bluuebunny/crossref_metadata_2025 huggingface dataset:
QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS
Authors: Debnath Dipraj , Hawary Ahmad Faizul , Ramdan Muhammad Iftishah , Alvarez Fernando Vanegas , Gonzalez Felipe
Published: November 2023

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.
Causal Factor Disentanglement for Few-Shot Domain Adaptation in Video Prediction
Authors: Cornille Nathan , Laenen Katrien , Sun Jingyuan , Moens Marie-Francine
Published: November 2023

An important challenge in machine learning is performing with accuracy when few training samples are available from the target distribution. If a large number of training samples from a related distribution are available, transfer learning can be used to improve the performance. This paper investigates how to do transfer learning more effectively if the source and target distributions are related through a Sparse Mechanism Shift for the application of next-frame prediction. We create Sparse Mechanism Shift-TempoRal Intervened Sequences (SMS-TRIS), a benchmark to evaluate transfer learning for next-frame prediction derived from the TRIS datasets. We then propose to exploit the Sparse Mechanism Shift property of the distribution shift by disentangling the model parameters with regard to the true causal mechanisms underlying the data. We use the Causal Identifiability from TempoRal Intervened Sequences (CITRIS) model to achieve this disentanglement via causal representation learning. We show that encouraging disentanglement with the CITRIS extensions can improve performance, but their effectiveness varies depending on the dataset and backbone used. We find that it is effective only when encouraging disentanglement actually succeeds in increasing disentanglement. We also show that an alternative method designed for domain adaptation does not help, indicating the challenging nature of the SMS-TRIS benchmark.
A Qualitative Study into Teacher–Student Interaction Strategies Employed to Support Primary School Children’s Working Memory
Authors: Sankalaite Simona , Huizinga Mariëtte , Pollé Sophie , Xu Canmei , De Vries Nicky , Hens Emma , Baeyens Dieter
Published: November 2023

The current qualitative study examined the teacher–student interaction and its influence on children’s working memory in primary schools in Belgium and the Netherlands. Eighteen primary school teachers participated in semi-structured interviews focusing on strategies employed to support students with working memory difficulties. The study offered a comprehensive overview of the strategies, categorised into instructional support, classroom organisation, and emotional support (based on the Teaching Through Interactions framework) that teachers use when dealing with working memory-related difficulties. Additionally, it provided unique insights into teachers’ underlying beliefs and rationales about the effectiveness of these strategies. Lastly, factors influencing the use and efficacy of these strategies (based on the Multilevel Supply–Use model) were explored. By integrating teachers’ voices and experiences, this research provides a unique opportunity to bridge theory and practice, and enrich the current understanding and interpretation of the teacher–student interaction and its implications for improving working memory performance in primary school students. Overall, the holistic approach, taking into account both direct and indirect approaches, offered a comprehensive understanding of the multifaceted challenges faced by students with working memory difficulties and the diverse strategies teachers can employ to address them, which can further inform classroom practices, professional development, and policy-making.
Using SAT Solvers to Reverse-Engineer FSM Models of Digital Devices
Authors: Cherepkov Danil , Mamoutova Olga , Dojnikov Anton , Bolsunovskaya Marina
Published: November 2023

Inferring a functional specification from an existing digital design is a challenge that is suitable with reverse-engineering methods. One of the most widely used functional specification formats is a finite state machine (FSM). This article studies the possibility of blind passive specification mining for a digital device, where the device is treated as a “black box”. The presented approach treats an input and output signal waveform as the transition graph of an incomplete deterministic FSM and learns the FSM through FSM minimization. It employs a Boolean satisfiability problem (SAT) solver to find a minimal FSM that complies with observed object behavior. The known approach to identifying state machines in discrete event systems is adapted to operate with variables in the form of coloring and transition tables. The developed implementation produces a synthesizable specification in hardware description language (HDL) and a state diagram in unified modeling language (UML). The proposed approach for inferring an FSM from a waveform trace can serve as a supplementary tool during reverse engineering to provide developers with meaningful insight regarding the analyzed device. The presented case study defines metrics of successful FSM inference and applies them to a synthetic FSM and a real-world example FSM to demonstrate the applicability of the approach.
Mean Field Multi-Agent Reinforcement Learning Method for Area Traffic Signal Control
Authors: Zhang Zundong , Zhang Wei , Liu Yuke , Xiong Gang
Published: November 2023

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches.
Design and Implementation of a New Framework for Post-Synthesis Obfuscation with a Mixture of Multiple Cells with an Integrated Anti-SAT Block
Authors: Rahman Hamidur , Rashid A. B. M. Harun-ur , Hasan Mahmudul
Published: November 2023

This paper proposes a new framework for post-synthesis obfuscation of digital circuits using a mixture of cells combined with an Anti-SAT block. Furthermore, a novel integrated framework has been established wherein obfuscation, along with Anti-SAT and validation of the benchmarks, progress through MATLAB®, Python, Cadence RTL Encounter® and Cadence LEC® to implement the proposed methodology. Area, delay, leakage power and total power are adopted as elements of the evaluation matrix. These parameters are compared between the original circuit, the circuit after obfuscation, the circuit after integration with Anti-SAT and the circuit after implementation of the proposed method of multiple-cell obfuscation with Anti-SAT. The probability of breaking a circuit is taken as the security criterion. It is mathematically proven that as the number of types of obfuscated cells used increases, then the probability of breaking the circuit decreases. The results obtained accord with the mathematical proof. The framework minimizes the delay by inserting obfuscation cells (OCs) in the non-critical paths, strengthens the security by using several types of OCs and allows the user to select a design based on justified area, leakage power and total power. However, against a Boolean SATisfiability (SAT) attack, obfuscation with multiple cells is not a sufficient defense. An Anti-SAT block performs better than obfuscation but has its own limitations. Thus, use of an Anti-SAT block in combination with multiple-cell obfuscation is proposed and implemented, giving better results against an efficient SAT attack. The number of iterations, as well as runtime to obtain the correct keys, increase significantly for the Anti-SAT block combined with multiple-cell obfuscation compared to the Anti-SAT or obfuscation block alone.
A New Multi-Scale Method to Evaluate the Porosity and MICP Curve for Digital Rock of Complex Reservoir
Authors: Xiong Ting , Chen Ming , Jin Yuan , Zhang Wei , Shao Haipeng , Wang Guanqun , Long Ethan , Long Wei
Published: November 2023

The evaluation of rock porosity and the mercury injection capillary pressure (MICP) curve is fundamental for oil and gas exploration and production. Digital rock (DR) technology, incorporating 3D micro-CT imaging and numerical methods, has been widely employed to predict these properties. However, analyzing the pore structure of heterogeneous rocks, such as fractured rocks or glutenite, solely through single-scale DR analysis poses challenges. Existing upscaling methods have limitations in fully representing the complete range of pore structures at different scales, with limited comparison to experimental data. To address this, we propose a novel method that upscales porosity and simulates the MICP curve from nano-scale to core scale by merging results from micro-CT (at resolutions of 35 μm and 2 μm) and SEM (at resolutions of 6.5 nm and 65 nm). We validate the developed DR model by applying it to sandstones, glutenite, and igneous rocks, and achieve excellent agreement between the experimental data and the multi-scale DR model across 67 samples. The results demonstrate that the multi-scale model effectively captures the porosity and pore structures across the entire range. In contrast, the single digital rock (DR) model underestimates the porosity measurements for both homogeneous sandstones and heterogeneous cores. While the MICP model based on a single DR proves suitable for homogeneous rock samples, it introduces noticeable discrepancies when applied to heterogeneous rock samples. The developed multi-scale method significantly enhances the confidence in using DR to assess the pore structure of complex rocks.
Decomposition of the Voltages in a Three-Phase Asymmetrical Circuit with a Non-Sinusoidal Voltage Source
Authors: Zajkowski Konrad , Duer Stanisław
Published: November 2023

This article presents the concept of a mathematical description of a three-phase, four-wire asymmetrical electric circuit in decomposition into Voltages’ Physical Components (VPC), associated with distinctive physical phenomena in the load. This is an alternative method of mathematical description to the Currents’ Physical Components (CPC) still being developed since the end of the last century. According to previous studies, the improvement of the power factor in three-phase systems is possible by observing several components. Compensation for the scattered power is possible only by using a reactive compensator connected in series with the load. Thanks to the presented analytical method, it is possible to design compensators connected in series with the load. The VPC power theory opens the possibility of improving the power factor in three-phase networks for loads with asymmetry between phases. Due to the unfavorable impact of high currents on the compensator branches, the method proposed in the article can improve the energy quality in local low-power grids. However, the possibility of its practical use in high-power industrial networks is questionable.
Photovoltaic Production Management under Constrained Regulatory Requirements: A Step towards a Local Energy Community Creation
Authors: Araújo Ivo , Nunes Leonel J. R. , Curado António
Published: November 2023

Addressing the urgent need for sustainable energy sources due to the high costs and environmental impacts of fossil fuels, this research explores the integration of decentralized solar photovoltaic (PV) systems in urban Southern European settings. Specifically, we examine the effectiveness of a solar carport PV installation at a higher education institution, focusing on the challenges of energy surplus management within regulated markets that restrict grid energy injection. Our study employed a mixed-methods approach, combining quantitative data analysis of energy production with qualitative assessments of regulatory frameworks. The innovative aspect of our method lies in the development of a smart management system tailored to circumvent legal barriers, ensuring optimal use of locally produced energy and fostering the creation of an energy community with shared energetic, environmental, and social benefits. Preliminary results demonstrate that our system not only prevents energy loss but also enhances investment return by enabling the institution to become an energy prosumer within its community. A comparative analysis with traditional energy exercises reveals that our approach significantly improves energy self-sufficiency and sustainability, positioning decentralized PV systems as a viable solution in similar urban contexts.
Refining the Selection of Historical Period in Analog Ensemble Technique
Authors: del Pozo Federico E. , Kim Chang Ki , Kim Hyun-Goo
Published: November 2023

A precise estimate of solar energy output is essential for its efficient integration into the power grid as solar energy becomes a more significant renewable energy source. Contrarily, the creation of solar energy involves fluctuation and uncertainty. The integration and operation of energy systems are complicated by the uncertainty in solar energy projection. As a post-processing technique to lower systematic and random errors in the operational meteorological forecast model, the analog ensemble algorithm will be introduced in this study. When determining the appropriate historical and predictive data required to use the approach, an optimization is conducted for the historical period in order to further maximize the capabilities of the analog ensemble. To determine statistical consistency and spread skill, the model is evaluated against both the raw forecast model and observations. The outcome lowers the uncertainty in the predicted data by demonstrating that statistical findings improve significantly even with 1-month historical data. Nevertheless, the optimization with a year’s worth of historical data demonstrates a notable decrease in the outcomes, limiting overestimation and lowering uncertainty. Specifically, analog ensemble algorithms calibrate analog forecasts that are equivalent to the latest target forecasts within a set of previous deterministic forecasts. Overall, we conclude that analog ensembles assuming a 1-year historical period offer a comprehensive method to minimizing uncertainty and that they should be carefully assessed given the specific forecasting aims and limits.
Optimization of Emergency Alternatives for Hydrogen Leakage and Explosion Accidents Based on Improved VIKOR
Authors: Cheng Fangming , Li Zhuo , Su Chang , Qu Jiao , Jiang Meng , Ge Hanzhang , Wang Linan , Gou Ziyan
Published: November 2023

Hydrogen leakage and explosion accidents have obvious dangers, ambiguity of accident information, and urgency of decision-making time. These characteristics bring challenges to the optimization of emergency alternatives for such accidents. Effective emergency decision making is crucial to mitigating the consequences of accidents and minimizing losses and can provide a vital reference for emergency management in the field of hydrogen energy. An improved VIKOR emergency alternatives optimization method is proposed based on the combination of hesitant triangular fuzzy set (HTFS) and the cumulative prospect theory (CPT), termed the HTFS-CPT-VIKOR method. This method adopts the hesitant triangular fuzzy number to represent the decision information on the alternatives under the influence of multi-attributes, constructs alternatives evaluation indicators, and solves the indicator weights by using the deviation method. Based on CPT, positive and negative ideal points were used as reference points to construct the prospect matrix, which then utilized the VIKOR method to optimize the emergency alternatives for hydrogen leakage and explosion accidents. Taking an accident at a hydrogen refueling station as an example, the effectiveness and rationality of the HTFS-CPT-VIKOR method were verified by comparing with the existing three methods and conducting parameter sensitivity analysis. Research results show that the HTFS-CPT-VIKOR method effectively captures the limited psychological behavior characteristics of decision makers and enhances their ability to identify, filter, and judge ambiguous information, making the decision-making alternatives more in line with the actual environment, which provided strong support for the optimization of emergency alternatives for hydrogen leakage and explosion accidents.
Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review
Authors: Necula Sabina-Cristiana
Published: November 2023

This systematic review investigates the role of artificial intelligence (AI) in advancing clean energy technologies within Europe, based on a literature survey from 2006 to 2023. The assessment reveals that AI, particularly through deep learning and neural networks, enhances the efficiency, optimization, and management of clean energy systems. Noteworthy is AI’s capacity to improve short-term energy forecasts, essential for smart cities and IoT applications. Our findings indicate that AI drives innovation in renewable energy, contributing to the development of smart grids and enabling collaborative energy-sharing models. While the research underscores AI’s substantial influence in Europe’s energy sector, it also identifies gaps, such as varied AI algorithm applications in different renewable energy sectors. The study emphasizes the need for integrating AI with emerging clean energy innovations, advocating for interdisciplinary research to navigate the socio-economic, environmental, and policy dimensions. This approach is crucial for guiding a sustainable and balanced advancement in the clean energy landscape, signifying AI’s pivotal role in Europe’s energy transition.
Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties
Authors: Li Zhiyong , Wu Wenbin , Si Yang , Chen Xiaotao
Published: November 2023

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system’s operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model’s scale. To ensure an efficient solution, a Benders’ decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM’s siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits.
Sizing Batteries for Power Flow Management in Distribution Grids: A Method to Compare Battery Capacities for Different Siting Configurations and Variable Power Flow Simultaneity
Authors: van Someren Christian , Visser Martien , Slootweg Han
Published: November 2023

Battery energy storage (BES) can provide many grid services, such as power flow management to reduce distribution grid overloading. It is desirable to minimise BES storage capacities to reduce investment costs. However, it is not always clear how battery sizing is affected by battery siting and power flow simultaneity (PFS). This paper describes a method to compare the battery capacity required to provide grid services for different battery siting configurations and variable PFSs. The method was implemented by modelling a standard test grid with artificial power flow patterns and different battery siting configurations. The storage capacity of each configuration was minimised to determine how these variables affect the minimum storage capacity required to maintain power flows below a given threshold. In this case, a battery located at the transformer required 10–20% more capacity than a battery located centrally on the grid, or several batteries distributed throughout the grid, depending on PFS. The differences in capacity requirements were largely attributed to the ability of a BES configuration to mitigate network losses. The method presented in this paper can be used to compare BES capacity requirements for different battery siting configurations, power flow patterns, grid services, and grid characteristics.
Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries
Authors: Wang Shun-Chung , Zhang Zhi-Yao
Published: November 2023

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively.
The Microbial Communities of Anaerobic Respiration and Fermentation Degrading Chitin Exist in the Anaerobic Sludge of Microbial Fuel Cell Anodes
Authors: Zhen Sheng-Hu , Yu Yang-Yang , Xie Rong-Rong , Xu Wei , Li Shan-Wei
Published: November 2023

Chitin is one of the most abundant polymers in nature, with chitinous biomass often discarded as food waste and marine debris. To explore an effective way to degrade chitin, in this work, anaerobic sludge was inoculated at the anode of a two-chamber microbial fuel cell (MFC), and chitin was degraded via anaerobic respiration and fermentation. The results showed that the anaerobic sludge could degrade chitin under both the anaerobic respiration and fermentation modes, with similar degradation rates (7.10 ± 0.96 and 6.96 ± 0.23 C-mg/L·d−1). The open-circuit voltage and output current density could roughly reflect the degradation of chitin in the MFC. The maximum current density generated through the anaerobic sludge degradation of chitin via anaerobic respiration was 160 mA/m2, and the maximum power density was 26.29 mW/m2. The microbial sequencing results revealed substantially different microbial community profiles, with electroactive bacteria (EAB) flora and fermentative bacteria (Longilinea) as the main microbial groups that degraded chitin via anaerobic respiration and fermentation, respectively. Therefore, anaerobic sludge may be a good choice for the treatment of refractory biomass due to its abundant electroactive and fermentative flora.
The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism
Authors: Hu Huiqiang , Xu Zhenyu , Wei Yunpeng , Wang Tingting , Zhao Yuping , Xu Huaxing , Mao Xiaobo , Huang Luqi
Published: November 2023

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.
Thermal and Modern, Non-Thermal Method Induction as a Factor of Modification of Inulin Hydrogel Properties
Authors: Florowska Anna , Florowski Tomasz , Kruszewski Bartosz , Janiszewska-Turak Emilia , Bykowska Weronika , Ksibi Nour
Published: November 2023

The aim of the study was to compare the properties of inulin hydrogels obtained with different methods, e.g., the traditional–thermal method and new, non-thermal methods, used in food production, like ultrasonic, high-pressure homogenization (HPH), and high hydrostatic pressures (HHPs). It was found that each of the compared induction methods allowed for obtaining inulin hydrogels. However, the use of non-thermal induction methods allows for obtaining a gel structure faster than in the case of thermal induction. In addition, hydrogels obtained with new, non-thermal methods differ from gels obtained with thermal treatment. They were characterized by higher stability (from 1.7 percent point-of-stability parameters for HHP 150 MPa to 18.8 for HPH II cycles) and in most cases, by improved microrheological properties–lower solid–liquid balance toward the solid phase, increased elasticity and viscosity indexes, and lowering the flow index. The gels obtained with the new, non-thermal method were also characterized by a more delicate structure, including lower firmness (the differences between thermal and non-thermal inductions were from 0.73 N for HHP at 500 MPa to 2.39 N for HHP at 150 MPa) and spreadability (the differences between thermal and non-thermal inductions were from 7.60 Ns for HHP at 500 MPa to 15.08 Ns for HHP at 150 MPa). The color of ultrasound-induced inulin gels, regarding the HPH and HHP technique, was darker (the differences in the L* parameter between thermal and non-thermal inductions were from 1.92 for HHP at 500 MPa to 4.37 for 10 min ultrasounds) and with a lower a* color parameter (the differences in the a* parameter between thermal and non-thermal inductions were from 0.16 for HHP at 500 MPa to 0.39 for HPH II cycles) and b* color parameter (the differences in the b* parameter between thermal and non-thermal inductions were from 1.69 for 5 min ultrasounds to 2.68 for HPH II cycles). It was also found that among the compared induction methods, the high-pressure technique has the greatest potential for modifying the properties of the created inulin hydrogels. Thanks to its application, depending on the amount of applied pressure, it was possible to obtain gels with very different characteristics, both delicate (i.e., soft and spreadable), using HHP at 150 MPa, and hard, using HHP at 500 MPa, the closest in characteristics to gels induced with the thermal method. This may allow the properties of hydrogels to be matched to the characteristics of the food matrix being created.
Nutritional Potential of Adzuki Bean Germplasm and Mining Nutri-Dense Accessions through Multivariate Analysis
Authors: D. Deepika D. , Padhi Siddhant Ranjan , Gore Padmavati G. , Tripathi Kuldeep , Katral Ashvinkumar , Chandora Rahul , Abhishek G. J. , Kondal Vishal , Singh Rakesh , Bharadwaj Rakesh , Bhatt Kailash C. , Rana Jai Chand , Riar Amritbir
Published: November 2023

The adzuki bean (Vigna angularis), known for its rich nutritional composition, holds significant promise in addressing food and nutritional security, particularly for low socioeconomic classes and the predominantly vegetarian and vegan populations worldwide. In this study, we assessed a total of 100 diverse adzuki bean accessions, analyzing essential nutritional compounds using AOAC’s official analysis procedures and other widely accepted standard techniques. Our analysis of variance revealed significant genotype variations for all the traits studied. The variability range among different traits was as follows: moisture: 7.5–13.3 g/100 g, ash: 1.8–4.2 g/100 g, protein: 18.0–23.9 g/100 g, starch: 31.0–43.9 g/100 g, total soluble sugar: 3.0–8.2 g/100 g, phytic acid: 0.65–1.43 g/100 g, phenol: 0.01–0.59 g/100 g, antioxidant: 11.4–19.7 mg/100 g GAE. Noteworthy accessions included IC341955 and EC15256, exhibiting very high protein content, while IC341957 and IC341955 showed increased antioxidant activity. To understand intertrait relationships, we computed correlation coefficients between the traits. Principal Component Analysis (PCA) revealed that the first four principal components contributed to 63.6% of the variation. Further, hierarchical cluster analysis (HCA) identified nutri-dense accessions, such as IC360533, characterized by high ash (>4.2 g/100 g) and protein (>23.4 g/100 g) content and low phytic acid (0.652 g/100 g). These promising compositions provide practical support for the development of high-value food and feed varieties using effective breeding strategies, ultimately contributing to improved global food security.
Sealing Wax and Bottles in Bags—A Paradigm Shift in Refined Olive Oil Packaging: Preliminary Results
Authors: Macaluso Monica , Mercanti Nicola , Scappaticci Giulio , Lannocca Elena , Rossi Linda , Guidi Luca , Tondello Paolo , Brazzarola Francesco , Zinnai Angela
Published: November 2023

Generally, olive oil possesses natural protection against oxidation due to antioxidant compounds such as phenols and tocopherols. However, in the case of refined olive oil, the refining process unavoidably reduces the presence of these compounds. Considering these considerations, the objective of this study was to address the issues related to the “tightness” of the cap used for packaging oil in SALOV, aiming to extend the product’s shelf life. The oil under investigation was packaged in 250 mL transparent glass bottles, each filled with either argon or air. Subsequently, the samples were divided into three groups: one group sealed with a conventional screw cap, another covered with a special protective bag, and a third one sealed with a wax cover directly on the cap. The storage period varied, during which the atmospheric conditions were monitored daily through both destructive and non-destructive analyses. The preliminary results indicate that alternative preservation techniques, such as the use of argon, sealing wax, and protective bags, can effectively enhance the shelf life of the oil and maintain its quality (reduce oxidation, preserve phenolic compounds, and reduce the degradation of pigments). Further research and development in this area could lead to the production of high-quality extra virgin olive oils with extended shelf life and improved sensory and nutritional properties.
Holographic Quantum-Foam Blurring Is Consistent with Observations of Gamma-Ray Burst GRB221009A
Authors: Steinbring Eric
Published: November 2023

Gamma-ray burst GRB221009A was of unprecedented brightness in the γ-rays and X-rays through to the far ultraviolet, allowing for identification within a host galaxy at redshift z=0.151 by multiple space and ground-based optical/near-infrared telescopes and enabling a first association—via cosmic-ray air-shower events—with a photon of 251 TeV. That is in direct tension with a potentially observable phenomenon of quantum gravity (QG), where spacetime “foaminess” accumulates in wavefronts propagating cosmological distances, and at high-enough energy could render distant yet bright pointlike objects invisible, by effectively spreading their photons out over the whole sky. But this effect would not result in photon loss, so it remains distinct from any absorption by extragalactic background light. A simple multiwavelength average of foam-induced blurring is described, analogous to atmospheric seeing from the ground. When scaled within the fields of view for the Fermi and Swift instruments, it fits all z≤5 GRB angular-resolution data of 10 MeV or any lesser peak energy and can still be consistent with the highest-energy localization of GRB221009A: a limiting bound of about 1 degree is in agreement with a holographic QG-favored formulation.
Injectable Decellularized Extracellular Matrix-Based Bio-Ink with Excellent Biocompatibility for Scarless Urethra Repair
Authors: Fang Wenzhuo , Yang Ming , Jin Yangwang , Zhang Kaile , Wang Ying , Liu Meng , Wang Yuhui , Yang Ranxing , Fu Qiang
Published: November 2023

In recent years, decellularized extracellular matrices (dECM) derived from organs have attracted much attention from researchers due to their excellent biocompatibility, capacity to promote cell proliferation and migration, as well as pro-vascularization. However, their inferior mechanical properties, slow cross-linking, weak strengths, and poor supporting properties remain their inevitable challenges. In our study, we fabricated a novel dECM hydrogel with better crosslinking strength and speed, stronger support properties, and better mechanical properties. The hydrogel, which we named gelatin-based dECM powder hydrogel (gelatin-dECM hydrogel), was prepared by mixing dECM microparticles in gelatin solution and adding certain amount of 1-Ethyl-3-(3-dimethyl-aminopropyl-1-carbodiimide) (EDC) and N-hydroxysuccinimide (NHS). We evaluated the utility of this hydrogel by assessing the degradation rate, mechanical properties, and biocompatibility. The results showed that the gelatin-dECM hydrogel has high mechanical properties and biocompatibility and also has the ability to promote cell proliferation and migration. After injection of this hydrogel around the surgical sites of urethras in rabbits, the incorporation of dECM powder was demonstrated to promote angiogenesis as well as scarless repair by histological sections after surgery. The application of this novel hydrogel provides a new perspective for the treatment of post-traumatic urethral stricture.
Short-Stranded Zein Fibers for Muscle Tissue Engineering in Alginate-Based Composite Hydrogels
Authors: Melzener Lea , Spaans Sergio , Hauck Nicolas , Pötgens André J. G. , Flack Joshua E. , Post Mark J. , Doğan Arın
Published: November 2023

Cultivated meat is a nascent technology that aims to create an environmentally and animal-friendly alternative to conventional meat. Producing skeletal muscle tissue in an animal-free system allowing for high levels of myofusion and maturation is important for the nutritional and sensorial value of cultivated meat. Alginate is an attractive biomaterial to support muscle formation as it is food-safe, sustainable and cheap and can be crosslinked using non-toxic methods. Although alginate can be functionalized to promote cell attachment, limitations in its mechanical properties, including form, viscosity, and stress relaxation, hinder the cellular capacity for myogenic differentiation and maturation in alginate-based hydrogels. Here, we show that the addition of electrospun short-stranded zein fibers increased hydrogel degradation, resulting in faster compaction, improved cell–gel interaction, and enhanced alignment of bovine muscle precursor cells. We conclude that fiber-hydrogel composites are a promising approach to support optimal formation of 3D constructs, by improving tissue stability and thus prolonging culture duration. Together, this improves muscle-related protein content by facilitating myogenic differentiation and priming muscle organoids for maturation.
Geoheritage and Geotourism Resources: Education, Recreation, Sustainability II
Authors: Drinia Hara , Voudouris Panagiotis , Antonarakou Assimina
Published: November 2023

In recent years, the world’s fascination with the geoenvironment has experienced a remarkable surge [...]
Rockfall Susceptibility Assessment and Landscape Evolution of San Nicola Island (Tremiti Islands, Southern Adriatic Sea, Italy)
Authors: Cinosi Jacopo , Piattelli Valerio , Paglia Giorgio , Sorci Adelmo , Ciavattella Francesco , Miccadei Enrico
Published: November 2023

San Nicola Island, pertaining to the Tremiti Archipelago (Southern Adriatic Sea, Italy), is widely affected by cliff retreat and gravitational phenomena which severely threaten its monumental historical and natural value. In this study, geomorphological features of the area were derived following a stepwise approach, combining multitemporal stereoscopic aerial photo interpretations with morphometric analyses and detailed field surveys. A rockfall susceptibility map was created following a heuristic approach based on morphometric and geothematic parameters, accounting for slope, slope aspect, outcropping lithologies, structural discontinuities density, distance from landslide scarps, and presence of anthropic caves. Cliff sectors set on dolomitic limestones feature the highest susceptibility values, especially along the southeastern sector; medium values, instead, are found along the island flanks and along scarps located within the inner sectors; and the lowest values are detected on summit tabular surfaces. The achieved results were compared with historical maps and seismic data derived from local and national archives and catalogues, respectively. These analyses allowed us to define the role played by litho-structural and tectonic features on landslide occurrence and distribution, and their interplay in driving landscape evolution over centuries. Finally, this work represents a valuable scientific tool to support geomorphological studies for landslide hazard assessment and proper territorial planning in any other small insular areas, showing similar geological–geomorphological features and landscape values.
Editorial on Special Issue “Geo-Information Applications in Active Mobility and Health in Cities”
Authors: Gudes Ori , Zarpelon Leao Simone
Published: November 2023

There is growing evidence that active mobility can have a range of positive outcomes for the wider community [...]
Functional Dimerization of Serotonin Receptors: Role in Health and Depressive Disorders
Authors: Mitroshina Elena V. , Marasanova Ekaterina A. , Vedunova Maria V.
Published: November 2023

Understanding the neurobiological underpinnings of depressive disorder constitutes a pressing challenge in the fields of psychiatry and neurobiology. Depression represents one of the most prevalent forms of mental and behavioral disorders globally. Alterations in dimerization capacity can influence the functional characteristics of serotonin receptors and may constitute a contributing factor to the onset of depressive disorders. The objective of this review is to consolidate the current understanding of interactions within the 5-HT receptor family and between 5-HT receptors and members of other receptor families. Furthermore, it aims to elucidate the role of such complexes in depressive disorders and delineate the mechanisms through which antidepressants exert their effects.
Regulation of Immune Checkpoint Antigen CD276 (B7-H3) on Human Placenta-Derived Mesenchymal Stromal Cells in GMP-Compliant Cell Culture Media
Authors: Amend Bastian , Buttgereit Lea , Abruzzese Tanja , Harland Niklas , Abele Harald , Jakubowski Peter , Stenzl Arnulf , Gorodetsky Raphael , Aicher Wilhelm K.
Published: November 2023

Therapies utilizing autologous mesenchymal cell delivery are being investigated as anti-inflammatory and regenerative treatments for a broad spectrum of age-related diseases, as well as various chronic and acute pathological conditions. Easily available allogeneic full-term human placenta mesenchymal stromal cells (pMSCs) were used as a potential pro-regenerative, cell-based therapy in degenerative diseases, which could be applied also to elderly individuals. To explore the potential of allogeneic pMSCs transplantation for pro-regenerative applications, such cells were isolated from five different term-placentas, obtained from the dissected maternal, endometrial (mpMSCs), and fetal chorion tissues (fpMSCs), respectively. The proliferation rate of the cells in the culture, as well as their shape, in vitro differentiation potential, and the expression of mesenchymal lineage and stem cell markers, were investigated. Moreover, we studied the expression of immune checkpoint antigen CD276 as a possible modulation of the rejection of transplanted non-HLA-matched homologous or even xeno-transplanted pMSCs. The expression of the cell surface markers was also explored in parallel in the cryosections of the relevant intact placenta tissue samples. The expansion of pMSCs in a clinical-grade medium complemented with 5% human platelet lysate and 5% human serum induced a significant expression of CD276 when compared to mpMSCs expanded in a commercial medium. We suggest that the expansion of mpMSCs, especially in a medium containing platelet lysate, elevated the expression of the immune-regulatory cell surface marker CD276. This may contribute to the immune tolerance towards allogeneic pMSC transplantations in clinical situations and even in xenogenic animal models of human diseases. The endurance of the injected comparably young human-term pMSCs may promote prolonged effects in clinical applications employing non-HLA-matched allogeneic cell therapy for various degenerative disorders, especially in aged adults.
Self-Entrapment of Antimicrobial Peptides in Silica Particles for Stable and Effective Antimicrobial Peptide Delivery System
Authors: Ki Mi-Ran , Kim Sung Ho , Park Tae In , Pack Seung Pil
Published: November 2023

Antimicrobial peptides (AMPs) have emerged as a promising solution to tackle bacterial infections and combat antibiotic resistance. However, their vulnerability to protease degradation and toxicity towards mammalian cells has hindered their clinical application. To overcome these challenges, our study aims to develop a method to enhance the stability and safety of AMPs applicable to effective drug–device combination products. The KR12 antimicrobial peptide was chosen, and in order to further enhance its delivery and efficacy the human immunodeficiency virus TAT protein-derived cell-penetrating peptide (CPP) was fused to form CPP-KR12. A new product, CPP-KR12@Si, was developed by forming silica particles with self-entrapped CPP-KR12 peptide using biomimetic silica precipitability because of its cationic nature. Peptide delivery from CPP-KR12@Si to bacteria and cells was observed at a slightly delivered rate, with improved stability against trypsin treatment and a reduction in cytotoxicity compared to CPP-KR12. Finally, the antimicrobial potential of the CPP-KR12@Si/bone graft substitute (BGS) combination product was demonstrated. CPP-KR12 is coated in the form of submicron-sized particles on the surface of the BGS. Self-entrapped AMP in silica nanoparticles is a safe and effective AMP delivery method that will be useful for developing a drug–device combination product for tissue regeneration.