bluuebunny/crossref_metadata_2025 huggingface dataset:
Quaternion Statistical Submanifolds and Submersions
Authors: Siddiqui Aliya Naaz , Alghamdi Fatimah
Published: December 2024

This paper aims to develop a general theory of quaternion Kahlerian statistical manifolds and to study quaternion CR-statistical submanifolds in such ambient manifolds. It extends the existing theories of quaternion submanifolds and totally real submanifolds. Additionally, the work examines quaternion Kahlerian statistical submersions, including illustrative examples. The exploration also includes an analysis of the total space and fibers under certain conditions with example(s) in support. Moreover, Chen–Ricci inequality on the vertical distribution is derived for quaternion Kahlerian statistical submersions from quaternion Kahlerian statistical manifolds.
Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks
Authors: Ceballos Manuel , Millán María
Published: December 2024

In this paper, we explore the connection between sensor networks and graph theory. Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. Specifically, we focus on vertex coloring, Eulerian paths, and Hamiltonian paths within the Delaunay graph associated with a sensor network. These concepts have critical applications in sensor networks, including connectivity analysis, efficient data collection, route optimization, task scheduling, and resource management. We derive theoretical results related to the chromatic number and the existence of Eulerian and Hamiltonian trails in the graph linked to the sensor network. Additionally, we complement this theoretical study with the implementation of several algorithmic procedures. A case study involving the monitoring of a sugarcane field, coupled with a computational analysis, demonstrates the performance and practical applicability of these algorithms in real-world scenarios.
Deformation Quantization of Nonassociative Algebras
Authors: Remm Elisabeth
Published: December 2024

We investigate formal deformations of certain classes of nonassociative algebras including classes of K[Σ3]-associative algebras, Lie-admissible algebras and anti-associative algebras. In a process which is similar to Poisson algebra for the associative case, we identify for each type of algebras (A,μ) a type of algebras (A,μ,ψ) such that formal deformations of (A,μ) appear as quantizations of (A,μ,ψ). The process of polarization/depolarization associates to each nonassociative algebra a couple of algebras which products are respectively commutative and skew-symmetric and it is linked with the algebra obtained from the formal deformation. The anti-associative case is developed with a link with the Jacobi–Jordan algebras.
Exploring the Structural Diversity and Biotechnological Potential of the Rhodophyte Phycolectome
Authors: Rodrigues Éllen F. , Verza Flavia Alves , Nishimura Felipe Garcia , Beleboni Renê Oliveira , Hermans Cedric , Janssens Kaat , De Mol Maarten Lieven , Hulpiau Paco , Marins Mozart
Published: December 2024

Lectins are non-covalent glycan-binding proteins found in all living organisms, binding specifically to carbohydrates through glycan-binding domains. Lectins have various biological functions, including cell signaling, molecular recognition, and innate immune responses, which play multiple roles in the physiological and developmental processes of organisms. Moreover, their diversity enables biotechnological exploration as biomarkers, biosensors, drug-delivery platforms, and lead molecules for anticancer, antidiabetic, and antimicrobial drugs. Lectins from Rhodophytes (red seaweed) have been extensively reported and characterized for their unique molecular structures, carbohydrate-binding specificities, and important biological activities. The increasing number of sequenced Rhodophyte genomes offers the opportunity to further study this rich source of lectins, potentially uncovering new ones with properties significantly different from their terrestrial plant counterparts, thus opening new biotechnological applications. We compiled literature data and conducted an in-depth analysis of the phycolectomes from all Rhodophyta genomes available in NCBI datasets. Using Hidden Markov Models capable of identifying lectin-type domains, we found at least six different types of lectin domains present in Rhodophytes, demonstrating their potential in identifying new lectins. This review integrates a computational analysis of the Rhodophyte phycolectome with existing information on red algae lectins and their biotechnological potential.
Pelvic Floor Muscle Training vs. Vaginal Vibration Cone Therapy for Postpartum Dyspareunia and Vaginal Laxity
Authors: Villani Federico , Petre Izabella , Buleu Florina , Iurciuc Stela , Marc Luciana , Apostol Adrian , Valentini Chiara , Donati Elisabetta , Simoncini Tommaso , Petre Ion , Furau Cristian
Published: December 2024

Background and Objectives: Pelvic floor dysfunction and sexual health issues are common postpartum due to weakened pelvic muscles, significantly impacting women’s quality of life (QoL). Pelvic floor muscle training (PFMT) is a widely used approach to address these issues. This study aimed to compare the effectiveness of two rehabilitation methods—vibrating vaginal cones (VCG) and PFMT exercises (CG)—in improving pelvic floor muscle strength, reducing dyspareunia, and enhancing sexual function in postpartum women. Materials and Methods: This 1-year retrospective observational analysis evaluated 57 postpartum women presenting with perineal muscle relaxation and sexual dysfunction. Participants were assessed 3 months postpartum (T0) and after 3 months of therapy (T1) at the Pelvic Floor Rehabilitation Clinic of Santa Chiara Hospital, Pisa. Outcomes were measured using the pubococcygeus (PC) test for pelvic floor strength and the Female Sexual Function Index (FSFI) for sexual function. Results: The results revealed significant improvements in pelvic floor muscle strength and sexual function across both groups. While both interventions effectively reduced dyspareunia, the VCG group demonstrated superior outcomes, with 96.67% of participants reporting no pain compared to 80.95% in the CG. FSFI scores improved significantly in both groups, with greater enhancements in arousal, desire, and pain domains observed in the VCG group (p < 0.01). Vaginal cone therapy also resulted in slightly higher gains in overall pelvic floor strength. Conclusions: These findings suggest that vibrating vaginal cones may be a promising option for postpartum pelvic floor rehabilitation, with potential benefits for improving sexual satisfaction and reducing pain.
Long-Distance Passive Sensing Tag Design Based on Multi-Source Energy Harvesting and Reflection Amplification
Authors: Li Gang , Pan Chong , Wu Bo , Xu Zhiliang , Li Shihua , Zhang Yehua , Yang Yongjun , Zou Zhuohang , Shi Chang , Wang Muze
Published: December 2024

Wireless sensor networks often rely on battery power, which incurs high costs, considerable volume, and a limited lifespan. Additionally, the communication range of existing passive sensor tags remains short, which challenges their suitability for evolving Internet of Things (IoT) applications. This paper, therefore, presents a long-distance passive RFID sensing tag that integrates multi-source energy harvesting and reflection amplification. Multi-source energy harvesting enhances tag receiving sensitivity and extends the system’s downlink communication distance, while reflection amplification increases tag reflection power and improves the uplink communication distance, thereby significantly expanding the overall communication range. The test results show that the tag achieves a receiving sensitivity of −45 dBm, a reflection gain of 44 dB, and a communication distance of up to 96 m.
Modeling and Characterization of Multilayer Piezoelectric Stacks via Dynamic Stiffness Method
Authors: Ding Wenxiang , Liang Zhaofeng , Zhao Wei , Zhong Hongmei , Chen Dan , Bavencoffe Maxime , Lethiecq Marc
Published: December 2024

Multilayer piezoelectric stacks, which are multiple layers of piezoelectric materials placed on top of each other, are widely used to achieve precise linear movement and high-force generation. In this paper, a dynamic stiffness (DS) method for the dynamic vibration analysis of multilayer piezoelectric stacks is presented. First, the general solutions for all physical quantities of the three vibration contributions (i.e., pure vibration, symmetrically coupled vibration, and anti-symmetrically coupled vibration) are derived from the governing equations of motion. Then, the DS matrices of each layer of the piezoelectric stack are obtained, and they are assembled to form a global DS matrix. The electrical impedances and the mode shapes of a piezoelectric stack consisting of two piezoelectric disks connected in series and in parallel are calculated using our method as well as by the finite element method. The comparison shows good agreement. Finally, the effect of the number of layers on the dynamic responses of piezoelectric stacks is investigated. The DS method developed here provides an efficient and accurate analytical tool for the parametric and optimization analysis of the coupled vibrations of multilayer piezoelectric structures.
Taxonomic and Functional Dynamics of Bacterial Communities During Drift Seaweed Vermicomposting
Authors: Aira Manuel , Gómez-Roel Ana , Domínguez Jorge
Published: December 2024

Seaweed is a valuable natural resource, but drift or beach-cast seaweed is considered a waste product. Although seaweed is traditionally used as an organic amendment, vermicomposting has the potential to transform the material into valuable organic fertilizer, thereby enhancing its microbial properties. This study aimed to investigate the dynamics of the taxonomic and functional bacterial communities in seaweed during the vermicomposting process by high-throughput sequencing of 16S rRNA gene amplicons. Vermicomposting changed the composition of the bacterial communities, as indicated by the low proportion of bacterial taxa common to the bacterial communities in the raw seaweed and vermicompost (21 to 56 ASVs from more than 900 ASVs per sample type). The observed increase in taxonomic diversity (32% mean increase across sampling times) also affected the functionality of the bacterial communities present in the vermicompost. The diverse bacterial community showed enriched functional pathways related to soil health and plant growth, including the synthesis of antibiotics, amino acids, and phytohormones, as well as the degradation of bisphenol. In conclusion, in terms of microbial load and diversity, vermicompost derived from seaweed is a more valuable organic fertiliser than seaweed itself.
Characteristics and Differences in the Antler Velvet Microbiota During Regeneration
Authors: Li Yongxiang , Zhu Yuhang , Yang Bo , Yu Shaochen , Li Songze , Wright André-Denis G. , Du Rui , Si Huazhe , Li Zhipeng
Published: December 2024

The skin surface has a complex and dynamic ecosystem inhabited by a diverse microbiota. The wound formed by antler velvet shedding can naturally achieve regenerative restoration, but the changes in microbial composition that occur during antler velvet regeneration are largely unknown. In this study, we analyzed the antler velvet microbiota of sika deer at 15 days (Half) and 30 days (Full) post-pedicle casting using 16S rRNA gene sequencing. A total of 2659 OTUs were identified, which were assigned to 26 phyla, 304 families, and 684 genera. The core microbiota of the two groups were mainly composed of Atopostipes spp., Corynebacterium spp., Burkholderia spp., Staphylococcus spp., and Paracoccus spp. In comparison with the Full group, the Shannon, Simpson, Ace, and Chao 1 indices were significantly decreased in the Half group (p < 0.05). Principal coordinate analysis showed that there were significant differences in the microbial community between the Half and Full groups based on Bray–Curtis dissimilarity, weighted Unifrac distance, and unweighted Unifrac distance (p < 0.05). The relative abundances of bacteria belonging to the genera Staphylococcus, Romboutsia_B, and Dietzia increased significantly in the Half group, while the abundances of bacteria belonging to the genera Atopostipes, Psychrobacter, and Faecousia increased significantly in the Full group (p < 0.05). Correlation analysis showed that the relative abundances of bacteria belonging to the genera Staphylococcus, Romboutsia_B, and Dietzia positively correlated with arginine and proline metabolism (p < 0.05). These findings demonstrate that antler velvet regeneration is accompanied by distinct changes in microbial composition and highlight the potential roles of key taxonomy in wound healing and tissue regeneration.
Correction: Ullah et al. Natural Polyphenols for the Preservation of Meat and Dairy Products. Molecules 2022, 27, 1906
Authors: Ullah Hammad , Hussain Yaseen , Santarcangelo Cristina , Baldi Alessandra , Di Minno Alessandro , Khan Haroon , Xiao Jianbo , Daglia Maria
Published: December 2024

In the original publication [...]
Effects of Integrated Virtual Reality and Galvanic Vestibular Stimulation on Standing Balance
Authors: Pradhan Gaurav N. , Kingsbury Sarah E. , Stepanek Jan , Cevette Michael J.
Published: December 2024

Background/Objectives: Galvanic vestibular stimulation (GVS) integrated into virtual reality (VR) environments enhances immersion and mitigates cybersickness. It is well known that GVS can affect standing balance. Most studies have investigated the effects of GVS in VR in seated conditions. The purpose of this study was to evaluate the impact of joint GVS and VR with moving visual stimulus on standing balance. Methods: Using a repeated measures counter-balanced design, motion sickness, postural sway, and velocity utilizing the center of pressure (COP) along the mediolateral (ML) and anteroposterior (AP) axes were obtained in 18 subjects during optokinetic (OPK) stimulus (black and white vertical bars moving from left to the right) in VR across three interventions: GVS in the same direction of visual stimulus—left to right ear (Positive GVS), GVS in the opposite direction of visual stimulus—right to left ear (Negative GVS), and without GVS (Null GVS). Motion sickness symptom scoring was obtained using the Pensacola Diagnostic Index. Results: The PDI score was increased significantly in the Negative GVS. The root mean square and sway range of COP along ML was greater during the Positive GVS and Negative GVS than the Null GVS, while, along AP, it was only greater during Negative GVS. During Positive GVS, mean positive and negative peak velocities, only in ML, were increased and decreased, respectively. During Negative GVS, only negative peak velocities in both ML and AP directions were decreased. Conclusions: This research highlights the importance of testing combined VR and GVS to assess standing balance while mitigating cybersickness.
Improved Antibacterial Properties of Fermented and Enzymatically Hydrolyzed Bee Pollen and Its Combined Effect with Antibiotics
Authors: Damulienė Vaida , Kaškonienė Vilma , Kaškonas Paulius , Mickienė Rūta , Maruška Audrius
Published: December 2024

Background/Objectives: A variety of phytochemicals from different plants are collected by bees into bee pollen granules. This research focused on evaluating the effects of lactic acid fermentation and enzymatic hydrolysis on the antibacterial activity of bee pollen and its interaction with antibiotics. There is limited knowledge regarding the interactions between treated bee pollen extracts and antibiotics, and this study contributes to the field by providing new insights into the antibacterial activity of pollen subjected to eight distinct treatment methods. Methods: Bee pollen’s bacterial fermentation using a Lacticaseibacillus rhamnosus culture and spontaneous fermentation were performed. Bee pollen hydrolysis was performed using commercial enzymes, including enzyme mixtures as well as pure enzymes. The agar well diffusion assay was employed to assess the antibacterial activity against Staphylococcus aureus, Listeria monocytogenes, and Salmonella enterica serovars Enteritidis and Typhimurium, as well as their interaction with antibiotics (ceftazidime, ciprofloxacin, oxytetracycline dihydrate, and erythromycin). Results: This study showed an enhancement in bee pollen’s antibacterial activity after both fermentation and enzymatic hydrolysis. The increase varied with the pollen’s origin, treatment type, and culture used for antimicrobial tests. More than 77% of bee pollen extracts demonstrated a synergistic effect with antibiotics across all tested bacterial strains, while antagonistic interactions were comparatively rare. Conclusions: The applied treatment methods can improve the antibacterial properties of bee pollen. Bee pollen extracts, in combination with antibiotics, can enhance their effectiveness. These findings provide new insights into the potential use of bee pollen in combating bacterial infections.
Aspergillus oryzae Fermented Plumula Nelumbinis Against Atopic Dermatitis Through AKT/mTOR and Jun Pathways
Authors: Chen Fengfeng , Liu Jing , Yu Xinwei , Jia Honglei , Yang Cheng , Zhao Bingtian
Published: December 2024

Background/Objectives: Atopic dermatitis (AD) is a chronic inflammatory skin disorder that has attracted global attention, and alkaloids from Plumula Nelumbinis have been shown to have anti-inflammatory activity. Fermentation has been used for the structural modification of natural compounds to improve bioavailability and activity, but the AD therapeutic efficacy and mechanism of the fermented Plumula Nelumbinis (FPN) are still unclear. Methods: The potential targets of FPN for AD were preliminarily screened using network pharmacology, and then PCR and WB were used to prove the therapeutic effect of FPN in AD. Results: Network pharmacology indicated that mTOR and Jun were key targets for AD. The experiments in vitro showed that FPN could effectively block AKT/mTOR and AKT/Jun-mediated inflammatory signaling pathways. Moreover, FPN can also alleviate SDS-induced inflammation in zebrafish. It is also found that the anti-inflammatory activity of Plumula Nelumbinis was enhanced by Aspergillus oryzae fermentation, and the oil phase of the fermentation product showed better activity, which may be due to microbial fermentation changing the structure of the original alkaloids. Conclusions: This study elucidated the potential mechanisms of alkaloids derived from fermented Plumula Nelumbinis against AD; it may also provide a scientific basis for the development of new drugs for AD.
Optimal Placement and Sizing of Modular Series Static Synchronous Compensators (M-SSSCs) for Enhanced Transmission Line Loadability, Loss Reduction, and Stability Improvement
Authors: Urrea-Aguirre Cristian , Saldarriaga-Zuluaga Sergio D. , Bustamante-Mesa Santiago , López-Lezama Jesús M. , Muñoz-Galeano Nicolás
Published: December 2024

This paper addresses the optimal placement and sizing of Modular Static Synchronous Series Compensators (M-SSSCs) to enhance power system performance. The proposed methodology optimizes four key objectives: reducing transmission line loadability, minimizing power losses, mitigating voltage deviations, and enhancing voltage stability using the L-index. The methodology is validated on two systems: the IEEE 14-bus test network and a sub-area of the Colombian power grid, characterized by aging infrastructure and operational challenges. The optimization process employs three metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO)—to identify optimal configurations. System performance is analyzed under both normal operating conditions and contingency scenarios (N − 1). The results demonstrate that M-SSSC deployment significantly reduces congestion, enhances voltage stability, and improves overall system efficiency. Furthermore, this work highlights the practical application of M-SSSC in modernizing real-world grids, aligning with sustainable energy transition goals. This study identifies the optimal M-SSSC configurations and placement alternatives for the analyzed systems. Specifically, for the Colombian sub-area, the most suitable solutions involve installing M-SSSC devices in capacitive mode on the Termocol–Guajira and Santa Marta–Guajira 220 kV transmission lines.
Heat Transfer Modeling of Geothermal Wells in Fractured Aquifers Considering the Joule–Thompson Effect
Authors: Li Mengmeng , Bi Gang , Shi Yu , Wang Yang
Published: December 2024

Geothermal energy, as a clean, low-carbon, widely distributed, renewable and environmentally friendly energy source, plays an important role in the transition from traditional energy sources dominated by coal and oil to clean energy. Ground source heat pump technology is a key technological tool for developing geothermal energy for widespread use. Coaxial-cased heat exchangers are the core component of the ground source heat pump system, and their heat transfer performance directly affects the heat transfer efficiency and service life of the ground source heat pump system. According to the actual working conditions of coaxial-cased heat exchangers in fractured aquifers, the coupled pressure–temperature model of the heat transfer outside the borehole was created by considering the influence of the Joule–Thompson effect. For heat transfer inside the wellbore, a multi-layer long concentric cylinder wall model was developed to obtain the fluid temperature distribution within the wellbore. Results show that the heat transfer efficiency increases with the increase of thermal conductivity, water production and effective permeability of fractures. The positive and negative values of the Joule–Thompson coefficient reflect the trend of fluid temperature changing with pressure. The larger the absolute value is, the greater the temperature change is. The increase in the initial temperature of the injected water will lead to a decrease in the theoretical heat transfer. With the increase of the water injection rate and horizontal wellbore length, the heat recovery power will also increase significantly, but the optimal value needs to be considered comprehensively. The findings of the study can not only lay a theoretical foundation for the performance evaluation and optimal design of coaxial-cased heat exchangers but also have great significance in promoting the efficient development of geothermal energy.
Generic Representation Language for Modeling Transport and Material Handling Systems in Smart Manufacturing Systems
Authors: Gonçalves Micael , Martins Paulo , Pereira Guilherme , Sousa Rui
Published: December 2024

This paper introduces a generic representation language to be used by organizations to represent physical and behavioral characteristics of Transport and Material Handling Systems (TMHS). This work implied a systematic observation, analysis and interpretation of several TMHS to ensure that most of the behaviors were covered. The generic representation language consists of three main types of elements: (i) objects transported, (ii) workstations and (iii) transport/handling equipment (device), and a small set of simple and easy-to-use properties to be defined by users of each organization to characterize each element of a TMHS. Each property is not related to any specific device and can be used to represent the behavior of different devices. A graphic representation for each element is proposed to make communication between users simpler and more effective, as well as to reduce the time to learn and apply the representation language. The representation of three concrete TMHS (with different behaviors, rules and restrictions) is shown, contributing to demonstrate the ability, flexibility and comprehensiveness of the developed representation language. These results point to the potential of implementing the developed generic representation language in IT (Information Technology) support systems, in particular, in Smart Manufacturing Systems, to control most of the TMHS.
Polymer Recycling: A Comprehensive Overview and Future Outlook
Authors: van den Tempel Paul , Picchioni Francesco
Published: December 2024

Polymer recycling is an essential and crucial topic in our sustainability-driven society. The depletion of oil and the increasing interest in biomass conversion clearly stimulate the search for alternative carbon sources. On the other hand, polymeric products (plastic, rubber etc.) are ubiquitous and are an integral part of our life. Recycling these products is thus of paramount importance, and perhaps crucially, from an environmental point of view. In this work, we will focus on the most common commodities, with the most important being (in terms of production volume) thermoplastics, rubbers and thermosets. A consequence of this choice is that the most common materials as well as chemical and biochemical recycling methods will be discussed. New advances in the corresponding technologies will be presented and critically evaluated. Finally, on the basis of this literature review, we will identify current trends and possible future developments.
Extra-Appendiceal Neuroendocrine Expressing Goblet Cell Adenocarcinoma of the Cecum—A Case Report and Brief Review of the Literature
Authors: Dinu Alexandra , Aşchie Mariana , Bălţătescu Gabriela Isabela , Enciu Manuela , Burlacu Ionuţ
Published: December 2024

Background and Clinical Significance: Neuroendocrine expressing goblet cell adenocarcinomas (GCAs) are uncommon clinically aggressive tumours of the digestive system, originating almost exclusively in the ileocecal appendix. GCA’s singularity comes from its amphicrine nature, expressing both neuroendocrine and exocrine characteristics. The case report’s objective is to raise awareness of this neoplasia’s possible extra-appendiceal localisation by showcasing a GCA involving the cecum with no detectable appendiceal tumour. Case Presentation: The authors present a case of GCA with neuroendocrine expression in an 82-year-old male patient with severe anaemia and comorbidities who underwent a right colectomy and had no histopathological evidence of appendiceal tumour involvement. Immunohistochemical testing was performed using synaptophysin, chromogranin A, neuronal specific enolase, CD56, CDX-2, CK20, CEA, MUC2 and Ki67, thus establishing the final diagnosis of high-grade extra-appendiceal goblet-cell adenocarcinoma of the cecum, G3. The patient died on postoperative day 26 due to pneumonia and acute renal failure in a chronic renal disease context. Conclusions: Extremely few cases of extra-appendiceal GCA have been reported. Appendiceal evaluation with the exclusion of this possible origin should be mandatory in such cases for a correct classification. These tumours do not benefit from any official management protocols concerning clinical evaluation, and their treatment is commonly based on the tumour’s stage, as in classical adenocarcinoma.
Fungal Submerged Fermentation of Coffee Silverskin: A Sustainable Source of Natural Meat Additives
Authors: Terán-Rivera Stephany Carolina , Torrescano-Urrutia Gastón Ramón , del Mar Torres-Martínez Brisa , Esqueda-Valle Martín , Ibarra-Arias Félix Joel , Sánchez-Escalante Armida , Vargas-Sánchez Rey David
Published: December 2024

The coffee industry, while processing coffee beans, generates residues like husk, pulp, and silverskin, which have been considered a promising source of bioactive metabolites. Recovering these metabolites offers a sustainable strategy to obtain natural food additives. Based on the above, this study aimed to determine the effect of the aqueous extract obtained from maceration and fungal fermented coffee silverskin (CSS) on ground pork meat’s oxidative and microbiological stability. Treatments used to recover bioactive compounds from CSS were the following: maceration extraction (ME) using 0, 1.5, and 3.0% of CSS (ME–0%, ME–1.5%, and ME–3%); fungal submerged fermentation extraction (FE) using 0, 1.5, and 3.0% of CSS (FE–0%, FE–1.5%, and FE–3%) and Pleurotus pulmonarius mycelium. Concerning metabolite content and bioactivity, results showed a decrease in the carbohydrate content of the ME and FE-obtained, as well as an increase in the phenol, flavonoid, and caffeoylquinic acid content. Also, an increase in radical cation scavenging activity, reducing power values and antibacterial activity of the extracts obtained with FE, was observed. Regarding pork meat homogenate treated with ME and FE extracts and subjected to oxidation with potassium ferrocyanide, results demonstrated that inclusion of FE-obtained extract led to decreased pH, lipid oxidation, metmyoglobin, and color changes (L*, a*, b*, C*, and h*), and microbial growth. These results demonstrate that CSS aqueous extract obtained with FE can be considered a potential additive for the meat industry with antioxidant and antibacterial activity. Nevertheless, further research is necessary to assess the sensory impact, biochemical mechanism, food safety, sustainability, and industrial feasibility of these extracts for broader applications.
Measuring the Impacts of Argentina’s Presidential Election Process in 2023 on the Stock Market Performance Using a Dynamic Event Study Methodology
Authors: Sandoval Álamos Eduardo Enrique , Molina Mac-Kay Claudio René , Taipe Aquino Erwin Octavio
Published: December 2024

This study measured the individual and conjoint effects of Argentina’s primaries and first- and second-voting presidential election results, as well as their post-election comparative effects, on the stock market performance of its most relevant economic sectors. Within four different estimation methods, the state-space specification outperformed the rest. The findings suggest that investors can under/overreact compared to post-election sectors performance, the public services sector being the exception. Therefore, those investors who anticipated the election results by liquidating positions in companies in the materials sector and investing more in companies in the energy and other industrial sectors achieved a superior performance.
An Improved Registration Method for UAV-Based Linear Variable Filter Hyperspectral Data
Authors: Wang Xiao , Yu Chunyao , Zhang Xiaohong , Liu Xue , Zhang Yinxing , Fang Junyong , Xiao Qing
Published: December 2024

Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency.
Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft–Vehicle–Airfield Collaboration
Authors: Yuan Dezhou , Zhong Yingxue , Zhu Xinping , Chen Ying , Jin Yue , Du Xinze , Tang Ke , Huang Tianyu
Published: December 2024

To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.
Study on the Mechanism of Energy Dissipation in Hemispherical Resonator Gyroscope
Authors: Yuan Lishan , Wang Ning , Xie Ronghao , Wei Zhennan , Zeng Qingshuang , Wang Changhong
Published: December 2024

The hemispherical resonator gyroscope is a gyroscope based on the principle of Coriolis vibration, widely used in inertial measurement systems of spacecraft. This article decomposes the gyroscope into two parts: the resonator shell and the gyroscope head, establishes the energy dissipation mechanism of the gyroscope, and conducts experimental verification. Firstly, based on the working principle of the gyroscope, a mechanical analysis model of the hemispherical resonator gyroscope head with a resonator spherical shell containing quality defects under second-order vibration state was established. The unbalanced force applied by the resonator spherical shell to the hemispherical resonator gyroscope head was analyzed, and the energy transfer path and dissipation mechanism from the spherical shell to the hemispherical resonator gyroscope head were explained. Finally, through the constructed testing platform, the circumferential quality factor test of the hemispherical resonator gyroscope before and after assembly was completed according to the designed experimental plan, and the consistency between theory and experimental phenomena was verified experimentally.
Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming
Authors: Nazar Ahmad M. , Selim Mohamed Y. , Qiao Daji
Published: December 2024

Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS.
Cilia-Inspired Bionic Tactile E-Skin: Structure, Fabrication and Applications
Authors: Yu Jiahe , Ai Muxi , Liu Cairong , Bi Hengchang , Wu Xing , Ying Wu Bin , Yu Zhe
Published: December 2024

The rapid advancement of tactile electronic skin (E-skin) has highlighted the effectiveness of incorporating bionic, force-sensitive microstructures in order to enhance sensing performance. Among these, cilia-like microstructures with high aspect ratios, whose inspiration is mammalian hair and the lateral line system of fish, have attracted significant attention for their unique ability to enable E-skin to detect weak signals, even in extreme conditions. Herein, this review critically examines recent progress in the development of cilia-inspired bionic tactile E-skin, with a focus on columnar, conical and filiform microstructures, as well as their fabrication strategies, including template-based and template-free methods. The relationship between sensing performance and fabrication approaches is thoroughly analyzed, offering a framework for optimizing sensitivity and resilience. We also explore the applications of these systems across various fields, such as medical diagnostics, motion detection, human–machine interfaces, dexterous robotics, near-field communication, and perceptual decoupling systems. Finally, we provide insights into the pathways toward industrializing cilia-inspired bionic tactile E-skin, aiming to drive innovation and unlock the technology’s potential for future applications.
BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals
Authors: Wang Weijie , Wang Xuben , Yu Xiaodong , Luo Debiao , Liu Xinyue , Yang Kai , Yang Wen , Yang Xiaolan , Hu Ke , Hu Wenyi
Published: December 2024

Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys.
Securing Cloud-Based Internet of Things: Challenges and Mitigations
Authors: Singh Nivedita , Buyya Rajkumar , Kim Hyoungshick
Published: December 2024

The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the convergence of traditional and cloud-based systems presents unique security threats in the absence of uniform regulations. Cloud-based IoT systems, enabled by Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) models, offer flexibility and scalability but also pose additional security risks. The intricate interaction between these systems and traditional IoT devices demands comprehensive strategies to protect data integrity and user privacy. This paper highlights the pressing security concerns associated with the widespread adoption of IoT devices and services. We propose viable solutions to bridge the existing security gaps while anticipating and preparing for future challenges. This paper provides a detailed survey of the key security challenges that IoT services are currently facing. We also suggest proactive strategies to mitigate these risks, thereby strengthening the overall security of IoT devices and services.
Comparative Analysis of Edge Detection Operators Using a Threshold Estimation Approach on Medical Noisy Images with Different Complexities
Authors: Maksimovic Vladimir , Jaksic Branimir , Milosevic Mirko , Todorovic Jelena , Mosurovic Lazar
Published: December 2024

The manuscript conducts a comparative analysis to assess the impact of noise on medical images using a proposed threshold value estimation approach. It applies an innovative method for edge detection on images of varying complexity, considering different noise types and concentrations of noise. Five edges are evaluated on images with low, medium, and high detail levels. This study focuses on medical images from three distinct datasets: retinal images, brain tumor segmentation, and lung segmentation from CT scans. The importance of noise analysis is heightened in medical imaging, as noise can significantly obscure the critical features and potentially lead to misdiagnoses. Images are categorized based on the complexity, providing a multidimensional view of noise’s effect on edge detection. The algorithm utilized the grid search (GS) method and random search with nine values (RS9). The results demonstrate the effectiveness of the proposed approach, especially when using the Canny operator, across diverse noise types and intensities. Laplace operators are most affected by noise, yet significant improvements are observed with the new approach, particularly when using the grid search method. The obtained results are compared with the most popular techniques for edge detection using deep learning like AlexNet, ResNet, VGGNet, MobileNetv2, and Inceptionv3. The paper presents the results via graphs and edge images, along with a detailed analysis of each operator’s performance with noisy images using the proposed approach.
EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application
Authors: Miller Leanne , Navarro Pedro J. , Rosique Francisca
Published: December 2024

This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle. To pretrain the architecture, a synthetic multimodal dataset for autonomous driving applications was created. The dataset includes driving data from 100 diverse weather and traffic scenarios, gathered from multiple sensors including cameras and an IMU as well as from vehicle control variables. The results show that including edge detection layers in the architecture improves performance for transfer learning when using synthetic and real-world data. In addition, pretraining with synthetic data reduces training time and enhances model performance when using real-world data.
Fault Diagnosis of Bearings with Small Sample Size Using Improved Capsule Network and Siamese Neural Network
Authors: Yasenjiang Jarula , Xiao Yang , He Chao , Lv Luhui , Wang Wenhao
Published: December 2024

This paper addresses the challenges of low accuracy and long transfer learning time in small-sample bearing fault diagnosis, which are often caused by limited samples, high noise levels, and poor feature extraction. We propose a method that combines an improved capsule network with a Siamese neural network. Multi-view data partitioning is used to enrich data diversity, and Markov transformation converts one-dimensional vibration signals into two-dimensional images, enhancing the visualization of signal features. The dynamic routing mechanism of the capsule network effectively captures and integrates key fault features, improving the model’s feature representation and robustness. The Siamese network shares weights to optimize feature matching, while SKNet dynamically adjusts feature fusion to enhance generalization performance. By integrating the Siamese neural network with SKNet, we improve transfer efficiency, reduce the number of parameters, and lighten the model to reduce complexity and shorten transfer time. Experimental results demonstrate that this method can accurately identify faults under conditions of limited samples and high noise, thereby improving diagnostic accuracy and reducing transfer time.
Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks
Authors: Wang Meng-Hui , Hong Jing-Xuan , Lu Shiue-Der
Published: December 2024

This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method.