bluuebunny/crossref_metadata_2025 huggingface dataset:
Pleuropulmonary Involvement in Patients with Systemic Lupus Erythematosus as Detected by High-Resolution CT Scans: Clinical and Immunological Association
Authors: Hasan Manal A. , Alismail Maram A. , Bokhari Danah R. , Alghamdi Rehab F. , Alhalal Zainab E. , Alqatari Safi G. , Al Shubbar Mohammed D.
Published: January 2025

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that leads to systemic inflammation and damage across various organs, including the respiratory system. The prevalence of pleuropulmonary manifestations in SLE patients, particularly in Saudi Arabia, remains underexplored. Objective: This study aims to determine the frequency of pleuropulmonary involvement in SLE patients at King Fahd Hospital of the University (KFUH), Khobar, Saudi Arabia, and identify factors associated with the presence of such findings. Method: A retrospective analysis was conducted on adult SLE patients at KFUH, using hospital medical records for data collection on demographic characteristics, clinical features, and immunological markers. Pleuropulmonary involvement was defined based on high-resolution computed tomography (HRCT) findings. Statistical analyses evaluated associations between pleuropulmonary findings and clinical or immunological parameters. Results: Out of 207 SLE patients (mean age 39.9 years, 92.8% female), 17.4% showed pleuropulmonary involvement on HRCT, with pleurisy being the most prevalent manifestation, followed by pulmonary nodules. Significant associations were found between pleuropulmonary involvement and positive rheumatoid factor (p = 0.002), cardiac involvement (p = 0.002), and higher SLICC/ACR damage index scores (p = 0.001). Patients with positive rheumatoid factor and elevated SLICC/ACR damage index had increased odds of developing pleuropulmonary complications (3.73- and 7.28-fold, respectively). Conclusions: Pleuropulmonary involvement in SLE patients is associated with clinical and immunological markers, including rheumatoid factor, cardiac involvement, and higher SLICC/ACR damage index scores. Recognizing these associations may improve the early detection and targeted management of high-risk patients.
Metal Additive Manufacturing and Molten Pool Dynamic Characterization Monitoring: Advances in Machine Learning for Directed Energy Deposition
Authors: He Wentao , Zhu Lida , Liu Can , Jiang Hongxiao
Published: January 2025

Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes.
Retinoids as Alternative Antifungal Agents Against Candida albicans: In Vitro and In Silico Evidence
Authors: Cosio Terenzio , Romeo Alice , Pistoia Enrico Salvatore , Pica Francesca , Freni Claudia , Iacovelli Federico , Orlandi Augusto , Falconi Mattia , Campione Elena , Gaziano Roberta
Published: January 2025

Candida albicans (C. albicans) is the most common pathogen responsible for a wide spectrum of human infections ranging from superficial mucocutaneous mycoses to systemic life-threatening diseases. Its main virulence factors are the morphological transition between yeast and hyphal forms and the ability to produce biofilm. Novel antifungal strategies are required given the severity of systemic candidiasis, especially in immunocompromised patients, and the lack of effective anti-biofilm treatments. We previously demonstrated that all-trans retinoic acid (ATRA), an active metabolite of vitamin A, exerted an inhibitory effect on Candida growth, yeast–hyphal transition and biofilm formation. Here, we further investigated the possible anti-Candida potential of trifarotene and tazarotene, which are the other two molecules belonging to the retinoid family, compared to ATRA. The results indicate that both drugs were able to suppress Candida growth, germination and biofilm production, although trifarotene was proven to be more effective than tazarotene, showing effectiveness comparable to ATRA. In silico studies suggest that all three retinoids may exert antifungal activity through their molecular interactions with the heat shock protein (Hsp) 90 and 14α-demethylase of C. albicans. Moreover, interactions between retinoids and ergosterol have been observed, suggesting that those compounds have great potential against C. albicans infections.
Autonomic Self-Healing of Polymers: Mechanisms, Applications, and Challenges
Authors: Wang Chenxu , Boulatov Roman
Published: January 2025

Mechanical loads degrade polymers by enabling mechanochemical fragmentation of macromolecular backbones. In most polymers, this fragmentation is irreversible, and its accumulation leads to the appearance and propagation of cracks and, ultimately, fracture of the material. Self-healing describes a diverse and loosely defined collection of approaches that aim at reversing this damage. Most reported synthetic self-healing polymers are non-autonomic, i.e., they require the user to input free energy (in the form of heat, irradiation, or reagents) into the damaged material to initiate its repair. Here, we critically discuss emerging chemical approaches to autonomic self-healing that rely on regenerating the density of load-bearing, dissociatively-inert backbone bonds either after the load on a partially damaged material dissipated or continuously and in competition with the mechanochemically driven loss of backbones in the loaded material. We group the reported chemistries into three broad types whose analysis yields a set of criteria against which the potential of a prospective approach to yield practically relevant self-healing polymers can be assessed quantitatively. Our analysis suggests that the direct chain-to-chain addition in mechanically loaded unsaturated polyolefins is the most promising chemical strategy reported to date to achieve autonomic synchronous self-healing of practical significance.
The Atmospheric Chemistry of Fluoroacetonitrile and the Characterization of the Major Product, Cyanoformyl Fluoride
Authors: Sapkota Ramesh , Nguyen Trang , Marshall Paul
Published: January 2025

Fluorinated nitriles have been proposed as low-global-warming-potential substitutes for industrial applications such as plasma etching and as dielectric materials in high-voltage equipment. FT-IR spectroscopy was used to measure the radiative efficiency of CH2FCN and its reactivity towards Cl and OH radicals, and to determine products from the Cl reaction. Relative rate experiments yielded rate constants for Cl and OH reactions of (2.1 ± 0.3) × 10−14 and (7.0 ± 1.0) × 10−14 cm3 molecule−1 s−1, respectively. The estimated atmospheric lifetime of CH2FCN with respect to radical attack was estimated to be 0.45 years, which, combined with the radiative efficiency of 0.042 W m−2 ppb−1, implies a 100-year global warming potential of 20. FCOCN was observed as the only organic product of the Cl-atom reaction in air, consistent with a dominant role for H-abstraction. Absolute infrared cross-sections for FCOCN were determined, to assist future experiments where this molecule may be formed. Quantum calculations at the CBS-APNO//B2PLYP-D3/cc-pVTZ level indicate similar energy barriers to addition and abstraction for OH radical attack, but the looser transition state and greater opportunity for tunneling also favor abstraction in this case.
Enhancing the Photocatalytic Efficacy of g-C3N4 Through Irradiation Modification and Composite Construction with Ti3C2 for Photodynamic Therapy
Authors: Huang Bin , Wang Yilun , Chen Xuguang , Wu Yue , Xu Kaidi , Xie Simeng , Qin Ziyang , Liu Xiang , Chen Huangqin , Li Yuesheng
Published: January 2025

Photodynamic therapy (PDT) holds considerable promise for advancing anticancer treatment, owing to its precision and minimally invasive nature. In this study, we successfully synthesized a series of titanium carbide (Ti3C2, TC)/graphitic carbon nitride (g-C3N4, CN) nanocomposite through a synergistic approach combining electron beam irradiation and 2D/2D composite formation. According to the results, 1TC/200-CN (1TC, which TC was 1, referred to the mass ratio; 200-CN, which CN was 200 kGy, referred to the irradiation metering) displayed a 94% degradation rate of methylene blue (10 mg/L) in 100 min. Furthermore, the proliferation rate of CAL-27 cells was suppressed to just 23.3% at a concentration of 320 μg/mL of 1TC/200-CN. Notably, the group treated with this concentration exhibited the largest residual scratch area, accompanied by a notable decrease in mitochondrial membrane potential. These enhanced effects were attributed to the efficient transfer of electron-hole pairs facilitated by the TC/CN composite. Our findings not only contribute to the development of efficient and stable nanocomposites for PDT applications but also provide valuable insights into the utilization of nanomaterials in the biomedical field, thereby paving the way for potential breakthroughs in cancer treatment.
Unravelling the Cu and Ce Effects in MnO2-Based Catalysts for Low-Temperature CO Oxidation
Authors: Blinov Egor D. , Kulchakovskaya Ekaterina V. , Sokovikov Nikolai A. , Svetlichnyi Valery A. , Kulinich Sergei A. , Vodyankina Olga V.
Published: January 2025

Cu-containing and Ce-modified OMS-2 catalysts were prepared at various calcination temperatures using the hydrothermal method and tested for low-temperature CO oxidation. The structure, chemical compositions, and physical–chemical properties of the catalysts were characterized using XRD, N2 physisorption, XRF, Raman spectroscopy, SEM, high-resolution TEM with EDX, TPR-H2, and XPS. The incorporation of Cu into the Ce-OMS-2 sample facilitated the transformation of pyrolusite into cryptomelane, as confirmed by Raman spectroscopy data. In the light-off mode, the Cu/Ce-OMS-2-300 and Cu/OMS-2 samples exhibited higher activity in low-temperature CO oxidation (T90 = 115 and 121 °C, respectively) compared to sample Cu/Ce-OMS-2-450. After a long-run stability test, the Cu/Ce-OMS-X samples demonstrated excellent performance: the T80 increased by 16% and 7% for the samples calcined at 300 °C and 450 °C, respectively, while the T80 for the Cu/OMS-2 increased by 40%. The Cu/OMS-2 and Cu/Ce-OMS-2-300 samples were found to have an increased content of nanodispersed copper sites on their surfaces. These copper sites contributed to the formation of the Cu2+-O-Mn4+ interface, which is responsible for the CO oxidation. The presence of Ce3+ in the catalyst was found to increase its stability in the presence of water vapor due to the higher reoxidation ability in comparison with Ce-free sample Cu/OMS-2.
Synthesis and Characterization of a Pla Scaffold with Pseudoboehmite and Graphene Oxide Nanofillers Added
Authors: Maidana Rafael Vieira , Munhoz Antônio Hortêncio , Ramos Filipe Figueiredo , Oliveira Alex Lopes de , Neto José César de Souza Almeida , Oliveira Victor Inácio de , Lima Bruno Luis Soares de , Almeida Fábio Jesus Moreira de
Published: January 2025

In cases of severe injuries or burns, skin grafts (scaffolds) are often required as skin substitutes. In order not to harm the patient or the donor, biodegradable and biocompatible materials are used, which validates the search for heterografts such as poly (L-lactic acid)—PLA. However, natural polymers applied to the skin suffer great degradation in environments with large amounts of carbon and water or via binders with considerable resistivity, which implies little durability due to their low ductility. For the proposal, this work investigates PLA-based scaffolds modified with a mixture of pseudoboehmite (PB) and graphene oxide (GO), produced via the sol–gel route. The nanomaterials are incorporated into the polymer at different loadings, seeking to improve mechanical and thermal properties. Analyses via SEM, EDS, and XRD confirm the presence and distribution of these fillers. Tensile and flexural tests indicate that adding the filler can increase stress resistance, prevent deformations before failure, and increase toughness when compared to pure PLA.
A Review of the Carcinogenic Potential of Thick Rigid and Thin Flexible Multi-Walled Carbon Nanotubes in the Lung
Authors: Ahmed Omnia Hosny Mohamed , Naiki-Ito Aya , Takahashi Satoru , Alexander William T. , Alexander David B. , Tsuda Hiroyuki
Published: January 2025

The carcinogenic potential of MWCNTs is not well defined. Currently, IARC has classified MWCNT-7 as a Group 2 B material, possibly carcinogenic to humans, and all other MWCNTs as Group 3 materials, inadequate evidence in experimental animals for their carcinogenicity and not classifiable as to their carcinogenicity to humans. In this review we discuss studies that investigated the lung toxicity of well characterized MWCNTs in mice and rats. Intraperitoneal and intrascrotal injection studies identified rigid MWCNTs as hazardous materials. The assessment of lung toxicity of MWCNTs in short and medium term instillation and inhalation studies were not conclusive; therefore, these studies do not confirm the hazard of MWCNTs. However, two-year carcinogenicity studies indicate that MWCNT-7 and other MWCNTs, both thick rigid MWCNTs and thin flexible MWCNTs, are carcinogenic in test animals. Therefore, the carcinogenicity of MWCNTs in experimental animals should be reassessed.
Pituitary Spindle Cell Oncocytoma: More than a Grade 1 Tumor?
Authors: Hammond Jonathan , Gagne Zacharie , Mitrovic Bojana , Priola Stefano M.
Published: January 2025

Background/Objectives: Spindle cell oncocytomas (SCOs) of the pituitary gland are rare tumors often misdiagnosed for nonfunctioning pituitary macroadenomas. Although classified as grade 1, they are often challenging in terms of diagnosis and treatment. Pituitary SCOs harbor peculiar features such as hypervascularity and stronger adherence to surrounding structures, with increased risk of hemorrhage, partial resection, and significantly higher recurrence rate. Almost 100 cases have been reported so far. The role of surgery is still crucial for the decompression of the optic chiasm as well as for achieving diagnosis. However, given the higher tendency of recurrence, the role of postoperative radiotherapy has been investigated over the last few years. Case presentation: Here, we reported a case of a 48-year-old female with a pituitary SCO treated at our institution, in which we focused on diagnosis, treatment, and follow-up. Conclusions: This type of tumor presents a challenge related to its higher vascularity and strong adherence to the surrounding structures. Adjuvant radiotherapy is something that should be considered, especially when gross total resection is not achieved, and finally, SCOs require diligent follow-up to monitor for any signs of disease recurrence or progression.
Associations Between Carbohydrate Intake Behaviours and Glycaemia in Gestational Diabetes: A Prospective Observational Study
Authors: Smith Roslyn , Borg Renee , Wong Vincent , Russell Hamish , Mak Ka Hi
Published: January 2025

Carbohydrate intake (CI) has the largest impact on the short-term glycaemia of all nutrients, yet optimal CI management in gestational diabetes remains unclear. Objective: To examine CI behaviours in individuals with recently diagnosed gestational diabetes and their association with self-monitored blood glucose. Methods: Data from 97 individuals were collected using food and blood glucose diaries. CI (including amounts, types, and timing) was manually assessed using 15 g servings over 5–8 days, while a 3-day computerised analysis examined a broader intake of macro- and micronutrients. Results: Elevated fasting glycaemia (EFG) was associated with lower total CI (Mdn 10.8 vs. 12.5 servings/day, p = 0.006), missed meals (Mdn 1.4 vs. 0.0/week, p = 0.007), missed snacks (Mdn 10.5 vs. 7.5/week, p = 0.038), low-carbohydrate meals (<30 g; Mdn 4.3 vs. 2.0/week, p = 0.004), and a higher proportion of energy intake from fat (Mdn 35% vs. 33%, p = 0.047), as compared with in-target fasting glycaemia. In contrast, elevated postprandial glycaemia (EPG) was not significantly associated with total CI, low-carbohydrate meals (<30 g), low-carbohydrate snacks (<15 g), or high-carbohydrate loads (>50 g). EPG was instead associated with high-glycaemic index meals (Mdn 1.6 vs. 0.9 lunch meals/week, p = 0.026; Mdn 0.9 vs. 0.0 dinner meals/week, p = 0.023); and a lower intake of energy (Mdn 7650 kJ vs. 9070 kJ/day, p = 0.031), protein (Mdn 91 g vs. 109 g/day, p = 0.015), fat (Mdn 61 g vs. 84 g/day, p = 0.003), and multiple micronutrients (p <0.05), as compared with in-target postprandial glycaemia. Conclusions: CI profiles differed for individuals with EFG, as compared with EPG, emphasising the need for dietary guidelines tailored for gestational diabetes subtypes. Further interventional studies are needed to explore these findings, particularly the associations between EFG and low CI behaviours.
Development of the NUCLEUS Detector to Explore Coherent Elastic Neutrino-Nucleus Scattering
Authors: Schermer Nicole
Published: January 2025

The NUCLEUS experiment, currently being commissioned at the Technical University of Munich, is designed to observe coherent elastic neutrino-nucleus scattering (CEνNS) from reactor neutrinos and measure its cross-section with a percent-level precision at recoil energies below 100 eV. As a Standard Model process, CEνNS provides a unique probe into neutrino properties, potential new physics, and background suppression techniques relevant to dark matter experiments. The experiment utilizes gram-scale cryogenic calorimeters operating at 10 mK with an energy threshold of 20 eV. Situated at a shallow overburden of 3 m of water equivalent, the experimental site necessitates an advanced shielding strategy combining active vetoes and passive layers to reduce background rates to approximately 100counts/(kg·day·keV), as confirmed by full setup simulations. The commissioning phase has successfully demonstrated the stable operation of the cryogenic target detectors, achieving baseline resolutions below 10 eV, and the integration of the various shielding systems. Following this milestone, the experiment is set to transition to the EdF Chooz B nuclear reactor in France in 2025, where it will enable precise measurements of CEνNS, contributing to the understanding of neutrino interactions and advancing the field of astroparticle physics.
The Body of Chagas Disease Vectors
Authors: Dujardin Jean-Pierre
Published: January 2025

Morphometry is an effort to describe or measure the morphology of the body, or parts of it. It also provides quantitative data on the interactions of living organisms with their environment, external or internal. As a discipline, morphometrics has undergone significant developments in the last decade, making its implementation more visual and less laborious. Chagas disease vectors, often referred to by the common name of “kissing bugs”, belong to the subfamily Triatominae. Due to their apparent morphological plasticity, they have been the subject of numerous morphometric studies. Most of these have been applied taking into account the particularities of this group of vectors, such as domesticity (synanthropy), food preferences, dispersal ability, insecticide resistance, as well as some taxonomic issues. This brief review over nearly three decades is organized here according to the body organs considered by the authors.
Understanding Host–Pathogen Interactions in Congenital Chagas Disease Through Transcriptomic Approaches
Authors: Cáceres Tatiana M. , Patiño Luz Helena , Ramírez Juan David
Published: January 2025

Chagas disease, caused by Trypanosoma cruzi, is a parasitic zoonosis with significant health impacts, particularly in Latin America. While traditionally associated with vector-borne transmission, increased migration has expanded its reach into urban and non-endemic regions. Congenital transmission has become a critical route of infection, involving intricate maternal–fetal immune interactions that challenge diagnosis and treatment. This review synthesizes findings from three RNA-seq studies that explore the molecular underpinnings of congenital Chagas disease, emphasizing differentially expressed genes (DEGs) implicated in host–pathogen interactions. The DAVID tool analysis highlighted the overexpression of genes associated with the innate immune response, including pro-inflammatory cytokines that drive chemotaxis and neutrophil activation. Additionally, calcium-dependent pathways critical for parasite invasion were modulated. T. cruzi exploits the maternal–fetal immune axis to establish a tolerogenic environment conducive to congenital transmission. Alterations in placental angiogenesis, cellular regeneration, and metabolic processes further demonstrate the parasite’s ability to manipulate host responses for its survival and persistence. These findings underscore the complex interplay between the host and pathogen that facilitates disease progression. Future research integrating transcriptomic, proteomic, and metabolomic approaches is essential to unravel the molecular mechanisms underlying congenital Chagas disease, with a particular focus on the contributions of genetic diversity and non-coding RNAs in immune evasion and disease pathogenesis.
Phytochemical Profile and Anticancer Potential of Helichrysum arenarium Extracts on Glioblastoma, Bladder Cancer, and Breast Cancer Cells
Authors: Nalkiran Ihsan , Sevim Nalkiran Hatice
Published: January 2025

Background/Objectives: Cancer is the second leading cause of death globally. Medicinal plants have emerged as fundamental sources of bioactive compounds with anticancer potential, largely attributed to their diverse secondary metabolites. This study aimed to investigate the cytotoxic effects of Helichrysum arenarium extracts from two distinct regions of Turkiye, Mersin, and Artvin, on cancerous (MDA-MB-231, RT4, T98G) and non-cancerous (ARPE-19, hGF) cell lines and to identify bioactive compounds responsible for these effects. Methods: H. arenarium plant extracts were prepared using ethanol and methanol as solvents, followed by lyophilization and dissolution in DMSO. The cytotoxic effects of the extracts were evaluated using Hoechst staining and MTS assays to assess cell viability. IC50 values and selectivity indices were calculated. Phytochemical composition was analyzed using Quadrupole Time-of-Flight mass spectrometry. Results: The ethanol extract from Mersin (HAE-M) demonstrated superior cytotoxicity, particularly against breast and bladder cancer cells, while showing minimal impact on non-cancerous cells. HAM-M, HAE-A, and HAM-A exhibited comparatively less potent effects. Phytochemical analysis of HAE-M identified 16 bioactive compounds, including Naringenin, Luteolin, and Quercitrin, known for their antioxidant and anticancer properties. Conclusions: These findings highlight the potential of H. arenarium extracts, particularly HAE-M, as a source of potent anticancer agents. This study is novel in its comprehensive analysis of different extraction methods and regional plant sources, combined with phytochemical profiling, to identify selective anticancer effects. Further investigations into the mechanisms of action of these extracts could contribute to the development of plant-derived anticancer therapies.
Correction: Fahmy et al. Optimized Icariin Cubosomes Exhibit Augmented Cytotoxicity against SKOV-3 Ovarian Cancer Cells. Pharmaceutics 2021, 13, 20
Authors: Fahmy Usama A. , Fahmy Omar , Alhakamy Nabil A.
Published: January 2025

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An Evaluation of the Subcutaneous Depot Release of TV-46000, A Novel Long-Acting Injectable (LAI) Formulation of Risperidone, Under Extreme Conditions in Dogs, Minipigs and Humans
Authors: Steiner Lilach , Bibi David , Merenlender Wagner Avia , Farkas Pavel , Rudnick-Glick Safra , Loupe Pippa , Hallak Hussein
Published: January 2025

Background: TV-46000 (Uzedy, Teva), a long-acting subcutaneous antipsychotic, is an injectable formulation of risperidone and is approved by the FDA for the treatment of schizophrenia in adults. Its innovative copolymer-based drug delivery depot technology (licensed from MedinCell, Jacou, France) allows for plasma concentrations of the total active moiety of risperidone (TAM) to reach clinically relevant levels within 6–24 h and the maintenance of these therapeutic levels with monthly and bimonthly dosing regimens. Objective: As part of the development program for TV-46000, the effect of extrinsic factors of manipulation on the site of injection, and on the pharmacokinetic (PK) profile of TAM following TV-46000 administration was evaluated. Methods: Studies were conducted assessing the effect of heat and rubbing with male Gottingen minipigs and the effect of rubbing with male beagle dogs. A pilot clinical study in healthy volunteers was performed to evaluate the effect of rubbing. Results: These investigations showed that heating or rubbing of the TV-46000 sc injection site immediately post-injection had no clinically meaningful impact on safety and no burst or uncontrolled release was evident. Furthermore, no impact of injection site manipulation on TAM exposure was observed after depot formation (≥0.5 h post-injection). Conclusions: The observed similarity in findings between the animal and human studies supports the suitability of animal models for evaluation of the effect of extrinsic factors on injection sites and its translatability to clinical settings.
Excited-State Proton Transfer for Biomarker Detection
Authors: Bose Debosreeta , Girigoswami Agnishwar
Published: January 2025

Biomarker detection is imperative in the realms of modern medicine, biology, and environmental science, owing to the numerous avenues for its application. The recent scientific upsurge in the development of molecules, materials, and mechanisms for such scientific development has garnered considerable attention among scientists. In this connection, excited-state intramolecular proton transfer (ESIPT) properties of photoluminescent compounds provide considerable insights into the designing, development, and detection of biomarkers. ESIPT molecules significantly show a Stokes-shifted emission due to their sensitive nature and unique photophysical properties. Leveraging this photophysical property and tunable nature, several fluorescent probes of this genre can be designed and synthesized for a plethora of application spheres. Schiff bases encompass one such category of functional molecules displaying ESIPT properties, which can be mitigated by adding several other functionalities and desired optical characteristics. The current review article spans the basics of ESIPT properties of certain photoluminescent molecules and also envisages biosensing applications of recently developed imine–functionalized Schiff base molecules with such properties as the prima-foci, along with other applications.
Nutrients, Phytochemicals, and In Vitro Biological Activities of Goldenberry (Physalis peruviana L.) Fruit and Calyx
Authors: Añibarro-Ortega Mikel , Dias Maria Inês , Petrović Jovana , Mandim Filipa , Núñez Sonia , Soković Marina , López Víctor , Barros Lillian , Pinela José
Published: January 2025

This study provides a comprehensive characterization of Physalis peruviana L., covering the nutritional composition of the fruit and the phytochemical profiles and in vitro bioactive properties of berry and calyx extracts. The fresh fruit stood out as a source of dietary fiber (5.16 g/100 g) and is low in fat (0.49 g/100 g). A 100-g serving also contained notable amounts of ascorbic acid (32.0 mg), tocopherols (2.34 mg), potassium (253 mg), phosphorus (45 mg), and magnesium (20 mg). HPLC-DAD-ESI/MS analysis tentatively identified five physalin derivatives and one withanolide in the fruit extract, which showed significant antiproliferative activity against human colorectal adenocarcinoma (Caco-2) and non-small-cell lung carcinoma (NCI-H460) cells. The calyx extracts contained three phenolic acids and four flavonoids, demonstrating high antioxidant activity through physiologically relevant cell-based assays, the ability to inhibit advanced glycation end products (AGEs) formation and nitric oxide production, and also antiproliferative properties. These findings highlight goldenberry as a nutrient-dense fruit rich in vitamins and functional compounds with potential health benefits, supporting its recognition as a “superfruit”. Furthermore, the fruit calyx emerged as a valuable source of bioactive secondary metabolites with potential applications in food and pharmaceutical industries and related sectors.
Magnetite Nitrogen-Doped Carbon Quantum Dots from Empty Fruit Bunches for Tramadol Removal
Authors: Ng Law Yong , Chiang Amelia Kar Mun , Ng Ching Yin , Ng Kai Joe , Mahmoudi Ebrahim , Lim Ying Pei , Ba-Abbad Muneer M.
Published: January 2025

Tramadol is a widely used pain medication detected in wastewater treatment plants, prompting concerns about its impact on the environment and the effectiveness of wastewater treatment. Nitrogen-doped carbon quantum dots (NCQDs) can be used to remove pollutants from the contaminated water sources. However, NCQDs can hardly be recovered after applications, leading to high regeneration costs. Thus, this study aims to explore the use of magnetite nitrogen-doped carbon quantum dots (magnetite NCQDs) fabricated from empty fruit bunches (EFBs) to remove tramadol from wastewater treatment. Various analytical methods were conducted to characterize the magnetite NCQDs. Magnetite NCQDs showed excellent separation and aggregate-free properties. This study investigated the effect of the initial concentration of tramadol, the dosage of magnetite NCQD adsorbent, and the contact time while keeping other parameters constant. Tramadol was efficiently adsorbed within 40 min with an adsorption efficiency of over 85.9% and further photodegraded by 4.5% after being exposed to UV light after undergoing photocatalysis for 50 min. Magnetite NCQDs exhibited outstanding properties in removing tramadol after undergoing five cycles. This research provides a promising approach for developing a highly efficient adsorbent for treating tramadol-contaminated wastewater.
The Implementation of Simulation in Designing Production Expansion
Authors: Kliment Marek , Kronová Jana , Pekarčíková Miriam , Trebuňa Peter , Baluch Michal
Published: January 2025

This paper addresses the design of production expansion by using simulation to optimize production and storage capacities. The primary objective is to apply the Siemens Tecnomatix Plant Simulation software version 2404 to analyze the current production process, identify potential bottlenecks, and propose improvements. The methodology involves the use of simulation to create digital models of the production process, allowing for the detection of issues and evaluation of alternative solutions without interfering with ongoing production. The novelty of this approach lies in its ability to test and optimize the production flow digitally, ensuring a smoother transition to expanded capacities. Several alternative scenarios for production expansion were developed using Siemens Tecnomatix Plant Simulation, with a focus on verifying production volumes, identifying bottlenecks, and proposing the necessary machines and technological equipment. The results of the simulations are used to assess the proposed improvements, while the layout of the process is optimized for efficient material flow. The paper also presents 3D models as part of the solution proposal. The findings demonstrate the effectiveness of simulation-based design for expanding production systems, offering a methodology that can be applied to similar manufacturing processes.
A Transformer Oil Temperature Prediction Method Based on Data-Driven and Multi-Model Fusion
Authors: Yang Lin , Chen Liang , Zhang Fan , Ma Shen , Zhang Yang , Yang Sixu
Published: January 2025

A power transformer is an important part of the power system, and the oil temperature of the transformer is an important state parameter that reflects the operation state of the transformer. The accurate prediction of the oil temperature of the transformer can ensure the safe and stable operation of the transformer. Given the lack of a practical and effective data processing process and the problem that most of the current research is conducted on small-scale ideal datasets, this paper proposes a transformer oil temperature prediction method based on data-driven and multi-model fusion. The method first analyses and processes the actual transformer inspection data; it then uses the multi-model fusion method to model and predict the transformer oil temperature. The base model was trained using the machine learning method, and the secondary learning model was trained using the improved TSSA-BP neural network. The improved sparrow search algorithm (TSSA) was used to optimise the parameters of the BP neural network to improve the convergence accuracy and prediction performance of the model. The transformer data are classified according to cooling mode, operating voltage, and other indicators, and then eight groups of experimental datasets under different actual conditions are constructed for modelling and prediction. The experimental results show that the maximum root mean square error and the mean absolute percentage error of this method on different datasets are 1.0877 and 1.58%, and compared with other prediction methods, the prediction accuracy of this method is better than other methods, which verifies the practicability and feasibility of modelling and predicting for the actual transformer inspection data.
Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances
Authors: Bermudez Vera Ivan Mauricio , Mosquera Restrepo Jaime , Manotas-Duque Diego Fernando
Published: January 2025

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk management by incorporating internal and external data to assess default risk. Data mining techniques are applied to address class imbalances, redefining the term “default” to include external credit information and increasing the representation of the minority class. The effectiveness of machine learning and statistical models is evaluated using class-balancing methods such as under-sampling, over-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation is based on the Balanced Accuracy metric and the holding power of the performance, ensuring a consistent predictive power of the model while avoiding overfitting. While machine learning methods can improve credit scoring, logistic regression-based models remain effective, especially when combined with class-balancing techniques. It is concluded that a balanced sample in a class size is essential to improve predictive performance.
Reconstructing Geometric Models from the Point Clouds of Buildings Based on a Transformer Network
Authors: Wang Cheng , Liu Haibing , Deng Fei
Published: January 2025

Geometric building models are essential in BIM technology. The reconstruction results using current methods are usually represented using mesh, which is limited to visualization purposes and hard to directly import into BIM or modeling software for further application. In this paper, we propose a building model reconstruction method based on a transformer network (DeepBuilding). Instead of reconstructing the polyhedron model of buildings, we strive to recover the CAD modeling operation of constructing the building models from the building point cloud. By representing the building model with its modeling sequence, the reconstruction results can be imported into BIM software for further application. We first translate the procedure of constructing a building model into a command sequence that can be vectorized and processed by the transformer network. Then, we propose a transformer-based network that can convert input point clouds into the vectorized representation of the modeling sequences by decoding the geometry information encoded in the point features. A tool is developed to convert the vectorized modeling sequence into a 3D shape representation (such as mesh) or file format that other BIM software supports. Comprehensive experiments are conducted, and the evaluation results demonstrate that our method can produce competitive reconstruction results with high geometric fidelity while preserving more details of the building reconstruction.
Multi-Baseline Bistatic SAR Three-Dimensional Imaging Method Based on Phase Error Calibration Combining PGA and EB-ISOA
Authors: He Jinfeng , Xie Hongtu , Liu Haozong , Wu Zhitao , Xu Bin , Zhu Nannan , Lu Zheng , Qin Pengcheng
Published: January 2025

Tomographic synthetic aperture radar (TomoSAR) is an advanced three-dimensional (3D) synthetic aperture radar (SAR) imaging technology that can obtain multiple SAR images through multi-track observations, thereby reconstructing the 3D spatial structure of targets. However, due to system limitations, the multi-baseline (MB) monostatic SAR (MonoSAR) encounters temporal decorrelation issues when observing the scene such as forests, affecting the accuracy of the 3D reconstruction. Additionally, during TomoSAR observations, the platform jitter and inaccurate position measurement will contaminate the MB SAR data, which may result in the multiplicative noise with phase errors, thereby leading to the decrease in the imaging quality. To address the above issues, this paper proposes a MB bistatic SAR (BiSAR) 3D imaging method based on the phase error calibration that combines the phase gradient autofocus (PGA) and energy balance intensity-squared optimization autofocus (EB-ISOA). Firstly, the signal model of the MB one-stationary (OS) BiSAR is established and the 3D imaging principle is presented, and then the phase error caused by platform jitter and inaccurate position measurement is analyzed. Moreover, combining the PGA and EB-ISOA methods, a 3D imaging method based on the phase error calibration is proposed. This method can improve the accuracy of phase error calibration, avoid the vertical displacement, and has the noise robustness, which can obtain the high-precision 3D BiSAR imaging results. The experimental results are shown to verify the effectiveness and practicality of the proposed MB BiSAR 3D imaging method.
Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data
Authors: Ghobadi Fatemeh , Tayerani Charmchi Amir Saman , Kang Doosun
Published: January 2025

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes.
Mapping of Fluvial Morphological Units from Sentinel-1 Data Using a Deep Learning Approach
Authors: Gargiulo Massimiliano , Cavallo Carmela , Papa Maria Nicolina
Published: January 2025

The identification of ongoing evolutionary trajectories, the prediction of future changes in the functioning of riverine habitats, and the assessment of flood-related risks to human populations all depend on regular hydro-morphological monitoring of fluvial settings. This paper focuses on the satellite monitoring of river macro-morphological units (assemblages of water, sediment, and vegetation units) and their temporal evolution. In particular, we develop a deep-learning semantic segmentation method using Synthetic Aperture Radar (SAR) Sentinel-1 dual-polarized data. The methodology is executed and tested on the Po River, located in Italy. The training of a relatively deep convolutional neural network requires a large amount of ground-truth data, which is often limited and challenging to acquire. To address this limitation, the dataset is augmented using a random forest (RF) classification algorithm. RF parameters are trained with both Sentinel-1 (S1) and Sentinel-2 (S2) data. The RF classification algorithm is very robust and achieves excellent performance. To overcome the limitation linked with the scarce availability of contemporary acquisition by S1 and S2 sensors, the deep learning (DL) model is trained by using only the Sentinel-1 input data and the ground truth from the RF result. The proposed approach achieves promising results in the classification of water, sediments, and vegetation along rivers such as the Italian Po River with low computational costs and no concurrency constraints between S1 and S2.
LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching
Authors: Kong Lingyu , Yan Zhiyuan , Shi Hanru , Zhang Ting , Wang Lei
Published: January 2025

Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they are not designed to effectively handle the unique characteristics of satellite videos. To address these challenges, we propose LocaLock, a joint detection and tracking framework for MOT that incorporates feature matching concepts from single object tracking (SOT) to enhance tracking stability and reduce intermittent tracking results. Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. This provides valuable object priors to the detection head, enabling the model to “lock” onto objects with greater accuracy and mitigate the instability associated with small object detection. Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. Furthermore, LocaLock incorporates a novel motion flow (MoF) module to accumulate and exploit temporal information, further enhancing feature robustness and consistency across frames. Rigorous evaluations on the VISO dataset demonstrate the superior performance of LocaLock, surpassing existing methods in tracking accuracy and precision within the demanding satellite video analysis domain. Notably, LocaLock achieved state-of-the-art performance on the VISO benchmark, achieving a multi-object tracking accuracy (MOTA) of 62.6 while ensuring fast running speed.
Robust Access Control for Secure IoT Outsourcing with Leakage Resilience
Authors: Riad Khaled
Published: January 2025

The Internet of Things (IoT) has revolutionized various industries by enabling seamless connectivity and data exchange among devices. However, the security and privacy of outsourced IoT data remain critical challenges, especially given the resource constraints of IoT devices. This paper proposes a robust and leakage-resilient access control scheme based on Attribute-Based Encryption (ABE) with partial decryption outsourcing. The proposed scheme minimizes computational overhead on IoT devices by offloading intensive decryption tasks to the cloud, while ensuring resilience against master secret key leakage, side-channel attacks, and other common security threats. Comprehensive security analysis demonstrates the scheme’s robustness under standard cryptographic assumptions, and performance evaluations show significant improvements in decryption efficiency, scalability, and computational performance compared to existing solutions. The proposed scheme offers a scalable, efficient, and secure access control framework, making it highly suitable for real-world IoT deployments across domains such as smart healthcare, industrial IoT, and smart cities.
Fast Registration Algorithm for Laser Point Cloud Based on 3D-SIFT Features
Authors: Yang Lihong , Xu Shunqin , Yang Zhiqiang , He Jia , Gong Lei , Wang Wanjun , Li Yao , Wang Liguo , Chen Zhili
Published: January 2025

In response to the issues of slow convergence and the tendency to fall into local optima in traditional iterative closest point (ICP) point cloud registration algorithms, this study presents a fast registration algorithm for laser point clouds based on 3D scale-invariant feature transform (3D-SIFT) feature extraction. First, feature points are preliminarily extracted using a normal vector threshold; then, more high-quality feature points are extracted using the 3D-SIFT algorithm, effectively reducing the number of point cloud registrations. Based on the extracted feature points, a coarse registration of the point cloud is performed using the fast point feature histogram (FPFH) descriptor combined with the sample consensus initial alignment (SAC-IA) algorithm, followed by fine registration using the point-to-plane ICP algorithm with a symmetric target function. The experimental results show that this algorithm significantly improved the registration efficiency. Compared with the traditional SAC−IA+ICP algorithm, the registration accuracy of this algorithm increased by 29.55% in experiments on a public dataset, and the registration time was reduced by 81.01%. In experiments on actual collected data, the registration accuracy increased by 41.72%, and the registration time was reduced by 67.65%. The algorithm presented in this paper maintains a high registration accuracy while greatly reducing the registration speed.