bluuebunny/crossref_metadata_2025 huggingface dataset:
Fusarium musae Infection in Animal and Plant Hosts Confirms Its Cross-Kingdom Pathogenicity
Authors: Tava Valeria , Reséndiz-Sharpe Agustin , Vanhoffelen Eliane , Saracchi Marco , Cortesi Paolo , Lagrou Katrien , Velde Greetje Vande , Pasquali Matias
Published: January 2025

Fusarium musae is a pathogen belonging to the Fusarium fujikuroi species complex, isolated from both banana fruits and immunocompromised patients, therefore hypothesized to be a cross-kingdom pathogen. We aimed to characterize F. musae infection in plant and animal hosts to prove its cross-kingdom pathogenicity. Therefore, we developed two infection models, one in banana and one in Galleria mellonella larvae, as a human proxy for the investigation of cross-kingdom pathogenicity of F. musae, along with accurate disease indexes effective to differentiate infection degrees in animal and plant hosts. We tested a worldwide collection of F. musae strains isolated both from banana fruits and human patients, and we provided the first experimental proof of the ability of all strains of F. musae to cause significant disease in banana fruits, as well as in G. mellonella. Thereby, we confirmed that F. musae can be considered a cross-kingdom pathogen. We, thus, provide a solid basis and toolbox for the investigation of the host–pathogen interactions of F. musae with its hosts.
Moesziomyces aphidis Bloodstream Infection in Oncologic Patient: First Report in Poland
Authors: Sulik-Tyszka Beata , Małyszko Jolanta , Pęczuła Agnieszka , Jarzynka Sylwia
Published: January 2025

Moesziomyces spp. (Pseudozyma) is a genus recognized as a new opportunistic human pathogen, causing systemic infections including premature neonates and adult patients. These fungi’s natural resistance to caspofungin enables them to spread through vascular catheter colonization, making them a new etiological agent associated with fungal bloodstream infections (FBIs) and a significant contributor to high mortality rates. In this report, we present a case of fungemia caused by Moesziomyces aphidis species in a patient with medical history that revealed pancreatic cancer infiltrating the duodenum and bile ducts. During hospitalization, the M. aphidis was cultured twice from peripheral blood samples on Sabouraud agar. The strain was sensitive to amphotericin B and voriconazole. In vitro susceptibility testing revealed resistance to fluconazole, caspofungin, anidulafungin, and micafungin. Antifungal therapy with voriconazole resulted in the resolution of clinical symptoms associated with fungal infection. Related to M. aphidis fungemia, we reviewed a total of three cases in Europe published in the PubMed database between 2003 and 2024. To the best of our knowledge, this is the first case of M. aphidis FBI in Poland and the fourth case in an adult patient in Europe.
Phototrophs in Unique Habitats of Thermomineral Springs in Central Serbia
Authors: Milićević Ana , Popović Slađana , Milovanović Vanja , Karadžić Vesna , Savković Željko , Bjelica Vukašin , Krizmanić Jelena , Subakov-Simić Gordana , Jakovljević Olga
Published: January 2025

Thermomineral springs are unique aquatic habitats characterized by high temperatures or mineral-rich water and often host specialized microbial communities. In Serbia, these springs represent an important but under-researched ecological resource whose diverse physicochemical properties are shaped by their geological context. In this study, the physical and chemical properties of Serbian thermomineral springs and their relationship with phototrophic communities in different substrates are investigated. Phototrophic biofilms were categorized into fully submerged and splash zone biofilms, with the former showing higher primary production. Cyanobacteria, Chlorophyta, and Bacillariophyta were recorded, with Bacillariophyta being the predominant division in terms of diversity, followed by Cyanobacteria. Among Cyanobacteria, coccoid forms like Aphanocapsa, Chroococcus, Gloeocapsa and Synechococcus dominated splash zones, while trichal forms such as Leptolyngbya, Oscillatoria and Pseudanabaena were abundant in submerged biofilms, forming thick mats. Unique cyanobacterial taxa, including Desertifilum, Elainella, Geitlerinema, Nodosilinea and Wilmottia, were identified through molecular analysis, underscoring the springs’ potential as habitats for specialized phototrophs. Diatom communities, dominated by Nitzschia and Navicula, exhibited site-specific species influenced by microenvironmental parameters. Statistical analysis revealed ammonia, total nitrogen, and organic carbon as key factors shaping community composition. This study enhances the understanding of these ecosystems, emphasizing their conservation importance and potential for biotechnological applications.
High-Yield Synthesis of N4-(3-Chloro-4-fluorophenyl)-7-((4-methoxybenzyl)thio)quinazoline-4,6-diamine via N-(3-Chloro-4-fluorophenyl)-7-((4-methoxybenzyl)thio)-6-nitroquinazolin-4-amine
Authors: Thapa Susila , Bolliger Jeanne L.
Published: January 2025

The target compound, N4-(3-chloro-4-fluorophenyl)-7-((4-methoxybenzyl)thio)quinazoline-4,6-diamine, was synthesized in two steps from N-(3-chloro-4-fluorophenyl)-7-fluoro-6-nitroquinazolin-4-amine by a nucleophilic aromatic substitution, yielding N-(3-chloro-4-fluorophenyl)-7-((4-methoxybenzyl)thio)-6-nitroquinazolin-4-amine which was characterized by 1H NMR, 13C NMR, 19F NMR, and HRMS. Subsequent reduction yielded the target molecule, N4-(3-chloro-4-fluorophenyl)-7-((4-methoxybenzyl)thio)quinazoline-4,6-diamine. The structure of the target compound was confirmed by 1H NMR, 13C NMR, 19F NMR, HSQC, HMBC, HRMS, and FTIR.
Ab Initio Molecular Dynamics Insights into Stress Corrosion Cracking and Dissolution of Metal Oxides
Authors: Felix Levi C. , Li Qin-Kun , Penev Evgeni S. , Yakobson Boris I.
Published: January 2025

Oxide phases such as α-Fe2O3 (hematite) and α-Al2O3 (corundum) are highly insoluble in water; however, subcritical crack growth has been observed in humidity nonetheless. Chemically induced bond breaking at the crack tip appears unlikely due to sterically hindered molecular transport. The molecular mechanics of a crack in corundum with a reactive force field reveal minimal lattice trapping, leading to bond breaking before sufficient space opens for water transport. To address this, we model a pre-built blunt crack with space for H2O molecule adsorption at the tip and show that it reduces fracture toughness by lowering the critical J-integral. Then, we explore stress-enhanced dissolution to understand the mechanism of crack tip blunting in the oxide/water system. Density functional theory combined with metadynamics was employed to describe atomic dissolution from flat hematite and corundum surfaces in pure water. Strain accelerates dissolution, stabilizing intermediate states with broken bonds before full atom detachment, while the free energy profile of unstrained surfaces is almost monotonic. The atomistic calculations provided input for a kinetic model, predicting the shape evolution of a blunt crack tip, which displays three distinct regimes: (i) dissolution primarily away from the tip, (ii) enhanced blunting near but not at the apex, and (iii) sharpening near the apex. The transition between regimes occurs at a low strain, highlighting the critical role of water in the subcritical crack growth of oxide scales, with dissolution as the fundamental microscopic mechanism behind this process.
Valence Variability Induced in SrMoO₃ Perovskite by Mn Doping: Evaluation of a New Family of Anodes for Solid-Oxide Fuel Cells
Authors: Sánchez de Bustamante Lucía , Santos Silva Romualdo , Martínez José Luis , Fernández-Díaz María Teresa , Aguadero Ainara , Alonso José Antonio
Published: January 2025

We report on a series of SrMo1−xMnxO3−δ perovskite oxides designed as potential anode materials for solid oxide fuel cells (SOFCs). These materials were synthesized using a citrate method, yielding scheelite-type precursors with nominal SrMo1−xMnxO4 compositions, which were further reduced to obtain the active perovskite oxides. Their structural evolution was examined through X-ray diffraction (XRD) and neutron powder diffraction (NPD). These techniques provided insights into the crystallographic changes upon Mn doping, revealing key factors influencing ionic conductivity. Whereas the oxidized scheelite precursors are tetragonal, space group I41/a, the reduced perovskite specimens are cubic, space group Pm-3m, and show the conspicuous absence of oxygen vacancies, even at the highest temperature of 800 °C. The transport properties were analyzed through electrical conductivity measurements, exhibiting a metallic-like behavior. Thermogravimetric analysis (TGA) and dilatometry give insights into the thermal stability and expansion behavior, essential for SOFC operation. Test single SOFCs were built in an electrolyte-supported configuration, on LSGM pellets of 300 μm thickness, assessing the performance of the title materials as anodes. This work emphasizes the critical relationship between the crystal structure and its electrochemical behavior, providing a deeper understanding of how doping strategies can optimize fuel cell performance.
Modeling, Analysis, and Transmission Dynamics of Cassava Mosaic Disease Through Stochastic Fractional Delay Differential Equations
Authors: Minhós Feliz , Raza Ali , Shafique Umar , Mohsin Muhammad
Published: January 2025

Cassava is the sixth most important food crop worldwide and the third most important source of calories in the tropics. More than 800 million people depend on this plant’s tubers and sometimes leaves. To protect cassava crops and the livelihoods depending on them, we developed a stochastic fractional delayed model based on stochastic fractional delay differential equations (SFDDEs) to analyze the dynamics of cassava mosaic disease, focusing on two equilibrium states, the state of being absent from cassava mosaic disease and the state of being present with cassava mosaic disease. The basic reproduction number and sensitivity of parameters were estimated to characterize the level beyond which cassava mosaic disease prevails or declines in the plants. We analyzed the stability locally and globally to determine the environment that would ensure extinction and its persistence. To support the theoretical analysis, as well as the reliable results of the model, the present study used a nonstandard finite difference (NSFD) method. This numerical method not only improves the model’s accuracy but also guarantees that cassava mosaic probabilities are positive and bounded, which is essential for the accurate modeling of the cassava mosaic processes. The NSFD method was applied in all the scenarios, and it was determined that it yields adequate performance in modeling cassava mosaic disease. The ideas of the model are crucial for exploring key variables, which affect the scale of cassava mosaic and the moments of intervention. The present work is useful for discerning the mechanism of cassava mosaic disease as it presents a solid mathematical model capable of determining the stage of cassava mosaic disease.
Joint Transmit Power and Power-Splitting Optimization for SWIPT in D2D-Enabled Cellular Networks with Energy Cooperation
Authors: Lim Dong-Woo , Kang Jae-Mo
Published: January 2025

In this paper, we propose a joint optimization scheme for a transmit power and power-splitting ratio in device-to-device (D2D)-enabled simultaneous wireless information and power transfer (SWIPT) cellular networks, considering energy signal transmission. This energy signal facilitates the energy cooperation between the D2D transmitter (DT) and the CU. Under the proposed scheme, the D2D rate is maximized while guaranteeing that the cellular user (CU) achieves the same performance as in scenarios without D2D communications. In order to solve the formulated nonconvex problem, we leverage the monotonically increasing property of logarithmic functions to transform it into an equivalent convex problem. As a result, we obtain the optimal solution in closed form. Also, the optimal D2D performance is analyzed, and useful insights into the performance improvements achievable through the proposed scheme are obtained. Numerical results demonstrate that the proposed scheme significantly outperforms the baseline scheme.
Matrix-Sequences of Geometric Means in the Case of Hidden (Asymptotic) Structures
Authors: Ahmad Danyal , Khan Muhammad Faisal , Serra-Capizzano Stefano
Published: January 2025

In the current work, we analyze the spectral distribution of the geometric mean of two or more matrix-sequences constituted by Hermitian positive definite matrices, under the assumption that all input matrix-sequences belong to the same Generalized Locally Toeplitz (GLT) ∗-algebra. We consider the geometric mean for two matrices, using the Ando-Li-Mathias (ALM) definition, and then we pass to the extension of the idea to more than two matrices by introducing the Karcher mean. While there is no simple formula for the geometric mean of more than two matrices, iterative methods from the literature are employed to compute it. The main novelty of the work is the extension of the study in the distributional sense when input matrix-sequences belong to one of the GLT ∗-algebras. More precisely, we show that the geometric mean of more than two positive definite GLT matrix-sequences forms a new GLT matrix-sequence, with the GLT symbol given by the geometric mean of the individual symbols. Numerical experiments are reported concerning scalar and block GLT matrix-sequences in both one-dimensional and two-dimensional cases. A section with conclusions and open problems ends the current work.
Characterizing the Ultraviolet (UV) Screening Ability of L-5-Sulfanylhistidine Derivatives on Human Dermal Fibroblasts
Authors: Luccarini Alessia , Marcheggiani Fabio , Galeazzi Roberta , Zuccarotto Annalisa , Castellano Immacolata , Damiani Elisabetta
Published: January 2025

Using sunscreens is one of the most widespread measures to protect human skin from sun ultraviolet radiation (UVR) damage. However, several studies have highlighted the toxicity of certain inorganic and organic UV filters used in sunscreens for the marine environment and human health. An alternative strategy may involve the use of natural products of marine origin to counteract UVR-mediated damage. Ovothiols are sulfur-containing amino acids produced by marine invertebrates, microalgae, and bacteria, endowed with unique antioxidant and UV-absorption properties. This study aimed to evaluate the protective effect of synthetic L-5-sulfanyl histidine derivatives, inspired by natural ovothiols, on human dermal fibroblasts (HDFs) upon UVA exposure. By using a custom-made experimental set-up to assess the UV screening ability, we measured the levels of cytosolic and mitochondrial reactive oxygen species (ROS), as well as cell viability and apoptosis in HDFs, in the presence of tested compounds, after UVA exposure, using flow cytometry assays with specific fluorescent probes. The results show that L-5-sulfanyl histidine derivatives display a UV screening capacity and prevent loss in cell viability, the production of cytosolic and mitochondrial ROS induced by UVA exposure in HDFs, and subsequent apoptosis. Overall, this study sheds light on the potential applications of marine-inspired sulfur-containing amino acids in developing alternative eco-safe sunscreens for UVR skin protection.
Automatic Extraction and Compensation of P-Bit Device Variations in Large Array Utilizing Boltzmann Machine Training
Authors: Zhang Bolin , Liu Yu , Gao Tianqi , Yin Jialiang , Guan Zhenyu , Zhang Deming , Zeng Lang
Published: January 2025

A Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two parameters, α and ΔV, is proposed. Then the weight compensation method is introduced, which can mitigate α and ΔV of P-Bit device variations by rederiving the weight matrix, enabling them to compute as ideal identical P-Bits without the need for weights retraining. Accurately extracting the α and ΔV simultaneously from a large P-Bit array which is prerequisite for the weight compensation method is a crucial and challenging task. To solve this obstacle, we present the novel automatic variation extraction algorithm which can extract device variations of each P-Bit in a large array based on Boltzmann machine learning. In order for the accurate extraction of variations from an extendable P-Bit array, an Ising Hamiltonian based on a 3D ferromagnetic model is constructed, achieving precise and scalable array variation extraction. The proposed Automatic Extraction and Compensation algorithm is utilized to solve both 16-city traveling salesman problem (TSP) and 21-bit integer factorization on a large P-Bit array with variation, demonstrating its accuracy, transferability, and scalability.
The Impact of Compounds Released from Damaged Salad Leaves on the Growth and Virulence of Listeria monocytogenes
Authors: Alsharif Asma , Aldawsari Lama , Koukkidis Giannis , Freestone Primrose
Published: January 2025

Background: Fresh produce such as leafy green salads have recently become recognized as a potential source of food-borne infection by enteric pathogens This study investigated whether compounds released from damaged salad leaves were recognized by Listeria monocytogenes strain EGD and if they impacted its growth and virulence. Methods: The effects of extracts of salad leaves or salad bag fluids were tested on the growth, biofilm formation, and colonization of salad leaves and host cell virulence. Results: The presence of salad extract at a concentration of less than 0.5% v/v and salad bag fluids at a concentration of 10% v/v enhanced the growth in water and serum-based medium by more than 10,000 times over un-supplemented control cultures. Light and scanning electron microscopy, as well as eukaryotic Caco-2 and Galleria mellonella models of infection, showed that leafy green extracts from rocket, lettuce, spinach, and their salad bag fluids significantly increased the ability of Listeria to establish biofilms and infect host cells. Conclusions: This investigation showed that salad leaf extracts can markedly enhance bacterial virulence, which has implications for bagged salad leaf consumer safety if the leaves become contaminated with pathogenic bacteria such as Listeria.
Internally Catalyzed Hydrogen Atom Transfer (I-CHAT)—A New Class of Reactions in Combustion Chemistry
Authors: Asatryan Rubik , Hudzik Jason , Amiri Venus , Swihart Mark T.
Published: January 2025

The current paradigm of low-T combustion and autoignition of hydrocarbons is based on the sequential two-step oxygenation of fuel radicals. The key chain-branching occurs when the second oxygenation adduct (OOQOOH) is isomerized releasing an OH radical and a key ketohydroperoxide (KHP) intermediate. The subsequent homolytic dissociation of relatively weak O–O bonds in KHP generates two more radicals in the oxidation chain leading to ignition. Based on the recently introduced intramolecular “catalytic hydrogen atom transfer” mechanism (J. Phys. Chem. 2024, 128, 2169), abbreviated here as I-CHAT, we have identified a novel unimolecular decomposition channel for KHPs to form their classical isomers—enol hydroperoxides (EHP). The uncertainty in the contribution of enols is typically due to the high computed barriers for conventional (“direct”) keto–enol tautomerization. Remarkably, the I-CHAT dramatically reduces such barriers. The novel mechanism can be regarded as an intramolecular version of the intermolecular relay transfer of H-atoms mediated by an external molecule following the general classification of such processes (Catal. Rev.-Sci. Eng. 2014, 56, 403). Here, we present a detailed mechanistic and kinetic analysis of the I-CHAT-facilitated pathways applied to n-hexane, n-heptane, and n-pentane models as prototype molecules for gasoline, diesel, and hybrid rocket fuels. We particularly examined the formation kinetics and subsequent dissociation of the γ-enol-hydroperoxide isomer of the most abundant pentane-derived isomer γ-C5-KHP observed experimentally. To gain molecular-level insight into the I-CHAT catalysis, we have also explored the role of the internal catalyst moieties using truncated models. All applied models demonstrated a significant reduction in the isomerization barriers, primarily due to the decreased ring strain in transition states. In addition, the longer-range and sequential H-migration processes were also identified and illustrated via a combined double keto–enol conversion of heptane-2,6-diketo-4-hydroperoxide as a potential chain-branching model. To assess the possible impact of the I-CHAT channels on global fuel combustion characteristics, we performed a detailed kinetic analysis of the isomerization and decomposition of γ-C5-KHP comparing I-CHAT with key alternative reactions—direct dissociation and Korcek channels. Calculated rate parameters were implemented into a modified version of the n-pentane kinetic model developed earlier using RMG automated model generation tools (ACS Omega, 2023, 8, 4908). Simulations of ignition delay times revealed the significant effect of the new pathways, suggesting an important role of the I-CHAT pathways in the low-T combustion of large alkanes.
The Effect of Sodium Dodecyl Sulphate Additives on the Electrochemical Performance of Aqueous Zinc Ion Batteries
Authors: Chen Na , Huang Ying , Lv Yuan , Wang Wenju
Published: January 2025

Aqueous zinc ion batteries are considered one of the most promising energy storage devices due to their high safety, low cost, and ease of fabrication. However, the growth of anode dendrites and continuous side reactions during cycling limit the practical application of zinc ion batteries. In this paper, sodium dodecyl sulfate (SDS) was used as an aqueous electrolyte additive to improve the surface deposition of Zn2+. The experimental results show that the SDS electrolyte additive forms a protective layer on the anode surface through electrostatic action and inhibits the growth of dendritic protruding dendrites by increasing the zinc deposition overpotential, as well as by limiting the two-dimensional diffusion of Zn2+ on the negative electrode surface of the aqueous zinc ion battery. As a result, adding SDS improves the discharge specific capacity of NVP/Zn batteries at high voltages and results in improved capacity retention. The cycling stability of NVP/Zn batteries was greatly enhanced by using a battery containing 1% SDS that still had a discharge specific capacity of 71 mAh/g after 100 cycles at a charging current density of 1 C, with a capacity retention rate of 89%. This work provides a simple and feasible solution to the anode problem of aqueous zinc ion batteries.
Advances in Nanoporous Composited Aerogels: Enhancing Durability and Expanding Applications
Authors: Lu Jialu , Siller Lidija , Zhou Bin , Du Ai
Published: January 2025

As highly porous nanomaterials, aerogels are fascinating due to their remarkable structural properties and performance [...]
Effects of Household Cooking on Mineral Composition and Retention in Widespread Italian Vegetables
Authors: Lisciani Silvia , Aguzzi Altero , Gabrielli Paolo , Camilli Emanuela , Gambelli Loretta , Marletta Luisa , Marconi Stefania
Published: January 2025

Background/Objectives: The process of cooking food can result in alterations to its nutrient composition due to changes in water content and the destruction or loss of certain micronutrients that occur in response to heat. This study examined the impact of diverse cooking techniques, namely grilling, microwave, and steam, on the macronutrients and minerals of vegetables commonly utilized in Italian cuisine (two varieties of zucchini, eggplants, and potatoes). Methods: The proximate composition was determined according to the Association of Official Analytical Chemists (AOAC) methods. The content of the minerals (Ca, K, P, Mg, Na, Fe, Zn, and Mn) was determined via ICP plasma after liquid washing. Results: Regarding macronutrients, the results revealed a notable difference in the carbohydrate profiles, whereas mineral retention demonstrated considerable heterogeneity. Some minerals, such as Na, Ca, Mn, and Fe, were found to be more prone to significant increases or losses. Moreover, the true retention factor (TR) calculations indicated that microwave cooking resulted in higher retention compared to the other methods for zucchini, while grilling demonstrated higher TR than microwave cooking for eggplants. Potatoes exhibited lower TR values than the other vegetables and their steaming resulted in higher retention than microwave cooking for K, P, Fe, and Zn. Conclusions: The results confirm the heterogeneous behaviors of minerals in commonly consumed Italian vegetables subjected to different cooking methods. The data underscore the need for additional research to understand the effects of heat treatments on mineral profiles and to determine specific retention factors linked to various cooking techniques. The significant gap between “true” and “apparent” retention factors, caused by changes in water content during cooking, highlight the need for new experimental data to update and enrich the existing literature on this topic.
Improved Performances in Point-to-Multipoint Flexible Optical Transceivers Utilizing Cascaded Discrete Fourier Transform-Spread Inverse Fast Fourier Transform/Fast Fourier Transform-Based Multi-Channel Aggregation/De-Aggregation
Authors: Chen Lin , Gao Yingxue , Jin Wei , Yang Han , Jiang Shenming , Liu Shu , Huang Yi , Tang Jianming
Published: January 2025

The previously proposed cascaded inverse fast Fourier transform/fast Fourier transform (IFFT/FFT)-based point-to-multipoint (P2MP) flexible optical transceivers have the potential to equip future intensity modulation and direct detection (IMDD) optical access networks with excellent flexibility, adaptability, scalability and upgradability. However, due to their cascaded IFFT-based multi-channel aggregations, P2MP flexible transceivers suffer high peak-to-average power ratios (PAPRs). To address the technical challenge, this paper proposes a novel P2MP flexible optical transceiver, which uses a cascaded discrete Fourier transformation-spread (DFT-Spread) IFFT/FFT-based multi-channel aggregation/de-aggregation and standard signal clipping to jointly reduce its PAPRs. The upstream performances of the proposed transceivers are numerically explored in a 20 km IMDD upstream passive optical network (PON). The results indicate that the proposed transceiver’s PAPRs are mainly dominated by the size of the last IFFT operation of the multi-channel aggregation, and are almost independent of modulation format and channel count. Compared to conventional cascaded IFFT/FFT-based P2MP transceivers with and without clipping operations, the proposed DFT-Spread P2MP transceivers can reduce PAPRs by 2.6 dB and 3.5 dB, respectively, for a final IFFT operation size of 1024. More significant PAPR reductions are achievable when the last IFFT operation size is increased further. As a direct result, compared to conventional P2MP transceivers adopting clipping operations only, the proposed transceiver can improve upstream receiver sensitivities by >1.9 dB and the aggregated upstream transmission capacities by >14.1%. Such aggregated upstream transmission capacity enhancements are independent of channel count and become more pronounced for longer transmission distances.
Exploration of Methylmercury Adsorption on Montmorillonite Surfaces Through Density Functional Theory
Authors: Du Jia , Xiao Hanxi , Ren Bao
Published: January 2025

To propel the development of a robust methylmercury immobilisation technology, CH3Hg+ adsorption on montmorillonite surfaces was simulated herein using density functional theory. This study involved a thorough molecular-level analysis, including factors such as electron potential energy, molecular orbital configurations, stable adsorption configurations, adsorption energies, charge distributions, and density of states. The principal findings are summarised as follows: (1) CH3Hg+ adsorption on the (001) surface was characterised by an adsorption energy ranging from −27 to −51.7 kJ/mol. In this case, Hg was attracted to the involved silicon–oxygen ring cavities. Meanwhile, on the (010) surface, CH3Hg+ exhibited an adsorption energy ranging between −119.4 and −154.3 kJ/mol. In this case, Hg was attracted to hydroxyl groups such as ≡Al(OH)(OH2) and ≡Si(OH), forming a covalent bond with the oxygen atom of these groups. (2) Comparative analysis revealed that the adsorption energy of CH3Hg+ on the (010) surface surpassed that on the (001) surface. On the (001) surface, electrostatic interactions were the predominant factor influencing adsorption, while on the (010) surface, electrostatic and covalent bonding interactions were important. Notably, the strength of electrostatic interactions was greater on the (001) surface than on the (010) surface. (3) The formation of covalent bonds between CH3Hg+ and the (010) surface was primarily attributed to the overlap of electron cloud between Hg and surface O atoms. In particular, the interaction between the s orbital of Hg and the p orbital of O facilitated the formation of a σ bond. Overall, these findings provide a theoretical framework for the advancement of efficient in situ immobilisation technologies for methylmercury.
Teaching and Learning in the Islamic West: Some Ideas Regarding the Almohad, Marinid, and Naṣrid Educational Systems
Authors: El Hour Rachid
Published: January 2025

The aim of the study is to highlight some characteristics of education during the Almohad, Marinid, and Naṣrid periods, taking into account to the influences exerted in the Andalusian educational system by the Marinids, especially with the establishment of the Andalusian madrasa. Part of the conclusions reached in this study is mainly based on the data offered by the Maghrebi hagiographic literature. The article provides new information on various aspects of education and the educational system in the Islamic West, especially those linked to models of sanctity and educational institutions in the Maghreb and al-Andalus. Other topics covered here include the historical overview of education in the Maghreb and al-Andalus; some examples of women’s education; education and the Berber language; sanctity and education; educational institutions such as the ribāṭ, mosque, madrasa, or school; and the role of the Berber language in the Maghrebi education. In al-Andalus, education was primarily conducted in Arabic, and it will be analyzed. All these aspects are key in understanding not only the processes of Arabization and Islamization, but also the history of religiosity throughout the Islamic West.
Mapping Vegetation Changes in Mongolian Grasslands (1990–2024) Using Landsat Data and Advanced Machine Learning Algorithm
Authors: Nyamtseren Mandakh , Pham Tien Dat , Vu Thuy Thi Phuong , Navaandorj Itgelt , Shoyama Kikuko
Published: January 2025

Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing and desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 to 2024, combined with vegetation indices such as NDVI and SAVI, along with NDWI and digital elevation models (DEMs), to analyze land cover dynamics in the Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing data into the advanced XGBoost (extreme gradient boosting) machine learning algorithm, we achieved high classification accuracy, with overall accuracies exceeding 94% and Kappa coefficients greater than 0.92. The results revealed a decline in montane grasslands (−6.2%) and an increase in other grassland types, suggesting ecosystem redistribution influenced by climatic and anthropogenic factors. Cropland exhibited resilience, recovering from a significant decline in the 1990s to moderate growth by 2024. Our findings highlight the stability of barren land and underscore pressures from ecological degradation and human activities. This study provides up-to-date statistical data to support decision-making in the conservation and sustainable management of grassland ecosystems in Mongolia under changing climatic conditions.
Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights
Authors: Miao Huiling , Zhang Rui , Song Zhenghua , Chang Qingrui
Published: January 2025

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models.
An Asymmetric Selective Kernel Network for Drone-Based Vehicle Detection to Build a High-Accuracy Vehicle Trajectory Dataset
Authors: Wang Zhenyu , Xiong Lu , Yu Zhuoping
Published: January 2025

To improve the detection accuracy of the drone-based oriented vehicle object detection network and establish high-accuracy vehicle trajectory datasets, we present a freeway on-ramp vehicle (FRVehicle) detection dataset with oriented bounding box annotations for vehicles in freeway on-ramp scenes from drone videos. Based on this dataset, we analyzed the dimension and angle distribution patterns of road vehicle object oriented bounding boxes and designed an Asymmetric Selective Kernel Network. This algorithm dynamically adjusts the receptive field of the backbone network’s feature extraction to accommodate the detection requirements for vehicles of different sizes. Additionally, we estimate vehicle heights with high-precision object detection results, further enhancing the accuracy of the vehicle trajectory. Comparative experimental results demonstrate that the proposed Asymmetric Selective Kernel Network achieved varying degrees of improvement in detection accuracy on both the FRVehicle dataset and DroneVehicle dataset compared to the symmetric selective kernel network in most scenarios, validating the effectiveness of the method.
Experimental Investigations on the Repeatability of the Fire-Resistance Testing of Electric Vehicle Post-Crash Safety Procedures
Authors: Darnikowski Daniel , Mieloszyk Magdalena
Published: January 2025

The widespread adoption of electric vehicles (EVs) has elevated the importance of rigorous safety standards, particularly for fire resistance in post-crash scenarios. Existing testing protocols, such as Regulation No. 100, utilize petrol pool fires to simulate real-world fire hazards but lack comprehensive analysis regarding their repeatability and reliability. This study addresses this critical gap by evaluating the variability and consistency of fire-resistance tests performed on multiple battery energy storage systems (BESSs) under standardized conditions. A custom-built measurement system incorporating thermocouples, anemometers, and hygrometers provided high-resolution data on flame dynamics, ambient conditions, and pool fire efficiency. Statistical evaluations following ISO 5725 series guidelines revealed substantial inconsistencies, including unstable exposure temperatures and sensitivity to local turbulence. These findings call into question the robustness of current testing methods, and we propose an alternative approach employing LPG burners for improved precision and repeatability. By identifying significant flaws in existing standards and offering scientifically grounded enhancements, this work contributes a novel perspective to the field of EV safety, advancing global fire-resistance testing protocols.
Implementation of Wearable Technology for Remote Heart Rate Variability Biofeedback in Cardiac Rehabilitation
Authors: Hu Tiehan , Zhang Xianbin , Millham Richard C. , Xu Lin , Wu Wanqing
Published: January 2025

Cardiovascular diseases pose a significant threat to global health, and cardiac rehabilitation (CR) has become a critical component of patient care. Heart Rate Variability Biofeedback (HRVB) is a non-invasive approach that helps modulate the Autonomic Nervous System (ANS) through Resonance Frequency (RF) breathing, supporting CR for cardiovascular patients. However, traditional HRVB techniques rely heavily on manual RF selection and face-to-face guidance, limiting their widespread application, particularly in home-based CR. To address these limitations, we propose a remote human-computer collaborative HRVB system, “FreeResp”, which features autonomous RF adjustment through a simplified cognitive computational model, eliminating the reliance on therapists. Furthermore, the system integrates wearable technology and the Internet of Things (IoT) to support remote monitoring and personalized interventions. By incorporating tactile guidance technology with an airbag, the system assists patients in performing diaphragmatic breathing more effectively. FreeResp demonstrated high consistency with conventional HRVB methods in determining RF values (22/24) from 24 valid training samples. Moreover, a one-month home-based RF breathing training using FreeResp showed significant improvements in Heart Rate Variability (HRV) (p < 0.05). These findings suggest that FreeResp is a promising solution for home-based CR, offering timely and precise interventions and providing a new approach to long-term cardiovascular health management.
DCLTV: An Improved Dual-Condition Diffusion Model for Laser-Visible Image Translation
Authors: Zhang Xiaoyu , Zhang Laixian , Guo Huichao , Zheng Haijing , Sun Houpeng , Li Yingchun , Li Rong , Luan Chenglong , Tong Xiaoyun
Published: January 2025

Laser active imaging systems can remedy the shortcomings of visible light imaging systems in difficult imaging circumstances, thereby attaining clear images. However, laser images exhibit significant modal discrepancy in contrast to the visible image, impeding human perception and computer processing. Consequently, it is necessary to translate laser images to visible images across modalities. Existing cross-modal image translation algorithms are plagued with issues, including difficult training and color bleeding. In recent studies, diffusion models have demonstrated superior image generation and translation abilities and been shown to be capable of generating high-quality images. To achieve more accurate laser-visible image translation, we designed an improved diffusion model, called DCLTV, which limits the randomness of diffusion models by means of dual-condition control. We incorporated the Brownian bridge strategy to serve as the first condition control and employed interpolation-based conditional injection to function as the second condition control. We also established a dataset comprising 665 pairs of laser-visible images to compensate for the data deficiency in the field of laser-visible image translation. Compared to five representative baseline models, namely Pix2pix, BigColor, CT2, ColorFormer, and DDColor, the proposed DCLTV achieved the best performance in terms of both qualitative and quantitative comparisons, realizing at least a 15.89% reduction in FID and at least a 22.02% reduction in LPIPS. We further validated the effectiveness of the dual conditions in DCLTV through ablation experiments, achieving the best results with an FID of 154.74 and an LPIPS of 0.379.
Digital Twin Smart City Visualization with MoE-Based Personal Thermal Comfort Analysis
Authors: Lam Hoang-Khanh , Lam Phuoc-Dat , Ok Soo-Yol , Lee Suk-Hwan
Published: January 2025

Digital twin technology us used to create accurate virtual representations of objects or systems. Digital twins span the object’s life cycle and keep updated with real-time data. Therefore, their simulation capabilities can be combined with deep learning to create a system that simulates scenarios, enabling analysis. As cities continue to grow and the demand for sustainable development increases, digital twin technology, combined with AI-driven analysis, will play a critical role in shaping the future of urban environments. The ability to accurately simulate and manage complex systems in real time opens up new possibilities for optimizing energy usage, reducing costs, and improving the quality of life for urban residents. In this study, a digital twin application is built to visualize a smart area in South Korea, utilizing a deep learning model for personal thermal comfort analysis, which can be useful for managing and saving building and household energy consumption. Using Cesium for Unreal, a powerful tool for integrating 3D geospatial data, and leveraging DataSmith to convert 3D data into Unreal Engine format, this study also contributes a roadmap for smart city application development, which is currently considered to be lacking. By creating a robust framework for smart city applications, this research not only addresses current challenges but also lays the groundwork for future innovations in urban planning and management.
Comprehensive Quantitative Analysis of Coal-Based Liquids by Mask R-CNN-Assisted Two-Dimensional Gas Chromatography
Authors: Fan Huan-Huan , Wang Xiang-Ling , Feng Jie , Li Wen-Ying
Published: January 2025

A comprehensive understanding of the compositions and physicochemical properties of coal-based liquids is conducive to the rapid development of multipurpose, high-performance, and high-value functional chemicals. However, because of their complex compositions, coal-based liquids generate two-dimensional gas chromatography (GC × GC) chromatograms that are very complex and very time consuming to analyze. Therefore, the development of a method for accurately and rapidly analyzing chromatograms is crucial for understanding the chemical compositions and structures of coal-based liquids, such as direct coal liquefaction (DCL) oils and coal tar. In this study, DCL oils were distilled and qualitatively analyzed using GC × GC chromatograms. A deep-learning (DL) model was used to identify spectral features in GC × GC chromatograms and predominantly categorize the corresponding DCL oils as aliphatic alkanes, cycloalkanes, mono-, bi-, tri-, and tetracyclic aromatics. Regional labels associated with areas in the GC × GC chromatograms were fed into the mask-region-based convolutional neural network’s (Mask R-CNN’s) algorithm. The Mask R-CNN accurately and rapidly segmented the GC × GC chromatograms into regions representing different compounds, thereby automatically qualitatively classifying the compounds according to their spots in the chromatograms. Results show that the Mask R-CNN model’s accuracy, precision, recall, F1 value, and Intersection over Union (IoU) value were 93.71%, 96.99%, 96.27%, 0.95, and 0.93, respectively. DL is effective for visually comparing GC × GC chromatograms to analyze the compositions of chemical mixtures, accelerating GC × GC chromatogram interpretation and compound characterization and facilitating comparisons of the chemical compositions of multiple coal-based liquids produced in the coal and petroleum industry. Applying DL to analyze chromatograms improves analysis efficiency and provides a new method for analyzing GC × GC chromatograms, which is important for fast and accurate analysis.
Embedding Sustainability: Sociotechnical Knowledge Management Guidelines for Digital Decarbonization in the Society 5.0 Era
Authors: Smuts Hanlie , van der Merwe Alta
Published: January 2025

Economic, social, and environmental sustainability emphasizes the need for organizations to integrate sustainability strategies into their core business and business development plans. The era of Society 5.0 is characterized by human-centeredness and digital leadership. It requires embedding sustainability practices and Green Information Technology (IT) while leveraging human–technology relationships to promote social good. However, embedding these practices into organizational culture is challenging due to resistance to change and the need for widespread mindset shifts. This study selected a focus group of eight South African participants to define sociotechnical knowledge management (KM) guidelines for embedding sustainable practices in organizations to promote digital decarbonization aligned with the Society 5.0 vision. Our findings suggest ten elements for the guidelines to incorporate: external environment, organizational context, business drivers, business outcomes, monitoring and evaluation, KM processes, technology enablers, sociotechnical KM tactics, knowledge assets, and execution considerations. By adopting such guidelines as a sustainability strategy, organizations can integrate KM practices into the human-centered and cyber-physical philosophy of Society 5.0. This approach aligns employee behavior with technological tools, enabling organizations to make data-driven decisions, reduce digital waste, and foster a culture of environmental responsibility. In addition, this approach enhances collaboration and innovation, benefiting all stakeholders and advancing sustainable development.