bluuebunny/crossref_metadata_2025 huggingface dataset:
Synchronization of Kuromoto Oscillators on Simplicial Complexes: Hysteresis, Cluster Formation and Partial Synchronization
Authors: Sahoo Samir , Gupte Neelima
Published: February 2025

The analysis of the synchronization of oscillator systems based on simplicial complexes presents some interesting features. The transition to synchronization can be abrupt or smooth depending on the substrate, the frequency distribution of the oscillators and the initial distribution of the phase angles. Both partial and complete synchronization can be seen as quantified by the order parameter. The addition of interactions of a higher order than the usual pairwise ones can modify these features further, especially when the interactions tend to have the opposite signs. Cluster synchronization is seen on sparse lattices and depends on the spectral dimension and whether the networks are mixed, sparse or compact. Topological effects and the geometry of shared faces are important and affect the synchronization patterns. We identify and analyze factors, such as frustration, that lead to these effects. We note that these features can be observed in realistic systems such as nanomaterials and the brain connectome.
Biogas Production Modelling Based on a Semi-Continuous Feeding Operation in a Municipal Wastewater Treatment Plant
Authors: Lima Derick , Li Li , Appleby Gregory
Published: February 2025

Anaerobic digestion is a common method for treating sewage sludge in municipal wastewater treatment plants (WWTPs). However, modelling this process can be very challenging due to the complexity of biochemical reactions. This paper presents a novel methodology that estimates biogas production from sewage sludge by considering the semi-continuous sludge-feeding process of the digester. In most WWTPs, the sewage sludge treatment operates in a dynamic process; therefore, using a time-dependent tool that represents this dynamic process is essential for accurately representing biogas production. The biogas production results from the proposed model are compared against the historical data for a large-scale WWTP located in Sydney, Australia. The proposed model shows great accuracy and follows the historical data trend very precisely. The average biogas production based on historical data for 2020, 2021, and 2022 was 37,337 m3/d, 31,695 m3/d, and 23,350 m3/d, whereas for the proposed model, it was 37,960 m3/d, 30,465 m3/d, and 23,080 m3/d. Over the three-year period, the average biogas production was 30,794 m3/d for historical data and 30,503 m3/d for the proposed model, which shows a great level of accuracy (R2 of 0.85 and average error of 4.64%) on the results of the proposed model and WWTP historical data.
Transformative Impacts of AI and Wireless Communication in CSP Heliostat Control Systems
Authors: Liu Quanwu , Dai Zengli , Wei Yuan , Wang Dongxiang , Xie Yu
Published: February 2025

In this review, the transformative impact of integrating artificial intelligence (AI) and wireless communication technologies into the heliostat control systems of concentrated solar power (CSP) plants are explored. Heliostat control systems are categorized based on wired and wireless implementations, and calibration methods are analyzed from traditional methods, auxiliary equipment, and AI in detail. The applications of artificial intelligence, machine learning, and deep learning techniques enhance the accuracy, control ability, and prediction performance of CSP heliostat control systems. At the same time, wireless communications play an important role in reducing costs, enhancing scalability, and enabling more flexible deployment. The synergistic impact of AI and wireless technologies improves the efficiency, reliability, and economic viability of heliostat systems, and shows great potential in global energy transition.
Absorption and Desorption Heat of Carbon Dioxide Capture Based on 2-Amino-2-Methyl-1-Propanol
Authors: Guo Jia , Wang Xin , Li Yi , Li Qingfang , Liu Haili , Wang Hui
Published: February 2025

In chemical absorption for carbon capture, the regeneration heat is a key factor determining solvent regeneration energy consumption, and the sterically hindered amine 2-amino-2-methyl-1-propanol (AMP) has great potential for application. In this paper, a CO2 reaction heat measurement system designed and constructed by our team was used to perform a comparative study on AMP and monoethanolamine (MEA). Moreover, five additives—MEA, diglycolamine (DGA), diethanolamine (DEA), methyldiethanolamine (MDEA), and piperazine (PZ)—were introduced into AMP-based solutions to investigate the promotion performance of these blended solvents. The results revealed that although AMP exhibited a slower absorption rate compared to MEA, it demonstrated a higher CO2 loading capacity and cyclic capacity, as well as a lower reaction heat, making it advantageous in terms of regeneration energy consumption. At the same total concentration, the absorption capacity of blended solutions (excluding AMP-MEA solutions) was generally lower than that of single-component AMP solutions. Among these additives, MEA and PZ could enhance the absorption rate clearly yet increase the reaction heat at the same time; DGA and DEA could decrease the overall absorption performance. Generally, AMP-MDEA solutions showed the best desorption performance, with the 15 wt% AMP + 5 wt% MDEA mixture demonstrating the lowest regeneration heat and good cyclic capacity.
Optimal Allocation and Sizing of BESS in a Distribution Network with High PV Production Using NSGA-II and LP Optimization Methods
Authors: Trivić Biljana , Savić Aleksandar
Published: February 2025

Battery energy storage systems (BESSs) can play a significant role in overcoming the challenges in Distribution Systems (DSs) with a high level of penetration from renewable energy sources (RESs). In this paper, the goal is to determine the optimal location, size, and charging/discharging dispatches of BESSs in DSs with a high level of photovoltaic (PV) installations. The problem of the location and size of BESSs is solved with multi-criteria optimization using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The criteria of the multi-criteria optimization are minimal investment costs for BESS and improvement of the network performance index. The network performance index includes the reduction in annual losses of active energy in DSs and the minimization of voltage deviations. The dispatch of a BESS is determined using auxiliary optimization. Linear Programming (LP) is used for auxiliary optimization, with the aim of dispatching the BESS to smooth the load profile in DS. The proposed optimization method differs from previous studies because it takes in its calculations all days of the year. This was performed using the K-means clustering technique. The days of one year are classified by the level of consumption and PV production. The optimization was performed for five different levels of PV penetration (60%, 70%, 80%, 90%, and 100%) and for two scenarios: the first with one BESS and the second with two BESSs. The proposed methodology is applied to the IEEE 33 bus balanced radial distribution system. The results demonstrate that with an optimal choice of location and parameters of the BESS, significant improvement in network performance is achieved. This refers to a reduction in losses of active power, improvement of voltage profile, smoothing the load diagram, and reducing the peak load. For the scenario with one BESS and PV penetration of 100%, the reduction in daily energy losses reaches a value of up to 10% compared to the base case (case without a BESS). The reduction in peak load goes to 20%. Further, the highest voltage during the day is significantly lower in all buses compared to the base case. Similarly, the lowest voltage during the day is considerably higher. The methodology from this paper can be applied to any radial distribution network with a variable number of BESSs. The testing results confirm the effectiveness of the proposed method.
Optimization of Fault Current Limiter Reactance Based on Joint Simulation and Penalty Function-Constrained Algorithm
Authors: Zhao Jun , Xing Chao , Zhang Zhigang , Liang Boyuan , Sun Lu , Wei Bin , Qin Weiqi , Gao Shuguo
Published: February 2025

This paper proposes a novel optimization method for fault current limiter (FCL) reactance configuration based on joint simulation and penalty function constraint optimization. By integrating MATLAB and ATP for joint simulation, the method accurately derives the constraint conditions of the objective optimization function, providing critical data support for the optimization process. To address the challenges of high computational complexity and solution difficulties in constrained optimization, the Penalty Function Method (PFM) is employed to transform the original constrained optimization problem into a standard unconstrained optimization problem, significantly reducing computational complexity and ensuring the feasibility of the solution. On this basis, the Gravitational Search Algorithm (GSA) is applied to compute the optimal reactance value. Through comparative analysis of engineering case studies, the superiority of the GSA over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in optimization performance is validated, further confirming the accuracy and efficiency of the proposed method. The results indicate that this method not only achieves precise calculation results but also significantly improves computational efficiency. Moreover, the integration of PFM and GSA demonstrates excellent robustness, providing reliable technical support for the optimized deployment of fast-switching fault current limiters in large-scale power grids.
Research on Export Oil and Gas Concentration Prediction Based on Machine Learning Methods
Authors: Wang Xiaochuan , Zhu Baikang , Zheng Huajun , Wang Jiaqi , Chen Zhiwei , Hong Bingyuan
Published: February 2025

With the oil industry’s increasing focus on environmental protection and the growing implementation of oil and gas recovery devices in depots, it is crucial to investigate the outlet concentrations of oil and gas from these devices. This research aims to reduce energy consumption while enhancing the efficiency of oil and gas recovery processes. This paper investigates the prediction of outlet oil and gas concentration based on the process parameters of oil and gas recovery devices in oil depots. This study employs both regression and classification machine learning models. Most regression models achieve a goodness-of-fit of approximately 0.9 and an accuracy error of about 30%. Additionally, most classification models attain over 90% accuracy, with predictions of high oil and gas concentrations reaching up to 84.5% accuracy. Both models demonstrate that the Random Forest method is more effective in predicting the exported oil and gas concentration with multiple-parameter inputs, providing a relevant basis for subsequent control of exported oil and gas concentration.
Enhancing Energy Autonomy in an e-Houseboat: Integration of Renewable Energy Sources with Hybrid Energy Storage Systems
Authors: Grela Jakub , Skała Aleksander , Latoń Dominik , Bańczyk Katarzyna
Published: February 2025

This paper explores the development and optimization of a hybrid renewable energy system (HRES) integrated with a hybrid battery energy storage system (HBESS) to achieve energy autonomy for an e-Houseboat. The e-Houseboat is a floating residential unit equipped with advanced sustainable technologies, including photovoltaic panels, wind turbines, and a hybrid battery storage system consisting of lithium iron phosphate (LFP) and lead-acid batteries. The primary goal of this study was to design an energy-autonomous e-Houseboat capable of sustaining energy demands for at least one month without external power sources, regardless of the season. This study included a comprehensive analysis of energy generation potential from renewable sources across different European locations, detailed simulations of the energy storage system, and the development of energy management function for a houseboat automation system. The results demonstrate the feasibility of achieving the desired energy autonomy by leveraging the synergistic benefits of multiple energy storage technologies and optimizing energy management strategies. The experiment demonstrated that the implemented solutions enabled the facility to achieve energy autonomy for a period of 7 months.
Examining Key Barriers and Relevant Promotion Strategies of Green Buildings Adoption in Tanzania
Authors: Ikingura Andrew , Grabiec Anna M. , Radomski Bartosz
Published: February 2025

Green buildings (GBs) offer significant potential to address environmental challenges and support nations to meet their sustainable development goals. Numerous developed countries have prioritized green building technologies (GBTs) adoption in their construction industry, whilst other nations are still hampered by several issues that slow down the level of their adoption. To effectively promote the adoption of GBTs, it is crucial to identify the key barriers and to prioritize relevant promotion strategies suitable to be emphasized towards specific geographic locations. This study aims to examine the key barriers hindering the adoption of GBTs and recommend suitable strategies to promote GBT adoption in the context of Tanzania. A questionnaire survey was carried out with 61 experts (architects, engineers, urban planners, economists, and green technologists) from Tanzania working in the field related to green buildings. A ranking analysis technique was used to rank the barriers and relevant promotion strategies to be prioritized. Measures of descriptive statistics, including mean and standard deviation, were carried out to sort the barriers and promotion strategies in the order of their potentiality. A Spearman’s rank correlation test was conducted to verify the consistency of the mean scores and derived ranks, and cluster analysis was also performed to group factors that share similar characteristics into the same clusters. Unfamiliarity with GBTs; a lack of expertise or few professionals in the GB field; the presence of few successful demonstrative projects to convince clients; and a low promotion of GBTs together with limited user knowledge were noted as the most critical barriers hindering the adoption of GBTs in Tanzania. Cluster analysis shows that knowledge-related barriers are majorly hindering the adoption of GBTs in Tanzania. Thus, a multi-faceted approach of institutional interventions and broadening public awareness should majorly be taken into account simultaneously to enhance adoption rates. The analysis also revealed that the establishment of an institutional framework; educational programs for policymakers, developers, and contractors related to GB guidelines; providing awareness to clients through advertisements; and imposing mandatory regulations in shifting towards GBTs are the most relevant promotion strategies to widen the adoption of GBs. The findings of this study provide valuable insights to various stakeholders for policy development in the construction industry and the future implementation of GBs in Tanzania.
A Novel Concept of High-Voltage Balancing on Series-Connected Transistors for Use in High-Speed Instrumentation
Authors: Despotuli Alexandr , Kazmiruk Viacheslav , Despotuli Anastasia , Andreeva Alexandra
Published: February 2025

The novel concept of reliable voltage balancing on N fast high-voltage (HV) transistors, connected in series, is verified by computer modeling/experimental testing. The essence of the concept is to transfer the balancing function from conventional snubbers, resistive dividers, varistors, etc., or sophisticated gate-side control techniques, to “individual” resistive loads (of transistors) connected to “individual” HV sources of power. The concept has been implemented in the recently patented architecture of HV rectangular pulse generators. The operation of any series-connected stack requires (1) synchronization of control actions on gates of all N transistors; (2) static HV balancing on all transistors in OFF states; and (3) dynamic HV balancing during ON↔OFF transients. The goals of the new design are to achieve an exceptionally high level of HV balancing in modes (2) and (3), as well as to simplify the process of configuring/customizing the circuit. Testing confirms that new generators exhibit minimal ripple during ON→OFF transients. Reliable operation with high-quality rectangular pulses is ensured even at a voltage slew rate of more than 100 kV/µs, while each transistor blocks a voltage close to the maximum value specified in its datasheet. The presented novelties are likely suitable for high-speed instrumentation.
Energy Efficiency Analysis of Water Treatment Plants: Current Status and Future Trends
Authors: Skoczko Iwona
Published: February 2025

The energy efficiency of water treatment plants (WTPs) plays a key role in the sustainable management of water resources. In the face of increasing water demand, climate change, and increasingly stringent environmental regulations, optimising the energy consumption of treatment processes is becoming a priority for water system operators and decision-makers alike. Water treatment plants, depending on the type of water source served (groundwater, infiltration, surface water), vary considerably in terms of their technological design, which directly affects their energy efficiency and operating costs. According to the International Water Association, the water sector accounts for approximately 4% of global electricity consumption, a significant proportion of which is consumed by water treatment and distribution processes. Electricity is used in many process steps, such as water pumping, aeration, filtration, disinfection, and filter flushing. The energy consumption of a System for Upgrading Water (SUW) depends not only on the quality of taken raw water, but also on the size of the station, used technologies, and operation organisation. This study shows that implementing high-efficiency pumping systems and AI-based optimisation can reduce energy consumption in WTPs by 20–30%. The introduction of membrane filtration in surface water plants has demonstrated a reduction in energy use by up to 50%, while the use of biogas from sludge treatment has cut external energy demand by 15–25%. The results emphasise the potential to reduce CO2 emissions by 10–20% compared to conventional treatment methods. However, achieving significant reductions in energy consumption in SUW requires a comprehensive understanding of the diversity of water facilities, technological processes, and specific energy requirements.
Measurement-Based Assessment of Energy Performance and Thermal Comfort in Households Under Non-Controllable Conditions
Authors: Stavrakakis George M. , Bakirtzis Dimitris , Tziritas Dimitrios , Zervas Panagiotis L. , Fotakis Emmanuel , Yfanti Sofia , Savvakis Nikolaos , Katsaprakakis Dimitris A.
Published: February 2025

The current research presents a practical approach to assess energy performance and thermal comfort in households through monitoring campaigns. The campaigns are conducted in a Greek city, involving the installation of low-intrusive recording devices for hourly electricity consumption, indoor temperature, and relative humidity in different residences in winter and summer periods. The recorded indoor environmental conditions are initially compiled to the Predicted Mean Vote (PMV) index, followed by the formulation of databases of hourly electricity consumption, PMV and local outdoor climate conditions retrieved by an official source of meteorological conditions. A special algorithm for database processing was developed which takes into account the eligibility of data series, i.e., only the ones corresponding to non-zero electricity consumption are treated as eligible. First, the sequential temporal progress of energy consumption and thermal comfort is produced towards the assessment of energy-use intensity and thermal comfort patterns. Secondly, through summing of the electricity consumption within 0.5-step PMV intervals, under three outdoor temperature intervals with approximately the same number of eligible measurements, reliable interrelations of energy consumption and PMV are obtained even for residences with limited amount of measured data. It is revealed that the weekly electricity consumption ranged within 0.15–3.59 kWh/m2 for the winter cases and within 0.29–1.72 kWh/m2 for the summer cases. The acceptable range of −1 ≤ PMV ≤ 1 interval holds an occurrence frequency from 69.46% to 93.39% and from 37.94% to 70.31% for the winter and summer examined cases, respectively. Less resistance to discomfort conditions is observed at most of the summer examined households exhibiting the electricity peak within the 1 ≤ PMV ≤ 1.5 interval, contrary to the winter cases for which the electricity peak occurred within the −1 ≤ PMV ≤ −0.5 interval. The study provides graphical relationships of PMV and electricity consumption under various outdoor temperatures paving the way for correlating thermal comfort and energy consumption.
Transient Overvoltage Prediction Method for Renewable Energy Stations via Knowledge-Embedded Enhanced Deep Neural Network
Authors: Wang Guangyao , Liu Jun , Liu Jiacheng , Li Yuting , Mo Tianxiao , Ju Sheng
Published: February 2025

When a line-commutated converter–high-voltage direct current (LCC-HVDC) transmission system with large-scale integration of renewable energy encounters HVDC-blocking events, the sending-end power system is prone to transient overvoltage (TOV) risks. Renewable energy units that are connected via power electronic devices are susceptible to large-scale cascading disconnections due to electrical endurance and insulation limitations when subjected to an excessively high TOV, which poses a serious threat to the safe and stable operation of the system. Therefore, the prediction of TOV at renewable energy stations (RES) under DC blocking (DCB) scenarios is crucial for developing strategies for the high-voltage ride-through of renewable energy sources and ensuring system stability. In this paper, an approximate analytical expression for the TOV at RES under DCB fault conditions is firstly derived, based on a simplified equivalent circuit of the sending-end system that includes multiple DC transmission lines and RES, which can take into consideration the multiple renewable station short-circuit ratio (MRSCR). Building on this, a knowledge-embedded enhanced deep neural network (KEDNN) approach is proposed for predicting the RES’s TOV for complex power systems. By incorporating theoretical calculation values of the TOV into the input features, the task of the deep neural network (DNN) shifts from mining relationships within large datasets to revealing the correlation patterns between theoretical calculations and real values, thereby improving the robustness of the prediction model in cases of insufficient training data and irrational feature construction. Finally, the proposed method is tested on a real-world regional power system in China, and the results validate the effectiveness of the proposed method. The approximate analytical expression for the TOV at an RES and the KEDNN-based TOV prediction method proposed in this paper can provide valuable references for scholars and engineers working in the field of power system operation and control, particularly in the areas of overvoltage theoretical calculation and mitigation.
Analysis and Research on the Influence of a Magnetic Field Concentrator on the Gear Heating Process Using a High-Frequency Resonant Inverter
Authors: Legutko Piotr
Published: February 2025

The article presents basic information about the induction heating of gears, which are widely used in various industries. This article presents the methodology and results of a coupled FEM simulation of a circuit model for a power electronics converter connected to an inductor-charged heating system. The induction heating of gears was performed using a high-frequency inverter with SiC MOSFET transistors. A prototype inverter was built using a full-bridge structure with a series-parallel resonant circuit. The operating frequency was 350 kHz, the output power of the inverter was 3.5 kW, and the drain efficiency was equal to 96%. Coupled simulation was performed for a charge in the form of a gear made of 42CrMo4 steel (material parameters are provided in the article) for two types of heating: with and without a magnetic field concentrator. In addition, the article presents the results of co-simulation studies in the following form: a distribution of magnetic induction in the gear, energy density in the gear, the characteristics of energy density in a single tooth on the 8 mm length and the temperature of the tooth tip for two types of induction heating.
Allometric Growth and Biomass Allocation in Haloxylon ammodendron Forests: Implications for Desertification Control and Ecosystem Rehabilitation in the Jilantai Salt Lake Region
Authors: Zhang Qian , Wang Ruidong
Published: February 2025

Haloxylon ammodendron plays a pivotal role in combating aeolian desertification and restoring degraded arid ecosystems. Strategic afforestation protocols for this xerophytic species offer dual benefits in ecological stabilization and socioeconomic development, particularly in ecotonal zones between desert and oasis ecosystems, as exemplified by the Jilantai Salt Lake region. This investigation employs allometric scaling analysis to elucidate biomass allocation strategies in H. ammodendron plantations under three distinct silvicultural approaches: soil moisture retention afforestation, water flushing afforestation, and mechanical hole afforestation. Key findings demonstrate that water flushing afforestation treatment induced significant biomass enhancement (total biomass: 1718.69 ± 214.28 g), with phylloclade (photosynthetic branch) and vegetative organ biomass increasing by 29.03% and 60.34%, respectively, compared to conventional methods. Conversely, soil moisture retention afforestation preferentially promoted lignification processes, maximizing biomass allocation to structural components (stems: 15.2% increase) and reproductive structures (inflorescences: 22.7% elevation). Standardized major axis regression revealed differential scaling exponents among organ pairs under varying treatments (stem-phylloclade: 1.798; inflorescence-phylloclade: 1.752; vegetative-reproductive: 1.672; p < 0.001), indicating treatment-specific allometric allocation patterns. Notably, soil moisture retention afforestation induced lateral crown expansion through enhanced meristematic activity in secondary branches (p < 0.01), contrasting with the apical dominance observed in water flushing afforestation and mechanical hole afforestation specimens. These morphological divergences suggest resource allocation trade-offs between vertical exploration and horizontal exploitation strategies. The differential growth trajectories were strongly correlated with edaphic moisture redistribution patterns (R2 = 0.83, p < 0.001), as quantified using soil water potential measurements. This study provides mechanistic insights into phenotypic plasticity responses to silvicultural interventions. These findings advance our understanding of allometric growth regulation in a psammophyte and establish an empirical basis for optimizing desert afforestation strategies in arid transitional ecotones.
Comparative Analysis of Drought-Driven Water-Use Strategies in Mangroves and Forests
Authors: Li Xin , An Feng , Wang You , Gong Manyao , Xu Huiting , Zheng Binbin , Dong Lu , Yu Rui
Published: February 2025

Mangroves grow in high-salinity environments with low soil water potential (Ψs), where high light intensity and strong winds increase the vapor pressure deficit (VPD), causing physiological drought and high transpiration demand (Δw), which limits carbon dioxide (carbon gain) for photosynthesis. This study explored how mangroves optimize their carbon-gain-to-water-loss ratio (water-use strategies) to maximize carbon gain during both dry and rainy seasons. We also calculated the relative costs of key leaf traits and compared them with those of terrestrial forests under the carbon gain optimization model. The results revealed that (1) with increasing Δw, terrestrial forests primarily adjusted leaf hydraulic conductance (Kleaf), while mangroves altered the difference in water potential (ΔΨ); (2) as Ψs decreased, πtlp of both terrestrial forests and mangroves increased; (3) terrestrial forests developed a more balanced distribution of leaf trait costs between osmotic pressure (46.7 ± 0.2%) and stomata (43.3 ± 1.2%), whereas mangroves had the highest cost in osmotic pressure (49.04 ± 0.03%) and the lowest cost in stomata (11.08 ± 3.00%) during the rainy season; and (4) although mangroves showed differences in trait values between dry and rainy seasons, their responses to drought stress remained consistent. These findings provided new theoretical insights into how mangroves maintain high carbon gain and water-use efficiency under extreme environmental conditions, which is important to improve mangrove conservation efforts and contribute to climate mitigation policies.
Applying Large Language Model Analysis and Backend Web Services in Regulatory Technologies for Continuous Compliance Checks
Authors: Li Jinying , Maiti Ananda
Published: February 2025

Regulatory technologies (RegTechs) are a set of electronic and digital technologies applied to check compliance in industrial processes. Such applications also aim to simplify the process of data collection and exchange according to the expected format over the cloud or the internet. Industrial processes are required to meet basic regulatory requirements according to law and follow a set of industry practices. Industry practices must be compliant with the basic regulatory requirements. Such applications also need a high level of privacy to protect the individual participant’s data from competitors but are revealed to the relevant regulatory agencies. However, there cannot be a standard data procurement method, as the industrial processes are different for individual businesses and often involve various stages of data collection with different aims. Also, the regulatory requirements may be changed over time. These challenges can be addressed over an online system that uses large language models (LLM) to perform continuous compliance checks. With LLMs, RegTech can be easily scaled up to meet new requirements. It can also help with data analysis and reformatting for different stakeholders in RegTech, such as producers, supply chains, regulators, and financial institutions. It can check for acceptable values with regards to RegTech through either numeric comparisons or enumerations matching. In this paper, we propose a comprehensive RegTech framework backed by LLM and web services. We propose a method to measure the accuracy of LLM in returning appropriate responses for RegTech queries and herein analyze several LLMs to conclude that they are satisfactory for basic tasks, but a dedicated LLM is needed for RegTech. Furthermore, we test the LLM’s tool-calling capabilities to identify and use dedicated functions in the form of web services to enhance the analytical accuracy and consistency of RegTech-related prompts.
Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels
Authors: Muralinath Rashmi N. , Pathak Vishwambhar , Mahanti Prabhat K.
Published: February 2025

Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlation and mean phase locking value (mPLV), with adjacency matrices generated via threshold-binarised connectivity. Graph kernels, including Random Walk, Weisfeiler–Lehman (WL), and spectral-decomposition (SD) kernels, were evaluated for latent space feature extraction by approximating Koopman spectral decomposition. The potential of graph Koopman embeddings in identifying latent metastable connectivity structures has been demonstrated with empirical analyses. The robustness of these features was evaluated using classifiers such as Decision Trees, Support Vector Machine (SVM), and Random Forest, on Epilepsy-EEG from the Children’s Hospital Boston’s (CHB)-MIT dataset and cognitive-load-EEG datasets from online repositories. The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. This work advances EEG-based neuro-marker estimation, facilitating reliable assistive tools for prognosis and cognitive training protocols.
Hybrid Carrageenans Versus Kappa–Iota-Carrageenan Blends: A Comparative Study of Hydrogel Elastic Properties
Authors: Monteiro Maria Alice Freitas , Faria Bruno , Moraes Izabel Cristina Freitas , Hilliou Loic
Published: February 2025

A comparison between the gel properties of blends of kappa- and iota-carrageenans (K+Is) and hybrid carrageenans (KIs) with equivalent chemical compositions is here presented. The objective is to assess under which conditions hybrid carrageenans are valuable alternative to blends of kappa- and iota-carrageenans for gelling applications and to contribute to the identification of phase-separated structures or co-aggregated helices. Phase states constructed in sodium chloride and in potassium chloride confirm that KIs build gels under a much narrower range of ionic strength and polysaccharide concentration. Hybrid carrageenans displayed salt specificity, forming gels in KCl but not in NaCl, highlighting their limited gelling potential in Na+ environments. A two-step gelation mechanism was found in both systems at lower ionic strengths and when iota carrageenan is the major component. The shear elastic moduli of KI gels are overall smaller than those of blends, but the opposite is observed at lower ionic strengths in KCl and in systems richer in iota-carrageenans. The nonlinear elastic properties of gels do not relate to the use of blends or hybrid carrageenans for their formulation. Instead, larger contents in iota-carrageenans lead to gels able to sustain larger strains before yielding to a fluid state. However, these gels are more prone to strain softening, whereas strain hardening is measured in gels containing more kappa-carrageenan, irrespective of their blend or hybrid structure.
Tuning the Swelling Behavior of Superabsorbent Hydrogels with a Branched Poly(aspartic acid) Crosslinker
Authors: Shin Sunggyu , Kim Sangjin , Hong Sukhyeon , Kim Namhyun , Kang Juhwan , Jeong Jaehyun
Published: February 2025

Superabsorbent hydrogels used in products like diapers, hygiene items, and medical patches depend on their swelling ratio. However, improving the swelling performance across hydrogel assemblies remains challenging. This study identifies a decline in the water absorption capacity in hydrogel assemblies with high swelling ratios, as confirmed through MRI analysis, and introduces a solution using a branched crosslinker to address this issue. The branched crosslinker was synthesized by grafting acrylate groups onto poly(aspartic acid)s. This branched poly(aspartic acid) crosslinker was incorporated into hydrogels with the same number of acrylate groups as PEGDA575, a conventional linear crosslinker, and their absorption performance and behavior were compared. The results showed that hydrogels with the branched crosslinker exhibited a swelling ratio twice as high as the PEGDA575 group, with a slower initial absorption rate, demonstrating a more gradual swelling behavior. Additionally, while the initial absorption rate was approximately 30% slower than the PEGDA575 group, the absorption rate showed a gradual decrease of less than 15% within the first 30 min, indicating sustained absorption behavior. Overall, the new strategy presented in this study of introducing a branched crosslinker into hydrogels is expected to be a useful application for existing industries by enhancing swelling ratios and promoting continuous absorption.
Worldwide Population Dynamics of Salmonella Saintpaul: Outbreaks, Epidemiology, and Genome Structure
Authors: Panzenhagen Pedro , Shah Devendra H. , Rodrigues Dalia dos Prazeres , Conte Junior Carlos Adam
Published: February 2025

Background/Objectives: Salmonella Saintpaul (SSa) is increasingly linked to foodborne outbreaks in Brazil and globally. Despite its rising public health significance, its epidemiology, genomic diversity, and pathogenic potential remain underexplored. This study addresses these gaps through a comprehensive global analysis of SSa population dynamics, outbreak patterns, and genetic structures, along with an in-depth phenotypic and genomic characterization of strain PP_BR059, isolated from a hospitalized patient in Ceará, Brazil. Methods: We analyzed 1,953 publicly available SSa genomes using core-genome multi-locus sequence typing (cgMLST), antimicrobial resistance (AMR) profiling, pan-genome analysis, and phylogenetic inference. A genome-wide association study (GWAS) identified genetic determinants of virulence and AMR. The invasiveness and intracellular survival of PP_BR059 were assessed using in vitro macrophage infection assays, while whole-genome sequencing (WGS) provided genetic insights. Results: Phylogenetic analysis identified 49 sequence types (STs), with ST-50 (787 genomes) and ST-27 (634 genomes) being most prevalent. ST-50 included all clinical strains from South America, including PP_BR059. AMR analysis showed 60% of SSa genomes were pan-susceptible, while ST-27 had the highest proportion of AMR strains. GWAS revealed distinct evolutionary lineages within ST-50 and ST-27. PP_BR059 exhibited lower macrophage invasion (3.82%) but significantly higher intracellular survival at 2 h (68.72%) and 20 h (25.68%) post-infection. WGS confirmed a pan-susceptible AMR profile and plasmid absence. Conclusions: This study highlights SSa’s global dissemination, evolutionary trends, and pathogenic variability, emphasizing the need for molecular surveillance to inform public health interventions.
Fostering Workforce Wellness: Insights from Nurse Managers and Early Childhood Educators
Authors: Charlot-Swilley Dominique , Zuskov Sabrina , Curtis Latisha , Mitchell Stephanie , Anderson Elva
Published: February 2025

Background/Objectives: The COVID-19 pandemic has highlighted the challenges faced by essential professionals, such as nurse managers and early childhood educators (ECEs), who grapple with heightened stress, burnout, and compassion fatigue. In response, the Compassion, Practice, Relationships, and Restoration (CPR2) program was designed as a virtual, structured wellness intervention to address these pressing concerns among caregiving professionals. Methods: A mixed-methods study was employed to evaluate the feasibility, acceptability, and preliminary outcomes of CPR2, implemented across two cohorts: nurse managers in a pediatric hospital and early childhood educators (ECEs) serving equity-deserving communities. Participants completed electronic surveys at pre- and post-test assessing mindfulness, professional quality of life, healthy lifestyle behaviors, and perceived social cohesion. One month post-program, focus groups were conducted using a facilitation guide to evaluate program elements, including group structure, expectations, discussion themes, and sustainability. Results: Quantitative findings suggest that while nurse managers experienced significant reductions in compassion fatigue, along with improvements in mindfulness and perceived cohesion, ECEs exhibited stable levels of stress and burnout. Both cohorts reported enhanced sleep quality, emphasizing the program’s potential to foster critical aspects of wellbeing. Qualitative participant feedback highlighted the importance of organizational readiness, leadership engagement, and program flexibility for successful implementation. Conclusions: The study highlights the need for tailored, context-sensitive wellness interventions that recognize the unique challenges faced by different caregiving roles. It also emphasizes the potential for sustained impact when wellness initiatives are integrated into the organizational culture, further reinforcing the importance of ongoing commitment to workforce wellbeing in high-stress environments.
Effects of Motor Skills and Physical Activity Interventions on Motor Development in Children with Autism Spectrum Disorder: A Systematic Review
Authors: Xing Yu , Wu Xueping
Published: February 2025

Background: Autism spectrum disorder (ASD) is an early childhood and lifelong neurodevelopmental disorder. Many studies have confirmed that motor skills and physical activity interventions can improve motor development in ASD individuals and ultimately improve their quality of life. However, systematic evidence is lacking on whether motor skills and physical activity interventions improve motor development among children with ASD. Methods: A systematic search of the CNKI, PsycINFO, PubMed, Web of Science, and Google Scholar databases was conducted for publications through 30 July 2023. Citation tracking and reference tracking were also used, and this study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Results: Of 8908 studies initially retrieved, 57 met the selection criteria and were evaluated. The overall quality of the evidence, assessed using PEDro, was low. The evaluated studies included 1622 children with ASD, among which 517 were males, from level II to IV, and ranging in age from 3 to 17 years. Five types (physical activity interventions, motor skill interventions, hippotherapy, equine-assisted or simulated horse riding interventions, exergaming interventions, and physical education interventions) of motor development interventions were used, and 57 studies achieved some positive results for improvements in motor development among children with ASD. Furtherly, eight studies reported motor development acquisition, retention, or transfer. Children with ASD learn well from different types of instructors, including teachers, coaches, camp counselors, physical therapists, and peers. Conclusions: Motor skills and physical activity interventions improved motor development among children with ASD, the effect of which would continue until the end of the interventions.
Salt Stress and Tomato Resilience: From Somatic to Intergenerational Priming Memory
Authors: Borromeo Ilaria , Del Gallo Maddalena , Forni Cinzia
Published: February 2025

To ameliorate plants’ response to environmental stresses, seed priming can be a useful tool; it consists of the pre-exposure of the seeds to mild stress, which improves plant adaptation to future exposure to adverse growth conditions. In our previous studies, seed priming with polyamines (2.5 mM putrescine, 2.5 mM spermine, and 2.5 mM spermidine) and salt acclimation have been proven to be an effective treatment in enhancing salt tolerance of tomato cultivars since they induced a better physiological response to salt stressful condition. The persistence of the memory of the first (priming) stress and retrieval of such remembered information upon exposure to later new stress play an important role in the applicability of seed priming in agriculture. Therefore, the aim of this work was the detection of the persistence of a stress memory induced by polyamine priming in tomatoes. Primed and not-primed seeds were stored at +4 °C for 2 years after the original priming treatment; then, germinated seeds were sown in non-saline soil and irrigated with 80 and 160 mM NaCl salt solutions until fruit production. The results confirm the increase in salt tolerance in primed plants compared to not-primed ones, indicating the presence of long-term somatic memory. In comparison with not primed, the primed plants produced better quality fruits, i.e., higher weight, water content, and higher amount of carotenoids, soluble sugars, and phenols. To determine if the memory can be inherited by the offspring, seeds were then collected from primed and not-primed plants (generation G1), and further experiments were undertaken by growing G1 plants under the same irrigation regime as the parental generation. After 45 days of growth, both antioxidants and osmolyte amounts were enhanced, leading to an improvement in the tolerance to saline conditions in the offspring of primed plants and confirming the results already observed in the parental generation. These results demonstrate, for the first time, the presence of both long-term somatic and intergenerational priming memory in tomatoes and may pave the pathway to future agricultural application of seed priming.
Family and Peer Support Facilitates Recall and Retelling of Traumatic Memories in War Refugee Children and Adolescents
Authors: Barazzetti Arianna , Milesi Stefano , Giordano Francesca , Noto Maria Chiara , Negri Attà
Published: February 2025

War refugees are more likely to present psychological disorders, such as PTSD. Memory impairments often hinder their ability to recall and integrate traumatic events. This study investigated the memory capacity of 133 Syrian refugee children and adolescents in Lebanon and examined the moderating role of resilience in the relationship between exposure to negative events, post-traumatic stress reactions, and traumatic events recollection. Participants completed the Childhood War Trauma Questionnaire (CWTQ), Post-Traumatic Symptom Reactions Checklist for Children (PTSR-C), and Child and Youth Resilience Measure-28 (CYRM-28). They also performed a timeline exercise, part of Narrative Exposure Therapy, in which they organized and described their positive and negative life memories. Results showed that memories of traumatic events were lower than potentially traumatic events that participants had witnessed. This difference increased as post-traumatic stress increased. Moreover, potentially traumatic events experienced predicted post-traumatic stress reactions, and these predicted negative memories recalled. Participants’ resilience moderated the association between post-traumatic reactions and remembered traumatic memories showing that only for children and adolescents with higher resilience post-traumatic symptoms predicted trauma-related memories. The results highlight meaning making and sharing capacities of natural relational contexts (family, peers, etc.) as potential key processes to be promoted to overcome and process traumatic events.