bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Catalytic Oxidation of 5-Hydroxymethylfurfural to 2,5-Furandicarboxylic Acid Using a Natural Mineral Vermiculite-Loaded Gold–Palladium Bimetallic Catalyst
Authors: Li Hongke , Li Yiwang , Xia Qinghua , Liu Yiran , Guan Wen , Chen Yao
Published: December 2024

Upgrading the bio-derived platform chemical 5-hydroxymethylfurfural (HMF) into the high value-added bioplastic monomer 2,5-furandicarboxylic acid (FDCA) is a promising pathway for biomass conversion. In this work, the natural and abundant available mineral vermiculite was employed as a carrier for loading a Au-Pd bimetal catalyst. Due to the high dispersion of bimetallic nanoparticles, this synthesized vermiculite-supported Au-Pd bimetal catalyst revealed excellent catalytic performance for the aerobic oxidation of HMF to FDCA. By adjusting the ratio of Au and Pd metals, the catalytic performance of the catalyst can be optimized. Finally, 100% HMF conversion and 99.9% FDCA yield could be obtained under the conditions of Au/Pd = 2/1, 2 h, 2 MPa O2, and 100 °C. The catalyst revealed good stability, and the FDCA yield can be maintained at 90.1% after five recycle usages. The physicochemical properties of the synthesized catalysts were characterized by various characterization methods. It could be concluded that the high dispersion and alloying effect of bimetallic nanoparticles promoted the activation of reactants and intermediates, resulting in the effective production of FDCA. This study could provide ideas and references for the development and utilization of natural minerals and also offer a new way to realize the efficient conversion of HMF to FDCA under green conditions.
Enhanced Diclofenac Removal from Constructed Wetland Effluent Using a Photoelectrocatalytic System with N-TiO2 Nanocrystal-Modified TiO2 Nanotube Anode and Graphene Oxide/Activated Carbon Photocathode
Authors: Liang Xiongwei , Yu Shaopeng , Meng Bo , Wang Xiaodi , Yang Chunxue , Shi Chuanqi , Ding Junnan
Published: December 2024

This investigation reports on the efficacy of a photoelectrocatalysis (PEC) system enhanced by a nitrogen-doped TiO2 nanocrystal-modified TiO2 nanotube array (N-TiO2 NCs/TNTAs) anode paired with a graphene oxide/activated carbon (GO/AC) photocathode for diclofenac removal from effluent. The FE-SEM and EDX analyses validated the elemental composition of the anode—27.56% C, 30.81% N, 6.03% O, and 26.49% Ti. The XRD results confirmed the anatase phase and nitrogen integration, essential for photocatalytic activity enhancement. Quantum chemical simulations provided a comprehensive understanding of the red-shifted absorption bands in N-TiO2, and UV-vis DRS demonstrated a red-shift in absorption to the visible spectrum, indicating improved light utilization. The PEC configuration achieved a photocurrent density of 9.8 mA/dm2, significantly higher than the unmodified and solely nitrogen-doped counterparts at 4.8 mA/dm2 and 6.1 mA/dm2, respectively. Notably, this system reduced diclofenac concentrations by 58% within 75 min, outperforming standard photocatalytic setups. These findings underscore the potential of N-TiO2 NCs/TNTAs-AC-GO/PTFE composite material for advanced environmental photoelectrocatalytic applications.
Magnetically Assembled Electrode Incorporating Self-Powered Tourmaline Composite Particles: Exploiting Waste Energy in Electrochemical Wastewater Treatment
Authors: Zhang Bo , Shao Dan , Wang Yaru , Xu Hao , Song Haojie
Published: December 2024

A magnetically assembled electrode (MAE) is a modular electrode format in electrochemical oxidation wastewater treatment. MAE utilizes magnetic forces to attract the magnetic catalytic auxiliary electrodes (AEs) on the main electrode (ME), which has the advantages of high efficiency and flexible adjustability. However, the issue of the insufficient polarization of the AEs leaves the potential of this electrode underutilized. In this study, natural tourmaline (Tml) particles with pyroelectric and piezoelectric properties were utilized to solve the above issue by harvesting and converting the waste energy (i.e., the joule heating energy and the bubble striking mechanical energy) from the electrolysis environment into additional electrical energy applied on the AEs. Different contents of Tml particles were composited with Fe3O4/Sb-SnO2 particles as novel AEs, and the structure–activity relationship of the novel MAE was investigated by various electrochemical measurements and orthogonal tests of dye wastewater treatment. The results showed that Tml could effectively enhance all electrochemical properties of the electrode. The optimal dye removal rate was obtained by loading the AEs with 0.2 g·cm−2 when the Tml content was 4.5 wt%. The interaction of current density and Tml content had a significant effect on the COD removal rate, and the mineralization capacity of the electrode was significantly enhanced. The findings of this study have unveiled the potential application of minerals and energy conversion materials in the realm of electrochemical oxidation wastewater treatment.
The Characteristics of Hydrodeoxygenation of Biomass Pyrolysis Oil over Alumina-Supported NiMo Catalysts
Authors: Seo Dong-Jin , Lee Jong Beom , Kim Yu-Jin , Cho Hye-Ryeong , Kim So-Yeon , Kim Ga-Eun , Park Young-Duk , Kim Geon-Hee , An Jung-Chul , Oh Kyeongseok , Park Joo-Il
Published: December 2024

The hydrodeoxygenation (HDO) of biomass pyrolysis oil (BPO) was evaluated in the presence of two commercial alumina-supported transition metal catalysts, NiMo/alumina-1 (NM1) and NiMo/alumina-2 (NM2). The study explored two characteristic aspects: how HDO reaction conditions affected the oxygen content, density, and boiling point distribution of BPO with varying temperature and HDO reaction time, and the roles of catalysts. Characterizations of HDO-treated oils included elemental analysis, GC-MS, SIMDIS, 13C NMR, and 1H NMR, and characterizations of catalysts included NH3-TPD, XRF, and TPO-MS analysis. The results show that both NM1 and NM2 catalysts removed oxygenated compounds effectively, which led to decreases in density and shifts toward higher boiling point distributions of BPO. Compared to the NM1 catalyst, NM2 had a higher acidity and enhanced HDO activity. The best HDO reaction performance was achieved in the presence of the NM2 catalyst at 300 °C. Furthermore, HDO reactions showed a significant amount of CO2, CH4, C2H6, and C3H8, which suggests that HDO reactions proceeded via a series of reactions of decarboxylation, water–gas shift, and methanation. In addition, hydrocarbon fraction tests suggested a favorable potential for the blending of HDO-treated biomass pyrolysis oil (HDO-BPO) with petroleum-derived fractions.
Temporal RAGE Over-Expression Disrupts Lung Development by Modulating Apoptotic Signaling
Authors: Clarke Derek M. , Kirkham Madison N. , Beck Logan B. , Campbell Carrleigh , Alcorn Hayden , Bikman Benjamin T. , Arroyo Juan A. , Reynolds Paul R.
Published: December 2024

Receptors for advanced glycation end products (RAGE) are multiligand cell surface receptors found most abundantly in lung tissue. This study sought to evaluate the role of RAGE in lung development by using a transgenic (TG) mouse model that spatially and temporally controlled RAGE overexpression. Histological imaging revealed that RAGE upregulation from embryonic day (E) 15.5 to E18.5 led to a thickened alveolar parenchyma and reduced alveolar surface area, while RAGE overexpression from E0 to E18.5 caused a significant loss of tissue and decreased architecture. Mitochondrial dysfunction was a hallmark of RAGE-mediated disruption, with decreased levels of anti-apoptotic BCL-W and elevated pro-apoptotic BID, SMAC, and HTRA2, indicating compromised mitochondrial integrity and increased intrinsic apoptotic activity. Extrinsic apoptotic signaling was similarly dysregulated, as evidenced by the increased expression of TNFRSF21, Fas/FasL, and Trail R2 in E0-18.5 RAGE TG mice. Additionally, reductions in IGFBP-3 and IGFBP-4, coupled with elevated p53 and decreased p27 expression, highlighted disruptions in the cell survival and cycle regulatory pathways. Despite the compensatory upregulation of inhibitors of apoptosis proteins (cIAP-2, XIAP, and Survivin), tissue loss and structural damage persisted. These findings underscore RAGE’s role as a pivotal modulator of lung development. Specifically, the timing of RAGE upregulation significantly impacts lung development by influencing pathways that cause distinct histological phenotypes. This research may foreshadow how RAGE signaling plausibly contributes to developmental lung diseases.
Limited Efficacy of Anti-EGFR Monoclonal Antibodies in Colorectal Cancer Patients with Rare RAS Variants: Analysis of the C-CAT Database
Authors: Suzuki Shuhei , Saito Yosuke , Saito Koki , Yamada Yuta , Takahashi Koshi , Kumanishi Ryosuke , Fukui Tadahisa , Yoshioka Takashi
Published: December 2024

Epidermal growth factor receptor (EGFR) inhibition is crucial in treating RAS wild-type metastatic colorectal cancer, yet current testing methods may miss rare RAS variants affecting treatment efficacy. We analyzed 4122 colorectal cancer patients receiving anti-EGFR antibodies from the Center for Cancer Genomics and Advanced Therapeutics database, identifying 54 patients (1.3%) with rare RAS variants undetectable by standard testing. These patients showed significantly lower response rates to anti-EGFR therapy (28.3%) compared to RAS wild-type cases (44.6%, p = 0.003). Disease control rates were also lower in rare variant cases (60.9%) versus wild-type cases (80.0%). Most common rare variants included KRAS Q22K, A59E, and A11_G12insGA. Comprehensive genomic profiling revealed additional alterations in TP53 (90.7%), APC (87.0%), and non-V600E BRAF mutations (25.9%). Our findings suggest that rare RAS variants predict poor anti-EGFR therapy response, highlighting the potential benefit of comprehensive genomic profiling before treatment initiation. This study provides real-world evidence supporting the clinical relevance of rare RAS variants in treatment decision-making for colorectal cancer. Future studies should focus on developing cost-effective comprehensive testing strategies and evaluating alternative treatment approaches for patients with rare RAS variants.
Tacrolimus- and Mycophenolate-Mediated Toxicity: Clinical Considerations and Options in Management of Post-Transplant Patients
Authors: Kaye Alan D. , Shah Shivam S. , Johnson Coplen D. , De Witt Adalyn S. , Thomassen Austin S. , Daniel Charles P. , Ahmadzadeh Shahab , Tirumala Sridhar , Bembenick Kristin Nicole , Kaye Adam M. , Shekoohi Sahar
Published: December 2024

Tacrolimus and mycophenolate are important immunosuppressive agents used to prevent organ rejection in post-transplant patients. While highly effective, their use is associated with significant toxicity, requiring careful management. Tacrolimus, a calcineurin inhibitor, is linked to nephrotoxicity, neurotoxicity, metabolic disturbances such as diabetes mellitus and dyslipidemia, and cardiovascular complications such as hypertension and arrhythmias. Mycophenolate, a reversible inhibitor of inosine monophosphate dehydrogenase, frequently causes gastrointestinal disturbances, including diarrhea and colitis, as well as hematologic side effects like anemia and leukopenia, which increase infection risk. Therapeutic drug monitoring (TDM) and pharmacogenomics have emerged as essential strategies for mitigating these toxicities. TDM ensures tacrolimus trough levels are maintained within a therapeutic range, minimizing the risks of nephrotoxicity and rejection. Pharmacogenomic insights, such as CYP3A5 polymorphisms, allow for personalized tacrolimus dosing based on individual metabolic profiles. For mycophenolate, monitoring inosine monophosphate dehydrogenase activity provides a pharmacodynamic approach to dose optimization, reducing gastrointestinal and hematologic toxicities. Emerging tools, including dried blood spot sampling and pharmacokinetic modeling, offer innovative methods to simplify monitoring and enhance precision in outpatient settings. Despite their utility, the toxicity profiles of these drugs, including those of early immunosuppressants such as cyclosporine and azathioprine, necessitate further consideration of alternative immunosuppressants like sirolimus, everolimus, and belatacept. Although promising, these newer agents require careful patient selection and further research. Future directions in immunosuppressive therapy include integrating individual pharmacogenetic data to refine dosing, minimize side effects, and improve long-term graft outcomes. This narrative review underscores the importance of personalized medicine and advanced monitoring in optimizing post-transplant care.
Harnessing Livestock and Vineyard Residues for Sustainable Energy Production in Portugal
Authors: Oliveira Miguel , Nunes Fernando Hermínio Ferreira Milheiro , Borges Amadeu Duarte da Silva
Published: December 2024

This study investigates the potential of utilizing livestock waste and vineyard residues for sustainable energy production in Portugal. Through the physical and chemical characterization of swine waste, grape seeds and skins, cork powder, sawdust, and biochar, 53 distinct samples, including 11 individual biomasses and their derived mixtures, were analyzed to identify optimal combinations for biofuel pellet production. The best-performing mixture, composed of 50% swine waste, 25% grape seeds and skins, and 25% cork powder, achieved a Lower Heating Value (LHV) of 18.34 MJ/kg and low ash content, qualifying it as a class B pellet. This mixture offers significant energy potential while minimizing environmental impacts. The research also presents three energy valorization scenarios, with the most balanced scenario meeting up to 6% of Portugal’s electricity demand and providing energy savings equivalent to 485,463 tons of oil equivalent (toe) annually. A case study on a “Case Study Farm” in the Douro region, managing 2000 pigs and producing 500 tons of wine grapes annually, demonstrated that implementing the optimal biomass mixture could generate 3854 MWh of heat and 1156 MWh of electricity per year. This could result in annual revenues of EUR 189,258 from pellet sales, covering the initial investment of EUR 283,938 within 6.36 years, with a total surplus of EUR 689,666 over 20 years. These findings highlight the economic viability and environmental benefits of converting agricultural waste into renewable energy, contributing to Portugal’s carbon neutrality and reducing energy dependence.
The Impact of Climate Change on Energy Consumption on Small Tropical Islands
Authors: Gargani Julien
Published: December 2024

The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production.
Impact of Surface Grinding on the Performance of Tilting Pad Bearings
Authors: Yang Peiji , Li Peng , Yuan Qi , Sun Quan , Xing Zhenya , Zhao Zhiming
Published: December 2024

Tilting pad bearings are core components of rotating machinery. Due to factors such as manufacturing, installation and operation, there may be a mismatch between the bearing pad surface and the shaft diameter, leading to issues like high bearing temperature and rotor system vibration. In engineering practice, surface grinding is often used to resolve these problems. This paper analyzed the mechanism of surface grinding, systematically studies the impact of individual or combined surface grinding on the static and dynamic characteristics of tilting pad bearings and summarizes reasonable pre-load adjustment schemes. The results show that the principle of pad surface grinding is to change the pre-load coefficient of the bearings. The grinding of the oil inlet and outlet sides of the pad surface changes the clearance of the pad, increases the pre-load coefficient, reduces the working temperature of the bearing, increases the thickness of the oil film, and increases power consumption; the grinding of the middle parting surface of the pad changes the clearance of the bearing, increases the pre-load coefficient, and increases the main stiffness and damping coefficient of the bearing. Using these results, a fault diagnosis was performed on a turbine unit with excessive vibration during an on-site operation, leading to effective control. The research provides technical references for the condition monitoring of turbine tilting pad bearing vibrations and the safe operation of units, offering significant engineering implications.
Distinguishing Reality from AI: Approaches for Detecting Synthetic Content
Authors: Ghiurău David , Popescu Daniela Elena
Published: December 2024

The advancement of artificial intelligence (AI) technologies, including generative pre-trained transformers (GPTs) and generative models for text, image, audio, and video creation, has revolutionized content generation, creating unprecedented opportunities and critical challenges. This paper systematically examines the characteristics, methodologies, and challenges associated with detecting the synthetic content across multiple modalities, to safeguard digital authenticity and integrity. Key detection approaches reviewed include stylometric analysis, watermarking, pixel prediction techniques, dual-stream networks, machine learning models, blockchain, and hybrid approaches, highlighting their strengths and limitations, as well as their detection accuracy, independent accuracy of 80% for stylometric analysis and up to 92% using multiple modalities in hybrid approaches. The effectiveness of these techniques is explored in diverse contexts, from identifying deepfakes and synthetic media to detecting AI-generated scientific texts. Ethical concerns, such as privacy violations, algorithmic bias, false positives, and overreliance on automated systems, are also critically discussed. Furthermore, the paper addresses legal and regulatory frameworks, including intellectual property challenges and emerging legislation, emphasizing the need for robust governance to mitigate misuse. Real-world examples of detection systems are analyzed to provide practical insights into implementation challenges. Future directions include developing generalizable and adaptive detection models, hybrid approaches, fostering collaboration between stakeholders, and integrating ethical safeguards. By presenting a comprehensive overview of AIGC detection, this paper aims to inform stakeholders, researchers, policymakers, and practitioners on addressing the dual-edged implications of AI-driven content creation.
Real-World Characterization of Toxicities and Medication Management in Recipients of CAR T-Cell Therapy for Relapsed or Refractory Large B-Cell Lymphoma in Nova Scotia, Canada
Authors: Shaw Jenna , Elsawy Mahmoud , Nielsen Rachel , Harrigan Amye Michelle , DiCostanzo Tara T. , Minard Laura V.
Published: December 2024

Nova Scotia (NS) began offering CAR T-cell therapy as a third-line standard of care for eligible patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) in 2022. Recipients of CAR T-cell therapy often experience acute toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), which require close monitoring and prompt management. This retrospective review aimed to describe the characteristics of adult patients with r/r LBCL deemed eligible to receive CAR T-cell therapy with axicabtagene ciloleucel in NS between January 2022 and June 2024, the toxicities experienced and toxicity management, hospital visits and intensive care unit (ICU) admissions, the utilization of toxicity management guidelines, and general efficacy outcomes. Twenty-seven patients received axicabtagene ciloleucel. All patients experienced CRS (7.4% grade ≥ 3), and 55.6% developed ICANS (25.9% grade ≥ 3). The median hospital stay was 18 days, with 40.7% requiring ICU admission. There was one treatment-related mortality. Most CRS (85.2%) and ICANS (80.0%) cases were managed according to the guidelines. By day +100, the best objective response rate was 81.5% (44.4% complete responses). Patients who received CAR T-cell therapy in NS, Canada, experienced comparable toxicities and efficacy to those reported in pivotal clinical trials and other real-world experiences.
Correlation Between Flying Insect Diversity and Environmental Factors in Various Land Use Types in Paseh District, Sumedang Regency, West Java
Authors: Withaningsih Susanti , Ilmi Bilhaq Fahmi , Parikesit Parikesit
Published: December 2024

Indonesia is known for its incredible diversity of insects. Being ectothermic, insects are influenced by environmental factors. The relationship between insect diversity and the environment can be understood using multivariate analysis. The Paseh District in Sumedang Regency has various land uses, including gardens, rice fields, and plantations. Changes in land use due to the construction of the Cisumdawu Toll Road can impact environmental factors, such as soil quality, microclimate, and water availability, which are critical for sustaining diverse insect communities. Similarly, changes in vegetation cover can alter temperature and humidity levels, impacting terrestrial insects adapted to specific climatic conditions. This study aims to gather information on the relationship between insect diversity and environmental factors in different land use types in the Paseh District. A preliminary survey was carried out to record land use types and determine sampling locations. An intensive survey was done to collect and identify flying insect samples, as well as to measure the environmental factors. The results were analyzed using the Shannon–Wiener Diversity Index (H’), Evenness Index (E’), Simpson’s Diversity Index (C), and Canonical Correspondence Analysis (CCA). The study found 115 species of flying insects, with mixed gardens having the highest diversity. The CCA results showed that temperature strongly and positively correlated with insect diversity across all land uses, while wind speed correlated positively with insect diversity in gardens. Altitude correlated negatively with insect diversity in mixed gardens but positively in rice fields. Humidity had a strong positive correlation with insect diversity in other land uses. This research is important for understanding how land use types and environmental factors influence flying insect diversity, which is crucial for conserving biodiversity and maintaining essential ecosystem services such as pollination and pest control. Its impact lies in providing scientific data to guide sustainable land management practices, support agricultural productivity, and inform policies for biodiversity conservation in the Paseh District and similar regions.
Parkinson’s Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data
Authors: Benredjem Sabrina , Mekhaznia Tahar , Abdulghafor Rawad , Turaev Sherzod , Bennour Akram , Sofiane Bourmatte , Aborujilah Abdulaziz , Al Sarem Mohamed
Published: December 2024

Background: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. Results: The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. Conclusions: The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD’s success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD.
Relationship Between Left Atrial Strain and Atrial Fibrillation: The Role of Stress Echocardiography
Authors: Mauriello Alfredo , Correra Adriana , Ascrizzi Antonia , Del Vecchio Gerardo Elia , Benfari Giovanni , Ilardi Federica , Lisi Matteo , Malagoli Alessandro , Mandoli Giulia Elena , Pastore Maria Concetta , Sperlongano Simona , Cameli Matteo , Russo Vincenzo , D’Andrea Antonello
Published: December 2024

Interest in advanced echocardiographic imaging methods is growing. Left atrial strain (LAS) is among recently developed echocardiographic parameters. LAS represents an index of tissue deformation of the left atrium (LA). This parameter is an expression of LA function. Several arrhythmias are caused by impaired LA function. LAS can be assessed with a resting echocardiogram. The evaluation of LAS during stress echocardiography represents another model for assessing LA function. The development of altered LAS during physical or pharmacological stress is a predictor of early LA disease. Our review aims to evaluate the relationship between alterations in LAS and the development of atrial fibrillation (AF), and the diagnostic and prognostic roles of the stress echocardiogram in clinical practice.
Oral Health of 7- to 9-Year-Old Children Born Prematurely—A Case–Control Observational Study with Randomized Case Selection
Authors: Schlesinger Heide , Heinrich-Weltzien Roswitha , Schüler Ina
Published: December 2024

Background: Along with the long-term sequelae of preterm birth for general health, oral health is potentially influenced by prematurity due to developmental and behavioral peculiarities. Objectives: This study aimed to compare oral health parameters in the mixed dentition of prematurely and full-term born children. Methods: Dental caries, developmental defects of enamel (DDE), and gingival inflammation were assessed in 7-to-9-year-old children (n = 38) born preterm (PT) compared to a matched control group born full-term (FT) in Germany. Dental caries was recorded using the International Caries Detection and Assessment System (ICDAS II) and DMFT/dmft-criteria. DDE was scored with modified DDE-Index and periodontal health by Periodontal Screening Index (PSI). Statistical analysis included McNemar’s test and Poisson regression. The significance level was p ≤ 0.05. Results: Caries prevalence was 47.4% in PT and 57.9% in FT. In the primary dentition, FT children were significantly more affected than PT children (1.6 dmft vs. 2.7 dmft; p = 0.035). PT children with extremely low birthweight (ELBW) had the highest caries experience (3.2 dmft; 1.0 DMFT). Prevalence of DDE in primary teeth was significantly higher in PT (55.3%) than in FT children (28.9%; p = 0.008). PSI was 3.8 in PT and 3.3 in FT children, but significantly higher in PT children with ELBW (7.4; p = 0.125). Conclusions: PT children are at higher risk for DDE in primary teeth and compromised periodontal health than FT children. Children with ELBW are most susceptible for dental caries and gingivitis.
A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems
Authors: Andreou Marios , Chen Nan
Published: December 2024

The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions. Desirably, it enjoys closed analytic formulae for the time evolution of its conditional Gaussian statistics, which facilitate the study of data assimilation and other related topics. In this paper, we develop a martingale-free approach to improve the understanding of CGNSs. This methodology provides a tractable approach to proving the time evolution of the conditional statistics by deriving results through time discretization schemes, with the continuous-time regime obtained via a formal limiting process as the discretization time-step vanishes. This discretized approach further allows for developing analytic formulae for optimal posterior sampling of unobserved state variables with correlated noise. These tools are particularly valuable for studying extreme events and intermittency and apply to high-dimensional systems. Moreover, the approach improves the understanding of different sampling methods in characterizing uncertainty. The effectiveness of the framework is demonstrated through a physics-constrained, triad-interaction climate model with cubic nonlinearity and state-dependent cross-interacting noise.
Representativeness, Complementarity, and Degree of Local Extirpation Risk for Thamnophis Species Inside and Outside of Protected Areas of Mexico
Authors: Venegas-Barrera Crystian Sadiel , Manjarrez Javier , Rodríguez-Moreno Ángel , Mendoza-Walle Yeimi Alexandra , Horta-Vega Jorge Víctor , Rodríguez-deLeón Itzel R. , Sunny Armando , Azuara Domínguez Ausencio
Published: December 2024

Protected areas (PAs) are geographical spaces intended to conserve populations, communities, and ecosystems, in which species richness must be maximized, the conserved area must be minimized, and anthropogenic pressure must be reduced. The present study analyzed the representativeness, complementarity, and degree of risk of 25 garter snake species of the genus Thamnophis in the PAs of Mexico. This study proposes that at least 17% of the potential geographic distribution (PGD) of species will be found inside PAs and in areas (Aichi Target 11) with a low human footprint (HF). The PGD of species was associated with the PAs and HF layers to identify where and which species could be at local extirpation risk by human activities. The results indicate that the federal PAs contain 85.2% of the species, while the state PAs contain 77.7% of the species. An average of 13.4% of the PGD of these species is found inside PAs, and two species are found outside. In 13 federal PAs and 10 state PAs, the Thamnophis species present high local extirpation risk from human activities. In total, 37% of species are found in PAs with a medium to very high human footprint; therefore, their persistence could be at local extirpation risk. Compared to other taxa, species of the genus Thamnophis are well represented. However, the PDG of more than half of the species achieves Aichi Target 11.
A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment
Authors: Rahman Shakila , Akter Shathee , Yoon Seokhoon
Published: December 2024

Unmanned aerial vehicles (UAVs) play a crucial role in various applications, including environmental monitoring, disaster management, and surveillance, where timely data collection is vital. However, their effectiveness is often hindered by the limitations of wireless sensor networks (WSNs), which can restrict communications due to bandwidth constraints and limited energy resources. Thus, the operational context of the UAV is intertwined with the constraints on WSNs, influencing how they are deployed and the strategies used to optimize their performance in these environments. Considering the issues, this paper addresses the challenge of efficient UAV navigation in constrained environments while reliably collecting data from WSN nodes, recharging the sensor nodes’ power supplies, and ensuring the UAV detours around obstacles in the flight path. First, an integer linear programming (ILP) optimization problem named deadline and obstacle-constrained energy minimization (DOCEM) is defined and formulated to minimize the total energy consumption of the UAV. Then, a deep reinforcement learning-based algorithm, named the DQN-based UAV detouring algorithm, is proposed to enable the UAV to make intelligent detour decisions in the constrained environment. The UAV must finish its tour (data collection and recharging sensors) without exceeding its battery capacity, ensuring each sensor has the minimum residual energy and consuming energy for transmitting and generating data, after being recharged by the UAV at the end of the tour. Finally, simulation results demonstrate the effectiveness of the proposed DQN-based UAV detouring algorithm in data collection and recharging the sensors while minimizing the total energy consumption of the UAV. Compared to other baseline algorithm variants, the proposed algorithm outperforms all of them.
Leveraging Transformer-Based OCR Model with Generative Data Augmentation for Engineering Document Recognition
Authors: Khallouli Wael , Uddin Mohammad Shahab , Sousa-Poza Andres , Li Jiang , Kovacic Samuel
Published: December 2024

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced an optical character recognition (OCR) system for text detection and recognition that leverages transformer-based generative deep learning models and transfer learning approaches to enhance text recognition accuracy in engineering documents. The proposed system was evaluated on a dataset collected from ships’ engineering drawings provided by a U.S. agency. Experimental results demonstrated that the proposed transformer-based OCR model significantly outperformed pretrained off-the-shelf OCR models.
Comparison of Different Modulation Control Strategies for Wireless Power Transfer System
Authors: Domajnko Jure , Težak Oto , Truntič Mitja , Prosen Nataša
Published: December 2024

When designing a wireless power transfer system, overall system efficiency is one of the key parameters. The efficiency depends on several parameters, which also includes control modulation, used for excitation of the transmitter coil. However, how much the modulation technique impacts the efficiency is studied rarely. In this paper, we compare the three most popular control modulation techniques for transmitter voltage of a wireless power transfer system. First, the theory behind the methods is presented by highlighting the difference between them and their implementation. The methods are implemented, together with a linear PI control scheme, to evaluate the efficiency and performance of each of the methods. Finally, the practical tests were performed, under different output power levels, in order to evaluate the practical performance of each modulation strategy further.
Lightweight Multi-Scale Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Authors: Li Jun , Huang Kaigen
Published: December 2024

Salient object detection in optical remote sensing images (ORSI-SOD) encounters notable challenges, mainly because of the small scale of salient objects and the similarity between these objects and their backgrounds in images captured by satellite and aerial sensors. Conventional approaches frequently struggle to efficiently leverage multi-scale and multi-stage features. Moreover, these methods usually rely on sophisticated and resource-heavy architectures, which can limit their practicality and efficiency in real-world applications. To overcome these limitations, this paper proposes a novel lightweight network called the Multi-scale Feature Fusion Network (MFFNet). Specifically, a Multi-stage Information Fusion (MIF) module is created to improve the detection of salient objects by effectively integrating features from multiple stages and scales. Additionally, we design a Semantic Guidance Fusion (SGF) module to specifically alleviate the problem of semantic dilution often observed in U-Net architecture. Comprehensive evaluations on two benchmark datasets show that the MFFNet attains outstanding performance in four out of eight evaluation metrics while only having 12.14M parameters and 2.75G FLOPs. These results highlight significant advancements over 31 state-of-the-art models, underscoring the efficiency of MFFNet in salient object-detection tasks.
An Adaptive Multimodal Fusion Network Based on Multilinear Gradients for Visual Question Answering
Authors: Zhao Chengfang , Tang Mingwei , Zheng Yanxi , Ran Chaocong
Published: December 2024

As an interdisciplinary field of natural language processing and computer vision, Visual Question Answering (VQA) has emerged as a prominent research focus in artificial intelligence. The core of the VQA task is to combine natural language understanding and image analysis to infer answers by extracting meaningful features from textual and visual inputs. However, most current models struggle to fully capture the deep semantic relationships between images and text owing to their limited capacity to comprehend feature interactions, which constrains their performance. To address these challenges, this paper proposes an innovative Trilinear Multigranularity and Multimodal Adaptive Fusion algorithm (TriMMF) that is designed to improve the efficiency of multimodal feature extraction and fusion in VQA tasks. Specifically, the TriMMF consists of three key modules: (1) an Answer Generation Module, which generates candidate answers by extracting fused features and leveraging question features to focus on critical regions within the image; (2) a Fine-grained and Coarse-grained Interaction Module, which achieves multimodal interaction between question and image features at different granularities and incorporates implicit answer information to capture complex multimodal correlations; and (3) an Adaptive Weight Fusion Module, which selectively integrates coarse-grained and fine-grained interaction features based on task requirements, thereby enhancing the model’s robustness and generalization capability. Experimental results demonstrate that the proposed TriMMF significantly outperforms existing methods on the VQA v1.0 and VQA v2.0 datasets, achieving state-of-the-art performance in question–answer accuracy. These findings indicate that the TriMMF effectively captures the deep semantic associations between images and text. The proposed approach provides new insights into multimodal interaction and fusion research, combining domain adaptation techniques to address a broader range of cross-domain visual question answering tasks.
A Practically Secure Two-Factor and Mutual Authentication Protocol for Distributed Wireless Sensor Networks Using PUF
Authors: Mo Jiaqing , Zhang Zhihua , Lin Yuhua
Published: December 2024

In a distributed wireless sensor network (DWSN), sensors continuously perceive the environment, collect data, and transmit it to remote users through the network so as to realize real-time monitoring of the environment or specific targets. However, given the openness of wireless channels and the sensitivity of collecting data, designing a robust user authentication protocol to ensure the legitimacy of user and sensors in such DWSN environments faces serious challenges. Most of the current authentication schemes fail to meet some important and often overlooked security features, such as resisting physical impersonation attack, resisting smartcard loss attack, and providing forward secrecy. In this work, we put forward a practically secure two-factor authentication scheme using a physically unclonable function to prevent a physical impersonation attack and sensor node capture attack, utilize Chebyshev chaotic mapping to provide forward secrecy, and improve the efficiency and security of session key negotiation. Furthermore, we use the fuzzy verifier technique to prevent attackers from offline guessing attacks to resist smartcard loss attacks. In addition, a BAN logic proof and heuristic security analysis show that the scheme achieves mutual authentication and key agreement as well as prevents known attacks. A comparative analysis with state-of-the-art schemes shows that the proposal not only achieves desired security features but also maintains better efficiency.
Assessment of the Impact of Multi-Agent Model-Based Traffic Optimization Interventions on Urban Travel Behavior
Authors: Pan Lihu , Yang Nan , Zhang Linliang , Zhang Rui , Xie Binhong , Yan Huimin
Published: December 2024

With the continuous increase in car ownership, alleviating traffic congestion and reducing carbon emissions have become key challenges in urban traffic management. This study constructs a multi-agent model to evaluate the impact of various traffic optimization interventions on citizens’ travel behavior and traffic carbon emission levels. Different from previous mathematical models, this model integrates computer technology and geographic information systems, abstracting travelers as agents with self-control capabilities who can make independent decisions based on their own circumstances, thus reflecting individual differences in travel behavior. Using the real geographical and social environment of the high-density travel area in Xiaodian District, Taiyuan City as a case study, this research explores the overall improvement in the urban transportation system through the implementation of multiple traffic optimization interventions, such as a parking reservation system, the promotion of the park-and-ride mode, and the optimization of public transportation services. Studies have demonstrated that, compared to reducing bus fares, travelers exhibit a greater sensitivity to waiting times. Reducing bus departure intervals can increase the proportion of park-and-ride trips to 25.79%, surpassing the 19.19% increase observed with fare adjustments. A moderate increase in the proportion of reserved parking spaces can elevate the public transport load to 49.85%. The synergistic effect of a combined strategy can further boost the public transport share to 50.62%, while increasing the park-and-ride trip proportion to 33.6%, thereby highlighting the comprehensive benefits of implementing multiple strategies in tandem. When the parking reservation system is effectively implemented, carbon dioxide emissions can be reduced from over 800 kg to below 200 kg, and the proportion of vehicle cruising can decrease from over 20% to under 15%. These results underscore the critical role of the parking reservation strategy in optimizing traffic flow and advancing environmental sustainability.
Performance Enhancement of a Building-Integrated Photovoltaic/Thermal System Coupled with an Air Source Heat Pump
Authors: Vuong Edward , Fung Alan S. , Kumar Rakesh
Published: December 2024

This study explores the improvement of building integrated photovoltaic–thermal (BIPV/T) systems and their integration with air source heat pumps (ASHPs). The BIPV/T collector needs a method to effectively extract the heat it collects, while ASHP can boost their efficiency utilizing preheated air from the BIPV/T collectors. Combining these two systems presents a valuable opportunity to enhance their performance. This paper discusses technological improvements and integration through a comprehensive modelling analysis. Two versions of the BIPV/T systems were assessed using a modified version of EnergyPlus V8.0, a building energy simulation program. This study involved sensitivity analysis of the internal channel surface and cover emissivity parameters of the opaque BIPV/T (OBIPV/T), transparent BIPV/T (TBIPV/T), and building-integrated solar air heater collectors (BISAHs). Various arrangements of the collectors were also studied. A BIPV/T-BISAH array design was selected based on the analysis, and its integration with a net-zero energy house. The BIPV/T-BISAH coupled ASHP system decreased space heating electricity consumption by 6.5% for a net-zero house. These modest savings are mainly attributed to the passive design of the houses, which reduced heating loads during sunny hours/days.
Improving Fuel Consumption Prediction for Marine Diesel Engines Using Hierarchical Neural Networks and Pulsating Exhaust Models
Authors: Salazar Anibal Aguillon , Salameh Georges , Chesse Pascal , Bulot Nicolas , Thevenoux Yoann
Published: December 2024

Predicting end-of-process variables in internal combustion engines, such as brake-specific fuel consumption or pollutant emissions, is crucial for engine design decisions. However, errors in common multi-layer-perceptron-based artificial neural network models often match the magnitude of the expected fuel consumption improvements, potentially leading to incorrect decisions. This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. To enhance metamodel accuracy, two modifications are proposed: incorporating a pulsating mass flow rate in the exhaust line to capture pulsating effects missing in mean-value engine models and using a hierarchical arrangement of several multi-layer perceptrons instead of a parallel arrangement. The pulsating mass flow rate approach improves the accuracy of all tested configurations by replicating pulsating effects from a detailed 1D engine model. Meanwhile, the hierarchical arrangement refines predictions of end-of-process variables, such as fuel consumption, by increasing the total layers, with a minimal trade-off in the accuracy of other variables. These findings are validated using a metamodel derived from a calibrated 1D engine model in GT-Suite. The proposed methods are expected to enhance the accuracy of data-driven artificial neural network approaches, contributing to more reliable engine design optimization.