bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metaphor-Driven Contextualization: Reordering Sustainability Information for Collective Intelligence
Authors: Aebi Carol 
Published: February 2025

Abstract
This chapter utilizes metaphorical reasoning and draws upon Gareth Morgan’s “Images of Organization” to provide a theoretical basis for understanding complex social value and sustainability dynamics. The selected metaphor of the “flux and transformation” lens/prism offers insights into the constant interplay between social stability and dynamic change in social development. Thought trials through metaphor explore interconnected concepts, including the relationships between systems and their environments, chaos and complexity theory, cybernetics, and the tensions between opposites. Concepts such as Stigmergy, Systems Thinking and Systems Dynamics offer contextualization. Chapter 3 bridges the social value and social impact literature examined in Chap. 2 and the model-building process outlined in Chap. 4 by providing a new perspective frame for a new theory’s emergence.
Research on DOA Estimation Method of Underwater Acoustic Signal Based on Machine Learning
Authors: Zhao Feiyu 
Published: February 2025

Abstract
This project is concerned with the development of a machine learning-based DOA estimation of underwater acoustic signals with the view of improving the efficiency and reliability of the signal processing in complex underwater environments. The feature analysis and classification of the underwater acoustic signals are done with the help of convolutional neural networks (CNNs). Through multi-step purification of the signal data and lowering the sampling frequency of the pre-processed signal data, a high-performance arrival angle assessment framework is constructed. The results indicate that the algorithm achieves high accuracy, recall and F1 in different noise intensity levels; particularly, low noise environment with an accuracy of 95%. 2%. The application verification of practical application also confirms that this technology has strong applicability and stability in complex seabed conditions.
Design and Implementation of Smart Home Control System Based on STM32
Authors: Gao Lang , Li Guo 
Published: February 2025

Abstract
With the rapid development of Internet of Things (IoT) technology, smart home control has become an indispensable part of modern life. This study designs a smart home control system based on the STM32 microcontroller, combined with Tencent CloudBase and WiFi communication technology, to achieve remote control of home devices and real-time monitoring of home environments. The system uses the MQTT protocol to transmit data and send control commands. At the same time, a WeChat mini-program is developed as the client for controlling devices. The feasibility of the system has been verified through testing.
Communication Anti-jamming System Based on Deep Reinforcement Learning
Authors: Feng Xuewei , Wen Hong , Zhao Runhui , Tang Tao , Shi Weihong , Peng Yulin 
Published: February 2025

Abstract
Due to the open characteristics of the electromagnetic channel, there are malicious nodes that jam the normal data flow, prevent the legitimate receiver from obtaining information, and then intercept and tamper with the data, which makes the research of communication anti-jamming becomes more and more important. Traditional anti-jamming methods employ a single anti-jamming approach and cannot adaptively adjust their anti-jamming strategy based on the environment, often can’t achieve a good anti-jamming effect in the complex communication environment. In order to solve these challenges, this paper studies the anti-jamming communication model based on deep reinforcement learning (DRL), and builds a simulation system to realize intelligent anti-jamming decision-making by using DRL algorithm. The simulation results show that the proposed intelligent anti-jamming decision can choose the best anti-jamming scheme according to the complex environment and effectively improve the communication quality.
Fault Diagnosis of Infrared Sensor Based on Convolutional Neural Network
Authors: Hu Zhenghao , Liu Yingyi , Cheng Lin , Wang Yan , Zhang Donglei , Tian Xun 
Published: February 2025

Abstract
Infrared sensors are critical equipment for monitoring substations, but they are subjected to complex multi-physical field stresses in substation applications, leading to a significantly higher failure rate of infrared sensors in substations compared to other applications. Effective fault diagnosis of infrared sensors is of great importance for improving the safety of substations. This article proposes a method for fault diagnosis of infrared sensors. First, the network weights are trained based on the ImageNet dataset and kept fixed. Then, a transfer learning approach is used to fine-tune the model based on a dataset of infrared sensor failures, resulting in the classification results. To verify the effectiveness of the proposed method, the improved transfer learning model is compared to the model before improvement. Experimental results show that the proposed method greatly reduces training time and improves classification accuracy compared to the previous model. In conclusion, the method presented in this article offers a new approach for fault diagnosis of infrared sensors, which can contribute to enhancing the reliability of infrared sensors in substations.
A Verifiable Ciphertext Retrieval Scheme for Smart Grids
Authors: Lin Xiuqing , Chen Jueyu , Qin Yupeng , Yang Zhou , Li Jinjin 
Published: February 2025

Abstract
With the expanding volume of smart grid data, cloud storage has emerged as a cost-effective scheme for their maintenance. However, storing plaintext data in the cloud may lead to a significant risk of privacy leakage of web data. Existing cryptography-based systems are often inefficient because data must be decrypted to access it. To address these challenges, a new approach for verifiable digital text discovery is proposed. The program allows recovery operations to be performed while ensuring data confidentiality and availability. This new scheme involves: Generating root hashes using Merkle trees and leverage Blockchain guarantees the trustworthiness of search results. In addition, a multi-cloud heterogeneous distributed system storage system is designed to store user data, which has the ability to repair damaged data. This feature not only improves data security, but also ensures data availability. Finally, the security analysis and simulation results verify the security and efficiency of the protocol.
Robust Multi-agent Federated Reinforcement Learning for Task Offloading
Authors: Yan Dibao , Wang Yongfeng , Hou Wenjing , Song Huanhuan , Wen Hong , Ma Wendi , Sun Fan 
Published: February 2025

Abstract
With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node.
Video Codec Method for Compressed Sensing Theory
Authors: Yan Haixia , Liu Yanjun 
Published: February 2025

Abstract
In order to realize the rapid H.265 codec method, a new H.265 codec method based on Compressed Sensing Theory is proposed. The H.265 codec method are divided into three different types, intra-frame type, interframe type and mix frame type. Combined with the Compressed Sensing Theory, the input images linearly transform into the residual data, scale and quantify the transformed coefficients; after loop filtering and adaptive compensation, the H.265 coding data are got. When decoding, the process is reversed. Experimental results show that this method improves the image video codec quality and the image video codec speeds.
An Offline Learning-Based Anti-interference Communication Scheme for UAV Networks
Authors: Tang Tao , Zhao Runhui , Wen Hong , Feng Xuewei , Shi Weihong , Peng Yulin 
Published: February 2025

Abstract
Unmanned Aerial Vehicles (UAV) face challenges from advanced interference technologies, making them susceptible to malicious node attacks, data interception, and tampering. Traditional anti-interference decisions have limitations, as they cannot adaptively adjust to changes in interference signals. Moreover, anti-interference communication models based on Deep Reinforcement Learning (DRL) require prolonged interactions with the environment, demanding high requirements for anti-interference environments. This paper investigates an offline anti-interference decision based on Decision-Transformers, which can quickly and stably acquire practical anti-interference decision models. Simulation experiments have verified the effectiveness of this algorithm in making anti-interference decisions under AWGN and fading channel conditions. Furthermore, this offline approach can achieve the expected reward targets with fewer training iterations.
Adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection
Authors: Shi Weihong , Tang Tao , Zhao Runhui , Wen Hong , Feng Xuewei 
Published: February 2025

Abstract
Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients.
Research on the Application of Internet of Things Technology in Power Grid Limited Space Security Monitoring
Authors: Jiang Zhijian 
Published: February 2025

Abstract
The goal of this paper is to study the practical application of Internet of Things technology in the safety supervision of specific areas of the power grid, so as to optimize the safety management process of the power grid and improve its management efficiency and accuracy. For the major components of power facilities including cable tunnels, substations, cable trenches, temperature, humidity, and gas sensors are installed. These sensors use wireless sensor network technology to send data to the next level, and then the data is sent to the big data analysis for further analysis and monitoring. The findings of the study indicate that the sensor network can monitor important environmental information in real time, with the data transmission rate of 250 kilobits per second and the probability of data loss of less than 0. 1%. This monitoring system can give an alarm 5 to 10 min before the fault is likely to happen with an accuracy of more than 95%. By using accurate measurement instruments and a big data processing center, the system has made some remarkable outcomes in the improvement of the power grid stability and security.
Commutative Algebras with One-Dimensional Square
Authors: Martín Barquero Dolores , Martín González Cándido , Sánchez-Ortega Juana 
Published: February 2025

Abstract
In this paper, we classify and study commutative algebras having a one-dimensional square. In finite dimension (see Theorem 3.9) besides some cases (which are all associative and nilpotent with nilpotency index 3), the algebras with zero annihilator are either of symplectic type (appearing only in characteristic 2), or evolution algebras. In infinite dimension, ruling out the associative case, we prove that our algebras are either of symplectic type or evolution algebras provided some technical conditions are satisfied (see Theorem 3.12). Our main tool is the theory of inner product spaces and quadratic forms. More precisely, if A denotes an evolution algebra with

$$\dim (A^2) = 1$$

dim
(

A
2

)
=
1

and a a generator of

$$A^2$$

A
2

, then A admits an inner product

$$\langle \cdot , \cdot \rangle $$

⟨
·
,
·
⟩

such that the product of A is given by

$$xy = \langle x, y\rangle a$$

x
y
=
⟨
x
,
y
⟩
a

. There are three classes to consider:

$$a\in \textrm{Ann}(A)$$

a
∈
Ann
(
A
)

;

$$a\notin \textrm{Ann}(A)$$

a
∉
Ann
(
A
)

and a is isotropic relative to

$$\langle \cdot , \cdot \rangle $$

⟨
·
,
·
⟩

;

$$a\notin \textrm{Ann}(A)$$

a
∉
Ann
(
A
)

and a is anisotropic relative to

$$\langle \cdot , \cdot \rangle $$

⟨
·
,
·
⟩

.

The isomorphism problem among these objects is investigated. For some of these algebras, we have also determined the existence of faithful associative representations in certain Clifford algebras.
Chirurgische Optionen zur Behandlung einer Patella baja
Authors: Blaukovitsch Michael , Mengis Natalie , Ammann Elias , Toth Laszlo , Hirschmann Michael T. , Koch Matthias 
Published: February 2025

Zusammenfassung
Die Patella baja stellt eine komplexe Herausforderung für den behandelnden Orthopäden und Unfallchirurgen dar. Sie kann akut in Folge von Verletzungen und postoperativen Veränderungen der Gelenksymmetrie entstehen, sowie sich auch im Verlauf nach operativen Eingriffen und Verletzungen entwickeln. Klinisch fällt sie insbesondere durch einen anterioren Knieschmerz sowie oft auch eine Einschränkung des Bewegungsumfangs auf. Die Diagnosestellung und Quantifizierung erfolgen anhand einer lateralen Bildgebung (Röntgenaufnahme/Magnetresonanztomographie [MRT]/Computertomographie [CT]) und unterschiedlichen Indices, die die Höhe der Kniescheibe beschreiben können. In Zusammenschau ist zwischen einer echten („true patella baja“) und vermeintlichen Patella baja („pseudo patella baja“), die vor allem nach Knieendoprothetik durch eine Verschiebung der Gelenklinie zustande kommt, zu unterscheiden. Die operativen Therapieformen erfolgen ursachenabhängig mittels Wiederherstellung der Kontinuität des Streckapparats im akuten Fall oder mittels Optimierung der Patellahöhe in Form einer proximalisierenden Tuberositas-tibiae-Osteotomie oder in der Revisionsendoprothetik durch die Wiederherstellung der Gelenklinie.
Two Generalizations of Almost Perfect Nonlinearity
Authors: Carlet Claude 
Published: February 2025

Abstract
Almost perfect nonlinear (in brief, APN) functions are vectorial functions

$$F:{\mathbb {F}}_2^n\rightarrow {\mathbb {F}}_2^n$$

F
:

F
2
n

→

F
2
n

playing roles in several domains of information protection, at the intersection of computer science and mathematics. Their definition comes from cryptography and is also related to coding theory. When they are used as substitution boxes (S-boxes, which are the only nonlinear components in block ciphers), APN functions contribute optimally to the resistance against differential attacks. This makes of course a strong cryptographic motivation for their study, which has been very active since the 90’s, and has posed interesting and difficult mathematical questions, some of which are still unanswered. Since the introduction of differential attacks, more recent types of cryptanalyses have been designed, such as integral attacks. No notion about S-boxes has been identified which would play a similar role with respect to integral attacks. In this paper, we study two generalizations of APNness that are natural from a mathematical point of view, since they directly extend classical characterizations of APN functions. We call these two notions strong non-normality and sum-freedom. The former existed already for Boolean functions (it had been introduced by Dobbertin), and the latter is new. We study how these two notions are related to cryptanalyses (the relation is weaker for strong non-normality). The two notions behave differently from each other, while they have similar definitions. They behave differently from differential uniformity, which is a well-known generalization of APNness. We study the different ways to define them. We prove their satisfiability, their monotonicity, and their invariance under classical equivalence relations, and we characterize them by the Walsh transform. We finally begin a study of the multiplicative inverse function (used as a substitution box in the Advanced Encryption Standard and other block ciphers) from the viewpoint of these two notions. In particular, we find a simple expression of the sum of the values taken by this function over affine subspaces of

$$\mathbb F_{2^n}$$

F

2
n

that are not vector subspaces. This formula shows that the sum never vanishes on such affine spaces. We also give a formula for the case of a vector space defined by one of its bases.
Intermediate complex structure limit for Calabi-Yau metrics
Authors: Li Yang 
Published: February 2025

Abstract
We study polarised degenerations of

$n$

n

-dimensional Calabi-Yau hypersurfaces

$\{ F_{0}F_{1}\ldots F_{m}+tF=0\} $

{

F
0

F
1

…

F
m

+
t
F
=
0
}

, where the essential skeleton has dimension

$1\leq m\leq n-1$

1
≤
m
≤
n
−
1

. We will describe the limiting behaviour of the Calabi-Yau potential at the

$C^{0}$

C
0

-level, in terms of an optimal transport problem, and in terms of non-archimedean potential theory.
Personalized physical activity recommendations for people with axial spondyloarthritis using wearable activity tracker data: an exploratory study
Authors: de Leeuw A.-W , van Wissen M.A.T. , Vlieland T.P.M. Vliet , van Tubergen A.M. , Gademan M.G.J. , Berger M.A.M. , van Weely S.F.E. 
Published: February 2025

Abstract

Objective
Benefits of physical activity (PA) on sleep in people with axial SpondyloArthritis (axSpA) are largely unknown. Our aim is to explore the relationships between PA and sleep on both a group level and an individual level using Wearable Activity Trackers (WATs) and machine learning.

Methods
A sample of 64 axSpA participants received a WAT to monitor their PA and sleep. Participants with more than 30 days data of PA and sleep duration were included in the analyses. Spearman’s correlation and the machine learning technique Subgroup Discovery were used to determine relationships between PA during the three prior days and light and deep sleep duration.

Results
Number of daily steps (n = 64) was (median (first quartile (Q1) - third quartile (Q3) )) 4026 (1915 - 6549), total sleep (daily light and deep sleep) duration of the participants was 7 h 29 min (6 h 41 min - 8 h 8 min). Nearly 30% (n = 18) of the participants were eligible for inclusion in analyses (> 30 days of data). No significant relationships between prior PA and sleep were obtained on a group level. On an individual level, for 8 of the 18 included participants, significant relationships (p < 0.05) could be identified between PA during the three prior days and daily sleep duration. These significant relationships differed from participant to participant with a varying qualification of PA (number of steps, intensity level PA) and relevant time window (previous one, two or three days).

Conclusion
Significant relationships between PA and daily sleep duration could be obtained on an individual level with details of the significant relationships varying between participants.

Registration number
Netherlands Trial Register NL8238, included in the International Clinical Trial Registry Platform (ICTRP) (https://trialsearch.who.int/Trial2.aspx?TrialID=NL8238).
Advancements in genetic research and RNA therapy strategies for amyotrophic lateral sclerosis (ALS): current progress and future prospects
Authors: Ruffo Paola , Traynor Bryan J. , Conforti Francesca Luisa 
Published: February 2025

Abstract
This review explores the intricate landscape of neurodegenerative disease research, focusing on Amyotrophic Lateral Sclerosis (ALS) and the intersection of genetics and RNA biology to investigate the causative pathogenetic basis of this fatal disease. ALS is a severe neurodegenerative disease characterized by the progressive loss of motor neurons, leading to muscle weakness and paralysis. Despite significant research advances, the exact cause of ALS remains largely unknown. Thanks to the application of next-generation sequencing (NGS) approaches, it was possible to highlight the fundamental role of rare variants with large effect sizes and involvement of portions of non-coding RNA, providing valuable information on risk prediction, diagnosis, and treatment of age-related diseases, such as ALS. Genetic research has provided valuable insights into the pathophysiology of ALS, leading to the development of targeted therapies such as antisense oligonucleotides (ASOs). Regulatory agencies in several countries are evaluating the commercialization of Qalsody (Tofersen) for SOD1-associated ALS, highlighting the potential of gene-targeted therapies. Furthermore, the emerging significance of microRNAs (miRNAs) and long RNAs are of great interest. MiRNAs have emerged as promising biomarkers for diagnosing ALS and monitoring disease progression. Understanding the role of lncRNAs in the pathogenesis of ALS opens new avenues for therapeutic intervention. However, challenges remain in delivering RNA-based therapeutics to the central nervous system. Advances in genetic screening and personalized medicine hold promise for improving the management of ALS. Ongoing clinical trials use genomic approaches for patient stratification and drug targeting. Further research into the role of non-coding RNAs in the pathogenesis of ALS and their potential as therapeutic targets is crucial to the development of effective treatments for this devastating disease.