bluuebunny/crossref_metadata_2025 huggingface dataset:
Easy Synthesis and In Vitro Evaluation of Halogenated Chalcones against Trypanosoma cruzi
Authors: Avila-Sorrosa Alcives , Laurel-Gochicoa Diana J. , Vargas-Díaz María Elena , Nogueda-Torres Benjamín , Gómez-Escobedo Rogelio I.
Published: October 2024

Chalcones are organic structures that occur naturally in flavonoids and isoflavonoids from diverse vegetables and fruits. Their properties have promising applications in medicinal chemistry as antiparasitic agents against malaria, leishmaniasis, and Chagas disease. Parasitic diseases, a global health challenge, affect thousands of people around the world. The lack of access to affordable treatments causes many deaths, especially in developing countries. Chagas disease, a neglected infection whose etiological agent is the protozoan Trypanosoma cruzi (T. cruzi), is currently incurable without timely treatment and depends on two primary nitrated chemotherapeutic agents: Nifurtimox (Nfx) and Benznidazole (Bzn). However, these drugs exhibit low selectivity and serious adverse effects, accentuating the critical need to develop new, safer chemotherapeutic options. In this context, herein we report the synthesis of halogen chalcone derivatives by an affordable and sustainable method. In vitro studies against T. cruzi demonstrated that the fluorine-containing structures have the best bioactive profile with inhibitions comparable to Nfx and Bzn. Additionally, ADME analysis was performed to determine the crucial physicochemical and pharmacokinetic descriptors of the series of compounds, which were shown to be suitable for enteral absorption and have a low risk of crossing the blood–brain barrier and damaging brain tissue.
Mechanisms of Water Pollutant Degradation under Electric Discharge Generated in a Cavitating Flow
Authors: Kamler Anna , Bayazitov Vadim , Sozarukova Madina , Nikonov Roman , Fedulov Igor , Cravotto Giancarlo , Abramova Irina
Published: October 2024

With the aim of developing an innovative water treatment approach for developing countries in the Global South, we have applied the method of treating a cavitating water stream with a plasma discharge under real conditions. To this end, we have optimised the approach after investigating the effects that occur in the treated medium during such a treatment. Based on the obtained light absorption curves of treated model solutions of titanium oxysulphate and potassium bichromate, it was found that inside the reactor the main role in the destruction of chemical contaminants is played by hydroxide ions, while outside the reactor the main chemical interaction takes place with hydrogen peroxide. The plasma treatment unit was tested in the biological wastewater treatment plant of a health resort in the territory of the Russian Federation (Almetyevsk, Republic of Tatarstan). Water samples taken directly from the tertiary decantation tank were used as real wastewater samples instead of adding chemical reagents for disinfection. It was found that with different modes of operation of the plasma treatment plant, the concentration levels of coliform bacteria, coliphages and Escherichia coli decreased significantly and fell below the limit of permissible concentrations for wastewater discharge. At the same time, the possible effect of the plasma on persistent inorganic compounds was investigated. It was shown that the plasma discharge in the flow of the incoming liquid can almost completely destroy compounds that are difficult to remove, such as hydrogen sulphide and chlorides. In the course of the study, the optimum frequency of electrical pulses of 68 kHz was selected, which ensures the lowest consumption of electrical energy while maintaining the required efficiency.
Techno-Economic Feasibility Analysis of Post-Combustion Carbon Capture in an NGCC Power Plant in Uzbekistan
Authors: Kamolov Azizbek , Turakulov Zafar , Furda Patrik , Variny Miroslav , Norkobilov Adham , Fallanza Marcos
Published: October 2024

As natural gas-fired combined cycle (NGCC) power plants continue to constitute a crucial part of the global energy landscape, their carbon dioxide (CO2) emissions pose a significant challenge to climate goals. This paper evaluates the feasibility of implementing post-combustion carbon capture, storage, and utilization (CCSU) technologies in NGCC power plants for end-of-pipe decarbonization in Uzbekistan. This study simulates and models a 450 MW NGCC power plant block, a first-generation, technically proven solvent—MEA-based CO2 absorption plant—and CO2 compression and pipeline transportation to nearby oil reservoirs to evaluate the technical, economic, and environmental aspects of CCSU integration. Parametric sensitivity analysis is employed to minimize energy consumption in the regeneration process. The economic analysis evaluates the levelized cost of electricity (LCOE) on the basis of capital expenses (CAPEX) and operational expenses (OPEX). The results indicate that CCSU integration can significantly reduce CO2 emissions by more than 1.05 million tonnes annually at a 90% capture rate, although it impacts plant efficiency, which decreases from 55.8% to 46.8% because of the significant amount of low-pressure steam extraction for solvent regeneration at 3.97 GJ/tonne CO2 and multi-stage CO2 compression for pipeline transportation and subsequent storage. Moreover, the CO2 capture, compression, and transportation costs are almost 61 USD per tonne, with an equivalent LCOE increase of approximately 45% from the base case. This paper concludes that while CCSU integration offers a promising path for the decarbonization of NGCC plants in Uzbekistan in the near- and mid-term, its implementation requires massive investments due to the large scale of these plants.
Chronic Pain in Spanish Physiotherapy Practice: Treatment Challenges and Opportunities in Diverse Healthcare Settings—A Qualitative Study
Authors: Díaz-Fernández Ángeles , Obrero-Gaitán Esteban , Cortés-Pérez Irene , Ortega-Martínez Ana Raquel , Osuna-Pérez María Catalina , Zagalaz-Anula Noelia , Ibancos-Losada María del Rocío , Lomas-Vega Rafael
Published: October 2024

Background/Objectives: Physiotherapists face significant challenges in managing chronic pain; a complex condition best addressed through a biopsychosocial (BPS) approach. Although substantial evidence exists globally, research specific to Spain remains limited. This study explores the experiences and challenges Spanish physiotherapists encounter in integrating BPS elements across public and private healthcare settings. Methods: Semi-structured interviews with 12 experienced physiotherapists were conducted, using a qualitative approach with purposive sampling and reflexive thematic analysis. The analysis, grounded in a constructivist/interpretivist framework, allowed themes to emerge naturally from the data, supported by relevant quotes. Results: Three primary themes were identified: (1) challenges in implementing the BPS approach, including patient resistance and limited training; (2) facilitators for adopting the BPS model, such as multidisciplinary support and positive patient outcomes; and (3) emotional and psychological aspects of the physiotherapists. The findings highlight the complexities of chronic pain management in physiotherapy, emphasizing the need for enhanced psychological training, integrated multidisciplinary care, early diagnosis, and effective family involvement. These insights are critical for improving both patient care and physiotherapist well-being. Conclusions: This study provides valuable guidance for future strategies, focusing on educational enhancements, multidisciplinary collaboration, healthcare policy reform, and emotional support for physiotherapists within the Spanish healthcare context.
Enhancement of Named Entity Recognition in Low-Resource Languages with Data Augmentation and BERT Models: A Case Study on Urdu
Authors: Ullah Fida , Gelbukh Alexander , Zamir Muhammad Tayyab , Riverόn Edgardo Manuel Felipe , Sidorov Grigori
Published: October 2024

Identifying and categorizing proper nouns in text, known as named entity recognition (NER), is crucial for various natural language processing tasks. However, developing effective NER techniques for low-resource languages like Urdu poses challenges due to limited training data, particularly in the nastaliq script. To address this, our study introduces a novel data augmentation method, “contextual word embeddings augmentation” (CWEA), for Urdu, aiming to enrich existing datasets. The extended dataset, comprising 160,132 tokens and 114,912 labeled entities, significantly enhances the coverage of named entities compared to previous datasets. We evaluated several transformer models on this augmented dataset, including BERT-multilingual, RoBERTa-Urdu-small, BERT-base-cased, and BERT-large-cased. Notably, the BERT-multilingual model outperformed others, achieving the highest macro F1 score of 0.982%. This surpassed the macro f1 scores of the RoBERTa-Urdu-small (0.884%), BERT-large-cased (0.916%), and BERT-base-cased (0.908%) models. Additionally, our neural network model achieved a micro F1 score of 96%, while the RNN model achieved 97% and the BiLSTM model achieved a macro F1 score of 96% on augmented data. Our findings underscore the efficacy of data augmentation techniques in enhancing NER performance for low-resource languages like Urdu.
Electrochemical Properties of Ultrathin LiNi1/3Mn1/3Co1/3O2 (NMC111) Slurry-Cast Li-Ion Battery
Authors: Park Byoung-Nam
Published: October 2024

In thin LiNi1/3Mn1/3Co1/3O2 (NMC111) electrodes, pseudocapacitive behavior is notably enhanced due to their increased surface-to-volume ratio, which intensifies the role of the electrode–electrolyte interface. This behavior is driven by fast, reversible redox reactions and ion intercalation occurring near the surface, where the shorter diffusion path allows for more efficient ionic transport. The reduced thickness of the electrodes shortens the Li-ion diffusion distance, improving the diffusion coefficient by a factor of 40 compared to thicker electrodes, where ion transport is hindered by longer diffusion paths. The increased surface area and shorter diffusion paths promote faster electrochemical kinetics, allowing for quicker ion intercalation and deintercalation processes. The thin-film configuration enhances pseudocapacitive charge storage, which is essential for applications requiring rapid charge and discharge cycles. As a result, the combination of improved Li-ion diffusion and enhanced surface activity contributes to superior electrochemical performance, offering higher power densities, faster energy delivery, and better rate capability. This improvement in performance makes thin NMC111 electrodes particularly advantageous for applications such as high-power energy storage systems, where fast kinetics and high power densities are critical. These findings highlight the importance of interface engineering and material morphology in optimizing the performance of Li-ion batteries and similar electrochemical energy storage devices.
Advancements in and Applications of Crystal Plasticity Modelling of Metallic Materials
Authors: Loukadakis Vasilis , Papaefthymiou Spyros
Published: October 2024

Integrated Computational Materials Engineering (ICME) is a set of methodologies utilized by researchers and engineers assisting the study of material behaviour during production processes and/or service. ICME aligns with societal efforts for the twin green and digital transitions while improving the sustainability and cost efficiency of relevant products/processes. A significant link of the ICME chain, especially for metallic materials, is the crystal plasticity (CP) formulation. This review examines firstly the progress CP has made since its conceptualization and secondly the relevant thematic areas of its utilization and portraits them in a concise and condensed manner. CP is a proven tool able to capture complex phenomena and to provide realistic results, while elucidating on the material behaviour under complex loading conditions. To this end, a significant number of formulations falling under CP, each with their unique strengths and weaknesses, is offered. It is a developing field and there are still efforts to improve the models in various terms. One of the biggest struggles in setting up a CP simulation, especially a physics-based one, is the definition of the proper values for the relevant parameters. This review provides valuable data tables with indicative values.
Terrestrial Aerophytic Cyanobacteria in the Canary Island Laurel-Forest (Laurisilva): Discovery of Brasilonema novocanariensis sp. nov. and Rhizonema melkonianarum sp. nov. from the Laurus Phyllosphere
Authors: Rancel-Rodríguez Nereida M. , Vieira Christophe , Sansón Marta
Published: October 2024

Cyanobacteria, known for their remarkable adaptability, are a diverse group of oxygenic photoautotrophic organisms found in various habitats worldwide. While their presence in aquatic ecosystems has been extensively studied, research on terrestrial and aerophytic cyanobacteria remains relatively limited, particularly concerning their taxonomic diversity and molecular characteristics. In this study, utilizing morphological and molecular data derived from the 16S rRNA gene, we unveiled and described two new species of filamentous cyanobacteria growing epiphytically on Laurus novocanariensis in the laurel forest (laurisilva) of the Canary Islands. The first species, Brasilonema novocanariensis sp. nov., belongs to a genus of aerophytic and subaerophytic nostocalean cyanobacteria. The second, Rhizonema melkonianarum sp. nov., belongs to the genus Rhizonema. The discovery of a new species of Brasilonema from the Canary Islands related to species initially found on the opposite side of the Atlantic Ocean, and recently in Asia, Europe, and Portugal, raises questions about their dispersal mechanisms, whether through natural means or human-mediated processes, prompting the need for future studies to investigate these propagation patterns. In the case of Rhizonema melkonianarum sp. nov., the transition from adapting to a basidiolichen host to inhabiting the phyllosphere of angiosperms raises ecological and evolutionary adaptation questions regarding cyanobacterial adaptability and their ecological roles in diverse habitats. This research expands our understanding of the diversity and geographic distribution of these terrestrial phyllosphere-associated cyanobacteria.
Monitoring Diversity Profiles of Forested Landscapes in the Mediterranean Spain: Their Contribution to Local and Regional Vascular Plant Diversity
Authors: García del Barrio José M. , Sánchez de Ron David , Auñón Francisco , Benavides Raquel , Alonso Ponce Rafael , González-Ávila Sergio , Bolaños Francisco , Roig Sonia , Ortega Quero Marta
Published: October 2024

Biodiversity monitoring is key for understanding the delivery of ecosystem functions and services. Mediterranean forests and woodlands harbor many characteristic species of the Mediterranean vascular flora, and hence, they are a good surrogate for detecting changes in biodiversity linked to global change. In this work, we present a database resulting from the study of vascular plant diversity in multi-scale plots of 0.1 ha, measured around the first decade of this century and located in Mediterranean forest environments. Diversity profiles are calculated from Hill numbers (0, 1 and 2) for local (α) and regional (ϒ) diversity, as well as a multiplicative calculation of differential diversity (β). The main Mediterranean forests sampled had a medium coverage of 51% and stand dominant height of 10.6 m, and they were monospecific in two-thirds of cases. Local diversity reaches its highest values (around 78 species per 0.1 ha) in Holm oak dehesas, with values below 50 species for the most productive forest stands dominated by species of the genus Pinus. As regards the contribution to regional diversity, broadleaf formations contribute the most, with stone pine forests and dehesas in an intermediate position, and pine forests contributing the lowest in species richness terms.
The Portofino Promontory: 200 Years of History of Marine Biology
Authors: Bavestrello Giorgio , Betti Federico , Bianchi Carlo Nike , Cappanera Valentina , Chiantore Mariachiara , Corradi Nicola , Montefalcone Monica , Mariotti Mauro Giorgio , Morri Carla , Povero Paolo , Relini Giulio , Schiaparelli Stefano , Bo Marzia
Published: October 2024

This paper outlines the history of scientific research developed in the Portofino Promontory, located in the centre of the Ligurian Sea. The chronicles span over two centuries, from the late 18th century to the present day. Portofino is now recognised as one of the best-known areas in the world regarding marine biological communities and their temporal dynamics, particularly in relation to current climate changes. In addition, since 1999, with the establishment of the Marine Protected Area, significant research related to marine environment conservation has developed in Portofino. The role of the University of Genoa, the Natural History Museum, other important institutions, and the researchers involved in the Portofino area has been outlined.
Ferulic Acid Ameliorates L-Methionine-Induced Hyperhomocysteinemia in Rats
Authors: Bhise Sunita , Aswar Urmila , Jadhav Akash , Aswar Manoj , Ganeshpurkar Ankit
Published: October 2024

Background/Objectives: Elevated plasma homocysteine levels constitute a risk factor for vascular and cardiovascular disorders. Ferulic acid (FA), a polyphenol is tested on L-methionine-induced hyperhomocysteinemia (hHcy). The present study investigated the protective effect of ferulic acid (FA) on hyperhomocysteinemia (hHcy) induced changes in hemodynamic, biochemical, anti-oxidant, anti-inflammatory parameters as well as histopathological changes in abdominal aorta and heart. Methodology: The Wistar rats were divided into six groups (n = 6) and treated orally for 36 days. The rats were treated with Met (1 gm/kg) to induce Hcy. They were treated with either standard (Vit. B12 + Folic acid; 15 + 70 mg) or test FA (20/40/60 mg/kg, respectively) post-Met treatment. Homocysteine, cholesterol, lactate dehydrogenase (LDH), creatinine kinase (CK-MB), and liver enzymes were estimated in blood followed by the measurement of hemodynamic parameters. The liver was estimated for antioxidant parameters and nitric oxide (NO). Heart and abdominal aorta were studied histopathologically. Result: Diseased rats showed increased Hcy, cholesterol, LDH, CK-MB, alanine transaminase (ALT), aspartate transaminase (AST), malondialdehyde (MDA), NO, and reduced glutathione (GSH). Following FA treatment, these parameters returned to normal. Atherosclerotic lesions in the aorta were observed in the hHcy group; however, in the FA treatment groups, they were lessened. Conclusions: Ferulic acid reduces oxidative and nitrosive stress, thereby reducing hypercyteinemia and improving the lipid profile. It might be acting by increasing the activity of methylation dependent on S-adenosylmethionine (SAM)/S-adenosylhomocysteine (SAH), which in turn prevents the formation of Hcy and reduces hHcy. The docking study supports these findings.
AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles
Authors: Shirzadi Mehdi , Martínez Mónica Rojas , Alonso Joan Francesc , Serna Leidy Yanet , Chaler Joaquim , Mañanas Miguel Angel , Marateb Hamid Reza
Published: October 2024

Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders.
The Validity of the Original and the Saudi-Modified Screening Tools for the Assessment of Malnutrition in Pediatrics: A Cross-Sectional Study
Authors: Alqahtani Sheikha , Aldubayan Khalid , Alshehri Saleh , Almuhareb Ghada , Mahnashi Amal
Published: October 2024

Background: Screening for malnutrition among hospitalized children is essential, and the Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) is a validated tool for this purpose. The study aimed to modify STAMP for a Saudi context and assess the sensitivity and specificity of both the original and modified tools. Method: A cross-sectional study was conducted among 307 hospitalized children, where both the original and Saudi-modified STAMP were applied. Anthropometric measurements were also recorded, and statistical analysis using SPSS and validity parameters was used to assess the tools’ validity. Results: The Saudi-modified STAMP identified a higher percentage of children at high risk of malnutrition compared to the original STAMP (91.6% vs. 62.9%). The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and agreement of the Saudi-modified STAMP compared to the original were 94.3%, 13.2%, 64.8%, 57.7%, 0.654, and 0.089, respectively. Conclusions: The Saudi-modified STAMP showed excellent sensitivity and varied negative predictive value, indicating its potential effectiveness in screening for the risk of malnutrition among hospitalized children compared to the original STAMP.
Impact of Psoas Muscle Area Index on Short- and Mid-Term Mortality in Patients Undergoing Valve Surgery for Infective Endocarditis: A Retrospective Analysis
Authors: Dinges Christian , Hammerer Matthias , Schörghofer Nikolaos , Knapitsch Christoph , Hecke Gretha , Klaus Sophie , Steindl Johannes , Rezar Richard , Seitelberger Rainald , Hoppe Uta C. , Hergan Klaus , Boxhammer Elke , Scharinger Bernhard
Published: October 2024

Background: Sarcopenia, characterized by the loss of skeletal muscle mass, is an emerging comorbidity associated with poor outcomes in cardiovascular surgery. Its impact on mortality in patients undergoing valve surgery for infective endocarditis (IE) remains underexplored. This study investigates the relationship between sarcopenia, measured by the Psoas muscle area index (PMAi), and mortality in patients with IE undergoing valve surgery. Materials and Methods: We retrospectively analyzed 68 patients with IE who underwent valve surgery at a tertiary care center from 2013 to 2021. Sarcopenia was defined as being in the lowest quartile of PMAi, measured via preoperative computed tomography (CT). Baseline characteristics, survival outcomes, and factors influencing mortality were analyzed using Kaplan–Meier survival curves and Cox proportional hazards regression. The predictive value of PMAi for 1-year and 3-year mortality was assessed via receiver operating characteristic (ROC) curves. Results: Sarcopenia was strongly associated with increased mortality at both 1-year (HR: 0.378, p = 0.010) and 3-year follow-ups (HR: 0.457, p = 0.012). Female sex (OR: 275.748, p < 0.001) and older age (OR: 9.995, p = 0.003) were significant predictors of sarcopenia. Chronic kidney insufficiency (CKI) and the use of heart failure medication therapy also significantly impacted survival outcomes. Conclusions: Sarcopenia is a strong independent predictor of short- and mid-term mortality in patients undergoing valve surgery for IE. Routine radiological assessment of sarcopenia using PMAi could improve risk stratification and guide preoperative interventions. Tailored management strategies, especially in older women and patients with CKI, may enhance outcomes in this high-risk population.
Factors Affecting Experiential Learning Experiences of University Students with Disabilities
Authors: Liu Shaohong , Bazzana-Adams Kayla D. , deBraga Michael , Kamenetsky Stuart B.
Published: October 2024

Background: Experiential learning (EL) experiences are an important component of a university education, positively impacting career-related attitudes, knowledge, and skills. Students also require EL opportunities to gain experiences required for admission to competitive graduate and professional programs. Students with disabilities face barriers accessing and benefiting from such opportunities. Purpose: This study examined the degree to which demographic factors, type and severity of disability, dispositional factors, and overall adjustment and well-being are predictive of the quality of EL experiences among university students with disabilities. Methodology/approach: A survey was distributed to undergraduate students with disabilities who have participated in EL courses. The results were analyzed using multiple linear regression. Findings/conclusions: Dispositional and adjustment and well-being variables, including the environmental mastery dimension of their psychological well-being rather than demographic factors, such as gender or type and severity of disability, are significant predictors of the quality of EL experiences among students with disabilities. Implications: For students with disabilities to have academically and professionally successful EL experiences, post-secondary institutions must continue to provide appropriate accommodations and educate instructors about the diverse and complex needs of this student group. This must include an understanding of the uniqueness of each individual student’s needs.
Fractional-Order Control Algorithm for Tello EDU Quadrotor Drone Safe Landing during Disturbance on Propeller
Authors: Rosmadi Nurfarah Hanim Binti , Bingi Kishore , Devan P. Arun Mozhi , Korah Reeba , Kumar Gaurav , Prusty B Rajanarayan , Omar Madiah
Published: October 2024

Quadcopter drones have become increasingly popular because of their versatility and usefulness in various applications, such as surveillance, delivery, and search and rescue operations. Weather conditions and obstacles can undoubtedly pose challenges for drone flights, sometimes causing the loss of one or two propellers. This is a significant challenge as the loss of one or more propellers leads to a sudden loss of control, potentially resulting in a crash, which must be addressed through advanced control strategies. Therefore, this article develops and implements a fractional-order control algorithm to enhance quadrotor drones’ safety and resilience during propeller failure scenarios. The research encompasses the complexities of quadrotor dynamics, fractional-order control theory, and existing methodologies for ensuring safe drone landings. The study emphasizes case validation on experimental results, where four distinct cases were tested using PID and Fractional-order PID (FOPID) controllers. These cases involve various simulated failure conditions to assess the performance and adaptability of the developed control algorithms. The results show the proposed FOPID control’s superior robustness and adaptability compared to traditional PID controllers. These offer significant advancements in navigating dynamic environments and managing disruptive elements introduced during propeller failure simulations in drone control technology.
Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning
Authors: Li Jinghang , Xu Shuting , Wu Yu , Zhang Zhe
Published: October 2024

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.
A Two-Player Game for Multi-Scale Topology Optimization of Static and Dynamic Compliances of Triply Periodic Minimal Surface-Based Lattice Structures
Authors: Strömberg Niclas
Published: October 2024

In this study, a novel non-cooperative two-player game for minimizing static (Player 1) and dynamic (Player 2) compliances is introduced, implemented, and demonstrated using a multi-scale topology optimization framework for triply periodic minimal surface (TPMS)-based lattice structures. Player 1 determines the optimal macro-layout by minimizing the static compliance based on a micro-layout provided by Player 2. Conversely, player 2 identifies the optimal micro-layout (grading of the TPMS-based lattice structure) by minimizing the dynamic compliance given a macro-layout from Player 1. The multi-scale topology optimization formulations are derived using two density variables in each finite element. The first variable is the standard density, which dictates whether the finite element is void or contains the graded lattice structure and is governed by the rational approximation of material properties (RAMP) model. The second density variable represents the local relative density of the TPMS-based lattice structure, determining the effective orthotropic elastic properties of the finite element. The multi-scale game is implemented for three-dimensional problems, and solved using a Gauss–Seidel algorithm with sequential linear programming. It is numerically demonstrated for several benchmarks that the proposed multi-scale game generates equilibrium designs with strong performance for both static and harmonic load cases, effectively avoiding resonance at harmonic load frequencies. Validation is achieved through modal analyses of finite element models of the optimal designs.
Density–Density Correlation Spectra of Ultracold Bosonic Gas Released from a Deep 1D Optical Lattice
Authors: Tan Yunzhi , Zhu Qiang , Wang Bing , Shi Jingran , Xiong Dezhi , Lyu Baolong
Published: October 2024

Density–density correlation analysis is a convenient diagnostic tool to reveal the hidden order in the strongly correlated phases of ultracold atoms. We report on a study of the density–density correlations of ultracold bosonic atoms which were initially prepared in a Mott insulator (MI) state in one-dimensional optical lattices. For the atomic gases released from the deep optical lattice, we extracted the normalized density–density correlation function from the atomic density distributions of freely expanded atomic clouds. Periodic bunching peaks were observed in the density–density correlation spectra, as in the case of higher-dimensional lattices. Treating the bosonic gas within each lattice well as a subcondensate without quantum tunneling, we simulated the post-expansion density distribution along the direction of the 1D lattice, and the calculated density–density correlation spectra agreed with our experimental observations.
Maxpro Designs for Experiments with Multiple Types of Branching and Nested Factors
Authors: Yang Feng , Zhou Zheng
Published: October 2024

Contemporary experiments often involve special factors known as branching factors. The levels of such factors determine the presence of some certain factors, referred to as nested factors. The design criteria for investigating the goodness of such designs are rarely developed. Furthermore, the existing criteria for such designs pay less attention to the space-filling property of low-dimensional projections of the design. The efficiencies of designs yielded by such criteria can markedly decrease when only a few factors are significant. To address this issue, this paper proposes a novel space-filling criterion based on the maximum projection criterion to evaluate the performance of the designs with branching and nested factors. A framework to construct optimal designs under the proposed criterion is also provided. Compared with the existing works, the resulting designs have better space-filling properties in all possible low-dimensional projections. Moreover, our strategy imposes no constraints on run size, level, and type of any factor, demonstrating its broad applicability.
Hierarchical Controlled Joint Remote Implementation of the Partially Unknown Operations of m Qudits via m High-Dimensional Entangled States
Authors: Jing Ruiheng , Lan Qi , Zhou Ping
Published: October 2024

We present a protocol for the hierarchical controlled joint remote implementation of the partially unknown operations of m qudits belonging to some restricted sets by using m multiparticle high-dimensional entangled states as the quantum channel. All the senders share the information of the partially unknown operations and cooperate with each other to implement the partially unknown operations on the remote receiver’s quantum system. The receivers are hierarchized in accordance with their abilities to reconstruct the desired state. The agents in the upper grade need only cooperate with one of the lower-grade agents, and the agents in the lower grade need the cooperation of all the other agents. The protocol has the advantage of having high channel capacity by using a high-dimensional entangle state as the quantum channel for the hierarchial controlled joint remote implementation of partially unknown quantum operations of m qudits.
Advanced Visitor Profiling for Personalized Museum Experiences Using Telemetry-Driven Smart Badges
Authors: Ivanov Rosen
Published: October 2024

This paper presents an innovative methodology for enhancing museum visitor experiences through personalized content delivery using a combination of explicit and implicit visitor profiling. The approach integrates Bluetooth Low Energy (BLE) smart badges to collect telemetry data, enabling precise visitor localization and dynamic group formation based on real-time proximity and shared interests. Initial profiling begins with OAuth registration and brief surveys and is then refined through the continuous tracking of exhibit interactions and the time spent at each exhibit. An AI-driven system delivers content to individual and group profiles, fostering both personalized learning and social interaction. This methodology addresses the limitations of traditional profiling by adapting to visitor behaviors in real time while maintaining a strong focus on data privacy and ethical considerations. The proposed system not only enhances engagement and satisfaction but also sets the stage for future advancements in personalized cultural experiences.
Overview of Reconfigurable Antenna Systems for IoT Devices
Authors: García Elena , Andújar Aurora , Anguera Jaume
Published: October 2024

The proliferation of Internet of Things (IoT) devices, such as trackers and sensors, necessitates a delicate balance between device miniaturization and performance. This extends to the antenna system, which must be both efficient and multiband operational while fitting within space-constrained electronic enclosures. Traditional antennas, however, struggle to meet these miniaturization demands. Reconfigurable antennas have emerged as a promising solution for adapting their frequency, radiation pattern, or polarization in response to changing requirements, making them ideal for IoT applications. Among various reconfiguration techniques (electrical, mechanical, optical, and material-based), electrical reconfiguration reigns supreme for IoT applications. Its suitability for compact devices, cost-effectiveness, and relative simplicity make it the preferred choice. This paper reviews various approaches to realizing IoT reconfigurable antennas, with a focus on electrical reconfiguration techniques. It categorizes these techniques based on their implementation, including PIN diodes, digital tunable capacitors (DTCs), varactor diodes, and RF switches. It also explores the challenges associated with the development and characterization of IoT reconfigurable antennas, evaluates the strengths and limitations of existing methods, and identifies open challenges for future research. Importantly, the growing trend towards smaller IoT devices has led to the development of antenna boosters. These components, combined with advanced reconfiguration techniques, offer new opportunities for enhancing antenna performance while maintaining a compact footprint.
An Efficient UAV Image Object Detection Algorithm Based on Global Attention and Multi-Scale Feature Fusion
Authors: Qian Rui , Ding Yong
Published: October 2024

Object detection technology holds significant promise in unmanned aerial vehicle (UAV) applications. However, traditional methods face challenges in detecting denser, smaller, and more complex targets within UAV aerial images. To address issues such as target occlusion and dense small objects, this paper proposes a multi-scale object detection algorithm based on YOLOv5s. A novel feature extraction module, DCNCSPELAN4, which combines CSPNet and ELAN, is introduced to enhance the receptive field of feature extraction while maintaining network efficiency. Additionally, a lightweight Vision Transformer module, the CloFormer Block, is integrated to provide the network with a global receptive field. Moreover, the algorithm incorporates a three-scale feature fusion (TFE) module and a scale sequence feature fusion (SSFF) module in the neck network to effectively leverage multi-scale spatial information across different feature maps. To address dense small objects, an additional small object detection head was added to the detection layer. The original large object detection head was removed to reduce computational load. The proposed algorithm has been evaluated through ablation experiments and compared with other state-of-the-art methods on the VisDrone2019 and AU-AIR datasets. The results demonstrate that our algorithm outperforms other baseline methods in terms of both accuracy and speed. Compared to the YOLOv5s baseline model, the enhanced algorithm achieves improvements of 12.4% and 8.4% in AP50 and AP metrics, respectively, with only a marginal parameter increase of 0.3 M. These experiments validate the effectiveness of our algorithm for object detection in drone imagery.
Optimizing S Chemical Looping Combustion with Cu-Fe Combined Oxygen Carriers: Performance and Mechanistic Insights
Authors: Peng Lihuai , Zheng Min
Published: October 2024

This study focuses on the S-to-H2SO4 industry by investigating the chemical looping combustion (CLC) process utilizing Fe-based and Cu-based oxygen carriers (OCs), which are widely applied in CLC technology. The primary objective is to conduct combined CLC reactions of these two metal carriers in a three-zone temperature tube furnace, aiming to achieve a higher SO2 yield than what is attainable by reacting a single metal carrier with S. The investigation reveals promising industrial applications, offering potential benefits in terms of reducing equipment costs, enhancing energy efficiency, and lowering the emissions of the H2SO4 production industry. Through a series of experiments, the study examines the effects of reaction temperature and material molar ratios on SO2 generation. The solid reaction products were characterized using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and X-ray photoelectron spectroscopy (XPS). The experimental results indicate that the combined Cu-based and Fe-based OCs exhibit a higher SO2 yield during the reduction stage compared to using either Fe-based or Cu-based OCs independently. Under optimal conditions, with a carrier gas flow rate of 300 mL/min, an Fe2O3/S molar ratio of 6:1 in the second temperature zone, and a reaction temperature of 900 °C, the total SO2 yield in the third temperature zone reached approximately 85%. This was achieved at a reaction temperature of 850 °C, with an Fe2O3/S molar ratio of 6:1 in the first half of the zone and a CuO/S molar ratio of 12:1 in the second half of the zone. SEM-EDS analysis further revealed that the combined OCs showed no significant signs of agglomeration or sintering after 10 cycles of the reaction. However, Cu-based carrier particles increased in size by 50%, while Fe-based carrier particles remained relatively stable. Additionally, the low mass-to-atom ratio of S on the surface of OCs after the cyclic reaction suggests that the reduced-state OCs can be fully oxidized and regenerated following the release of SO2 during oxidation.
An Open-Source Tool for Composite Power System Reliability Assessment in Julia™
Authors: Figueroa Josif , Bubbar Kush , Young-Morris Greg
Published: October 2024

This paper introduces an open-source tool capable of performing the Composite System Reliability evaluation developed in the high-level, dynamic Julia™ programming language. Employing Monte Carlo Simulation and parallel computing, the tool evaluates probabilistic adequacy indices for combined generation and transmission systems, focusing on both individual delivery points and the broader system. Proficiency in Optimal Power Flow problem formulations is demonstrated through two distinct methods: DC and linearized AC, enabling comprehensive resource and transmission adequacy analysis with high-performance solvers. Addressing replicability and the insufficiency of available software, the tool supports diverse analyses on a unified platform. The paper discusses the tool’s design and validation, particularly focusing on the two optimal power flow problem formulations. These insights significantly contribute to understanding transmission system performance and have implications for power system planning.
Mean Droplet Size Prediction of Twin Swirl Airblast Nozzle at Elevated Operating Conditions
Authors: Miao Jiaming , Wang Bo , Ren Guangming , Gan Xiaohua
Published: October 2024

This study introduces a novel predictive model for atomization droplet size, developed using comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel–air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of We1/Re1 ≥ 0.13 and highlights the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.
Two-Stage Robust Resilience Enhancement of Distribution System against Line Failures via Hydrogen Tube Trailers
Authors: Yang Libin , Li Zhengxi , Liu Tingxiang , An Na , Zhou Wanpeng , Si Yang
Published: October 2024

Due to the properties of zero emission and high energy density, hydrogen plays a significant role in future power system, especially in extreme scenarios. This paper focuses on scheduling hydrogen tube trailers (HTTs) before contingencies so that they can enhance resilience of distribution systems after contingencies by emergency power supply. The whole process is modeled as a two-stage robust optimization problem. At stage 1, the locations of hydrogen tube trailers and their capacities of hydrogen are scheduled before the contingencies of distribution line failures are realized. After the line failures are observed, hydrogen is utilized to generate power by hydrogen fuel cells at stage 2. To solve the two-stage robust optimization problem, we apply a column and constraint generation (C&CG) algorithm, which divided the problem into a stage-1 scheduling master problem and a stage-2 operation subproblem. Finally, experimental results show the effectiveness of enhancing resilience of hydrogen and the efficiency of the C&CG algorithm in scheduling hydrogen tube trailers.
Review of AEM Electrolysis Research from the Perspective of Developing a Reliable Model
Authors: Bernat Rafal , Milewski Jaroslaw , Dybinski Olaf , Martsinchyk Aliaksandr , Shuhayeu Pavel
Published: October 2024

This review thoroughly examines recent progress, challenges, and future prospects in the field of alkaline exchange membrane (AEM) electrolysis. This emerging technology holds promise for eco-friendly hydrogen production. It blends the benefits of traditional alkaline and proton-exchange membrane technologies, enhancing affordability and operational efficiencies by utilizing non-precious metal catalysts and operating at reduced temperatures. This study discusses key developments in materials, electrode design, and performance enhancement techniques. It also highlights the strategic role of AEM electrolysis in meeting global energy transition targets, like achieving Net Zero Emissions by 2050. An in-depth exploration of the operational fundamentals of AEM water electrolysis is provided, noting the technology’s early stage development and the ongoing need for research in membrane-electrode assembly assessment, catalyst efficiency, and electrochemical ammonia production. Moreover, this review compiles results on different cell components, electrolyte types, and experimental approaches, providing insights into operational parameters critical to optimizing AEM performance. The conclusion emphasizes the necessity for continuous research and commercialization efforts to exploit AEM electrolysis’s full potential across diverse industries.