bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information
Authors: Alodjants Alexander P. , Avdyushina Anna E. , Tsarev Dmitriy V. , Bessmertny Igor A. , Khrennikov Andrey Yu.
Published: October 2024

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the “entanglement” in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.
Finite-Blocklength Analysis of Coded Modulation with Retransmission
Authors: Jiang Ming , Wang Yi , Ding Fan , Xu Qiushi
Published: October 2024

The rapid developments of 5G and B5G networks have posed higher demands on retransmission in certain scenarios. This article reviews classical finite-length coding performance prediction formulas and proposes rate prediction formulas for coded modulation retransmission scenarios. Specifically, we demonstrate that a recently proposed model for correcting these prediction formulas also exhibits high accuracy in coded modulation retransmissions. To enhance the generality of this model, we introduce a range variable Pfinal to unify the predictions with different SNRs. Finally, based on simulation results, the article puts forth recommendations specific to retransmission with a high spectral efficiency.
Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis
Authors: Chen Wenyu , Zhan Ling , Jia Tao
Published: October 2024

Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions.
Mapping Guaranteed Positive Secret Key Rates for Continuous Variable Quantum Key Distribution
Authors: Sayat Mikhael T. , Thearle Oliver , Shajilal Biveen , Kish Sebastian P. , Lam Ping Koy , Rattenbury Nicholas J. , Cater John E.
Published: October 2024

The standard way to measure the performance of existing continuous variable quantum key distribution (CVQKD) protocols is by using the achievable secret key rate (SKR) with respect to one parameter while keeping all other parameters constant. However, this atomistic method requires many individual parameter analyses while overlooking the co-dependence of other parameters. In this work, a numerical tool is developed for comparing different CVQKD protocols while taking into account the simultaneous effects of multiple CVQKD parameters on the capability of protocols to produce positive SKRs. Using the transmittance, excess noise, and modulation amplitude parameter space, regions of positive SKR are identified to compare three discrete modulated (DM) CVQKD protocols. The results show that the M-QAM protocol outperforms the M-APSK and M-PSK protocols and that there is a non-linear increase in the capability to produce positive SKRs as the number of coherent states used for a protocol increases. The tool developed is beneficial for choosing the optimum protocol in unstable channels, such as free space, where the transmittance and excess noise fluctuate, providing a more holistic assessment of a protocol’s capability to produce positive SKRs.
Causal Learning: Monitoring Business Processes Based on Causal Structures
Authors: Montoya Fernando , Astudillo Hernán , Díaz Daniela , Berríos Esteban
Published: October 2024

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.
First Hitting Times on a Quantum Computer: Tracking vs. Local Monitoring, Topological Effects, and Dark States
Authors: Wang Qingyuan , Ren Silin , Yin Ruoyu , Ziegler Klaus , Barkai Eli , Tornow Sabine
Published: October 2024

We investigate a quantum walk on a ring represented by a directed triangle graph with complex edge weights and monitored at a constant rate until the quantum walker is detected. To this end, the first hitting time statistics are recorded using unitary dynamics interspersed stroboscopically by measurements, which are implemented on IBM quantum computers with a midcircuit readout option. Unlike classical hitting times, the statistical aspect of the problem depends on the way we construct the measured path, an effect that we quantify experimentally. First, we experimentally verify the theoretical prediction that the mean return time to a target state is quantized, with abrupt discontinuities found for specific sampling times and other control parameters, which has a well-known topological interpretation. Second, depending on the initial state, system parameters, and measurement protocol, the detection probability can be less than one or even zero, which is related to dark-state physics. Both return-time quantization and the appearance of the dark states are related to degeneracies in the eigenvalues of the unitary time evolution operator. We conclude that, for the IBM quantum computer under study, the first hitting times of monitored quantum walks are resilient to noise. However, a finite number of measurements leads to broadening effects, which modify the topological quantization and chiral effects of the asymptotic theory with an infinite number of measurements.
A Power Boosting Method for Wireless Power Transfer Systems Based on a Multilevel Inverter and Dual-Resonant Network
Authors: Ge Xuejian , Wang Yiwen , Tang Minghao , Wang Lei
Published: October 2024

With the deepening of research on wireless power transfer (WPT) technology, there is an urgent need to improve the output power of WPT systems. Therefore, this paper proposes a power boosting method based on a multilevel inverter and dual-resonant network. The system adopts a five-level inverter whose output voltage contains rich fundamental and harmonic components. The transmitting side adopts a dual-resonant compensation network, and, through its parameter configuration, the system can be in a resonant state at both the fundamental and third harmonic frequencies. The receiving side adopts two receiving channels which can simultaneously receive power at both frequencies, thus boosting the output power. Firstly, the overall structure and operating principle of the system are introduced. Then, the relationship between the system output power and efficiency with the inverter modulation parameters is obtained and the strategy for selecting the optimal operating points of the inverter is designed by comprehensively considering the THD of the inverter output voltage and the efficiency of the system. Furthermore, the proposed system is simulated in a Matlab/Simulink platform. Finally, an experimental setup is created to verify the proposed method. The results show that, compared with the single-channel WPT system, the proposed method can enhance the output power from 160 W to 310 W with a boosting coefficient of nearly double and with the highest efficiency exceeding 92%.
Geometry-Aware Enhanced Mutual-Supervised Point Elimination with Overlapping Mask Contrastive Learning for Partitial Point Cloud Registration
Authors: Dai Yue , Wang Shuilin , Shao Chunfeng , Zhang Heng , Jia Fucang
Published: October 2024

Point cloud registration is one of the fundamental tasks in computer vision, but faces challenges under low overlap conditions. Recent approaches use transformers and overlapping masks to improve perception, but mask learning only considers Euclidean distances between features, ignores mismatches caused by fuzzy geometric structures, and is often computationally inefficient. To address these issues, we introduce a novel matching framework. Firstly, we fuse adaptive graph convolution with PPF features to obtain rich feature perception. Subsequently, we construct a PGT framework that uses GeoTransformer and combines it with location information encoding to enhance the geometry perception between source and target clouds. In addition, we improve the visibility of overlapping regions through information exchange and the AIS module, aiming at subsequent keypoint extraction, preserving points with distinct geometrical structures while suppressing the influence of non-overlapping regions to improve computational efficiency. Finally, the mask is refined through contrast learning to preserve geometric and distance similarity, which helps to compute the transformation parameters more accurately. We have conducted comprehensive experiments on synthetic and real-world scene datasets, demonstrating superior registration performance compared to recent deep learning methods. Our approach shows remarkable improvements of 68.21% in RRMSE and 76.31% in tRMSE on synthetic data, while also excelling in real-world scenarios with enhancements of 76.46% in RRMSE and 45.16% in tRMSE.
Optimization of Triply Periodic Minimal Surface Heat Exchanger to Achieve Compactness, High Efficiency, and Low-Pressure Drop
Authors: Liu Jian , Cheng David , Oo Khin , Pan Wang , McCrimmon Ty-Liyiah , Bai Shuang
Published: October 2024

With advancements in additive manufacturing (AM) techniques, high-quality triply periodic minimal surface (TPMS) structures can now be produced. TPMS walled heat exchangers (HX) hold significant potential for industrial applications and are receiving increasing attention. This paper explores the impact of various TPMS design variables on flow and thermal performance to optimize TPMS heat exchangers for compactness, high efficiency, and low pressure drop. The design variables examined include the type of TPMS lattice, unit cell size, wall thickness, aspect ratio, TPMS orientation, and equivalent thickness. The study reveals that the flow and heat transfer performance of TPMS structures are significantly affected by these design variables. For the Gyroid, Diamond, and SplitP lattices, performance is nearly identical when the surface-to-volume ratio is kept constant. The average velocity of the fluid in the TPMS HX should be 0.3 m/s. The corresponding Re is between 300~800. Thin wall thickness, small equivalent thickness, and flat lattice configurations can significantly reduce pressure drop while maintaining the overall heat transfer coefficient. Additionally, the angle between the flow direction and TPMS orientation can increase pressure drop. Three aluminum heat exchangers were successfully printed using an AM machine, and testing results are comparable with theoretical prediction.
Enhancing Semiconductor Chiller Performance: Investigating the Performance Characteristics of Ultra-Low-Temperature Chillers Applying a Liquid Receiver
Authors: Lee Joon-Hyuk , Jung Hye-In , Lee Su-Been , Son Chang-Hyo
Published: October 2024

This study investigates the implementation of a cryogenic chiller utilizing a mixed-refrigerant cascade refrigeration cycle (MRCRC). In this setup, R-404A is employed in the high-temperature circuit (HTC), while a mixture of refrigerants is utilized in the low-temperature circuit (LTC). Unlike a conventional MRCRC that operates without a receiver to maintain the composition ratio, this research explores the impact of receiver installation on system performance. Experiments were conducted with and without a receiver to assess performance improvements and device behavior. With a fixed refrigerant charge of 4 kg, the suction and discharge pressures of the LTC compressor remained low and stable after the receiver’s installation. The addition of a receiver significantly reduced the cooling time, with further reductions observed as the refrigerant charge increased. The system achieved evaporative heat capacities of 0.59, 1.76, and 2 kW for refrigerant charges of 4, 7, and 9 kg, respectively. Notably, at the maximum refrigerant charge of 11 kg, the evaporative heat capacity peaked at 3.3 kW. These findings indicate that incorporating a receiver is crucial for enhancing the cooling performance of cryogenic coolers using mixed refrigerants and stabilizing device operation. This contrasts with previous studies that omitted receivers due to concerns over potential alterations in the composition ratio of the mixed refrigerant.
Model Order Reduction Methods for Rotating Electrical Machines: A Review
Authors: Kiss Kristóf Levente , Orosz Tamás
Published: October 2024

Due to the rise of e-mobility applications, there is an increased demand to create more accurate control methods, which can reduce the loss in an e-drive system. The accurate modeling of the rotating machines needs to resolve a partial differential equation system that describes the thermal and mechanical behavior of the different parts in addition to the electromagnetic design. Due to these models’ limited resources and high computation demand, they cannot be used directly for real-time control. Model order reduction methods have been of growing interest in the past decades and offer solutions for this problem. According to the processed literature, many model order reduction-based methods are used for a wide range of problems. However, a paper has not been published that discusses a model order reduction-based real-time control model that is actually used in the industry. This paper aims to summarize and systematically review the model order reduction methods developed for rotating electrical machines in the last two decades and examine the possible usage of these methods for a real-time control problem.
Bionic Strategies for Pump Anti-Cavitation: A Comprehensive Review
Authors: Li Jian , Zhou Xing , Zhao Hongbo , Mou Chengqi , Meng Long , Sun Liping , Zhou Peijian
Published: October 2024

The cavitation phenomenon presents a significant challenge in pump operation since the losses incurred by cavitation adversely impact pump performance. The many constraints of conventional anti-cavitation techniques have compelled researchers to explore biological processes for innovative alternatives. Consequently, the use of bionanotechnology for anti-cavitation pumping has emerged as a prominent study domain. Despite the extensive publication of publications on biomimetic technology, research concerning the use of anti-cavitation in pumps remains scarce. This review comprehensively summarizes, for the first time, the advancements and applications of bionic structures, bionic surface texture design, and bionic materials in pump anti-cavitation, addressing critical aspects such as blade leading-edge bionic structures, bionic worm shells, microscopic bionic textures, and innovative bionic coatings. Bionic technology may significantly reduce cavitation erosion and improve pump performance by emulating natural biological structures. This research elucidates the creative contributions of biomimetic designs and their anti-cavitation effects, hence boosting the anti-cavitation performance of pumps. This work integrates practical requirements and anticipates future applications of bionic technology in pump anti-cavitation, offering a significant research direction and reference for scholars in this domain.
A Review on the Recent Development of High-Frequency Inverters for Wireless Power Transfer
Authors: Liu Ying , Pan Liangyi , Yao Shunyu , Zhang Jiantao , Cui Shumei , Zhu Chunbo
Published: October 2024

With the demand for the miniaturization and integration of wireless power transfer (WPT) systems, higher frequency is gradually becoming the trend; thus, the power electronic device has become one of the main reasons for limiting the development. Therefore, further research on high-frequency inverters and purposeful design according to the characteristics of WPT systems are of great significance to promote the development of high-frequency WPT technology. There is still no literature that summarizes all the inverter circuits that can be applied to WPT systems, which makes it extremely difficult to find an inverter that meets the designer’s requirements. This paper reviews the high-frequency inverters for WPT systems, summarizes the derived topologies based on power amplifiers and H-bridge inverters, investigates the main factors restricting the development of high-frequency inverters, and analyzes the research directions for future development.
Storing Excess Solar Power in Hot Water on Household Level as Power-to-Heat System
Authors: Kotte Ivar , Snaak Emma , van Sark Wilfried
Published: October 2024

PV technology has become widespread in the Netherlands, reaching a cumulative installed capacity of 22.4 GWp in 2023 and ranking second in the world for solar PV per capita at 1268 W/capita. Despite this growth, there is an inherent discrepancy between energy supply and demand during the day. While the netting system in the Netherlands can currently negate the economic drawbacks of this discrepancy, grid congestion and imbalanced electricity prices show that improvements are highly desirable for the sustainability of electricity grids. This research analyzes the effectiveness of a Power-to-Domestic-Hot-Water (P2DHW) system at improving the utilization of excess PV electricity in Dutch households and compares it to similar technologies. The results show that the example P2DHW system, the WaterAccu, compares favorably as a low cost and flexible solution. In particular, for twelve different households differing in size (1–6 occupants), PV capacity (2.4–8 kWp), and size of hot water storage boiler (50–300 L), it is shown that the total economic benefits for the period 2024–2032 vary from −€13 to €3055, assuming the current net metering scheme is abolished in 2027. Only for large households with low PV capacity are the benefits a little negative. Based on a multi-criteria analysis, it is found that the WaterAccu is the cheapest option compared to other storage options, such as a home battery, a heat pump boiler, and a solar boiler. A sensitivity study demonstrated that these results are overall robust. Furthermore, the WaterAccu has a positive societal impact owing to its peak shaving potential. Further research should focus on the potential of the technology to decrease grid congestion when implemented on a neighborhood scale.
Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function Across the Ten States of the Eastern US
Authors: Khadka Saaruj , He Hong S. , Bardhan Sougata
Published: October 2024

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley’s K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks’ dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM.
Charting the Research Status for Bamboo Resources and Bamboo as a Sustainable Plastic Alternative: A Bibliometric Review
Authors: Zhang Hanjiao , Zhang Xu , Ding Yulong , Huang Feiyi , Cai Zhuoyu , Lin Shuyan
Published: October 2024

With the increasing pollution caused by plastic products, people’s awareness of environmental protection has gradually increased. Based on the advantages of China’s bamboo resources and bamboo industry, the International Network for Bamboo and Rattan launched the ‘Utilizing bamboo as a sustainable alternative to plastic’ initiative in November 2022 to accelerate the implementation of the United Nations 2030 Agenda for Sustainable Development. This initiative provides a nature-based solution for global sustainable development, which will have a profound impact on promoting global green development, industrial science and technology, social services, cultural exchanges, and other fields. It has a huge market capacity and application potential. This study aims to understand the research status, hotspots, development trends, and prospects of this initiative. WoS and China National Knowledge Infrastructure (CNKI) were searched for the relevant literature on ‘utilizing bamboo as a sustainable alternative to plastic bamboo as plastic’ and ‘bamboo resources’ from the establishment of the database to 2024. Bibliometric methods and VOSviewer were used to analyze 1855 literatures with the above two keywords published in the Web of Science core set database from 2000 to 2024. A visual analysis was performed on the number of publications, national research institutions, researchers, number of citations, and research topics of the literature. For example, China, the United States, and India ranked in the top three in terms of the number of articles published, with 1103, 267, and 167, respectively. China had the highest number of citations, reaching 26,607. At the same time, the literature with ‘bamboo’ as the keyword in CNKI was analyzed to understand the species of bamboo currently studied. A literature investigation was carried out around the resource cultivation of the bamboo industry in our country, and it was found that the research on ‘use bamboo as plastic’ and ‘bamboo resources’ had been valued by scholars at home and abroad, and had formed a relatively stable research group. Using scientific and technological innovation to improve the product quality of ‘bamboo instead of plastic’, expanding its application scope and market demand, and realizeing the sustainable industry development of ‘bamboo instead of plastic’ is the current research hotspot and the future development direction.
Internet of Things Adoption in Technology Ecosystems Within the Central African Region: The Case of Silicon Mountain
Authors: Kuaban Godlove Suila , Nkemeni Valery , Nwobodo Onyeka J. , Czekalski Piotr , Mieyeville Fabien
Published: October 2024

The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various sectors and industries worldwide. Despite its global significance, the adoption and implementation of IoT technologies in emerging technology ecosystems within the Central African region still need to be studied and explored. This paper presents a case study of the Silicon Mountain technology ecosystem, located in Fako division of the southwest region of Cameroon, focusing on the barriers and challenges to adopting and integrating IoT technologies within this emerging tech ecosystem. Through a survey-based approach, we investigate the factors influencing IoT adoption in the Silicon Mountain tech ecosystem, including technological, economic, social, and regulatory factors. Our study reveals key insights into the current state of IoT adoption, opportunities for growth and innovation, and IoT adoption challenges. Key among the challenges identified for impeding IoT uptake were issues related to standardization and financial resources, labor shortage in the industry, educational and knowledge gaps, market challenges, government policies, security and data privacy concerns, and inadequate power supply. Based on our findings, we provide recommendations for policymakers, industry stakeholders, and academic institutions to promote and facilitate the widespread adoption of IoT technologies in Silicon Mountain and the Central African region at large.
Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster–Support Vector Machine (VC-SVM) Hybrid Models
Authors: Buoio Eleonora , Colombo Valentina , Ighina Elena , Tangorra Francesco
Published: October 2024

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster–support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.
Formation and Applications of Typical Basic Protein-Based Heteroprotein Complex Coacervations
Authors: Xie Yufeng , Liu Qingchen , Ge Yubo , Liu Yongqi , Yang Rui
Published: October 2024

Lactoferrin, lysozyme, and gelatin are three common basic proteins known for their ability to interact with acidic proteins (lactoglobulin, ovalbumin, casein, etc.) and form various supramolecular structures. Their basic nature makes them highly promising for interaction with other acidic proteins to form heteroprotein complex coacervation (HPCC) with a wide range of applications. This review extensively examines the structure, properties, and preparation methods of these basic proteins and delves into the internal and external factors influencing the formation of HPCC, including pH, ionic strength, mixing ratio, total protein concentration, temperature, and inherent protein properties. The applications of different HPCCs based on these three basic proteins are discussed, including the encapsulation of bioactive molecules, emulsion stabilization, protein separation and extraction, nanogel formation, and the development of formulas for infants. Furthermore, the challenges and issues that are encountered in the formation of heteroprotein complexes are addressed and summarized, shedding light on the complexities and considerations involved in utilizing HPCC technology in practical applications. By harnessing the basic proteins to interact with other proteins and to form complex coacervates, new opportunities arise for the development of functional food products with enhanced nutritional profiles and functional attributes.
Ecocritical Concerns in the Selected Poems of Mahmoud Darwish and Naomi Shihab Nye
Authors: Shamim Amna
Published: October 2024

Ecocriticism is an advancing field in literature that has opened up avenues in reading world literature from a whole new perspective. This paper seeks to flesh out ecocritical concerns in the selected poems of Mahmoud Darwish and Naomi Shihab Nye by using selected concepts of the theory of ecocriticism given by Greg Garrard: pastoral, wilderness, and the sublime. An analysis of the poetry by the selected writers, sharing their roots from the Arab world, reveals their agenda of using nature as a trope in the form of resistance to colonialism. The writers give a glimpse of the people of their homeland and their culture imbued in nature.
Perinatal and Demographic Risk Factors Associated with Autism Spectrum Disorder: A National Survey of Potential Predictors and Severity
Authors: Sousamli Aikaterini , Dragioti Elena , Metallinou Dimitra , Lykeridou Aikaterini , Dourou Panagiota , Athanasiadou Chrysoula Rozalia , Anagnostopoulos Dimitrios , Sarantaki Antigoni
Published: October 2024

INTRODUCTION: This study investigates autism spectrum disorders (ASD) in Greece, focusing on estimating prevalence and identifying regional disparities in children aged 4 to 7 years. MATERIALS AND METHODS: Utilizing a quantitative, descriptive, and exploratory methodology, the research employed a structured questionnaire to gather extensive maternal and child health data. RESULTS: The sample consisted of 517 mothers of children diagnosed with ASD from all over Greece, contributing to a nuanced understanding of ASD predictors. This study aims to elucidate the role of prenatal factors in the likelihood of an ASD diagnosis and their impact on the subsequent functionality of children with ASD. The study identified significant predictors of lower functionality in children with ASD, including higher maternal age, delayed ASD diagnosis, lower family income, and higher birth order. Prenatal health issues, such as vaginal bleeding and infections, also influenced functional outcomes. Notably, a family history of neurological or psychiatric conditions appeared protective. DISCUSSION: The regression model demonstrated robust predictive power, underscoring the complexity of genetic, environmental, and socioeconomic factors in ASD development. The findings advocate for early diagnosis and intervention, systematic screening, and addressing socioeconomic disparities to improve functional outcomes. The results support evidence-based service development and policy adjustments to enhance early identification, intervention, and rehabilitation for children with ASD. CONCLUSIONS: Establishing standardized case-recording procedures and an ASD register at national and regional levels is recommended for systematic monitoring and resource evaluation.
Hepatocellular Carcinoma Immunotherapy: Predictors of Response, Issues, and Challenges
Authors: Rizzo Alessandro , Brunetti Oronzo , Brandi Giovanni
Published: October 2024

Immune checkpoint inhibitors (ICIs), such as durvalumab, tremelimumab, and atezolizumab, have emerged as a significant therapeutic option for the treatment of hepatocellular carcinoma (HCC). In fact, the efficacy of ICIs as single agents or as part of combination therapies has been demonstrated in practice-changing phase III clinical trials. However, ICIs confront several difficulties, including the lack of predictive biomarkers, primary and secondary drug resistance, and treatment-related side effects. Herein, we provide an overview of current issues and future challenges in this setting.
Exploring the Therapeutic Potential of N-Methyl-D-Aspartate Receptor Antagonists in Neuropathic Pain Management
Authors: Pușcașu Ciprian , Chiriță Cornel , Negreș Simona , Blebea Nicoleta Mirela
Published: October 2024

Neuropathic pain (NeP) is a complex and debilitating condition that impacts millions of people globally. Although various treatment options exist, their effectiveness is often limited, and they can be accompanied by significant side effects. In recent years, there has been increasing interest in targeting the N-methyl-D-aspartate receptor (NMDAR) as a potential therapeutic approach to alleviate different types of neuropathic pain. This narrative review aims to provide a comprehensive examination of NMDAR antagonists, specifically ketamine, memantine, methadone, amantadine, carbamazepine, valproic acid, phenytoin, dextromethorphan, riluzole, and levorphanol, in the management of NeP. By analyzing and summarizing current preclinical and clinical studies, this review seeks to evaluate the efficacy of these pharmacologic agents in providing adequate relief for NeP.
Atractylodes lancea Rhizome Polysaccharide Alleviates MCD Diet-Induced NASH by Inhibiting the p53/mTOR Pathway
Authors: Pi Dajin , Liang Zheng , Pan Maoxing , Zhen Jianwei , Zheng Chuiyang , Pan Jinyue , Fan Wen , Song Qingliang , Yang Qinhe , Zhang Yupei
Published: October 2024

Nonalcoholic steatohepatitis (NASH) is a form of chronic liver disease that is characterized by liver inflammation and steatosis, with possible progression to fibrosis. Currently, no drugs have been approved for the treatment of NASH. In this study, we isolated a polysaccharide from Atractylodes lancea rhizome (AP) and established a methionine- and choline-deficient (MCD) diet -induced NASH mouse model to investigate the preventive effect and potential mechanism of AP on NASH. The results showed that AP effectively reduced liver lipid accumulation and inflammation and reduced autophagy and ferroptosis in hepatocytes, thereby preventing the development of NASH. These findings suggest that AP may be a promising natural candidate for the treatment of NASH.
In Silico Insights Reveal Fibronectin 1 as a Theranostic Marker in Gastric Cancer
Authors: Millapán Tatiana , Gutiérrez Álvaro , Rosas Krisnna , Buchegger Kurt , Ili Carmen Gloria , Brebi Priscilla
Published: October 2024

Gastric cancer (GC) is a complex and highly variable disease, ranking among the top five cancers diagnosed globally, and a leading cause of cancer-related deaths. Emerging from stomach lining cells amid chronic inflammation, it often advances to preneoplastic stages. Late-stage diagnoses and treatment challenges highlight the critical need for early detection and innovative biomarkers, motivating this study’s focus on identifying theranostic markers through gene ontology analysis. By exploring deregulated biological processes, this study aims to uncover insights into cancer progression and associated markers, potentially identifying novel theranostic candidates in GC. Using public data from The Human Protein Atlas, this study pinpointed 299 prognostic genes, delineating 171 with unfavorable prognosis and 128 with favorable prognosis. Functional enrichment and protein–protein interaction analyses, supported by RNAseq results and conducted via Metascape and Cytoscape, highlighted five genes (vWF, FN1, THBS1, PCDH7, and F5) with promising theranostic potential. Notably, FN1 and THBS1 exhibited significant promise, with FN1 showing a 370% expression increase in cancerous tissue, and it is possible that FN1 can also indicate the stratification status in GC. While further validation is essential, these findings provide new insights into molecular alterations in GC and potential avenues for clinical application of theranostic markers.
Studying the Stability of Anthocyanin Pigments Isolated from Juices of Colored-Fleshed Potatoes
Authors: Tkaczyńska Agnieszka , Sendra Esther , Jiménez-Redondo Nuria , Rytel Elżbieta
Published: October 2024

The aim of this study was to obtain extracts of anthocyanin pigments from red and purple-fleshed potato juices characterized by stable color. For this purpose, potato juices were pasteurized at different temperatures or fruit and vegetable concentrates were added to them. Color stability tests of the obtained pigments were carried out in model pH and temperature conditions and after adding to natural yogurt. Both the pasteurization process and the addition of fruit and vegetable concentrates to the potato juices positively affected their color and its stability in time. However, the pasteurization of the potato juices had a negative effect on the content of biologically active compounds, in contrast to the juices stabilized with the addition of fruit and vegetable concentrates. Anthocyanin pigments from red-fleshed potato juices were more stable than those isolated from the purple-fleshed potato juices. The results of model tests of the anthocyanin pigment concentrates from the colored-flesh potatoes and natural yoghurts with their addition confirmed the high stability of the tested concentrates.
Interactive Structural Analysis of KH3-4 Didomains of IGF2BPs with Preferred RNA Motif Having m6A Through Dynamics Simulation Studies
Authors: Fakhar Muhammad , Gul Mehreen , Li Wenjin
Published: October 2024

m6A modification is the most common internal modification of messenger RNA in eukaryotes, and the disorder of m6A can trigger cancer progression. The GGACU is considered the most frequent consensus sequence of target transcripts which have a GGAC m6A core motif. Newly identified m6A ‘readers’ insulin-like growth factor 2 mRNA-binding proteins modulate gene expression by binding to the m6A binding sites of target mRNAs, thereby affecting various cancer-related processes. The dynamic impact of the methylation at m6A within the GGAC motif on human IGF2BPs has not been investigated at the structural level. In this study, through in silico analysis, we mapped IGF2BPs binding sites for the GGm6AC RNA core motif of target mRNAs. Subsequent molecular dynamics simulation analysis at 400 ns revealed that only the KH4 domain of IGF2BP1, containing the 503GKGG506 motif and its periphery residues, was involved in the interaction with the GGm6AC backbone. Meanwhile, the methyl group of m6A is accommodated by a shallow hydrophobic cradle formed by hydrophobic residues. Interestingly, in IGF2BP2 and IGF2BP3 complexes, the RNA was observed to shift from the KH4 domain to the KH3 domain in the simulation at 400 ns, indicating a distinct dynamic behavior. This suggests a conformational stabilization upon binding, likely essential for the functional interactions involving the KH3-4 domains. These findings highlight the potential of targeting IGF2BPs’ interactions with m6A modifications for the development of novel oncological therapies.
Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data
Authors: Remori Veronica , Airoldi Manuel , Alberio Tiziana , Fasano Mauro , Azzi Lorenzo
Published: October 2024

Oral cancer, representing 2–4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein–Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.
High-Throughput Luminescence-Based Serum Bactericidal Assay Optimization and Characterization to Assess Human Sera Functionality Against Multiple Shigella flexneri Serotypes
Authors: Caradonna Valentina , Pinto Marika , Alfini Renzo , Giannelli Carlo , Iturriza Miren , Micoli Francesca , Rossi Omar , Mancini Francesca
Published: October 2024

Shigellosis represents a significant global health concern particularly affecting children under 5 years in low- and middle-income countries (LMICs) and is associated with stunting and antimicrobial resistance. There is a critical need for an effective vaccine offering broad protection against the different Shigella serotypes. A correlate of protection has not yet been established but there is a general consensus about the relevant role of anti-O-Antigen-specific IgG and its functionality evaluated by the Serum Bactericidal Assay (SBA). This study aims to characterize a high-throughput luminescence-based SBA (L-SBA) against seven widespread Shigella serotypes. The assay was previously developed and characterized for S. sonnei and S. flexneri 1b, 2a, and 3a and has now been refined and extended to an additional five serotypes (S. flexneri 4a, 5b, 6, X, and Y). The characterization of the assay with human sera confirmed the repeatability, intermediate precision, and linearity of the assays; both homologous and heterologous specificity were verified as well; finally, limit of detection and quantification were established for all assays. Moreover, different sources of baby rabbit complement showed to have no impact on L-SBA output. The results obtained confirm the possibility of extending the L-SBA to multiple Shigella serotypes, thus enabling analysis of the functional response induced by natural exposure to Shigella in epidemiological studies and the ability of candidate vaccines to elicit cross-functional antibodies able to kill a broad panel of prevalent Shigella serotypes in a complement-mediated fashion.
Integrating Knowledge Graphs into Autonomous Vehicle Technologies: A Survey of Current State and Future Directions
Authors: Htun Swe Nwe Nwe , Fukuda Ken
Published: October 2024

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions.