bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Toxic Element Levels and Health Risks in Different Soybean Species (Glycine max, Vigna radiata, Vigna angularis, Vigna mungo)
Authors: Jáudenes-Marrero Juan R. , Giannantonio Greta , Paz-Montelongo Soraya , Hardisson Arturo , Darias-Rosales Javier , González-Weller Dailos , Gutiérrez Ángel J. , Rubio Carmen , Alejandro-Vega Samuel
Published: December 2024

Background: Soybeans are a widely consumed legume, essential in Western diets and especially prominent in vegan and vegetarian nutrition. However, environmental contamination from anthropogenic sources, such as industrial emissions, wastewater, and pesticide use, has led to the accumulation of non-essential and toxic elements in legumes, potentially impacting human health. Method: This study quantified the levels of 11 potential toxic elements (Al, B, Ba, Cd, Co, Cr, Li, Ni, Pb, Sr, V) in 90 samples of four soybean species (Glycine max, Vigna radiata, Vigna angularis, Vigna mungo) using inductively coupled plasma optical emission spectrometry (ICP-OES). Results: Results showed that boron had the highest mean content (9.52 mg/kg ww), followed by aluminum (6.73 mg/kg ww). Among the toxic metals, cadmium was most concentrated in green soybeans (0.03 mg/kg ww), and black soybeans had the highest level of lead (0.07 mg/kg ww). Based on an average soybean consumption of 50 g/day, no immediate health risk was detected. However, lithium and nickel were present in substantial amounts, with lithium contributing 31.43–48.57% and nickel 6.81–39.56% of their respective provisional daily intake limits, especially from red soybeans (V. angularis). Conclusions: This study highlights the importance of monitoring toxic elements in soybeans and calls for stricter environmental management practices to minimize contamination, ensuring the safety of soy products as their global consumption rises.
Association of TyG Index and TG/HDL-C Ratio with Trajectories of Depressive Symptoms: Evidence from China Health and Retirement Longitudinal Study
Authors: Guo Tingting , Zou Qing , Wang Qi , Zhang Yi , Zhong Xinyuan , Lin Hantong , Gong Wenxuan , Wang Yingbo , Xie Kun , Wu Kunpeng , Chen Feng , Chen Wen
Published: December 2024

Objectives: To explore whether the triglyceride–glucose (TyG) index and the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio are associated with the trajectories of depressive symptoms. Methods: In this longitudinal study, 4215 participants aged 45 years and older were recruited from the China Health and Retirement Longitudinal Study from 2011 to 2018. The trajectories of depressive symptoms, measured by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), were identified using group-based trajectory modeling. Multinomial logistic models and restricted cubic spline analysis were used to investigate the relationships between the TyG index and the TG/HDL-C ratio and the trajectories of depressive symptoms. Stratified analyses were conducted based on sex, age, place of residence, and body mass index (BMI). Results: Five distinct trajectories of depressive symptoms characterized by stable low, stable moderate, decreasing, increasing, and stable high were identified during a follow-up of 7 years. The associations of the TyG index and the TG/HDL-C ratio with trajectories of depressive symptoms are not entirely consistent. After adjusting for covariates, a higher TyG index at baseline was associated with lower odds of being on the decreasing trajectory of depressive symptoms (ORad = 0.61, 95% CI: 0.40–0.92) compared to the stable low trajectory, and restricted cubic spline analysis revealed a negative linear relationship between the TyG index and the likelihood of a decreasing trajectory of depressive symptoms. However, the relationship between the TG/HDL-C ratio and the decreasing trajectory of depressive symptoms was no longer statistically significant when all confounders were controlled (ORad = 0.72, 95% CI: 0.50−1.04). Additionally, this negative association between the TyG index and decreasing trajectory of depressive symptoms was observed among 45–64-year-old individuals, female participants, those living in rural areas, and those with a normal BMI. Limitations: This study was conducted in a middle-aged and elderly population in China, and extrapolation to other regions and populations requires further confirmation. Conclusions: Compared to the TG/HDL-C ratio, the TyG index may be a better predictor for trajectories of depressive symptoms in middle-aged and older adults. Considering that the pathology of depression progresses long term, our findings may have utility for identifying available and reliable markers for the development of depression.
From Emergence to Evolution: Dynamics of the SARS-CoV-2 Omicron Variant in Florida
Authors: Ali Sobur , Giovanetti Marta , Johnston Catherine , Urdaneta-Páez Verónica , Azarian Taj , Cella Eleonora
Published: December 2024

The continual evolution of SARS-CoV-2 has significantly influenced the global response to the COVID-19 pandemic, with the emergence of highly transmissible and immune-evasive variants posing persistent challenges. The Omicron variant, first identified in November 2021, rapidly replaced the Delta variant, becoming the predominant strain worldwide. In Florida, Omicron was first detected in December 2021, leading to an unprecedented surge in cases that surpassed all prior waves, despite extensive vaccination efforts. This study investigates the molecular evolution and transmission dynamics of the Omicron lineages during Florida’s Omicron waves, supported by a robust dataset of over 1000 sequenced genomes. Through phylogenetic and phylodynamic analyses, we capture the rapid diversification of the Omicron lineages, identifying significant importation events, predominantly from California, Texas, and New York, and exportation to North America, Europe, and South America. Variants such as BA.1, BA.2, BA.4, and BA.5 exhibited distinct transmission patterns, with BA.2 showing the ability to reinfect individuals previously infected with BA.1. Despite the high transmissibility and immune evasion of the Omicron sub-lineages, the plateauing of cases by late 2022 suggests increasing population immunity from prior infection and vaccination. Our findings underscore the importance of continuous genomic surveillance in identifying variant introductions, mapping transmission pathways, and guiding public health interventions to mitigate current and future pandemic risks.
Antibacterial and Anti-Influenza Activities of N-Heterocyclic Carbene–Gold Complexes
Authors: Pellegrino Michele , Checconi Paola , Ceramella Jessica , Prezioso Carla , Limongi Dolores , Marra Maria , Mariconda Annaluisa , Catalano Alessia , De Angelis Marta , Nencioni Lucia , Sinicropi Maria Stefania , Longo Pasquale , Aquaro Stefano
Published: December 2024

Background/Objectives: Infectious diseases represent a serious threat due to rising antimicrobial resistance, particularly among multidrug-resistant bacteria and influenza viruses. Metal-based complexes, such as N-heterocyclic carbene–gold (NHC–gold) complexes, show promising therapeutic potential due to their ability to inhibit various pathogens. Methods: Eight NHC–gold complexes were synthesized and tested for antibacterial activity against Escherichia coli, Enterococcus faecalis, Staphylococcus aureus, and for anti-influenza activity in lung and bronchial epithelial cells infected with influenza virus A/H1N1. Antibacterial activity was assessed through the determination of the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC), while the viral load was quantified using qRT-PCR. Results: Complexes 3, 4, and 6 showed significant antibacterial activity at concentrations of 10–20 µg/mL. Additionally, these complexes significantly reduced viral load, with complexes 3 and 4 markedly inhibiting replication. Conclusions: These findings support the potential use of NHC–gold complexes in combined antimicrobial and antiviral therapies, representing an attractive option for fighting resistant infections.
The Role of Extracellular Vesicles in the Development and Treatment of Psoriasis: Narrative Review
Authors: Tang Bin , Bi Yang , Zheng Xuwei , Yang Yujie , Huang Xiaobing , Yang Kexin , Zhong Haixin , Han Ling , Lu Chuanjian , Chen Haiming
Published: December 2024

Psoriasis is a chronic inflammatory polygenic disease with significant impacts on skin and joints, leading to substantial treatment challenges and healthcare costs. The quest for novel therapeutic avenues has recently highlighted extracellular vesicles (EVs) due to their potential as biomarkers and therapeutic agents in autoimmune diseases, including psoriasis. EVs are nano-sized, lipid membrane-bound particles secreted by cells that have emerged as promising tools for targeted drug delivery, owing to their unique structure. This review delves into how EVs, either as mediators of cell communication or via their cargo (such as miRNA), directly participate in the pathology of psoriasis, influencing processes such as immune regulation, cell proliferation, and differentiation. Furthermore, this review explores the innovative application of EVs in psoriasis treatment, both as direct therapeutic agents and as vehicles for drug delivery, offering a novel approach to overcoming the current treatment limitations.
Evaluation of the Cancer-Preventive Effect of Resveratrol-Loaded Nanoparticles on the Formation and Growth of In Vitro Lung Tumor Spheroids
Authors: Torrico Guzmán Elisa A. , Gravely Mitchell , Meenach Samantha A.
Published: December 2024

Background: Resveratrol (RSV) is a natural polyphenol that offers antioxidant, anti-inflammatory, and chemopreventive benefits. This project determined the ability of RSV-loaded nanoparticles (NP) to inhibit the growth of lung tumor spheroids in vitro. Methods: RSV was encapsulated in NP comprised of the biodegradable polymer, acetalated dextran. A549 lung cancer cells in two-dimensional and three-dimensional cell culture models were exposed to free RSV and RSV NP to evaluate their effect on cell proliferation and spheroid formation and growth. For prevention studies, spheroids were exposed to free RSV and RSV NP on day 0, and for treatment studies, spheroids were dosed with the same formulations on day 5 after the spheroids had fully formed. Results: The resulting RSV NP were 200 nm in diameter with neutral surface charge and exhibited the ability to control the release of RSV in vitro based on environmental pH. In comparison to free RSV, the RSV NP exerted a greater inhibitory effect on the proliferation and growth of cancer cells and spheroids. Conclusions: RSV NP have the potential to be used as a chemopreventive agent for lung cancer.
Development and Characterization of Novel Combinations and Compositions of Nanostructured Lipid Carrier Formulations Loaded with Trans-Resveratrol for Pulmonary Drug Delivery
Authors: Khan Iftikhar , Sunita Sunita , Hussein Nozad R. , Omer Huner K. , Elhissi Abdelbary , Houacine Chahinez , Khan Wasiq , Yousaf Sakib , Rathore Hassaan A.
Published: December 2024

Background/Objectives: This study aimed to fabricate, optimize, and characterize nanostructured lipid carriers (NLCs) loaded with trans-resveratrol (TRES) as an anti-cancer drug for pulmonary drug delivery using medical nebulizers. Methods: Novel TRES-NLC formulations (F1–F24) were prepared via hot, high-pressure homogenization. One solid lipid (Dynasan 116) was combined with four liquid lipids (Capryol 90, Lauroglycol 90, Miglyol 810, and Tributyrin) in three different ratios (10:90, 50:50, and 90:10 w/w), with a surfactant (Tween 80) in two different concentrations (0.5 and 1.5%), and a co-surfactant, soya phosphatidylcholine (SPC S-75; 50 mg). Results: Amongst the analyzed 24 TR-NLC formulations, F8, F14, and F22 were selected based on their physicochemical stability when freshly prepared and following storage (4 weeks 25 °C), as well as in terms of particle size (<145 nm), polydispersity index (PDI; <0.21) and entrapment efficiency (>96%). Furthermore, F14 showed greater stability at 4 and 25 °C for six months and exhibited enhanced aerosolization performance, demonstrating the greater deposition of TRES in the later stages of the next-generation impactor (NGI) when using an air-jet nebulizer than when using an ultrasonic nebulizer. The F14 formulation exhibited greater stability and release in acetate buffer (pH 5.4), with a cumulative release of 95%. Conclusions: Overall, formulation F14 in combination with an air-jet nebulizer was identified as a superior combination, demonstrating higher emitted dose (ED; 80%), fine particle dose (FPD; 1150 µg), fine particle fraction (FPF; 24%), and respirable fraction (RF; 94%). These findings are promising in the optimization and development of NLC formulations, highlighting their versatility and targeting the pulmonary system via nebulization.
U-Net Driven High-Resolution Complex Field Information Prediction in Single-Shot Four-Step Phase-Shifted Digital Holography Using Polarization Camera
Authors: Mehdi Askari , Lim Yongjun , Oh Kwan-Jung , Park Jae-Hyeung
Published: December 2024

We present a novel high-resolution complex field extraction technique utilizing U-Net-based architecture to effectively overcome the inherent resolution limitations of polarization cameras with micro-polarized arrays. Our method extracts high-resolution complex field information, achieving a resolution comparable to that of the original polarization camera. Utilizing the parallel phase-shifting digital holography technique, we extracted high-resolution complex field information from four high-resolution phase-shifted interference patterns predicted by our network directly at the hologram plane. Extracting the object’s complex field directly at the hologram plane rather than the object’s plane, our method eliminates the dependency on numerical propagation during dataset acquisition, enabling reconstruction of objects at various depths without DC and conjugate noise. By training the network with real-valued interference patterns and using only a single pair of low- and high-resolution input and ground truth interference patterns, we simplify computational complexity and improve efficiency. Our simulations demonstrate the network’s robustness to variations in random phase distributions and transverse shifts in the input patterns. The effectiveness of the proposed method is demonstrated through numerical simulations, showing an average improvement of over 4 dB in peak-signal-to-noise ratio and 25% in intensity normalized cross-correlation metrics for object reconstruction quality.
Environmentally Sustainable and Green Polymeric Method for Chitosan (CH) Film Synthesis Using Natural Acids and Impact of Zinc Ferrite Nanoparticles (NPs) on Water Solubility (WS) and Physical Properties
Authors: Hassan Dilawar , Sani Ayesha , Chanihoon Ghulam Qadir , Antonio Pérez Aurora , Ehsan Muhammad , Torres Huerta Ana Laura
Published: December 2024

Currently, there is a rush to develop green polymeric films such as biodegradable chitosan (CH) films to control and prevent plastic pollution from degrading the environment. This study reports a novel and sustainable green approach to the development of CH films using lemon juice (LJ) and lemon peel extract (LPE), the latter to dilute the LJ. The LPE was also utilized for the synthesis of ZnFe2O4 nanoparticles (NPs), adding to this work’s novelty. The crystalline size of the ZnFe2O4 NPs was computed to be ~16 nm. The introduction of 1% and 2% ZnFe2O4 NPs improved not only the mechanical properties of the films, but also their barrier properties and water solubility (WS). The tensile strength increased from 0.641 MPa to 0.835 MPa when 2% NPs were incorporated, which is almost 1.30 times greater; the NPs also enhanced the surface strength by 2.66 times, which was demonstrated by the puncture strength. The introduction of NPs occupied the vacant spaces and improved the barrier capabilities of the CH film by reducing the water vapor permeability (WVP) value from 8.752 ± 0.015 for bare CH films to 6.299 ± 0.009 for 2% NP-containing CH films. Overall, the introduction of ZnFe2O4 NPs boosted the mechanical and barrier properties of the CH films, and offers a promising method for developing sustainable, eco-friendly, and biodegradable polymeric films for potential packaging and medical applications to contribute to circular economic efforts.
Analysis of the Impact of the Post-Consumer Film Waste Scenario and the Source of Electricity on the Harmfulness of the Mass Packaging Process
Authors: Walichnowska Patrycja , Kruszelnicka Weronika , Piasecka Izabela , Flizikowski Józef , Tomporowski Andrzej , Mazurkiewicz Adam , Valle José Miguel Martínez , Opielak Marek , Polishchuk Oleh
Published: December 2024

Life cycle analysis (LCA) is a popular tool for determining the environmental impacts of a product in use. The aim of this study is to carry out a life cycle analysis, gate-to-gate, of a mass packaging process using a polyethylene shrinking film with a focus on energy consumption, raw material use and associated emissions, and film post-consumer disposal scenarios. Two different scenarios for the disposal of the shrinking film used in the packaging process were analyzed, namely recycling and landfills. The analysis showed that choosing recycling as the post-consumer management of film waste within the studied system boundaries reduces the negative environmental impact by approximately 17%. The study showed significantly higher environmental benefits in terms of harmfulness to human health for recycling than for landfills. A study of the environmental impact of the mass packaging process depending on the energy source showed that using a renewable source minimizes environmental damage. Three sources of energy options were analyzed, including the country’s energy mix, wind, and solar. The research shows that changing sources to wind power reduces potential damage to human health by 91%, to ecosystems by 89%, and to resources by 92% compared to the country’s energy mix power option. When comparing the results for the renewable energy options, the variant with energy from wind presents lower harm in all three damage categories compared to the solar option.
Investigation of Solid-State Thermal Decomposition of Ammonia Borane Mix with Sulphonated Poly(ellagic Acid) for Hydrogen Release
Authors: Astorino Carmela , De Nardo Eugenio , Lettieri Stefania , Ferraro Giuseppe , Bartoli Mattia , Etzi Marco , Chiodoni Angelica Monica , Pirri Candido Fabrizio , Bocchini Sergio
Published: December 2024

The utilization of hydrogen in safety conditions is crucial for the development of a hydrogen-based economy. Among all methodologies, solid-state hydrogen release from ammonia borane through thermal stimuli is very promising due to the high theoretical hydrogen release. Generally, carbonaceous or inorganic matrices have been used to tune the reactivity of ammonia borane. Nevertheless, these solutions lack chemical tunability, and they do not allow one to properly tune the complex chemical pathway of hydrogen release from ammonia borane. In this study, we investigated the effect of a bioderived multifunctional polymeric matrix on hydrogen release from ammonia borane, reaching pure hydrogen release of 1.2 wt.% at 94 °C. We also describe new chemical pathways involving the formation of anchored intermediates, namely BxNy species.
Recovery of Cellulose Contained in Mixed Fabrics
Authors: Conesa Juan A. , Córdoba Viviana N.
Published: December 2024

The present work focuses on the recovery of cellulose from mixed fabrics containing polyester and cotton through the use of different chemical and hydrothermal treatment methods. Through the application of various analytical techniques, such as thermogravimetry (TG) and infrared spectroscopy (IR), we seek to determine the effectiveness of the methods used. The results indicate that different treatments with NaOH and distilled water at high temperatures and pressures are particularly effective for the extraction of the cellulose fraction. Furthermore, these methods were compared with previous studies to evaluate their feasibility and sustainability. The findings underline the importance of selecting appropriate experimental conditions to maximize the purity of the cellulose obtained and minimize fiber degradation, thus promoting more efficient and sustainable textile recycling processes. These results have significant implications for industrial applications by enabling the development of scalable recycling methods and contribute to reducing the environmental impact of textile waste.
Advances in Palladium-Based Membrane Research: High-Throughput Techniques and Machine Learning Perspectives
Authors: Kolor Eric , Usman Muhammad , Boonyubol Sasipa , Mikami Koichi , Cross Jeffrey S.
Published: December 2024

The separation of high-purity hydrogen from mixed gasses using dense metallic alloy membranes is essential for advancing a hydrogen-based economy. Palladium-based membranes exhibit outstanding catalytic activity and theoretically infinite hydrogen selectivity, but their high cost and limited performance in contaminant-rich environments restrict their widespread use. This study addresses these limitations by exploring strategies to develop cost-effective, high-performance alternatives. Key challenges include the vast compositional design space, lack of systematic design principles, and the slow pace of traditional material development. This review emphasizes the potential of high-throughput and combinatorial techniques, such as composition-spread alloy films and the statistical design of experiments (DoE), combined with machine learning and materials informatics, to accelerate the discovery, optimization, and characterization of palladium-based membranes. These approaches reduce development time and costs while improving efficiency. Focusing on critical properties such as surface catalytic activity, resistance to chemical and physical stresses, and the incorporation of low-cost base metals, this study introduces domain-specific descriptors to address data scarcity and improve material screening. By integrating computational and experimental methods, future research can identify hidden material correlations and expedite the rational design of next-generation hydrogen separation membranes.
A Fast Two-Dimensional Direction-of-Arrival Estimator Using Array Manifold Matrix Learning
Authors: Lu Jieyi , Yang Long , Yang Yixin , Wang Lu
Published: December 2024

Sparsity-based methods for two-dimensional (2D) direction-of-arrival (DOA) estimation often suffer from high computational complexity due to the large array manifold dictionaries. This paper proposes a fast 2D DOA estimator using array manifold matrix learning, where source-associated grid points are progressively selected from the set of predefined angular grids based on marginal likelihood maximization in the sparse Bayesian learning framework. This grid selection reduces the size of the manifold dictionary matrix, avoiding large-scale matrix inversion and resulting in reduced complexity. To overcome grid mismatch errors, grid optimization is established based on the marginal likelihood, with a dichotomizing-based solver provided that is applicable to arbitrary planar arrays. For uniform rectangular arrays, we present a 2D zoom fast Fourier transform as an alternative to the dichotomizing-based solver by transforming the manifold vector in a specific form, thus accelerating the computation without compromising accuracy. Simulation results verify the superior performance of the proposed methods in terms of estimation accuracy, computational efficiency, and angle resolution compared to state-of-the-art methods for 2D DOA estimation.
A Sequence-to-Sequence Transformer Model for Satellite Retrieval of Aerosol Optical and Microphysical Parameters from Space
Authors: Zhang Luo , Gu Haoran , Li Zhengqiang , Liu Zhenhai , Zhang Ying , Xie Yisong , Zhang Zihan , Ji Zhe , Li Zhiyu , Yan Chaoyu
Published: December 2024

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, and health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on a global scale. However, traditional physical and statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence methods are increasingly applied to aerosol parameter retrieval, yet most current approaches focus on end-to-end single-parameter retrieval without considering the inherent relationships among multiple aerosol properties. In this study, we propose a sequence-to-sequence aerosol parameter joint retrieval algorithm based on the transformer model S2STM. Unlike conventional end-to-end single-parameter retrieval methods, this algorithm leverages the encoding–decoding capabilities of the transformer model, coupling multi-source data such as polarized satellite, meteorological, model, and surface characteristics, and incorporates a physically coherent consistency loss function. This approach transforms traditional single-parameter numerical regression into a sequence-to-sequence relationship mapping. We applied this algorithm to global observations from the Chinese polarimetric satellite (the Particulate Observing Scanning Polarimeter, POSP) and simultaneously retrieved multiple key aerosol optical and microphysical parameters. Event analyses, including dust and pollution episodes, demonstrate the method’s responsiveness in hotspot regions and events. The retrieval results show good agreement with ground-based observation products. This method is also adaptable to satellite instruments with various configurations (e.g., multi-wavelength, multi-angle, and multi-dimensional polarization) and can further improve its spatiotemporal generalization performance by enhancing the spatial balance of ground station training datasets.
Extended Reality to Assess Short-Term Spatial Memory—A Comparative Study of Mixed Reality, Augmented Reality, and Virtual Reality
Authors: Ponce David , Mendez-Lopez Magdalena , Lluch Javier , Juan M.-Carmen
Published: December 2024

A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets. When comparing the three technologies (MR, AR, VR) for the performance variables, there were no statistically significant differences for either the total number of correctly placed objects or the total number of attempts. However, the MR application required more time than the AR and VR applications in the evaluation phase and more time than the VR application in the learning phase. Our arguments for the longer time are the novelty of the MR application for the participants and the characteristics of the applications. The key results from the MR study include the following: (1) the objects used in the MR application were correctly positioned on a map, which implies that the memory acquired with the MR application is effectively transferred to the user’s mental map; (2) for the performance variables, there were no significant differences in the results by gender; (3) and the usability rating decreased with computer experience. The results show that the MR application is effective for spatial memory assessment and was well rated by the participants. The three technologies, along with suitable hardware, are effective for spatial memory assessment. However, MR using optical see-through headsets offers advantages over mobile AR and VR using headsets, discussed in this publication.
Limiting Performance of Radar-Based Positioning Solutions for the Automotive Scenario
Authors: Bandiera Francesco , Ricci Giuseppe
Published: December 2024

Road safety applications for automotive scenarios rely on the ability to estimate vehicle positions with high precision. Global navigation satellite systems (GNSS) and, in particular, the global positioning system (GPS), are commonly used for self localization. But, especially in urban vehicular scenarios, due to obstructions, they may not provide the requirements for crucial position-based applications. In this paper, we investigate the potential of GPS-free positioning schemes and, in particular, we compute the ultimate performance, i.e., Cramér–Rao lower bounds (CRLB), of localization schemes in which each vehicle estimates its position exploiting range and/or angle measurements of an assigned set of landmarks with a known position.
Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence
Authors: Abacı Hüseyin , Seçkin Ahmet Çağdaş
Published: December 2024

Wireless Fidelity (Wi-Fi) based positioning has gained popularity for accurate indoor robot positioning in indoor navigation. In daily life, it is a low-cost solution because Wi-Fi infrastructure is already installed in many indoor areas. In addition, unlike the Global Navigation Satellite System (GNSS), Wi-Fi is more suitable for use indoors because signal blocking, attenuation, and reflection restrictions create a unique pattern in places with many Wi-Fi transmitters, and more precise positioning can be performed than GNSS. This paper proposes a machine learning-based method for Wi-Fi-enabled robot positioning in indoor environments. The contributions of this research include comprehensive 3D position estimation, utilization of existing Wi-Fi infrastructure, and a carefully collected dataset for evaluation. The results indicate that the AdaBoost algorithm attains a notable level of accuracy, utilizing the dBm signal strengths from Wi-Fi access points distributed throughout a four-floor building. The mean average error (MAE) values obtained in three axes with the Adaptive Boosting algorithm are 0.044 on the x-axis, 0.063 on the y-axis, and 0.003 m on the z-axis, respectively. In this study, the importance of various Wi-Fi access points was examined with explainable artificial intelligence methods, and the positioning performances obtained by using data from a smaller number of access points were examined. As a result, even when positioning was conducted with only seven selected Wi-Fi access points, the MAE value was found to be 0.811 for the x-axis, 0.492 for the y-axis, and 0.134 for the Z-axis, respectively.
A Combined CNN-LSTM Network for Ship Classification on SAR Images
Authors: Toumi Abdelmalek , Cexus Jean-Christophe , Khenchaf Ali , Abid Mahdi
Published: December 2024

Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images. To address these challenges, we propose a shallow CNN architecture specifically designed to optimize performance on SAR datasets. Evaluations were performed on three datasets: FUSAR-Ship, OpenSARShip, and MSTAR. While the FUSAR-Ship and OpenSARShip datasets present difficulties due to their limited and imbalanced class distributions, MSTAR serves as a benchmark with balanced classes. To compare and optimize the proposed shallow architecture, we examine various properties of CNN components, such as the filter numbers and sizes in the convolution layers, to reduce redundancy, improve discrimination capability, and decrease network size and learning time. In the second phase of this paper, we combine the CNN with Long short-term memory (LSTM) networks to enhance SAR image classification. Comparative experiments with six state-of-the-art CNN architectures (VGG16, ResNet50, Xception, DenseNet121, EfficientNetB0, and MobileNetV2) demonstrate the superiority of the proposed approach, achieving competitive accuracy while significantly reducing training times and network complexity. This study underscores the potential of customized architectures to address SAR-specific challenges and enhance the efficiency of target classification.
Analytical Equations for the Prediction of the Failure Mode of Reinforced Concrete Beam–Column Joints Based on Interpretable Machine Learning and SHAP Values
Authors: Karampinis Ioannis , Karabini Martha , Rousakis Theodoros , Iliadis Lazaros , Karabinis Athanasios
Published: December 2024

One of the most critical components of reinforced concrete structures are beam–column joint systems, which greatly affect the overall behavior of a structure during a major seismic event. According to modern design codes, if the system fails, it should fail due to the flexural yielding of the beam and not due to the shear failure of the joint. Thus, a reliable tool is required for the prediction of the failure mode of the joints in a preexisting population of structures. In the present paper, a novel methodology for the derivation of analytical equations for this task is presented. The formulation is based on SHapley Additive exPlanations values, which are commonly employed as an explainability tool in machine learning. Instead, in the present paper, they were also utilized as a transformed target variable to which the analytical curves were fitted, which approximated the predictions of an underlying machine learning model. A dataset comprising 478 experimental results was utilized and the eXtreme Gradient Boosting algorithm was initially fitted. This achieved an overall accuracy of ≈84%. The derived analytical equations achieved an accuracy of ≈78%. The corresponding metrics of precision, recall, and the F1-score ranged from ≈76% to ≈80% and were close across the two modes, indicating an unbiased model.
Urban-Scale Rooftop Photovoltaic Potential Estimation Using Open-Source Software and Public GIS Datasets
Authors: Cenky Matej , Bendik Jozef , Janiga Peter , Lazarenko Illia
Published: December 2024

This paper aims to effectively estimate urban-scale rooftop photovoltaic potential using strictly open-source software and publicly available GIS data. This approach is often neglected; however, its importance is significant regarding technology transfer and general commercial or academic ease of use. A complete methodology is introduced, including the building shadow analysis. Although many papers are published in similar areas, very few reveal the specific steps and functions in the software used, or the computational core of some part of the estimation is a “black box” of a commercial service. Detailed irradiation parameters can be obtained using the proposed methodologies, and the maximum photovoltaic (PV) power output in the area can be estimated. The great advantage of this model is its scalability and the easy way of modifying every computational parameter. The results and limitations of the proposed methodology are discussed, and further development is suggested. The presented model is based on a sample location in Bratislava, Slovakia, with an area of circa 2.5 km2.
Studies of Applications of Cold Plasma Systems in Cancer Treatment: Mechanisms of Oxidant Stress and Pathway Signaling
Authors: Durán Martínez David , Valladares Méndez Adriana , Rivera Islas Jesús , Sánchez-Carranza Jessica Nayelli
Published: December 2024

Cold atmospheric plasma (CAP) has gained attention as a non-invasive therapeutic option in oncology due to its selective cytotoxicity against cancer cells. CAP produces a complex mixture of reactive oxygen and nitrogen species (RONS), which induce oxidative stress, leading to various forms of cell death, including apoptosis, necrosis, autophagy, and ferroptosis. These mechanisms allow CAP to target cancer cells effectively while sparing healthy tissue, making it a versatile tool in cancer treatment. This review explores the molecular pathways modulated by CAP, including PI3K/AKT, MAPK/ERK, and p53, which are crucial in the regulation of cell survival and proliferation. Additionally, in vivo, in vitro, and clinical studies supporting the efficacy of CAP are collected, providing additional evidence on its potential in oncological therapy.
Sustainability of the Origin Indication of Sugar Cane Spirit from Abaíra Microregion, Bahia, Brazil Under the Aegis of the Sustainable Development Goals (SDGs)
Authors: Saldanha Cleiton Braga , Silva Daliane Teixeira , Martins Luís Oscar Silva , Fraga Igor Dantas , Silva Marcelo Santana
Published: December 2024

This study evaluates the economic, social, environmental, and institutional impacts of the Geographical Indication (GI) of the Indication of Origin (IO) type in the Abaíra Microregion, Bahia, Brazil, and its alignment with the Sustainable Development Goals (SDGs), particularly SDGs 2, 4, 8, 12, and 16. The research focuses on assessing the long-term sustainability effects of the Abaíra GI on local communities and territories after its recognition in 2014. Using an exploratory, qualitative approach, this study applies the Economic, Social, and Environmental Impact Assessment Methodology for GIs developed by EMBRAPA, along with semi-structured interviews and field visits with local producers and cooperative members. The analysis highlights the importance of post-GI sustainability assessment and identifies significant impacts on local economic development, cultural preservation, and environmental management. The research reveals improvements in product visibility and commercial value, as well as the adoption of sustainable and innovative practices by local cooperatives and producers. Among the key findings are the strengthening of local traditions, enhanced product quality, and increased market access despite challenges such as rural exodus. When compared with Embrapa Grape and Wine’s methodology, this study proposes a comprehensive framework for evaluating other GIs. Additionally, the results show that support from SEBRAE and educational institutions through training and technical assistance has positively impacted territorial conditions, contributing to the broader goals of sustainable development.
Probabilistic Analysis of Extreme Water Demand Peak Factors for Sustainable Resource Management
Authors: Moretti Manuela , Guercio Roberto
Published: December 2024

Water management has evolved significantly, but sustainability remains a critical challenge. Ancient Roman aqueducts, despite their engineering marvel, operated with constant flow, leading to substantial water waste. Later, rooftop reservoir systems continued this inefficiency, as excess water would overflow. Only recently have demand-driven networks shown potential for reducing waste, though substantial water leaks continue to undermine these efforts. Achieving true sustainability in water distribution requires minimizing leaks through the use of models that adopt accurate water demand scenarios and identifying an optimal peak factor (PF). In fact, water distribution networks (WDNs) are commonly designed, analyzed, and calibrated using deterministic demand scenarios based on average annual consumption and scaled by a chosen PF. However, for efficient design and management, it is essential to associate a probabilistic value with the consumption data used in the analyses. This study introduces a novel methodology for estimating PFs with a specific return period at the District Meter Area (DMA) scale, utilizing extreme value statistical analysis. The generalized Pareto distribution (GPD) models were applied to provide more reliable PF estimates. The proposed methodology was validated using hourly residential consumption data from a DMA located in Southern Italy, demonstrating its effectiveness in improving the accuracy of WDN design.
Managing Digital Evidence in Cybercrime: Efforts Towards a Sustainable Blockchain-Based Solution
Authors: Ratul Md Hasibul Alam , Mollajafari Sepideh , Wynn Martin
Published: December 2024

Digital evidence plays a crucial role in cybercrime investigations by linking individuals to criminal activities. Data collection, preservation, and analysis can benefit from emerging technologies like blockchain to provide a secure, distributed ledger for managing digital evidence. This study proposes a blockchain-based solution for managing digital evidence in cybercrime cases in the judicial domain. The proposed solution provides the basis for the development of a new model that leverages a consortium blockchain, allowing secure collaboration among judicial stakeholders, while ensuring data integrity and admissibility in court. An extensive literature review demonstrates blockchain’s potential to create a more secure, efficient evidence management system. The proposed model was implemented in a test environment using a localised blockchain for developing and testing smart contracts, as well as integrating a web interface, with off-chain storage for managing evidence data. The system was subsequently deployed in both the Polygon and Ethereum test networks, simulating real-world blockchain environments, revealing that the operational cost in the Polygon network is reduced by 99.96% compared to Ethereum, thereby offering scalability without compromising security. This study underscores blockchain’s potential to revolutionise the chain of custody procedures, improving dependability and security in evidence management and providing more sustainable solutions within the criminal justice system.
Towards Effective Integrated Risk Management: Enhancing Compliance and Competitiveness for Sustainable Access to the EU Citrus Market
Authors: Phologane Samantha A. , Valentine Lucrecia , Yan Bingwen , Tambovceva Tatjana
Published: December 2024

The tightening of sanitary and phytosanitary (SPS) regulations, along with stringent private standards in international fresh produce trade, requires citrus producers, especially in developing regions, to adopt robust compliance mechanisms. This study examines the integration of a Risk Management System (RMS) into the existing Quality Management System (QMS) of citrus producers in the Western Cape (WC), South Africa, aimed at improving adherence to European Union (EU) market requirements. Through a comprehensive survey of citrus producers, the research identifies the primary barriers to effective risk management integration, such as financial constraints, limited human resources, and the prevalent non-compliance in the export chain, including pest infestations and exceeding Maximum Residue Limits (MRLs). The results highlight the fact that producers of varying scales face substantial challenges in resource allocation for RMS implementation. The study advocates for a strategic, top-down integration approach, which includes targeted financial support, the establishment of dedicated teams, and well-structured implementation plans. Additionally, the role of both governmental and private sector support is emphasised, particularly in providing training, technical assistance, and infrastructure investments to strengthen compliance efforts. These actions are crucial for ensuring sustained market access, enhancing the competitiveness of citrus producers, and promoting sustainable agricultural practices in the global market.
Shaping the Rural Landscape: Institutions of Land Use Change in Non-Urbanized Areas in Poland
Authors: Ślawska Justyna
Published: December 2024

Although extensive research has examined land use dynamics in urban settings, non-urbanized areas remain understudied, particularly in post-socialist contexts. This study investigates the political, economic, and institutional drivers of land use changes in the non-urbanized areas of Poland through the Political Market Framework. It focuses on how local government structures and interest group interactions shape land development decisions. The two-stage Heckman model was used to address sample selection bias. The results reveal three key findings: (1) municipal council composition significantly influences land use outcomes, with farmers and highly educated councilors generally opposing intensive development, while partisan councilors favor it; (2) interest groups, particularly production companies and the tourism sector, drive both the initiation and scale of land use changes; and (3) institutional factors, including agricultural land quality and farm size, moderate these relationships. This study advances our understanding of rural land use governance by demonstrating how political–economic forces interact with local institutional contexts to shape spatial development patterns. These findings have important implications for land use policy in transitional economies and contribute to broader discussions on rural governance.
A Study on the Response Characteristics of Carbon Flux Exchange in Chinese Fir Forests to Vapor Pressure Deficit
Authors: Liu Zhenxiang , Wang Yongqian , Sun Luming , Jiang Jing , Jiang Lan , Wang Mengtao , Ye Jingjing , Cheng Zhiqing
Published: December 2024

Forest carbon exchange is affected by various environmental variables, among which photosynthetically active radiation, temperature, saturated water vapor pressure deficit, and soil moisture content dominate. The global atmospheric temperature has risen significantly in recent decades, and the saturated water vapor pressure deficit has also increased, which has had a widespread and lasting impact on terrestrial carbon sinks. Here, using flux data from Mazongling in Jinzhai County from July 2020 to June 2023, the relationship between saturated water vapor pressure deficit and forest carbon flux was investigated on the basis of carbon flux changes in the forest ecosystem in response to environmental factors. Results revealed that vapor pressure deficit (VPD) and net ecosystem productivity (NEP) exhibited a quadratic relationship at the daily and monthly scales. When the VPD was greater than 1.2 kPa at the monthly scale, the NEP of the fir forest ecosystem decreased with increasing VPD. At the daily scale, the impact of the VPD on NEP was studied by month and season. The results revealed that the threshold value at which the VPD affected NEP differed across different months and seasons. Therefore, the VPD is an important factor in forest ecosystems and should be considered in the assessment of ecosystem carbon sinks. It also has far-reaching significance in the carbon cycle and ecological sustainable development.
Multi-Scenario Simulation of Future Land Use in the Beijiang River Basin Under Multidimensional Ecological Constraints
Authors: Wang Yi , Wang Jun , Hao Beibei , Zhang Siyi , Ding Junwei , He Bin
Published: December 2024

This study takes the Beijiang River Basin in Guangdong Province as an example, examining the changes in land usage throughout time and space between 1980 and 2020. Using multidimensional ecosystem service functions and the loop theory, this study constructs ecological constraints (ES) for the Beijiang River Basin. Based on these ecological constraints, an ES-PLUS model is developed to simulate future land cover changes under multiple scenarios in the Beijiang River Basin by 2050. The results indicate the following: (1) Currently, the major land use types in the Beijiang River Basin are forest, cropland, and grassland, accounting for over 95% of the area. Significant changes in land use were observed between 1980 and 2020, including the severe degradation of forests and grasslands, a notable expansion of construction land, intense human–land conflicts, and the highest single land use dynamic degree for unused land at 5.67%, with a comprehensive land use dynamic degree of 0.18%. (2) In the four development scenarios of the Beijiang River Basin in 2050, construction land increased by 32.97%, 74.75%, 26.01%, and 45.50%, respectively, suggesting that ecological constraints as flexible constraint spaces can effectively control the disorderly expansion of construction land. Therefore, formulating ecological protection policies, optimizing the land use structure in the Beijiang River Basin, and constructing ecological sources and corridors in line with the distribution of urban areas, roads, and railroads in the basin may offer direction for the best use of land resources, the preservation of the environment, and sustainable growth in the Beijiang River Basin.