bluuebunny/crossref_metadata_2025_split huggingface dataset:
MambaLF: An Efficient Local Feature Extraction and Matching with State Space Model
Authors: Bian Houqin , Chen Qifei , Zhang Haolin , Qin Lunming , Xue Liang , Cui Haoyang , Wang Xi 
Published: November 2024

Abstract

Local feature extraction and matching has lately attracted increasing attention due to its wide application, especially in real-time automated systems. However, existing image matching methods struggle to balance the global receptive field and the efficient computation, which limits the practical applications. Recently, the State Space Model (SSM) has shown great potential in linear complexity and long-range dependency modeling. Therefore, in this paper, a local feature extraction and matching method using the SSM is proposed, which aims to achieve the tradeoff between global information extraction and model complexity. Firstly, a Local and Global Information Fusion (LGIF) block is developed to integrate local and global information and reduce model parameters through parallel SSM. Secondly, a backbone based on Euclidean group E(2) equivariant steerable Convolution (E2Conv) is designed to improve the model's robustness against geometric transformations. Finally, a self-supervised learning framework is constructed, which optimizes the ability of the network in local feature detection and description by combining four loss functions: keypoint localization loss, keypoint confidence score loss, descriptor triplet loss, and keypoint correspondence loss. Experimental results on public benchmark datasets Hpatches and RDNIM demonstrate that the proposed method has a significant advantage over existing methods in homography estimation tasks. Notably, our method outperforms the end-to-end dense matching method LoFTR by 6.11% under the 1-pixel error threshold on the Hpatches dataset, simultaneously with a smaller number of parameters and less average matching time.
AI/ML-based model to investigate the variability of the Fully Depleted SOI MOSFET
Authors: Chanda Manash , Roy Swarnil , Ghosh Sudipta , Debnath Papiya 
Published: November 2024

Abstract

A machine learning-based approach to predict the variability of Fully Depleted Silicon on Insulator (FDSOI) MOSFETs is demonstrated in this paper. Here, mainly On and OFF current (ION and IOFF), Threshold Voltage (VTH), and sub-threshold slope (SS) variability due to device geometries like channel length, body thickness, oxide thickness etc have been investigated in depth using AI/ML framework. The Random Forest Regression (RFR) model has demonstrated substantial effectiveness in evaluating the device characteristics. SILVACO ATLAS-based device simulation produces a dataset of 14627 different readings. Out of different regression and classification methods, the RFR-ML model attains maximum accuracy. The efficacy of the ML frameworks has been measured using mean square error (RMSE) (~ 0.0001), R2 score (~ 0.9986), and accuracy (~ 99.60%) as figures of merit, demonstrating its superiority over state-of-the-art literature. In addition to offering higher accuracy, the model can predict data within seconds, in contrast to the traditional ways. This rapid and precise design approach is highly beneficial for the current VLSI industries in terms of accuracy and precision and achieving scalability and cost-effectiveness.
Efficient FPGA Implementation of the Twofish Encryption Algorithm for High-Security Applications
Authors: Amara Wided , Ghodhbani Refka , Ghayoula Ridha , Smida Amor , Saidani Taoufik , Fattahi Jaouhar , Said Yahia 
Published: November 2024

Abstract

This paper presents an efficient implementation of the Twofish encryption algorithm on FPGA chips, with a specific focus on its utilization in applications requiring a high level of security. The Twofish algorithm is renowned for its robust security and efficiency, making it suitable for a wide range of security-sensitive applications. The primary objective of this study is to demonstrate how Twofish 1 can be successfully deployed on FPGAs, capitalizing on their parallelism and cus-tomization capabilities to achieve high encryption performance while preserving data security. We provide detailed insights into the hardware design and specific optimizations required to fully harness FPGA technology. Additionally, this study compares the implementation of the Twofish encryption algorithm across four FPGA families: Artix-7, Kintex, Stratix, and Vitex. The efficiency of this FPGA implementation is assessed through performance tests, highlighting its ability to deliver fast and secure encryption and decryption processes. Furthermore, we explore the practical applications of this solution, including securing communications , safeguarding sensitive data, and other scenarios where data security is paramount. This study serves as a valuable resource for researchers, engineers, and practitioners working in fields where data security is a top priority, offering a detailed insight into the successful implementation of Twofish on FPGAs and its potential applications.
“When You Pop, You Know Everybody That Rolls”: Social and Contextual Pragmatism in Substance Initiation and Transition
Authors: Sibley Adams L. , Colston David C. , Joniak-Grant Elizabeth , Mortensen Hillary L. , Swilley-Martinez Monica E. , Pence Brian W. , Ranapurwala Shabbar I. 
Published: November 2024

Abstract

Background
The overdose epidemic is presently driven by polydrug use, sparking renewed interest in why people initiate use of certain drugs or drug combinations. Current research privileges the physiological ends of consumption, often ignoring the social and environmental context of use. Framed by social cognitive theory, the purpose of this study was to characterize factors precipitating substance initiation, transition, and combination beyond the immediate effects of the substance(s).
Methods
We conducted 30 semi-structured interviews with people who use drugs across North Carolina, exploring substance use history and risk and protective factors of polydrug use. Participants also completed a visual timeline activity. We used a staged analytic approach, beginning with deductive Structural Coding and ending with inductive Reflexive Thematic Analysis at both the transcript and excerpt levels.
Results
We conceptualized substance transitions as pragmatic processes within environments of constraints and opportunities. Socially, drug choices were often driven by a desire for interpersonal bonding, pressure to assimilate to practices in one’s social circles, and the ubiquity of use within one’s milieu. Transitions were also shaped by environmental context – which substances are locally available, the logistical convenience of competing substances, and the material costs of use.
Conclusions
Beyond the desire for new or enhanced physiological effects, substance transitions serve social and practical functions, like facilitating emotional closeness and ensuring stable supply. Interventions to reduce the risks of use should account for these contextual factors – for instance, by educating on strategies to avoid normative pressures and by promoting safe, affordable, and accessible supply.
Synthesis of Chromene-linked Bis-indole Derivatives as Selective Tumor-associated Carbonic Anhydrase IX Inhibitors
Authors: Singh Priti , Nerella Sridhar Goud , Swain Baijayantimala , Angeli Andrea , Kausar Samreen , Ullah Qasim , Supuran Claudiu T. , Arifuddin Mohammed 
Published: November 2024

Background:
Sulfonamide derivatives are well-reported hCA IX inhibitors; however, they inhibit all
types of hCA without any selectivity, leading to severe adverse effects. Hence, developing a novel nonsulfonamide
class of tumor-associated hCA IX inhibitors through non-classical inhibition may provide greater
selectivity and better pharmacokinetics.

Objective:
The objective of this study was to develop non-sulfonamide derivatives as potential human carbonic
anhydrase (hCA) inhibitors and develop a new series of chromene-linked bis-indole derivatives.

Methods:
We synthesized and characterized the chromene-linked bis-indole derivatives and further evaluated
them against four hCA isoforms, i.e., hCA I, hCA II, hCA IX, and hCA XII, and determined the ADMET parameters
by the In-silico method.

Results:
Most of the compounds showed significantly greater affinity and selectivity towards the tumorassociated
hCA IX over other hCA isoforms within the lower micromolar to submicromolar range. In particular,
the bromo-substituted bis-indole derivative 6t showed an excellent inhibition of hCA IX isoform with an affinity
(Ki) of 2.61 μM. In contrast, the cyano group substituted bis-indole derivative 6s and also displayed a strong
inhibition of hCA IX isoform with an affinity (Ki) of 2.73 μM. Many other potential candidates, including 6g, 6i,
6k, 6m, 6o, 6p, and 6r, showed higher affinity at tumor-associated hCA IX with lower than 10 μM compared to
other hCA isoforms.

Conclusion:
Therefore, the chromene-linked bis-indole derivatives can serve as a novel non-sulfonamide class of
tumor-associated hCA IX inhibitors.
Enhanced Computer-aided Digital Imaging Technique for Predictions in Breast Cancer
Authors: Nagdeote Sushma , Prabhu Sapna , Chaudhari Jayashri 
Published: November 2024

Background:
Breast cancer (BRCA) is the most frequently diagnosed cancer in
women, with a rise in occurrences and fatalities. The field of BRCA prediction and diagnosis has
witnessed significant advancements in recent years, particularly emphasizing enhanced
computer-aided digital imaging techniques, and has emerged as a powerful ally in the prediction
of BRCA through histopathology image analysis. A number of approaches have been suggested
in recent years for the categorization of histopathology BRCA images into benign and malignant
as it examines the images at cellular level. The histopathology slides must be manually analysed
which is time consuming and tiresome and is prone to human error. Additionally, different
laboratories occasionally have different interpretation of these images.

Methods:
This paper focuses on implementing a framework for Computer-Aided digital imaging
technique that can serve as a decision support. With recent advancements in computing power
the analysis of BRCA histopathology image samples has become easier. Stain normalization
(SN), segmentation, feature extraction and classification are the steps to categorize the cancer
into benign and malignant. Nuclei segmentation is a crucial step that needs to be taken into
account in order to establish malignancy. These are considered essential for early diagnosis of
BRCA. A unique method proposed for BRCA prediction is put forward. To maximize the
prediction accuracy, the suggested method is integrated with machine learning (ML) techniques
and clinical data is used to evaluate the suggested approach.

Results:
This strategy is adaptable to many cancer types and imaging techniques. The suggested
technique is applied to clinical data and is integrated with logistic regression and K-Nearest
Neighbor resulting in accuracy of 92.10% and 86.89% respectively for BRCA histopathology
images.

Conclusion:
The objective of this work is to validate the proposed model which takes input as
feature pattern for a given label. For the collected clinical samples, the model is able to classify
the input as benign or malignant. The proposed model worked efficiently for different BC
datasets and performed classification task successfully. Integrating mathematical model (MM)
with ML model for interpreting histopathology BRCA is a potential area of research in the field
of digital pathology.
Identifying and Estimating Frailty Phenotypes by Vocal Biomarkers: Cross-Sectional Study
Authors: Lin Yu-Chun , Yan Huang-Ting , Lin Chih-Hsueh , Chang Hen-Hong 
Published: November 2024

Background
Researchers have developed a variety of indices to assess frailty. Recent research indicates that the human voice reflects frailty status. Frailty phenotypes are seldom discussed in the literature on the aging voice.

Objective
This study aims to examine potential phenotypes of frail older adults and determine their correlation with vocal biomarkers.

Methods
Participants aged ≥60 years who visited the geriatric outpatient clinic of a teaching hospital in central Taiwan between 2020 and 2021 were recruited. We identified 4 frailty phenotypes: energy-based frailty, sarcopenia-based frailty, hybrid-based frailty–energy, and hybrid-based frailty–sarcopenia. Participants were asked to pronounce a sustained vowel “/a/” for approximately 1 second. The speech signals were digitized and analyzed. Four voice parameters—the average number of zero crossings (A1), variations in local peaks and valleys (A2), variations in first and second formant frequencies (A3), and spectral energy ratio (A4)—were used for analyzing changes in voice. Logistic regression was used to elucidate the prediction model.

Results
Among 277 older adults, an increase in A1 values was associated with a lower likelihood of energy-based frailty (odds ratio [OR] 0.81, 95% CI 0.68-0.96), whereas an increase in A2 values resulted in a higher likelihood of sarcopenia-based frailty (OR 1.34, 95% CI 1.18-1.52). Respondents with larger A3 and A4 values had a higher likelihood of hybrid-based frailty–sarcopenia (OR 1.03, 95% CI 1.002-1.06) and hybrid-based frailty–energy (OR 1.43, 95% CI 1.02-2.01), respectively.

Conclusions
Vocal biomarkers might be potentially useful in estimating frailty phenotypes. Clinicians can use 2 crucial acoustic parameters, namely A1 and A2, to diagnose a frailty phenotype that is associated with insufficient energy or reduced muscle function. The assessment of A3 and A4 involves a complex frailty phenotype.
Do Public Financial Statements Influence Private Equity and Venture Capital Financing?
Authors: Baik Brian K. , Berfeld Natalie , Verdi Rodrigo S. 
Published: November 2024

ABSTRACT
We study whether the availability of public audited financial statements influences the probability of private firms receiving private firm equity financing. Using a setting in the EU with plausibly exogenous variability in the extent to which private firms issue public financial reports, we find that private firms subject to public reporting requirements have a higher probability of obtaining private equity (PE) financing. In addition, we show that the increase in PE financing occurs in industries in which PE funds have prior experience. Last, we show that our findings also extend to other forms of private firm financing such as private firm acquisitions and venture capital. Our evidence highlights the importance of public financial statements in the decision-making of PE investors, an important and understudied segment of the private firm financing market.
Data Availability: Data are available from the public sources cited in the text.
JEL Classifications: M41; M2; G24; G34.
Cooperative learning as a methodological, inspiration in the implementation of a science club
Authors:   University States of Rio Grande do Sul, Brazil, Caroline Ragiuk de Oliveira Daysi 
Published: November 2024

The purpose of this study is to introduce a Science Club to develop activities related to the socio-environmental area in a state school in the city of Guaíba, in the state of Rio Grande do Sul / Brazil. These activities cover the reoccupation of the school’s physical spaces, with the aim of making it more sustainable, and the formation of more critical and autonomous students, actively involved in the construction of their knowledge and learning. To achieve this goal, the Science Club was chosen as the means of conducting this research. The methodology for developing the Club’s activities will incorporate elements of Cooperative Learning, since it is believed that this approach can be a significant alternative for addressing current educational challenges, promoting the renewal of teaching and the teacher-student relationship in the classroom. Although the structure of the Science Club is distinct from a traditional classroom, the dissemination and emphasis on the benefits of Cooperative Learning are seen as potential catalysts for new research in the classroom. The research was conducted during Science Club meetings, using Cooperative Learning as a methodology, with a focus on socio-environmental issues pertinent to sustainability. The results indicate that the participants experienced contentment, academic improvements, attitudinal changes and intellectual development after joining and participating in the Science Club activities. However, issues such as participant turnover over the course of the meetings were identified as points that merit review in future research. Thus, despite these challenges, it is believed that the research was able to provide relevant reflections on the subject, potentially stimulating new studies in the area. Finally, as an educational development, a MOOC course is proposed to help train new coordinators and disseminate Science Clubs throughout the country.
At every poit a tale – ethnomathematics in basic education
Authors:   University States of Rio Grande do Sul, Brazil, Karine Silva 
Published: November 2024

The result of this project is a descriptive account of the experience developed around Ethnomathematics with the enculturation of the practice of crochet and the integration of the management movement with computational thinking. This report is developed with objectivity and theoretical contributions focused on a general understanding of the keywords; Ethnomathematics, Computational Thinking, Abstraction, Decomposition, Generalization, Algorithm and Crochet. Taking account of these literacies by embracing a transdisciplinary methodology. With the movement to carry out a project that makes a positive and relevant contribution to my area of expertise. Focusing on the search for considerations and reflections that will bring about biopsychological maturation in the student to develop the subject act, leading them towards the formation of a cognizant subject with their thinking, learning and doing. On their lived experiences with the interactions established in the contexts of observations, perceptions, and constructions. Finally, the aim is to report non-biased observations, valuing ethics and critical thinking.
Copper oxide–ferric oxide nanocomposite: Synthesis, characterization, and antibacterial and antifungal properties
Authors: Elnouby Mohamed , Nabil Marwa , Al-Askar Abdulaziz A. , Kowalczewski Przemysław , Behiry Said , Abdelkhalek Ahmed 
Published: November 2024

Abstract
Recently, copper oxide–ferric oxide nanocomposites (CuO/Fe2O3-NCs) have gained popularity and are widely employed in various applications. However, their effectiveness against phytopathogens has not been studied yet. This study investigates the synthesis and characterization of CuO/Fe2O3-NCs using the hydrothermal technique. X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared spectroscopy (FTIR) were used to characterize the produced nanocomposite (NC). EDX and TEM analyses revealed the presence of Cu, Fe, and O elements. The NC had a polygonal shape with sides around 12 nm, spherical CuO particles of 7–10 nm, and plate-like Fe2O3. XRD measurements confirmed the crystal and hexagonal structures of CuO and Fe2O3. The XRD patterns of CuO/Fe2O3 showed the characteristic peaks of (−111) and (004) reflections for CuO at 35.69° and 37.73°. The FTIR spectra showed characteristic lines at 525 and 567 cm−1 for the Cu–O bond and Fe–O stretching modes of Fe2O3, respectively. The antifungal activity of CuO/Fe2O3-NCs showed significant growth inhibition of Fusarium oxysporum, Rhizoctonia solani, and Botrytis cinerea by up to 71, 50, and 81%, respectively, at 100 µg/mL. At 50 µg/mL, the antibacterial test revealed inhibition zones of 12.33 mm for Pectobacterium carotovorum, 9.33 mm for Streptomyces scabies, 10.67 mm for Pectobacterium atrosepticum, and 14.67 mm for Ralstonia solanacearum. The results show that CuO/Fe2O3-NCs can efficiently suppress the growth of various fungal and bacterial strains, making them potential antimicrobial agents against phytopathogenic microorganisms.
Imaging-tracked progression of primary leptomeningeal gliomatosis: A case report
Authors: Nomura Noriaki , Nagasaka Shohei , Suzuki Kohei , Yamamoto Junkoh 
Published: November 2024

Background:
Primary leptomeningeal gliomatosis (PLG) is a rare neoplasm characterized by the diffuse spread of glial tumor cells throughout the leptomeninges without any evidence of a primary tumor source in the brain or spinal cord parenchyma. Here, we present a case of PLG potentially linked to prior interventional radiotherapy.

Case Description:
The patient was a 75-year-old woman with a history of interventional radiology for a left internal carotid cavernous sinus fistula 13 years before presentation. Routine follow-up fluid-attenuated inversion recovery magnetic resonance imaging revealed a high intensity region spreading from the deep white matter of the subventricular zone (SVZ) to the insular cortex and medial temporal lobe. Subsequently, contrast-enhanced T1-weighted imaging revealed an enhanced effect consistent with extensive leptomeninges extending from the basilar cistern to the left Sylvian fissure. The patient underwent surgery, and subsequently histological examination of extracted tissue revealed a glioblastoma (GBM). Despite postoperative concurrent chemoradiotherapy and adjuvant temozolomide chemotherapy, the tumor increased in size, and the patient died 2 months postoperatively.

Conclusion:
This case highlights the importance of careful follow-up and early therapeutic intervention in PLG, as it can be difficult to diagnose leptomeningeal lesions alone. This case also raises the possibility of radiation-induced GBM, and the criteria for diagnosis were fully met. The progression of PLG from the SVZ to the leptomeningeal site was tracked using imaging, providing valuable insights into the pattern of spread of this rare condition.
Pelaksanaan Pembelajaran Berdiferensiasi Pada Mata Pelajaran Ilmu Pengetahuan Alam dan Sosial (IPAS) Kelas IV SDN Embung Karung
Authors: Siswani Siswani , Sudirman Sudirman , Angga Prayogi Dwina 
Published: November 2024

Pembelajaran berdiferensiasi adalah pendekatan yang menyesuaikan konten, proses, dan produk pembelajaran agar selaras dengan kebutuhan, minat, dan kemampuan siswa yang beragam. Penelitian ini bertujuan untuk menghasilkan pemahaman mendalam mengenai persiapan, pelaksanaan, dan evaluasi pembelajaran berdiferensiasi yang dilakukan oleh guru. Metode penelitian yang digunkaan adalah metode kualitatif dengan desain deskriptif yang melibatkan guru dan siswa kelas IV SDN Embung Karung. Metode pengumpulan data dikakukan melalui wawancara, observasi, dan dokumentasi. Teknik analisis data yang digunakan yakni; pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan. Keabsahan data diuji melalui triangulasi teknik dan sumber. Hasil penelitian menunjukkan bahwa Guru melaksanakan pembelajaran berdiferensiasi melalui persiapan, pelaksanaan hingga pada tahap evaluasi dengan baik. Guru melakukan persiapan awal dengan melakukan tes diagnostik kemudian menyusun strategi pembelajaran yang sesuai. Diferensiasi konten, diferensiasi proses, dan diferensiasi produk juga dilakukan oleh guru berdasarkan pemetaan kebutuhan siswa. Faktor pendukung berasal dari siswa yang antusias, suasana belajar yang menyenangkan dan sarana prasarana yang memadai. Faktor penghambat utama dalam pembelajaran ini membutuhkan waktu lebih lama. Sehingga, guru dan kepala sekolah melakukan refleksi bersama di setiap minggunya. Dapat disimpulkan bahwa guru di SDN Embung Karung berhasil menerapkan pembelajaran berdiferensiasi melalui persiapan, pelaksanaan, dan evaluasi yang baik, meskipun terkendala waktu yang lebih lama dan memerlukan refleksi mingguan untuk perbaikan.