bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive Multicore Dual‐Path Fusion Multimodel Extraction of Heterogeneous Features for FAIMS Spectral Analysis
Authors: Zhang Ruilong , Du Xiaoxia , Xiao Wenxiang , Li Hua 
Published: December 2024

ABSTRACTWith the increasing application scenarios and detection needs of high‐field asymmetric waveform ion mobility spectrometry (FAIMS) analysis, deep learning–assisted spectral analysis has become an important method to improve the analytical effect and work efficiency. However, a single model has limitations in generalizing to different types of tasks, and a model trained from one batch of spectral data is difficult to achieve good results on another task with large differences. To address this problem, this study proposes an adaptive multicore dual‐path fusion multimodel extraction of heterogeneous features for FAIMS spectral analysis model in conjunction with FAIMS small‐sample data analysis scenarios. Multinetwork complementarity is achieved through multimodel feature extraction, adaptive feature fusion module adjusts feature size and dimension fusion to heterogeneous features, and multicore dual‐path fusion can capture and integrate information at all scales and levels. The model's performance improves dramatically when performing complex mixture multiclassification tasks: accuracy, precision, recall, f1‐score, and micro‐AUC reach 98.11%, 98.66%, 98.33%, 98.30%, and 98.98%. The metrics for the generalization test using the untrained xylene isomer data were 96.42%, 96.66%, 96.96%, 96.65%, and 97.60%. The model not only exhibits excellent analytical results on preexisting data but also demonstrates good generalization ability on untrained data.
Consumption Poverty Alleviation: The Role of Poverty Attribution and Attitude in Poverty Alleviation Product Purchase
Authors: Zhang Tong , Jiang Qi , Hu Wuyang , Zhu Zhanguo 
Published: December 2024

ABSTRACTOne approach to alleviate poverty is to identify and allow consumers to purchase poverty alleviation products (PAPs) from low‐income agricultural producers in rural areas. We explore whether consumers' attribution of the cause of poverty affects their purchase intentions for PAPs. We differentiate consumer attribution into situational and dispositional and find that situational attribution does not affect purchase intentions, whereas dispositional attribution negatively does. Furthermore, we find that consumer attitudes toward the government's consumption poverty alleviation (CPA) policies may mediate the direct impact of poverty attribution on PAP purchase intention. Consumers generally value CPA policies consistent with their attribution and the mediation effect strengthens the direct effects. This study offers insights into consumer purchase of products designed to fight poverty and provides policy recommendations that can mobilize poverty reduction through product development and consumer endorsement.
Engineering the Local Electronic Configuration of Diatomic Iron–Nickel Site for Enhanced Nitrate and Ammonia Co‐Electrolysis Activity
Authors: Sun Jie , Xu Shaohan , Gao Weiqi , Zhao Guohua 
Published: December 2024

AbstractAnthropogenic activities have caused a significant rise in nitrate and ammonia nitrogen levels in natural water bodies, disrupting the balance of the nitrogen cycle. The electrocatalytic reduction of nitrate and the oxidation of ammonia are promising strategies for converting polyvalent nitrogen into nontoxic and harmless N2. Herein, a bifunctional electrode loaded with diatomic iron–nickel site on porous N‐doped carbon (FeNi‐NC) is designed and successfully applied for the co‐electrolysis of nitrate and ammonia. The incorporation of the Fe atom shifts the partial density of states of Ni 3d away from the Fermi level and suppresses the 3d–2π* coupling between Ni sites and superficial N2, leading to the easy desorption of N2 intermediates. Consequently, the Faradaic efficiencies of FeNi‐NC for N2 production at the cathode and anode are 90.3% and 99.4% at 1.8 V, respectively, and an electricity consumption saving of 19.4% is achieved. This work provides a feasible strategy to regulate the electronic configuration of atomically dispersed catalysts for sewage treatment.
Synergistic Phase and Structural Engineering for Enhanced Zinc Storage in V2O3@N‐Doped Carbon Nanofibers
Authors: Wang Jinkai , Liu Boshi , Wang Zhengyu , Liu Zhi , Li Ling , Wang Min , Meng Xiaoman , Yong Yiming , Wang Hongkang , Yin Zongyou 
Published: December 2024

AbstractVanadium‐based oxide cathodes are promising energy‐storage systems for aqueous zinc‐ion batteries (AZIBs) because of their high energy density and safety, and low cost. However, their limited ion/electron transfer rates and rapid capacity decay pose challenges to their practical application. To overcome these limitations, V2O3 nanoparticles are developed with surface oxygen vacancies integrated with N‐doped carbon nanofibers (V2O3@NCNFs), using an electrospinning method combined with an in situ oxidation/reduction strategy. By precisely controlling the reaction atmosphere, synergistic regulation of the phase transition and structural evolution of vanadium oxide are achieved. The unique combination of oxygen vacancies and N‐doped carbon nanofibers enhances the zinc storage capacity, rate capability, and cycle stability of the V2O3@NCNFs electrode, achieving high reversible capacities of 554.6 mAh g−1 at 0.1 A g−1 with a high loading mass of ≈2.0–2.5 mg cm−2. Moreover, the V2O3@NCNFs electrode can achieve a high initial capacity of 434.3 and 266.6 mAh g−1 even at high current densities of 1.0 and 5.0 A g−1, respectively, with minimal capacity decay rates of 0.012% per cycle over 500 cycles and 0.003% per cycle over 2000 cycles. More importantly, this innovative approach can be universally applied to the design of novel nanostructured Mn‐ and V‐based oxide cathodes, which is promising for the development of advanced electrodes for high‐performance energy storage devices.
Automotive Cybersecurity Scheme for Intrusion Detection in CAN‐Driven Artificial Intelligence of Things
Authors: Dangwal Gagan , Wazid Mohammad , Nizam Sarah , Chamola Vinay , Das Ashok Kumar 
Published: December 2024

ABSTRACTThe Artificial Intelligence of Things (AIoT) is applicable for various domains, that is, smart healthcare, smart cities, industrial sectors, transportation systems, and many more. Controller area network (CAN) facilitates the integration of the sensing devices, thus enables them to send their data for analysis to various artificial intelligence (AI) algorithms. CAN also provides reliability and fault tolerance to the AIoT applications, as it has been designed to deal with noisy environments. Thus, CAN‐driven AIoT improves the efficiency, reliability, and functionalities of the devices and systems, which is very much needed for various AIoT applications. However, it is vulnerable to various cyber‐attacks like message replay, modification attack, fuzzy attack, denial of service, and spoofing the RPM gauge or drive gear. Therefore, an intrusion detection system (IDS) is required to detect attacks on the CAN bus. In this paper, we propose a lightweight and efficient intrusion detection system which successfully detects multiple intrusions based on the type of attack on CAN bus without causing additional traffic overhead to the ongoing communications (in short, ACID‐CAN). The presented mechanism is very much needed for the CAN‐driven AIoT applications. Experimental results show that the proposed ACID‐CAN successfully detects intrusions even when the amount of intrusion data is reduced to of normal data. The obtained results were compared with those of previous studies in the field of CANs intrusion detection, and it has been noted that the proposed ACID‐CAN offers comparable and better results.
Caught Off Guard: When Experts Fall for Quishing, Is Awareness Enough?
Authors: Rys Maciej , Ślusarek Agata , Zieliński Krzysztof 
Published: December 2024

ABSTRACTThis study delves into social engineering vulnerabilities among cybersecurity experts, using an experimental approach. The motivation is to challenge the assumption that cybersecurity professionals are immune to social engineering attacks by examining their susceptibility to emerging threats like quishing, identifying gaps in current practices and training programs, and raising awareness about the risks posed to the general public. Two scenarios involving QR codes offering free training sessions and promotional hoodie giveaways were set up at and despite ethical considerations, the experiment yielded enlightening results: around 19% of participants interacted with the QR codes, questioning the perceived invulnerability of cybersecurity experts to such tactics. These results question the feasibility of traditional educational approaches in fully mitigating such risks, highlighting the need for updated and effective training, innovative strategies, and automated defenses to better protect both professionals and the general public from evolving social engineering threats.
IDDLE: A Novel Deep Learning–Based Approach for Intrusion Detection Problem Using Feature Extraction
Authors: Göktepe Yunus Emre , Uzun Yusuf 
Published: December 2024

ABSTRACTThe internet has revolutionized access to information, enabling users to retrieve vast amounts of data effortlessly, transcending geographical and temporal barriers. This unprecedented connectivity has advanced research, education, and communication, but it has also created vulnerabilities that cybercriminals exploit to steal confidential information, interfere with processes, and carry out extensive attacks. Consequently, robust security measures are essential to safeguard the integrity and confidentiality of online data. Intrusion detection systems (IDS) are crucial in defending against such threats, employing both signature‐based and anomaly‐based models. Even though signature‐based IDS are highly effective at countering recognized threats, they struggle with novel, “zero‐day” attacks. Conversely, anomaly‐based detection systems can identify unknown threats but often generate high false positive rates. A hybrid IDS, combining elements of both approaches, offers a more comprehensive defense. This study presents a new intrusion detection model utilizing deep learning, evaluated on the KDD'99 and NSL‐KDD datasets. The proposed model (IDDLE—intrusion detection deep learning engine) incorporates advanced preprocessing techniques, including normalization, feature extraction, and categorical encoding. Empirical findings demonstrate that the proposed model outperforms existing state‐of‐the‐art approaches. This study underscores the capability of deep learning to enhance it in improving IDS performance, emphasizing the importance of continuous innovation and collaboration among security researchers, developers, and users to stay ahead of evolving cyberthreats. The findings underscore the significance of advanced feature extraction and hybrid detection strategies in developing robust intrusion detection systems.
Managing Obligation Delegation: Incentive Model and Experiment
Authors: Chen Liang , Zeng Cheng , Vidalis Stilianos , Jie Wei 
Published: December 2024

ABSTRACTObligations are essential part of security policies, which specify what actions a user is obliged to perform in the future. One interesting feature of obligations is unenforceable, that is, the system cannot guarantee that each obligation will be fulfilled. Indeed, obligations go unfulfilled for a variety of reasons. For example, a user may have a family emergency that leads her having little time to discharge assigned obligations. We argue that delegation of obligations can be regarded as a means of providing opportunity for obligations to be discharged. However, this opportunity will be wasted if users who received delegation do not fulfill the obligations eventually. In this paper, we propose a mechanism that incentivizes users to accept and fulfill obligations for others by rewarding users credits. The amount of credits that can be earned depends on their trust rating, which reflects precisely how diligent of individuals in fulfilling obligations in the past. Users are motivated to raise up their trust ratings by fulfilling obligations for others, in order to earn more credits in the future. To evaluate our approach, we develop a multiple‐agent system that simulates a number of different profiles for agents and run experiments for one‐hop delegation and cascaded delegation with those agents. The experiments show a rich set of results, one of which confirms that delegation with incentives achieves the best outcome in terms of the number of obligations being fulfilled. Also, we implemented the modified ‐greedy algorithm, one of the closely related existing works, in our experimental framework and compared its performance to our approach. The results show that our approach offers greater flexibility and efficiency, as well as a higher obligation fulfillment rate.
Molecular Heterogeneous Photocatalysts for Visible‐Light‐Driven CO2 Reduction
Authors: Qi Yuanyuan , Sun Hai , She Ping , Qin Jun‐Sheng , Rao Heng 
Published: December 2024

AbstractPhotoreduction of CO2 to high‐value chemical fuels presents an effective strategy to reduce reliance on fossil fuels and mitigate climate change. The development of a photocatalyst characterized by superior activity, high selectivity, and good stability is a critical issue for PCR. Molecular heterogeneous photocatalytic systems integrate the advantages of both homogeneous and heterogeneous catalysts, creating a synergistic enhancement effect that increases photocatalytic performance. This review summarizes recent advancements in molecular heterogeneous photocatalysts for CO2 reduction. Much of the discussion focuses on the types of molecular heterogeneous photocatalysts, and their photocatalytic performance in CO2 reduction is summarized. The synthesis strategies for molecular heterogeneous photocatalysts are thoroughly discussed. Finally, the challenges and future prospects of molecular heterogeneous photocatalysts for PCR are addressed.
Tubular ectasia of rete testis with obstructive azoospermia and infertility: A case report and literature review
Authors: Al Saeedi Ahmed , Majzoub Ahmad , ElBardisi Haitham , Arafa Mohamed 
Published: December 2024

AbstractBackgroundTubular ectasia of rete testis (TERT) is a benign condition due to dilatation of the rete testis as a consequence of multiple etiologies, including postinfectious, trauma, prior inguinal or scrotal surgeries, mechanical compression of extratesticular excretory ducts, and congenital malformation. TERT is a rare and underreported condition in the medical literature. Its association with infertility is lacking in the literature.Case PresentationWe present a rare case of primary infertility and obstructive azoospermia associated with TERT. We performed extensive literature regarding the possible etiologies, associations, clinical diagnosis, and different management options of this entity. Our patient is a 33‐year‐old man presented with primary infertility. He was found to have obstructive azoospermia. During scrotal exploration, the diagnosis of intratesticular obstruction due to TERT was made. The couple was advised to undergo intracytoplasmic sperm injection using testicular sperms. This is the third report of TERT associated with azoospermia.ConclusionTERT is usually asymptomatic but can occasionally be associated with pain or abnormal semen analysis, but further studies are required to confirm its effect on fertility. Fortunately, it has a benign course and typically does not require surgical intervention. It is vital to differentiate it from neoplastic conditions.
Investigation of Trace and Macro Element Contents in Commercial Cat Foods
Authors: Bilgiç Bengü , Tarhan Duygu , Ateş Fatma , Parkan Yaramiş Çağla , Koenhemsi Lora , Or Mehmet Erman 
Published: December 2024

ABSTRACTBackgroundNutritional profiles and guidelines are determined by various associations to ensure optimum health of cats and provide pet food manufacturers nutritional recommendations to ensure the well‐balanced and nutritionally adequate pet food.ObjectivesIt was aimed to determine some trace elements and macro minerals in prescription and non‐prescription dry cat foods and compare the contents with the suggested guidelines to evaluate the potential in‐compliance.MethodsA total of 96 dry cat foods were evaluated. Cu, Fe, Mn, Se, Zn, Mg, Ca and P concentrations were analysed in prescription dry cat foods developed for gastrointestinal diseases—GI (n = 18), renal diseases—R (n = 15), urinary diseases—U (n = 8), obesity—O (n = 10) and non‐prescription—N‐P (n = 45) foods from different flavours and brands by ICP‐OES.ResultsTen precent of the O group exceeded the upper limit, and 15.5% of the N‐P group failed to provide a nutritional minimum level. 4.44% of prescription foods were below the minimum recommended Mn level according to the European Pet Food Industry Federation (FEDIAF), and 66% of N‐P foods were below the minimum adult maintenance value recommended by the Association of American Feed Control Officials (AAFCO). One hundred percent non‐compliance with the guidelines in the GI and U groups was recorded. 22.2% of the GI group was lower, and 5.55% of the GI group was higher than the recommended levels by FEDIAF, which reflects the 27.7% in compliance. While all the prescription foods have 100% compliance with the determined minimum level of guidelines, 11.1% of N‐P foods were below the minimum recommended level. In compliance with the guidelines for Ca was noted in the O and U groups (10% and 12.5%, respectively). The P ratio in all prescription food groups except for GI was below the minimum level determined by AAFCO.ConclusionsIn all food groups, mean Cu, Fe, Mn, Mg, Ca and P concentrations were between the minimum and maximum limits recommended by FEDIAF. However, the mean Se level was above the upper legal limit in all food groups, and the Zn level was below the minimum recommended level in the N‐P food group. Besides the mean values, a high number of foods in each group show a broad in‐compliance with the guidelines.
Systematics of U-spin sum rules for systems with direct sums
Authors: Gavrilova Margarita , Schacht Stefan 
Published: December 2024

Abstract
A rich mathematical structure underlying flavor sum rules has been discovered recently. In this work, we extend these findings to systems with a direct sum of representations. We prove several results for the general case. We derive an algorithm that enables the determination of all U-spin amplitude sum rules at arbitrary order of the symmetry breaking for any system containing a direct sum of the representations 0 ⨁ 1. Potential applications are numerous and include, for example, higher order sum rules for CP-violating charm decays with an arbitrary number of final states.
Dark matter, CEνNS and neutrino new physics scrutinized by a statistical method in Xenon-based experiments
Authors: Tang Jian , Zhang Bing-Long 
Published: December 2024

Abstract
Dark matter direct detection experiments are approaching the neutrino floor, with a significant probability of measuring coherent elastic neutrino-nucleus scattering (CEνNS) and exploring potential neutrino-related new physics (νNP). In the present study, the simultaneous presence of dark matter and νNP is emphatically investigated, revealing a response similar to Standard Model neutrino backgrounds in Xenon-based dark matter experiments. Through analyses of three U(1) extension models, it is determined that dark matter signals can be differentiated from an excess or a depletion of neutrino contributions from νNP by applying a statistically defined distinction method to nuclear and electronic recoil spectra. Additionally, an investigation is conducted into how νNP affects the exclusion limits for spin-independent dark matter-nucleon interactions. The present findings could facilitate the identification of new physics in future dark matter experiments.
Type II orientifold flux vacua in 3D
Authors: Arboleya Álvaro , Guarino Adolfo , Morittu Matteo 
Published: December 2024

Abstract
We initiate a systematic study of type II orientifold flux vacua in three dimensions including gauge and metric fluxes, O-planes and D-branes. We derive simple flux models (we dub them RSTU-models) that admit a description in terms of gauged supergravities with half-maximal $$ \mathcal{N} $$
N
= 8 supersymmetry in three dimensions. As a landscape appetizer, we present various multi-parametric families of supersymmetric and non-supersymmetric AdS3 and Mkw3 vacua. Remarkably, negative masses turn out to be always absent in the spectrum of scalar fluctuations, thus making all the vacua perturbatively stable within half-maximal supergravity. We provide examples of non-supersymmetric type IIB AdS3 flux vacua which feature parametrically-controlled scale separation and come along with integer-valued conformal dimensions of the would-be dual CFT2 operators. We also comment on the implications of our results in light of the Swampland Program.
Der Diskurs um § 218 StGB seit der deutschen Wiedervereinigung – Geschichtliche, rechtliche und ethische Aspekte des Schwangerschaftsabbruchs
Authors: Dienerowitz Florian Michael 
Published: December 2024

Zusammenfassung
Der Schwangerschaftsabbruch ist bis heute Gegenstand harter Diskussionen. Der Beitrag gibt einen Überblick über geschichtliche, ethische und rechtliche Aspekte der Abtreibungsdebatte in Deutschland mit dem Fokus auf die Entwicklungen seit der Wiedervereinigung. Dabei wird die Reform der §§ 218–219 StGB aus dem Jahr 1995 mit der Beratungsregelung im Zentrum beleuchtet. Auch werden Folgen der medizinischen Indikation und juristische Spannungsfelder thematisiert. Vor dem Hintergrund einer sich abzeichnenden Transformation des Schwangerschaftsabbruchs von einem strafrechtlichen Verbot zu einem reproduktiven Recht werden abschließend verschiedene rechtsphilosophische Positionen und praktische Problemstellungen der aktuellen Debatte dargestellt.
Predicting terrain effects on blast waves: an artificial neural network approach
Authors: Leconte R. , Terrana S. , Giraldi L. 
Published: December 2024

Abstract
Large yield airbursts generate powerful outdoor blast waves. Over long propagation distances, the blast is significantly altered by the topographical relief. Usually, the terrain effects are quantified by running accurate but expensive hydrodynamics or CFD codes. We present an alternative approach based on artificial neural networks, which is applicable wherever the blast–relief interaction can be approximated by an axisymmetric configuration. A database of overpressures associated with a very large sample of the French topography is constructed by running a high-fidelity hydrodynamics code. The proposed neural networks then learn the relationship between the relief geometry and the ground overpressures. The predictive ability of the networks is assessed extensively over a test database for several error metrics.

$${97}{\%}$$

97
%

of the peak overpressure predictions can be considered accurate for most practical purposes, and the pressure impulse predictions are even more accurate. Finally, specific artificial neural networks able to estimate the model uncertainties are presented and their performances are discussed.