bluuebunny/crossref_metadata_2025 huggingface dataset:
An Autonomous UAV Obstacle Avoidance Trajectory Planning Method Based on Convex Optimized Particle Swarm Algorithm
Authors: Wang Jun , Zou Biao , Zhu Songtao , Gao Yubo , Zhao Ziyan
Published: February 2025

The dynamics of unmanned aerial systems (UAS) are often nonlinear, especially at high speed and high maneuverability flight. The inherent nonlinearity of the system renders conventional linear control techniques inadequate for achieving optimal control outcomes. Consequently, unmanned aerial vehicle (UAV) trajectory planning is fundamentally a nonlinear optimal control challenge, characterized by the difficulty of swiftly obtaining a stable solution and the propensity to converge on suboptimal local solutions during the solving process. In response to this, a method for autonomous UAV obstacle avoidance trajectory planning is introduced, leveraging a convex optimization-based particle swarm algorithm. The UAV uses sensors to record the information about obstacles, adopts a one-dimensional time-parameterized polynomial trajectory to construct the obstacle avoidance control input, combines the safety penalty function between the navigation path and obstacles to generate the obstacle avoidance trajectory planning objective function, and obtains the path points in the obstacle avoidance trajectory; the particle swarm algorithm is used to solve the objective function, the parameters such as obstacle avoidance control input and safety penalty function are used as particles, and the optimal parameter values are obtained through the iterative updating process of position and speed. Aiming at the acquired obstacle avoidance trajectory path points, the convex optimization algorithm is used to construct a non-convex optimal control model that meets the requirements of energy optimization, and is rooted in the concave-convex process, so that the control model’s objective function and inequality constraint are both convex, while the formula constraint is affine. By transforming the control model into a convex optimization problem, it aligns with the energy optimization requirements. The sequential convex optimization framework is employed to solve this problem, enabling the optimization of the UAV’s trajectory for obstacle avoidance. Experimental outcomes demonstrate that this approach effectively captures the coordinate details of obstacles, navigates through various dense obstacle scenarios, and simultaneously guarantees an energy-efficient path to the target destination.
Optimization Analysis of Power Grid Data Quality by Federal Average Algorithm
Authors: Tang Li , Li Biaoqi , Zeng Ting , Liu Haibin , Shen Yu
Published: February 2025

This paper explores the application and benefits of the Federated Averaging (FedAvg) algorithm in optimizing power grid data quality as the power grid evolves toward more intelligent, data-driven systems, ensuring high-quality data becomes critical to the effective operation and management of the grid. However, optimizing data quality is a complex challenge due to the involvement of multiple data holders, each with privacy concerns that prevent the sharing of sensitive information. The FedAvg algorithm offers a promising solution by enabling the aggregation of data insights across distributed systems without the need to share raw data, thus preserving privacy while improving data quality. This study provides a comprehensive evaluation of the FedAvg algorithm’s impact on power grid data quality through a detailed implementation process. The research outlines the algorithm’s step-by-step optimization procedure, highlighting key design choices, such as model aggregation strategies, communication protocols, and iterative updates. By analyzing real-world application cases, we demonstrate how FedAvg addresses challenges such as data heterogeneity, missing data, and inconsistencies across different grid regions. Additionally, we present a series of experimental results that include a range of data quality metrics — such as accuracy, consistency, and reliability — to assess the algorithm’s effectiveness. The findings of this study show that the FedAvg algorithm can significantly enhance the accuracy and consistency of power grid data. Through its distributed approach, it not only improves the quality of data but also enhances the operational efficiency and reliability of the grid. The paper provides a clearer understanding of how FedAvg can be effectively implemented in power grid systems and its direct impact on data quality. This research contributes to the broader field of grid management by offering practical insights into leveraging federated learning techniques for data optimization while maintaining privacy, thus offering a more comprehensive and scalable solution for modern power grids.
Coal Transportation Cost Prediction under Mixed Uncertainty Based on Graph Attention Networks
Authors: Wang Xiaoyu
Published: February 2025

In order to solve the high uncertainty and dynamics of coal transportation costs and reduce the subjectivity of artificial prediction, a method of coal transportation cost prediction under mixed uncertainty based on a graph attention network (GAT) is proposed. The analytic hierarchy process (AHP) is used to build the factor model of coal transportation cost prediction, calculate the weight coefficient of each factor, and clarify the relative importance of each factor in the prediction process. Principal component analysis (PCA) is used to reduce the dimensions of various factors affecting the cost of coal transportation, eliminate the noise and redundant information in the data, and retain the main information characteristics. The processed data set is input into the STDGAT model that combines the GAT and the long-term and short-term memory neural network (LSTM). Through GAT, the spatial correlation characteristics in the coal transportation demand are extracted and combined with LSTM to capture the dynamic characteristics in the time dimension, and the time-space joint prediction of the coal transportation cost is realized. The experimental results show that the prediction results are highly close to the actual cost, and the error remains at a low level no matter in what time period, short distance, medium distance or long-distance transportation, and different transportation modes. By adjusting the number of attention heads, it was found that the prediction performance of the model was the best when the number of attention heads was 6.
Planning Method for Latin Aerobics Exercise Plan for Female College Students Based on Genetic Algorithm
Authors: Feng Li Bao
Published: February 2025

For female college students, due to individual differences such as age, physical fitness and sports foundation, a unified Latin aerobics exercise plan is difficult to meet everyone’s needs. Genetic algorithm, with its powerful search ability and global optimization performance, can be applied to the planning of Latin aerobics exercise plans for female college students and can generate optimal solutions for Latin aerobics exercise plans. Therefore, this study proposes a planning method for female college students’ Latin aerobics exercise plan based on a genetic algorithm. This method is based on Bluetooth power consumption technology to collect heart rate data from Latin aerobics exercises, collect images of female college students’ Latin aerobics exercises and reconstruct them, recognize Latin aerobics exercise postures based on K-nearest neighbor algorithm, and implement motion trajectory monitoring based on Kinovea software. On this basis, a model of Latin aerobics exercise plan for female college students is constructed, and the Latin aerobics exercise plan is optimized through a genetic algorithm. By using the search and optimization ability of the genetic algorithm, the optimal combination of exercise plans can be found, and an exercise plan that meets the physiological and psychological characteristics of female college students, effectively enhances physical fitness and improves dance skills can be formulated. Through experimental verification, it has been found that the proposed genetic algorithm has good global search ability. The planned Latin aerobics exercise plan can ensure that the heart rate falls within a safe and effective range, reduce the deviation of the body’s left and right swing amplitude during Latin aerobics exercise, and improve the personalized satisfaction of female college students. By applying genetic algorithms, female college students can achieve their goals of physical health and body shaping while enjoying the fun of Latin aerobics.
Optimization of Energy Finance Risk Compliance Early Warning System Based on Machine Learning
Authors: Bu Su , Zhu Mengni , Niu Chenlin
Published: February 2025

The energy finance sector is characterized by its complexity and volatility, driven by fluctuating commodity prices, regulatory changes and evolving market dynamics. Energy companies face financial, regulatory and operational risks, relying on historical data and static risk assessment frameworks that may not accurately reflect real-time market changes. This research aims to develop an energy finance risk compliance early warning system, leveraging a machine learning approach to enhance the early detection of compliance risks, enabling proactive decision-making and improving organizational resilience. Initially, data were collected from various sources, including historical financial records, market trends and regulatory frameworks. These data are essential for developing an early warning system that aims to enhance compliance risk detection in the energy sector. The collected data are preprocessed using cleaning and normalization and prepared for analysis. Exploratory Data Analysis (EDA) was conducted using statistical methods such as correlation analysis and regression analysis to identify patterns and relationships between variables. The study proposes a novel Revenue Optimizer with a weighted Support Vector Machine (RO-WSVM) model to enhance risk detection and ensure regulatory compliance in energy finance. Optimizing revenue while adhering to compliance standards provides proactive insights for effective risk management and decision-making. The result demonstrates that the application of the proposed RO-WSVM model works successfully and this is because RO-WSVM has higher accuracy (90%), error value (0.1%) and less time taken to process the data than any other model (6[Formula: see text]s). The study highlights that the innovative approach RO-WSVM enhances the prediction model in the finance risk compliance early warning of energy finance.
Innovation and Inheritance Strategies of Ink Art Under the Software-Defined Internet of Things Architecture
Authors: Xu Jing , Yupu Zheng
Published: February 2025

Supported by the Software-Defined Internet of Things (SD IoT) architecture, this paper aims to promote the modern innovation and inheritance of ink art, and proposes an innovative ink art style transformation Generative Adversarial Network (GAN) algorithm. This algorithm utilizes a carefully constructed GAN model to achieve efficient and accurate transformation of ink art styles. In order to comprehensively evaluate the performance of the algorithm, this study conducted a comparative analysis between GAN and Long Short Term Memory Network (LSTM) based on multiple key indicators such as response time, style classification accuracy, and error rate. The results showed that GAN was significantly better than LSTM in all indicators, manifested in faster response time, higher classification accuracy, and lower error rate. This study not only opens up new technological paths for the digital innovation and inheritance of ink art, but also verifies the excellent performance of computer algorithms, especially GAN, in the task of ink art style conversion. Empowered by the SD IoT architecture, the GAN-based ink art style conversion algorithm shows great potential and is expected to become a bridge connecting modern expression of ink art with traditional essence, providing a new perspective and strategy for the digital development of traditional art. This study not only enriches the expression forms of ink art, but also explores new possibilities for the application of SD IoT architecture in traditional art fields.
Research on Digital Publishing and Communication Strategies for IoT Media Using Software-Defined Network Control and Management
Authors: Sun Shanshan , Zheng Yifeng
Published: February 2025

With the continuous advancement of software-defined networking (SDN) technology, the arrival of the Internet of Things (IoT) media era has brought unprecedented opportunities and challenges to the field of digital publishing and communication. This paper focuses on exploring how to optimize the digital publishing and dissemination strategy of IoT media using SDN control and management technology to adapt to this emerging information dissemination ecosystem. Firstly, we conducted an in-depth analysis of the application of SDN technology in IoT media digital publishing, covering multiple aspects from intelligent management of content creation and distribution, precise implementation of personalized content recommendations, to deep mining and real-time analysis of user behavior data. These applications not only improve the efficiency and quality of content production, but also significantly enhance the personalization and accuracy of information dissemination. Subsequently, we adopted a case study method to deeply analyze the innovative communication strategies implemented by media organizations using SDN control and management technology in the context of IoT media. These strategies include but are not limited to: utilizing SDN technology to achieve dynamic optimization and intelligent distribution of content to attract a wider audience, strengthening interaction and collaboration between IoT devices through SDN, promoting user participation through social media, online communities, and other platforms, and building an active community ecosystem and implementing cross-platform integration strategies, leveraging the flexibility and scalability of SDN, to build a complete media matrix and achieve multi-channel and all-round information dissemination. The research results indicate that the deep integration of SDN technology significantly enhances the personalization and interactivity of IoT media digital publishing and dissemination, bringing users a richer and more immersive reading experience. This not only improves communication efficiency, but also deepens the connection between media and audiences, injecting new vitality into the development of IoT media digital publishing.
A Study on Multidimensional Fuzzy Retrieval of Visual Communication Design Resources
Authors: Pu Lili , Gao Zhongli , Pu Ziyao
Published: February 2025

The retrieval of visual communication graph sources is a necessary yet complex task, pivotal for various applications in virtual asset management, creative industries, and design training. Traditional visual resource retrieval methods, often constrained by unimodal data processing, fail to capture the intricate interplay between visual elements and their semantic interpretations, leading to suboptimal search outcomes. To address this limitation, this paper introduces a multidimensional framework that leverages the strengths of Convolutional Neural Networks (CNNs) for image feature analysis and Recurrent Neural Networks (RNNs) for text feature processing. Our approach seamlessly integrates these multimodal features through a sophisticated deep learning architecture, aiming to bridge the gap between visual understanding and linguistic context. Experimental results, benchmarked against conventional retrieval techniques, demonstrate a significant improvement in retrieving relevant diagram resources, highlighting the effectiveness of our integrated approach. This study offers a unique perspective in the field of design resource retrieval, with the potential to enhance user experience and streamline the search process in creative domains.
Computational heat and mass transfer analysis of magnetized nanofluid flow under the influences of motile microorganisms and thermal radiation
Authors: Chaudhry Munaza , Basit Muhammad Abdul , Akhtar Tayyaba , Imran Muhammad , Tahir Madeeha , Saleem S. , Galal Ahmed M.
Published: February 2025

A stagnation-point flow in a two-dimensional (2D) geometry across a nonlinear stretchable surface with varying viscosity and bioconvection is considered. The prominent feature of mass and heat transport is further explained by the instantaneous influences of variable mass diffusivity, chemical reaction, changing thermal conductivity and magnetohydrodynamics. The mathematical model for the above problem is developed in the form of partial differential equations. After applying the proper similarity variables, the PArtial Differential Equation (PDE) are converted to a system of Ordinary Differential Equation (ODE) and are numerically targeted by utilizing a built-in BVP4C technique in MATLAB software. The discussion regarding the problem focuses on determining the crucial values that correspond to the suction and stretching parameters. A parametric analysis of the temperature field, concentration distribution, and axial velocity has been carried out. The numerically produced outcomes show a good fit with results in existing literature. The study underscores the importance of understanding the interplay between various physical parameters in nanofluid flow systems involving bioconvection. Magnetic parameters contribute to an increased axial velocity profile on the other hand diffusive constant parameters [Formula: see text] initially reduce but eventually enhance velocity. Additionally by fixing the values of parameters as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], it is observed that the higher Prandtl numbers reduce thermal conductivity, leading to a lower temperature profile. Variable viscosity introduces circular streamline patterns, indicating vortex formation and a gradual change in flow curvature from lower to higher viscosity regions. The magnetic field enhances flow stability, producing smoother streamlines and effectively controlling heat distribution in magnetohydrodynamic systems.
Integration of artificial intelligence (AI) to predict the behavior of milk enhanced with silver and zinc oxide nanoparticles when subjected to rapid heating in a movable electromagnetic channel
Authors: Karmakar Poly , Das Sanatan
Published: February 2025

The implementation of artificial intelligence (AI) to predict and control the behavior of milk enhanced with silver and zinc oxide nanoparticles during electromagnetic heating enhances the precision and energy efficiency of pasteurization and sterilization processes. This approach ensures precise temperature management, reducing the risk of overheating and maintaining the milk’s nutritional and sensory integrity. The research examines milk flow dynamics (Ag-ZnO/milk) in a suddenly heated movable electromagnetic channel under sudden pressure variations. It incorporates significant physical phenomena such as radiant heat emission and Darcy drag forces, with Darcy’s model addressing drag within the porous medium. The dynamics of milk flow are thoroughly defined mathematically and physically, with solutions succinctly derived using the Laplace transform (LT) method. The findings, including analyses of shear stress (SS) and rate of heat transfer (RHT), are presented tabularly and graphically. The study indicates an annex in milk momentum with higher modified Hartmann numbers and an enfeeblement with wider electrode widths. Both hybrid nano-milk (HNM) and nano-milk (NM) exhibit thermal degradation, where rising Casson parameters amplify SS, whereas elevated radiation parameters lead to a reduction in RHT. Additionally, the AI-driven artificial neural network (ANN) model demonstrates remarkable predictive precision, achieving 95.175% accuracy during testing and 99.64% during cross-validation for SS predictions, while attaining a perfect 100% accuracy for RHT predictions across both testing and cross-validation phases. This model could lead to the development of advanced pasteurization equipment that utilizes electromagnetic technology for more consistent heating and better preservation of milk’s nutritional and sensory qualities.
Does Business Strategy Influence Cybersecurity Risk Disclosures?
Authors: Hasan Mostafa Monzur , Chen Xiaomeng Charlene , He Liyu
Published: February 2025

Synopsis

The research problem

This study examines the influence of firms’ business strategies on their cybersecurity risk disclosures (CRDs).

Motivations

The exponential expansion of the digital economy and the increasing reliance on online data storage and processing have made cybersecurity breaches a critical issue for businesses worldwide. The disclosure of cybersecurity risks is vital in fostering transparency and communication between firms and external stakeholders. This study draws from the Miles and Snow ( 1978 ) strategic typology to explore how firms’ chosen strategies influence their CRDs. This investigation is important because firms that adopt a prospector- or a defender-type strategy have different strategic focuses, leading to varying degrees of exposure to cybersecurity risks. As a result, these firms have various incentives to engage in CRDs. By enhancing our understanding of CRDs’ determinants, this study provides insights into the way in which business strategies shape firms’ approaches to communicating cybersecurity risks.

Despite the increasing scholarly attention paid to firms’ disclosure of cybersecurity risks, there remains a lack of literature concerning the factors influencing the extent of CRDs. Our study fills the gap in the literature by investigating how firms’ business strategies shape CRDs. By uncovering the influence of business strategies on CRDs, our study provides valuable insights into the information environment within firms.

The test hypotheses

Hypothesis 1a posits that firms adopting a prospector-type strategy are more likely to make more CRDs than firms following a defender-type strategy. Conversely, Hypothesis 1b suggests that firms with a prospector-type strategy are less inclined to provide extensive CRDs than their defender-type counterparts.

Target population

Regulators, investors, and other stakeholders.

Adopted methodology

Ordinary least squares regressions.

Analyses

Our independent variable of interest is business strategies, which we categorized into prospectors, analyzers, and defenders based on the Miles & Snow ( 1978 ) strategic typology. As our dependent variable, we employ the CRD score developed by Florackis et al. ( 2023 ), which utilizes machine learning-based textual analysis to quantify CRDs. As for the analysis of consequences, we use Tobin’s Q as an indicator of firm value.

Findings

We find that firms adopting a prospector-type strategy are more inclined to provide extensive CRDs than firms following a defender-type strategy. Moreover, we observe that the impact of business strategies on CRDs is heightened in firms with strong corporate governance attributes, including effective boards, robust internal controls, and the engagement of industry expert auditors or Big Four accounting firms. In addition, our findings indicate that the strengthened relationship between business strategies and CRDs contributes positively to firm value.
The Betsiaka Group in northern Madagascar: a Paleoproterozoic terrane along the northern margin of the Bemarivo Domain
Authors: Rasaona Imboarina T. , Edwards Lesley E. , Collins Alan S. , Rakotondrazafy Amos F. M. , Bauer Wilfried
Published: February 2025

The Betsiaka Group, located on the northern edge of the Bemarivo Domain in northern Madagascar, consists of metamorphic felsic rocks, amphibolite, and marbles. These rocks are overlain by the Permo-Triassic sedimentary cover to the northwest and are separated by a fault from the Neoproterozoic volcanic rocks of the Bemarivo Domain. This study aims to determine the provenance and geodynamic evolution of the Betsiaka Group.

Orthoamphibolites are associated with siliciclastic sedimentary rocks and exhibit geochemical characteristics of back-arc basin basalts. A quartzite from the Betsiaka Group contains detrital zircon with a youngest single grain U–Pb age of 1771 ± 28 Ma, with peaks at 2500and 1850 Ma. An orthogneiss intruding this group has concordant zircon grains interpreted to date the protolith’s crystallization at 841 ± 3.2Ma.

The detrital zircon age pattern corresponds to that of the Aravalli Craton in India, suggesting that the northern Madagascar crustal fragment may have been formed off the coast of NW India between 1770 and 1000 Ma. This fragment could have been connected with other crustal fragments, now part of the SE Arabian plate, showing similar detrital zircon patterns in Neoproterozoic sedimentary rocks and sharing a calc-alkaline magmatism around 840 Ma.
HEROES: Humanitarian Emergency Response based on UAV-enabled Integrated Sensing and Communication, Positioning, and Satisfaction Games
Authors: Siraj Md Sadman , Charatsaris Panagiotis , Diamanti Maria , Tsiropoulou Eirini Eleni , Papavassiliou Symeon
Published: February 2025

In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This paper presents a novel Positioning, Navigation, and Timing (PNT) framework, named HEROES, which leverages the UAV and Integrated Sensing and Communication (ISAC) technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an Emergency Control Center (ECC). HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a Reinforcement Learning (RL) model based on the Optimistic Q-learning with Upper Bound Confidence algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a Satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including Minimum Efficient Satisfaction Equilibrium (MESE), ensuring that the UAVs meet their Quality of Service (QoS) constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies.
A Comprehensive Review on IoT Marketplace Matchmaking: Approaches, Opportunities and Challenges
Authors: An Qi , Jiang Frank , Neiat Azadeh , Yeoh William , Venayagamoorthy Kumar , Zaslavsky Arkady
Published: February 2025

Service discovery matchmaking plays a vital role in the cyber marketplace for the Internet of Things (IoT), especially in peer-to-peer environments where buyers and sellers dynamically register and match resource profiles online. As the IoT marketplace expands, efficient resource allocation through matchmaking is increasingly important. However, the growing complexity of service discovery, coupled with data security and privacy challenges, complicates the identification of suitable services. To address these issues, this study conducts a comprehensive review of matchmaking algorithms within the IoT marketplace by examining their key attributes, strengths, and limitations as documented in academic literature. This paper categorises and summarises state-of-the-art approaches, identifying research gaps and proposing future directions. Our comparative analysis highlights the strengths and weaknesses of current methodologies, advocating for deep learning and context-aware solutions to improve service efficiency. Additionally, blockchain-based approaches are discussed for their potential to improve security, trust, and privacy-preserving transactions. This research lays a critical foundation for the advancement of secure, efficient IoT-enabled marketplaces.
Eye Tracking for Infant Populations: A Comparison of Methods and Best Practices: Eye Tracking for Infant Populations
Authors: Aukerman Julia F. , Newland Jamie , Scott Lisa S.
Published: February 2025

Analyzing infant visual fixations and gaze patterns from the earliest stages of life can provide valuable insights into early cognition and development. Eye-tracking methodologies provide a means to measure infant visual perception with high temporal and spatial accuracy, and in increasingly naturalistic contexts with the innovation of head-mounted eye tracking. The current review provides a comparison between screen-based and head-mounted eye-tracking methods for use with infant populations, reviewing the advantages and disadvantages of each approach. This review also aims to summarize recommendations for best practices in infant eye tracking for both screen-based and head-mounted technology, including suggestions for designing experiments, calibration techniques, and analysis approaches. Moreover, we discuss the need for standardized procedures for experimental design and reporting data quality for investigations using eye tracking to examine infant development.
Distributed MobilityDB: A Scalable Moving Object Database Management System
Authors: Bakli Mohamed , Sakr Mahmoud , Zimányi Esteban , Dijk Nils , Slot Marco
Published: February 2025

As the volume and complexity of spatiotemporal data continue to expand rapidly across various domains such as urban planning, environmental monitoring, and logistics, the demand for comprehensive data management systems becomes increasingly urgent. Handling such data entails intricate topological and analytical operations, emphasizing the necessity for robust and adaptable solutions capable of addressing diverse user queries.

This paper introduces Distributed MobilityDB

1

, an open-source system engineered to manage big spatiotemporal trajectory datasets within SQL environments. Distributed MobilityDB offers capabilities for scalable spatiotemporal data management, facilitating efficient distributed query processing while seamlessly integrating with existing MobilityDB SQL operations. Key contributions highlighted in the paper encompass an adaptive spatiotemporal SQL query engine. This engine channels user SQL queries through various planning strategies for optimizing the distributed query plan, then distributing the query execution across cluster nodes transparently to the user. Various spatiotemporal query types are supported for distribution, including range selections, and joins proximity. Distributed MobilityDB is implemented as an add-on extension to PostgreSQL, which facilitates installing it on a readily running server. The paper further presents extensive experiments conducted on both cloud and on-premise environments using both real and synthetic datasets, including AIS for ship trajectories and BerlinMOD for simulated person trips.
Knowledge Management in a World of Generative AI: Impact and Implications
Authors: Storey Veda Catherine
Published: February 2025

The management of organizational and worker knowledge is a critical part of any organization. Knowledge management recognizes the explicit knowledge that workers contribute to an organization, as well as the organizational knowledge and skills acquired over time. The potential impact of generative AI (GenAI) raises many issues and opportunities related to effective knowledge management as organizations explore the impact and implications of its adoption. This research explores issues related to an increasing world of GenAI and proposes a
Framework for Knowledge Management in the GenAI Era
. Knowledge management workers who are novices might be more inclined to use generative AI for performing their tasks. Expert knowledge workers might focus on quality evaluation and quality assessment. Adoption of GenAI may help with the progression from novice to expert, while freeing up time and resources for other work. Challenges include the potential loss of critical organizational knowledge, definition of job-related requirements, expectations, and tasks, as well as over-reliance, and loss of tacit or unarticulated knowledge. These challenges must be balanced against available access to large amounts of general and domain-specific knowledge from GenAI to capture the value realized from its adoption.
Bispectral Index and Surgical Space Conditions in Day Surgery Benign Gynecological Laparoscopies: A Double‐Blinded Randomized Clinical Trial
Authors: Crescioli Elena , Thyrrestrup Peter Søndergaard , Almås Thale
Published: February 2025

Background: Ambulatory surgery is increased in numbers, healthcare costs are reduced, and patient safety is high when patient characteristics and type of surgery are properly evaluated. To maintain efficiency and patient safety, anesthesia and surgery must be optimized. The bispectral index (BIS) is a widely used and simplified measure of the depth of anesthesia and may provide a more stable anesthesia and avoid insufficient levels of anesthesia. We investigated whether BIS–guided anesthesia could increase the frequency of excellent overview of the surgical field.

Methods: This is a single‐center double‐blinded randomized clinical trial. We enrolled patients undergoing gynecologic laparoscopies on a benign indication from April 2019 to March 2021. Using closed envelopes, patients were randomized to receive either a BIS–assisted anesthesia, or to receive anesthesia without BIS monitoring (i.e., control group). Surgeons graded their surgical field overview using a four‐grade numerical scale at three predefined time points. The primary outcome was the proportion of patients graded with an excellent surgical overview.

Results: We included 160 women: 80 patients were randomized to the BIS group and 80 to the control group. There was no statistically significant difference between groups in excellent surgical overview at any time point. Among secondary outcomes, a lower remifentanil consumption in the control group was found in comparison with the BIS group.

Conclusions: This trial found no clinically relevant differences regarding surgical overview quality during gynecological laparoscopic surgery when using BIS to titrate anesthesia. To our knowledge, this is the first study investigating this aspect.

Trial Registration: ClinicalTrials.gov identifier: NCT03911544
The Glycosyltransferase XYLT1 Activates NF-κB Signaling to Promote Metastasis of Early-Stage Lung Adenocarcinoma
Authors: Han Jian , Du Jianan , Li Xiangmeng , Zhou Qingbo , Zeng Jiayu , Lin Jun-Tao , Zhou Wenle , Cai Jiayi , Ye Yaokai , Yang Bosui , Wang Junsheng , Zhou Xiang , Lian Rong , Yang Yi , Zhu Xun , Guan Hongyu , Liu Liping , Cai Junchao , Wu Jueheng , Li Yun , Li Mengfeng , Tian Han
Published: February 2025

Early-stage lung adenocarcinoma (LUAD) generally has a favorable prognosis. However, more than 30% of early-stage LUAD cases relapse within 5 years of initial treatment, even following complete removal of the primary tumor. Identification of the factors contributing to early-stage LUAD metastasis is needed to develop effective prevention and treatment strategies. Here, we found upregulation of xylosyltransferase 1 (XYLT1), a glycosyltransferase that initiates biosynthesis of sulfated glycosaminoglycan (sGAG) chains, in metastatic recurrent lesions of early-stage LUAD, which correlated with poor prognosis. In vitro and in vivo experiments showed that XYLT1 promoted LUAD cell survival and metastasis by activating the NF-κB pathway. Mechanistically, XYLT1 interacted with IκBα and facilitated biosynthesis of sGAG-conjugated IκBα, which enhanced the interaction between IκBα and IKKs to promote proteasomal degradation of IκBα. These results illustrate that proteoglycan modification-mediated activation of NF-κB signaling is a driver of early-stage LUAD metastasis, providing a possibility for detection and intervention of early LUAD metastasis.
Table S4 from Phase Ib Study of Immunocytokine Simlukafusp Alfa (FAP-IL2v) Combined with Pembrolizumab for Treatment of Advanced and/or Metastatic Melanoma
Authors: Munoz-Couselo Eva , Soria Rivas Ainara , Sandhu Shahneen , Long Georgina V. , Sanmamed Miguel F. , Spreafico Anna , Buchbinder Elizabeth , Sznol Mario , Prenen Hans , Fedenko Alexander , Milhem Mohammed , Arance Fernandez Ana Maria , Grob Jean-Jacques , Demidov Lev , Robert Caroline , Habigt Christin , Evers Stefan , Sleiman Nassim , Dejardin David , Ardeshir Caroline , Martin Nicole , Boetsch Christophe , Charo Jehad , Teichgräber Volker , Kraxner Anton , Keshelava Nino , Bechter Oliver
Published: February 2025

Characterization of patients with complete or partial response
Table 2 from Phase Ib Study of Immunocytokine Simlukafusp Alfa (FAP-IL2v) Combined with Pembrolizumab for Treatment of Advanced and/or Metastatic Melanoma
Authors: Munoz-Couselo Eva , Soria Rivas Ainara , Sandhu Shahneen , Long Georgina V. , Sanmamed Miguel F. , Spreafico Anna , Buchbinder Elizabeth , Sznol Mario , Prenen Hans , Fedenko Alexander , Milhem Mohammed , Arance Fernandez Ana Maria , Grob Jean-Jacques , Demidov Lev , Robert Caroline , Habigt Christin , Evers Stefan , Sleiman Nassim , Dejardin David , Ardeshir Caroline , Martin Nicole , Boetsch Christophe , Charo Jehad , Teichgräber Volker , Kraxner Anton , Keshelava Nino , Bechter Oliver
Published: February 2025

Overview of AEs
Figure S1 from Phase Ib Study of Immunocytokine Simlukafusp Alfa (FAP-IL2v) Combined with Pembrolizumab for Treatment of Advanced and/or Metastatic Melanoma
Authors: Munoz-Couselo Eva , Soria Rivas Ainara , Sandhu Shahneen , Long Georgina V. , Sanmamed Miguel F. , Spreafico Anna , Buchbinder Elizabeth , Sznol Mario , Prenen Hans , Fedenko Alexander , Milhem Mohammed , Arance Fernandez Ana Maria , Grob Jean-Jacques , Demidov Lev , Robert Caroline , Habigt Christin , Evers Stefan , Sleiman Nassim , Dejardin David , Ardeshir Caroline , Martin Nicole , Boetsch Christophe , Charo Jehad , Teichgräber Volker , Kraxner Anton , Keshelava Nino , Bechter Oliver
Published: February 2025

Study flow chart
Figure 3 from Phase Ib Study of Immunocytokine Simlukafusp Alfa (FAP-IL2v) Combined with Pembrolizumab for Treatment of Advanced and/or Metastatic Melanoma
Authors: Munoz-Couselo Eva , Soria Rivas Ainara , Sandhu Shahneen , Long Georgina V. , Sanmamed Miguel F. , Spreafico Anna , Buchbinder Elizabeth , Sznol Mario , Prenen Hans , Fedenko Alexander , Milhem Mohammed , Arance Fernandez Ana Maria , Grob Jean-Jacques , Demidov Lev , Robert Caroline , Habigt Christin , Evers Stefan , Sleiman Nassim , Dejardin David , Ardeshir Caroline , Martin Nicole , Boetsch Christophe , Charo Jehad , Teichgräber Volker , Kraxner Anton , Keshelava Nino , Bechter Oliver
Published: February 2025

Pharmacodynamic results in tumor tissue. Tumor tissue from biopsies taken at baseline and on treatment (C2D8) was assessed for changes in TIL density with respect to CD3 T cells, CD8 T cells, proliferating CD8 T cells, NK cells, and cytotoxic T lymphocytes (A) and Tregs as well as PD-L1 expression on immune and tumor cells (Ventana SP263 assay; B). For these exploratory PD assessments, the investigator assessment of tumor response was used. BL, baseline; C, cycle; D, day; IC, immune cell; PD, progressive disease; SD, stable disease; TC, tumor cell.
Prosodic Cues Support Inferences About the Question’s Pedagogical Intent
Authors: Bascandziev Igor , Shafto Patrick , Bonawitz Elizabeth
Published: February 2025

Questions may be asked with an intent to acquire new information from the recipient (i.e., information-seeking questions) or with the intent to teach (i.e., pedagogical questions). Understanding how the questions’ recipients infer the intent of questions is important, because the recipients’ inferences have important consequences for reasoning and learning. In the present series of studies, we tested the hypothesis that i) askers use prosodic cues—an ever-present signal—to encode information-seeking and pedagogical intent both in deliberate and spontaneous speech and that ii) adults and children can draw appropriate inferences about the question’s intent on the basis of prosody alone. In Experiments 1 and 2, we found that naïve adult listeners and children aged 5 years and above have the capacity to explicitly identify which asker has an intention to teach on the basis of prosody alone. In Experiment 3, we found that parents’ spontaneous speech in pedagogical or information-seeking contexts is appropriately recognized by naïve listeners as pedagogical or information-seeking. Thus, the intent of pedagogical and information-seeking questions is acoustically encoded by askers, and it can be appropriately decoded by recipients.
Personal Values and Student Volunteering
Authors: Vasileva Inna V. , Chumakov Mikhail V.
Published: February 2025

Background. Against the background of the transformation of civic consciousness in a period of social instability, the state turns to the activity of citizens, many of whom are ready and eager to be active in order to transform the living space. However, not everyone is prone to this kind of activity. Thus, it is important to identify the values of the individual that contribute to prosocial activity.
Objectives. The paper studies the difference in the value sphere (ten basic values according to S. Schwartz) among university students involved in the volunteer movement and not participating in it.
Study Participants. Students of the Tyumen State and Kurgan State Universities studying in the direction of “«Psychology”»: 172 people (32 boys, 140 girls) from 18 to 39 years old (Mage = 21.25; SD = 3.96).
Methods. Questionnaire of values by S. Schwartz (adapted by V.N. Karandashev). Screening survey: gender, age, presence / absence of experience in volunteering, type of volunteering. The presence and nature of differences were tested using the two-sample Kolmogorov — -Smirnov test.
Results. Students participating in volunteering and not participating in it have a similar structure of personal values, both at the level of normative ideals and at the level of individual priorities. At the level of normative ideals, the values of universalism, independence and kindness are the most significant; at the level of individual priorities, the value of security prevails. Statistically significant differences between student volunteers and those who are not involved in volunteer activities consist in the expression of the value of kindness at the level of normative ideals and the value of tradition at the level of individual priorities.
Conclusions. There are differences in the structure of individual preferences and normative ideals of values among students who are involved in the volunteer movement and those who do not participate in it. The main differences are observed in the values of kindness, which are more pronounced among students participating in volunteer activities.
New fossil species from two genera of the subfamily Hormiinae (Hymenoptera: Braconidae) from Eocene Baltic amber
Authors: BELOKOBYLSKIJ SERGEY A. , MANUKYAN ANDRANIK R.
Published: February 2025

New species of the genus Prochremylus Brues, 1933, P. museumoceanus Belokobylskij, sp. nov., from the tribe Chremylini and of the genus Aulosaphes Muesebeck, 1935, Aulosaphes fumipennis Belokobylskij, sp. nov., from the tribe Lysitermini of the subfamily Hormiinae are described and illustrated from the late Eocene Baltic amber. The genus Aulosaphes Muesebeck is recorded as a fossil in Baltic amber for the first time. The new subgenus of the genus Aulosaphes, Antiquasaphes Belokobylskij, subgen. nov., is described and key to subgenera is provided. A detailed redescription of the fossil genus Prochremylus Brues is provided. The position of the hormiine taxa previously described as fossils is discussed.
Notes on the genus Draconarius Ovtchinnikov, 1999 (Araneae: Agelenidae) with description of two new species from Laos
Authors: HOANG QUANG DUY , NOPHASEUD LIPHONE , JÄGER PETER
Published: February 2025

Two new species of the Draconarius venustus group are described from the Vang Vieng karst region of Laos: D. laosensis spec. nov. (♂♀) from various caves, and D. externus spec. nov. (♂) from the leaf litter. Detailed descriptions, illustrations and photographs of copulatory organs along with somatic features of the new species are provided. Additionally, the male palp of D. joshimath from India is illustrated in detail and the species is assigned to the D. venustus group. Distribution maps for all known records of the genus Draconarius s.l. are given along with an analysis of cave-dwelling species and its elevational distribution.
Racist Policy Shocks in the United States and Latino Elites’ Identities and Actions: Prop 187, SB 1070, and Trump’s Racism
Authors: Vallejo Jody Agius
Published: February 2025

This article draws on interviews conducted with 65 upper-middle class and upper-class Latinos in the business and corporate sector to investigate how racist and exclusionary immigration policy shocks at the state and federal level in the United States—such as Prop 187 in California, SB1070 in Arizona in the 2010s and Trump’s criminalizing rhetoric and immigration policies before and during his first term—shape the racial/ethnic identification and philanthropic activities of Latino economic elites. I argue that well-publicized racialized policies, what I refer to “racist policy shocks,” have enduring effects because they shock systems at multiple levels of society that ripple through institutions and everyday life and across time. Racist policy shocks can result in more than changes in public opinion, a thickening of ethnic identity, and political protest in the moment that groups are being targeted—they can also have long-lasting impacts on racial/ethnic identity, reinforce ethnic solidarity, and spur people to economic action and ethnoracial uplift. I argue that individual level factors rooted in ethnorace and class intersections—such as growing up in low-income communities and experiences with discrimination—combine with an exclusionary macro level context—such as negative political rhetoric that constantly demonizes Latinos as a threat—to crystallize a sense of ethnic solidarity and responsibility for the broader Latino community.