bluuebunny/crossref_metadata_2025 huggingface dataset:
Distress respiratorio del neonato: dall'Apgar all'ecografia polmonare
Authors: Carnazzo Salvatore Michele , Balconara Desiree , Calareso Lara , Pecora Giulia , Papale Maria , Parisi Giuseppe Fabio , Leonardi Salvatore
Published: January 2025

The use ultrasound for diagnosing lung diseases is relatively recent: it was around the 1960s when the first observations of the utility of chest ultrasound in pulmonary diseases were made. This article examines the findings of lung ultrasound in newborns from the first minutes after birth, represented by artifacts such as horizontal lines (A-lines) and vertical lines (B-lines). The application of ultrasound in diagnosing specific neonatal pulmonary conditions - such as meconium aspiration syndrome, transient tachypnea of the newborn (TTN), hyaline membrane disease (HMD) and pneumothorax - can provide valuable information. According to evidences, lung ultrasound has been proven to be an effective method for diagnosing neonatal pulmonary conditions. Its reproducibility, portability and lack of ionizing radiation, along with sensitivity and specificity comparable to or exceeding traditional diagnostic methods, highlight its potential as a tool in neonatal care. The establishment of validated ultrasound scoring systems could further complement the Apgar score in routine neonatal assessments for determining pulmonary function at birth.
Genç Erişkinlerde Pre-Hipertansiyon, Sedanter Yaşam Tarzı Ve Obezite Prevalansının İncelenmesi
Authors: Shalabı Sumaya , Uyaroğlu Mehmet Burak , Aktolgalı İlmiye , Taghvatalab Seyedehatefeh , Kuzu Betül , Pehlivan Esra
Published: January 2025

ÖZ
Amaç: Çalışmamızın amacı genç erişkinlerde pre-hipertansiyon (pre-HT) ve ilişkili faktörlerin prevalansının belirlenmesidir.
Gereç ve Yöntemler: Araştırmamızın örneklemini 18-25 yaş arası 382 kişi oluşturmaktadır. Veriler Aralık 2022 -Nisan 2023 tarihleri arasında araştırmacılar tarafından hazırlanan vücut kitle indeksi, skinfold deri altı yağ kalınlığı ölçümü ve sistolik (SKB) ve diyastolik (DKB) kan basınç ölçümlerini içeren değerlendirme formu, Uluslararası Fiziksel Aktivite Anketi (UFAA), Pittsburg Uyku Kalitesi İndeksi (PUKİ), Serbest Zaman Egzersiz Anketi (SZEA), Beck Depresyon Envanteri (BDE), Algılanan Stres Ölçeği (ASÖ) ve Hollanda Yeme Davranışı Anketinin (HYDA) kullanılarak toplanmıştır.
Bulgular: Katılımcıların %21,7'si pre-HT olarak sınıflandırılmıştır. Ortalama SKB/DKB değerleri 121,75 ± 14,92 / 78,44 ± 11,35 mmHg olarak belirlenmiştir. UFAA skoruna göre katılımcıların %45'i düşük derecede aktiftir. SZEA skoruna göre katılımcıların %33,8’ü yüksek şiddetli, %27,2’si orta şiddetli ve %39,0’u hafif şiddetli egzersize katıldığı bulunmuştur. BDE skoruna göre katılımcıların %52,62'sinde depresyon saptanmıştır. PUKİ skoru ortalaması 6,01 ± 2,9, HYDA skor ortalaması 85,05 ± 17,80, ASÖ ortalaması 19,74 ± 5,04 ve VKİ ortalaması ise 22,65 ± 3,94 kg/m2 olarak kaydedilmiştir. Skinfold biceps / triceps / abdomen ölçümleri sırasıyla 5,22 ± 5,19 / 7,84 ± 5,90 / 11,28 ± 6,86 mm olarak belirlenmiştir.
Sonuç: Çalışmamızın sonuçları, genç erişkinlerde görülen pre-HT’nin, önemli bir sağlık sorunu haline gelmekte olduğunu ve inaktivite, depresyon gibi birçok olumsuz faktörle yakından ilişkili olduğunu göstermektedir. Katılımcıların sedanter yaşam sürdüğü ve uyku kalitelerinin kötü olduğu bulunmuştur. Katılımcıların normal ağırlıkta ve deri altı yağ kalınlığı ölçümleri normal değerler arasında olduğu belirlenmiştir. Sedanter yaşamının pre-HT ile ilişkisinin farkındalığını arttıracak faaliyetlerin yapılması önemli görülmektedir.
Patoloji Tanısı Adenomyozis ya da Leiomyoma Uteri Olan Histerektomize Olgularda, Klinik Semptomatolojinin ve Transvajinal Ultrasonografik Değerlendirmenin Retrospektif Korelasyonu
Authors: Fırtına Tuncer Serap
Published: January 2025

ÖZ
Amaç: Adenomyozis ve leiomyoma uteri hastalarında semptomların ve ultrasonografi bulgularının doğru kombinasyonu sonucunda tanının doğru konulması tedaviye yön gösterecektir. Biz bu amaçla, çalışmamızda klinik semptomların ve Transvajinal Ultrasonografi (TVUSG) bulgularının, patoloji sonuçları ile karşılaştırılması amaçlanmıştır.
Gereç ve Yöntemler: Akdeniz Üniversitesi Tıp Fakültesi Kadın Hastalıkları ve Doğum Kliniğinde, benign nedenlerle histerektomi operasyonu olan ve sitopatolojik inceleme sonucu, leiomyoma uteri ve/veya adenomyozis olarak rapor edilen olgular retrospektif olarak değerlendirilmiştir.
Bulgular: İki yüz bir olgunun histerektomi materyalinin incelenmesinde; 94 olguda (%46,8) leiomyoma uteri, 33 olguda adenomyozis (%16,4), 37 olguda (%18,4) leiomyoma uteri+adenomyozis saptanmıştır. Parite sayısı (mean±Sd: 3,36±1,912 ve 2,07±1,065; p
Bank capital requirements and risk-taking: evidence from Basel III
Authors: Anguren Rebeca , Jiménez Gabriel , Peydró José-Luis
Published: January 2025

We study the short-term effects of both tighter and looser bank capital requirements on bank risk-taking in a crisis period. We exploit credit register data matched with firm and bank level data in conjunction with changes in capital requirements stemming from Basel III, including the introduction of a SME supporting bank capital factor in the European Union. We find that tighter capital requirements reduce the supply of bank credit to firms, while looser capital requirements mitigate the credit supply effects of increasing capital. Importantly, at the loan level (credit supply), banks more affected by capital requirements temporarily change less the supply of credit to riskier than to safer firms, and these asymmetric effects occur for both the tightening and the loosening of bank capital requirements. Finally, these effects are also important at the firm-level for total credit availability and for firm survival. Interestingly, our results suggest that those banks most impacted by the tighter Basel III capital requirements prioritize credit among ex-ante riskier firms to avoid their closure, consistent with loan evergreening.
Resilience of Antibiotic Usage and Vital Signs in COVID-19 Patients: A Post-Pandemic Analysis in Pakistan
Authors: Muhammad Islam  , Imtiaz Ahmed  , Muhammad Shehzad  , Kashif Nadeem
Published: January 2025

COVID-19 has had a profound global impact, wreaking havoc on economies and causing a significant loss of lives. Pakistan has also faced severe consequences from the pandemic. To combat the virus, antibiotics such as Azithromycin, Ceftriaxone, Cefixime, Clarithromycin, Levofloxacin, Moxifloxacin, Meropenem, Tazobactam, Tienam, and Vancomycin have been administered to COVID-19 patients. Various vital signs such as temperature, respiratory rate, oxygen saturation, pulse rate, and blood pressure have been closely linked to the symptoms of COVID-19. This study examines the impact of antibiotic usage and vital signs on the survival and mortality of COVID-19 patients using a dataset collected from four major hospitals in Pakistan. The analytical approach includes descriptive analysis, a t-test, and a chi-square test of independence. The originality of this research lies in its comprehensive exploration of the relationship between specific antibiotics and the monitoring of vital signs in the context of COVID-19. It offers a systematic analysis of existing information to derive meaningful insights. Azithromycin emerged as the predominant antibiotic used to treat COVID-19 patients in Pakistan, with other antibiotics showing varying usage patterns. Strong, significant associations were identified between Azithromycin, Tazobactam (Tanzo), and the binary response variable (died/alive). Regarding vital signs, significant relationships were observed for patient age, systolic blood pressure, and diastolic blood pressure when compared to the response variable. This study's findings underscore the importance of Azithromycin and Tazobactam in COVID-19 treatment and highlight the role of vital signs in predicting patient outcomes.
Date Palm Cultivation, Consumption and Export: Current Status and Future Challenges - A Review
Authors: Solangi Najamuddin , Abul-Soad Adel Ahmed , Jatoi Mushtaque Ahmed , Mirani Abdul Aziz , Markhand Ghulam Sarwar
Published: January 2025

The date palm is an important horticultural crop, cultivated in all provinces of Pakistan, particularly in Khairpur and Sukkur districts of Sindh. Khairpur is biodiversity center for dates, with cultivation of elite commercially important cultivars like Aseel (predominant cultivar), Karbalain, Otakin, Kurh, Dedhi, Dhakki, Kashuwari, Kupro and Fasli etc. Aseel dates are consumed locally as table dates as well as exported to Germany, USA, Canada and Japan in the form of pitted and chopped dates by the different dates factories established in the country. Most of the Aseel dates are exported to India as yellow and brown chhuhara (boiled dried dates). Dates are also used to make different value-added products (date syrup, date paste, date pickles, date bars, date powder, date extracts, date jam). Poor pre- and post-harvest management and poor cultural practices are major obstacles in obtaining dates of international standards. Currently, date palms in Sindh face the effects of natural disasters, i.e., floods, post-flood diseases (sudden decline disease in the crown caused by Fusarium solani), and pests, such as, Red Palm Weevil (Rhynchophorus ferrugineus). Multiplication of elite and rare date palm cultivars by tissue culture technique is pre-requisite for large-scale cultivation which may fill the gap of destruction of indigenous date palm cultivars. Currently, thousands of grown-up trees of date palm have been destroyed with post-flood sudden decline disease. Proper pre- and post-harvest management and cultural practices and in addition cultivation of disease resistant cultivars are need of the time. In this review article, different aspects of date palm problems have been discussed and their solutions are suggested to benefit the date palm growers who can manage the crop in a better way.
Investigation of Paternally Inherited Allele Mutation at Short Tandem Repeat (STR) Locus D7S820 Leading to Parent-Child Mismatch
Authors: Abdul Hameed  , Hafsah Muhammad  , Muhammad Ajmal  , Siddique Nayyer
Published: January 2025

During paternity investigation with Identifiler™ set of autosomal Short Tandem Repeats (STRs), a genetic mismatch was observed with D7S820 between the disputed father and child. The genotype at this locus in the disputed father, mother and child was 10/10, 11/12 and 11/11, respectively.  The combined paternity index and probability of paternity after including the mutation in the calculation were 7.6×107 and 0.9998, respectively. Both values supported the suspicious father as the biological father of the child. Further analysis of Y-STRs revealed matching of all the alleles of the child with that of the suspicious father. It suggested that the mismatch at the D7S820 locus might be a case of mutation. DNA sequencing of D7S820 PCR products of the child and both the parents helped in determining that the child inherited the expanded repeat of the paternal allele 10, which was transmitted as allele 11 in the child from the suspicious father.
A Morphometric Study of Epidermal Appendages in Commonly Existed Angiosperms in Faisalabad, Pakistan
Authors: Farooq Ahmad  , Hafiza Komal Naeem  , Maheen Iqbal  , Farah Maqsood  , Samia Kanwal  , Sehrish Imran  , Urooj Fatima
Published: January 2025

Epidermal appendages are mono-cellular or multicellular, root hairs, and trichomes that grow on an epidermis. Commonly found members of angiospermic families were selected focusing on their presence or absence, position, and types of surface appendages (colliculate, hairy, wart and papil). Trichomes types (simple hairs, tubercle-based hair minerals) concrete, secretory hairs, vesicular hairs, moniliform hairs, and dendritic hairs were also considered. The epidermal appendages were studied in thirty species of angiospermic families collected from Botanical Garden University of Agriculture Faisalabad, Pakistan, by using light microscope. The data was then subjected to ANOVA and cluster analysis to investigate relationship among different angiosperm species. The Maximum cell length/width on abaxial epidermis is recorded in Strelitzia reginae Regel & Koch and Terminalia bellirica (Gaertn.) Roxb, which is closely related to the Alpinia allughas Retz., and Crinum asiaticum L.. Minimum cell length on upper epidermis is recorded in Ficus lyrata which is closely related to the Colocasia esculentum. Maximum number of stomata is observed in Kigela africana.  Minimum number of stomata is recorded in Terminalia bellirica and Ficus lyrata. Maximum number of stomata on adaxial epidermis is recorded in Pentas lanceolata. Minimum number of stomata is recorded in Ravenala medaghas cariensis.  On adaxial surface, the maximum number of trichomes is recorded in Terminallia bellirica and Kigela Africana, and the minimum number is recorded in Campsis rodicum. The present study emphasizes the distinctive and similar features of those epidermal appendages. These o
Accesibilidad de Recursos Educativos Digitales en libros de texto de Educación Básica en Chile
Authors: Ortiz Ruiz Yorka Tatiana , Casis Raposo Luis Marcelo
Published: January 2025

El presente artículo muestra los resultados obtenidos con apoyo de un Semillero de Investigación ejecutado por estudiantes de la carrera de Pedagogía en Educación Básica, mención Lenguaje y Matemática, de la Universidad de Los Lagos. A través de una metodología cualitativa y mediante el análisis de contenido, se estudió la accesibilidad de los recursos educativos digitales propuestos en los libros de texto del estudiante de educación básica, en las asignaturas de Matemática, Lenguaje, Historia y Ciencias en el año 2022.
Se analizaron aspectos clave en el uso de estos recursos, como la accesibilidad, disponibilidad o vigencia; las vías de ingreso por parte del estudiantado a estos recursos; estrategias de trabajo propuestas; aciertos y errores detectados en las diferentes propuestas por especialidad y tipo de recurso. Los resultados ponen de manifiesto graves deficiencias en la accesibilidad de los recursos tecnológicos; poca o nula actualización de los recursos; y la inexistencia de lineamientos claros y precisos en cuanto a la integración eficiente de estos recursos en el proceso de enseñanza y aprendizaje.
Áreas de demanda y competencias de ingenieros en PYMES del sector textil salvadoreño
Authors: Grande Sánchez Alfredo de Jesús , Quintana Pacheco Patricia Betsabé
Published: January 2025

Este estudio se enfoca en las áreas de demanda y competencias requeridas para ingenieros textiles e industriales en el sector textil de El Salvador. La metodología empleada combinó enfoques cualitativos y cuantitativos para comprender a fondo las demandas y competencias necesarias para estos profesionales, con un enfoque en la región occidental del país, una zona económicamente relevante por la actividad de la industria textil. Se recopilaron datos a través de encuestas electrónicas y entrevistas con ejecutivos y profesionales del sector de forma voluntaria. Las áreas de demanda de estos profesionales son operaciones generales, administración, procesos, desarrollo de productos y ventas. Las competencias blandas y duras esenciales para tener éxito en el sector incluyen la integridad, el liderazgo, la comunicación efectiva, las habilidades de planificación y organización, entre otras.
Adopción de herramientas de inteligencia artificial en la Universidad Francisco Gavidia
Authors: Humberstone Morales James Edward
Published: January 2025

La inteligencia artificial está transformando radicalmente la Educación Superior. No obstante, su adopción plantea desafíos significativos relacionados con la privacidad de los datos y la ética. El presente estudio corresponde a una investigación descriptiva con enfoque cuantitativo sobre la adopción de herramientas de inteligencia artificial por parte de los estudiantes y los docentes de la Universidad Francisco Gavidia. Los datos se recolectaron en un solo punto en el tiempo a través de cuestionarios distribuidos tanto a estudiantes como a docentes. La muestra incluyó a quienes respondieron voluntariamente los cuestionarios durante el período establecido. Los resultados revelaron que existe una falta de comunicación entre docentes y estudiantes respecto a las políticas de uso de la inteligencia artificial en el aula. Además, se identificó un potencial aún no explotado en el uso de estas herramientas en el ámbito educativo, ya que el 50 % de los estudiantes y el 38 % de los docentes manifestaron no utilizarlas. Ambos grupos mostraron un gran interés en fortalecer sus competencias en el uso adecuado de estas herramientas; sin embargo, sus estrategias de aprendizaje presentaron algunas diferencias notables. Por lo tanto, resulta relevante comprender estas diferencias para diseñar programas de capacitación y recursos educativos más efectivos.
Antonio José Coelho: un brasileño en los orígenes de la educación salvadoreña
Authors: Cañas Dinarte Carlos
Published: January 2025

En la tercera década del siglo XIX, el gobierno salvadoreño tomó la decisión de implantar el sistema lancasteriano de enseñanza mutua o memorística entre la población de la ciudad de San Salvador. En esa labor jugó un papel esencial un educador originario de Brasil, de cuya vida y obra pedagógica se sabe muy poco. Un aspecto muy interesante fue el rechazo social que Coelho generó debido a su cultura sincrética de origen, su cuestionamiento de las tradiciones locales y su escasa adhesión al catolicismo predominante. Además, su trabajo en cuanto a la aclimatación de especies vegetales también es un aspecto muy desconocido de su labor en el territorio nacional. En ese sentido, sus experimentaciones con el café y el mango resultarían llamativas para desarrollar más estudios acerca de la historia de la botánica aplicada en El Salvador, mucho antes de los trabajos pioneros de científicos como los de los doctores David Joaquín Guzmán Martorell, Darío González Guerra y otros. Este texto -investigado y escrito de manera discontinua entre 2001 y 2024- pretende sentar las bases para una investigación mayor acerca del desarrollo educativo nacional en las primeras décadas de vida independiente, tras la separación política de España y México y mientras duró la vinculación del Estado con la república federal regional.
Caracterización óptica y estructural de nanopuntos de carbono sintetizados mediante carbonización directa de extractos de biomasa
Authors: Ponce Elías Hamilton Alexander
Published: January 2025

Los nanopuntos de carbono (CNDs) son estructuras a escalas nanométricas, con propiedades ópticas y electrónicas de interés para su implementación en el desarrollo de tecnologías sostenibles. Estos se pueden obtener de manera fácil y con un reducido impacto ambiental mediante la carbonización directa de biomasa en presencia de oxígeno. Sin embargo, debido a los pocos estudios sobre CNDs sintetizados mediante este método, aún no se comprenden por completo sus propiedades ópticas, electrónicas y estructurales. Por lo tanto, con el fin de abonar a la comprensión de estas propiedades de los CNDs sintetizados, en este trabajo se presenta la síntesis de CNDs mediante la carbonización directa de diferentes fuentes de biomasa como extractos de limón, naranja, melón, cúrcuma, zanahoria, papa, tomate, chile y ciruela. Las muestras de CNDs se caracterizaron utilizando las técnicas de microscopía electrónica de barrido (SEM), espectroscopia de absorbancia (Ab) y fotoluminiscencia (PL). Las medidas SEM muestran tamaños de CNDs alrededor de 30 nm a 60 nm de diámetro, por otra parte, los espectros de Ab mostraron tres bandas en común a 230 nm, 300 nm y 380 nm. Atribuibles a transiciones π-π* de los enlaces C=C, transiciones n-π* de los enlaces C=O y al dopamiento con N y Si, respectivamente. Se observó una intensa PL en el rango de 425 nm a los 750 nm, lo que las hace buenas candidatas para aplicaciones en iluminación de luz blanca, entre otras aplicaciones.
Exploring the Frontier of Genetic Diversity: Recent Advances in Pan-genomics
Authors: Shen Chao
Published: January 2025

Pan-genomics has revolutionized our understanding of genetic diversity within species by providing a comprehensive view of both core and variable genes across multiple genomes. Recent advances in this field, driven by innovations in sequencing technologies, computational methods, and integrative approaches with other -omics fields, have unveiled new layers of complexity in genome architecture, gene regulation, and evolutionary dynamics. This mini-review explores key developments in pan-genomics, including the construction of graph-based pan-genomes, applications in crop improvement, integration with epigenomics, and the emerging role of machine learning. These advances underscore the potential of pan-genomics to drive future discoveries in genomics, with significant implications for agriculture, evolutionary biology, and personalized medicine.
REVITALISASI JUDICIAL PARDON DALAM PEMBARUAN HUKUM ACARA PIDANA DI INDONESIA
Authors: Sutrisno Umar Wiyogo , Irawan Andrie , Pradana Aditya Fajri Kurnia
Published: January 2025

This study aims to determine and analyse the relevance of revitalising judicial pardon in the field of criminal procedure law reform in Indonesia. In general, judicial pardon, or the original legal term judicial clemency, is one of the legal instruments that considers judges in giving punishment to convicts who are legally found guilty according to criminal procedure law in consideration of justice and humanity. In Indonesia, legal management in the field of judicial pardon is still less than optimal. This research is conducted in a normative juridical manner with a qualitative approach, by analysing primary and secondary legal materials and in-depth literature. Thus, the results of this research can conclude that the revitalisation of judicial pardon can make a positive contribution to a fairer and more humanist criminal justice system. This revitalisation includes strengthening the legal foundation, increasing the capacity of judges, and establishing a more transparent and accountable oversight mechanism. As a result of better implementation, judicial pardons can reduce injustice in the criminal justice system, overcrowding of correctional institutions, and support the rehabilitation and reintegration of prisoners. Therefore, the reform of Indonesia's criminal procedure law could include judicial pardons as one of the necessary aspects to achieve closer protection of sub/iller justice.
Health Status Assessment of a Scraper Conveyor Mechanical System
Authors: Bai Hailin , Liu Jinyou , Wang Jiguo , Zhang Peng , Bu Kankan
Published: January 2025

A method leveraging cooperative game theory is proposed to determine the combined weights of the mechanical system of a scraper conveyor, with a particular mining face serving as the research object. Additionally, the immune principle is introduced to estimate the health status of the mechanical system. First, a hierarchical analysis model and an immune hierarchical analysis model are established for the mechanical system of the scraper conveyor. The analytic hierarchy process, fuzzy statistics method, and entropy weight method are employed to determine the individual weights of each component in the mechanical system. Subsequently, the combined weights of various components in the mechanical system of the scraper conveyor are calculated using the cooperative game method. Second, the health values and health status of each component are calculated based on the sequence vector of component states output by the immune principle and the comprehensive weight vector. Finally, the weight vector of the mechanical system of the scraper conveyor is obtained based on the analytic hierarchy process. The health status of the entire mechanical system is then evaluated using the health status data of each component as the initial data for the system layer. The research results demonstrate that the presented method effectively obtains the health status of the mechanical system of the scraper conveyor by considering the physical characteristics of each component. Moreover, it facilitates the effective formulation of maintenance and repair plans for various components of the system.
VLSI Implementation of AES Block Cipher Based Data Hiding in Image Processing
Authors:   Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India, Kumarganesh Kumarganesh ,   PG Scholar, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu,     India,   Received: October 02, 2024 Revised: November 12, 2024 Accepted: January 07, 2025, P. Socratees. , G. Rajamanickam.
Published: January 2025

A common symmetric key block cypher for protecting electronic data is called the Advanced Encryption Standard (AES). The implementation of the AES algorithm using Very-Large-Scale Integration (VLSI) is examined in this study. Cryptographic algorithms can be realised in hardware thanks to VLSI, which has advantages over software implementations in terms of increased processing speed and security. In this work, an AES block cypher core designed and implemented with VLSI techniques is described. We investigate various architectural designs to maximise performance indicators such as area, power consumption, and throughput. The trade-offs between different implementation options and design concerns for important components such as S-boxes will be covered. To illustrate the performance and usefulness of the implemented AES core, simulation results will be given. This paper examines the VLSI implementation of the Advanced Encryption Standard (AES), with a focus on image processing applications, even though it is essential for general data security. Effective on-chip encryption and decryption can be included into image processing systems by implementing the AES algorithm into specialised hardware circuits. Benefits of this VLSI design include enhanced performance over software-based solutions on resource-constrained image processing devices, the ability to encrypt images in real-time for secure transmission or storage, and the potential for reduced power usage for battery-powered applications.
Cooperative Spectrum Sensing Architecture for Energy-Efficient Data-Fusion-Based Cognitive Radio Network
Authors:   Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India, admin admin ,   PG Scholar, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India,   Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India,   Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India, Israel D. , Veerakumar S. , Praveenkumar T.
Published: January 2025

This demand can be satisfied by cognitive radio (CR) technology thanks to the growing desire to utilize existing radio frequency bands more effectively. This paper suggests a hardware-efficient, very-large spectrum sensor. In cooperative cognitive radio networks, data fusion is not provided by a new-scale integration (VLSI) architecture. The cooperative method to spectrum sensing and management as a proposed concept uses approaches for data fusion to address the difficulties. Our VLSI system delivers high throughput with exceptional performance by combining the latest sensing algorithms with an effective hardware architecture. The overall performance of the spectrum and spectrum awareness are enhanced by the cooperative theoretical radio communication system. In order to enable the network to make judgements that can be modified utilizing combined data from scattered spectrum sensors, the study examines the integration of network fusion techniques. The suggested scheme's primary characteristics are its hardware efficiency, low power consumption, and real-time flexibility for changing spectrum conditions. Through simulations and comparison with existing methods, it is assessed. System performance is tracked, and the results indicate that faster and more accurate spectrum sensing is required in order to apply notions of spectrum sharing that make sense.
Smart Accident Detection using IoT Technology
Authors:   Assistant Professor, School of Electronics, Vellore Institute of Technology, Chennai, Tamil Nadu, India, Sindhuja Sindhuja ,   Assistant Professor, Department of ECE, Paavai Engineering College, Namakkal, Tamil Nadu, India,   Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu , India, S. Vijay Murugan. , S. Elarmathi.
Published: January 2025

Road accidents and emergency services delay are the main significant issues. To overcome these issues need to develop a system. Efficient handling of accidents through the immediate detection and provide timely aid are more crucial. Accident detection and emergency system depends on IoT (Internet of things) with minimum delay are gaining significant attention towards industry and academic literature. Several researches are investigated using IOT technology to detect accidents. In this work, we proposed an effective accident detection method by employing five sensors not only to detect accident but also to report type of accident such as collision, no accident, roll over or fall off. In addition to that, the status of the accident is communicated to the IBM Watson Cloud platform. The incoming data received in the node red platform is integrated with the Google Maps to show location and other information about the accident that can be accessed by the hospital through website and also sending alert messages to victim acquaintances. In addition, two Machine Learning (ML) models based on K-Nearest Neighbor (KNN) model and the Naïve Bayes (NB) model are compared to find out the best accident detection model. It is noticed that the KNN model is the very effective ML model, which employed to know the accident status and also to enhance the system by providing patient’s details, a kill switch and sending messages often till acknowledgement is received.
Joint PAPR and Spectrum Sensing in CRNS: A VLSI-Based Approach for Secondary User Integration
Authors:   Associate Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu,India, P. P. ,   Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu,India,   PG Scholar, Department of ECE, Knowledge Institute of Technology, Salem,TamilNadu, India, Vasanthi M. , Sangeetha P.
Published: January 2025

In Cognitive Radio Networks (CRNs), Peak-to-Average-Power-Ratio (PAPR) reduction is crucial for mitigating distortion in signals while optimizing spectral efficiency. This work offers a novel strategy for effectively reducing that PAPR in CRN systems, especially when secondary users are incorporated, by utilizing VLSI (Very-Large-Scale Integration) design approaches. The proposed strategy investigates VLSI methods for PAPR reduction, such as Partial-Transmit-Sequence (PTS) techniques. The system is appropriate for CRN applications because it can accomplish real-time PAPR reduction while preserving low power consumption and compact size by implementing these approaches in VLSI hardware. This could entail particular strategies for controlling PAPR with secondary users, such as joint PAPR and spectrum sensing approaches, dynamic power allocation, or user scheduling algorithms. Utilizing the predetermined values of pilot tones, the suggested decoder investigates every possible combination of weighting variables to determine which combination the transmitter has chosen and employed. There appears to be no data rate loss with the proposed decoder since it doesn't require any more pilot tones. This study next gives a digital execution of the described PTS decoder and illustrates its low power qualities, as well as the design and The encoder required at the transmitter to operate the suggested system is being developed using VLSI. The suggested architecture makes it easier for SUs to integrate with CRNs seamlessly. It allows SUs to effectively take advantage of available spectrum opportunities while complying with CRN restrictions and reducing interference with primary users by tackling PAPR and spectrum sensing concurrently. Furthermore, the study discusses the difficulties of incorporating secondary users into CRNs while retaining PAPR management.
High-Throughput Screening of Plant Extracts for Targeted Control of Burkholderia glumae, Causing Rice Sheath and Panicle Blight
Authors: Lee Seungchul , Jeong Yong Tae , Jang Seokhun , Jung Taeho , Hwang Buyng Su , Choi Ji Su , Oh Young Taek , Mannaa Mohamed , Seo Young-Su
Published: January 2025

Rice (Oryza sativa), a staple crop worldwide, is severely threatened by bacterial panicle blight caused by Burkholderia glumae, leading to substantial yield losses. The lack of effective chemical treatments and resistant rice cultivars highlights the urgent need for alternative solutions. In this study, 1,134 plant extracts were screened for antibacterial activity against B. glumae using agar disc diffusion and liquid broth assays. Thirty-three extracts exhibited significant growth inhibition on agar plates. These 33 extracts were further tested in Luria- Bertani broth, where five showed notable activity, and two extracts—Trapa japonica (FBCC-EP312) and Rumex crispus (FBCC-EP487)—were selected for detailed analysis. Both extracts significantly reduced bacterial motility and disease severity in rice, while having no effect on non-target bacteria such as Escherichia coli. These findings highlight the potential of these plant-derived compounds as effective biocontrol agents, offering an eco-friendly alternative to synthetic pesticides and promising applications in sustainable agriculture.
Diagnosis Anthracnose of Chili Pepper Using Convolutional Neural Networks Based Deep Learning Models
Authors: Kim Hae-In , Yoon Ju-Yeon , Ju Ho-Jong
Published: January 2025

Chili pepper (Capsicum annuum L.), one of the most economically important vegetable crops globally, faces significant economic risks from anthracnose, leading to yield losses of 10% as well as decreasing marketability. Early and accurate detection is essential for mitigating these effects. Recent advancements in deep learning, particularly in image recognition, offer promising solutions for plant disease detection. This study applies deep learning models—MobileNet, ResNet50v2, and Xception—using transfer learning to diagnose anthracnose in chili peppers. A key challenge is the need for large, labeled datasets, which are costly to obtain. The study aims to identify the minimum dataset size required for accurate and efficient disease diagnosis using limited data. Performance metrics, including precision, recall, F1-score, and accuracy, were evaluated across different dataset sizes (500, 1,000, 2,000, 3,000, and 4,000 samples). Results indicated that model performance improves with larger datasets, with ResNet50v2 and Xception requiring more data to achieve optimal accuracy, while MobileNet showed strong generalization even with smaller datasets. These findings underscore the effectiveness of transfer learning-based models in plant disease detection, offering practical guidelines for balancing data availability and model performance in agricultural applications. Source code available at https://github.com/smart-able/Anthracnose.git.
Changes in Teachers' Roles in the Context of Multicultural Education
Authors: Yang Yifan
Published: January 2025

With the acceleration of globalisation, the field of education is gradually becoming multicultural, especially in schools where the number of students from different cultural backgrounds is increasing. Multicultural education aims to promote understanding and respect among different cultures, and therefore the requirements for teachers' roles have changed significantly. In this context, teachers not only need to impart knowledge, but also need to be guides, mediators and facilitators of learning about cultural diversity. This paper discusses the change of teachers' roles in the context of multicultural education, analyses how teachers can adapt to this change, enhance the effectiveness of education, and promote the overall development of students, and puts forward suggestions on teacher training and curriculum design to better adapt to the needs of multicultural education.
Exploration of Technological Innovation and Market Strategy of Auction Houses
Authors: Gao Jiarun
Published: January 2025

The global art auction market has long been dominated by two major auction houses, Christie's and Sotheby's, whose global networks and expertise in high-end art transactions have established their leading position. However, in recent years, the rise of the Chinese market has injected new vitality into the global art auction landscape. Although local competitors such as China Guardian and Poly Auction have performed well in regional markets, they face cultural and economic context constraints in their global expansion. This study reviews the latest developments in the art auction industry, with a particular focus on the strategic choices of auction houses and potential opportunities for international competition, while exploring the profound impact of technological innovation on transaction efficiency, market expansion, and customer experience. The study found that Western auction houses rely on diversified asset portfolios and technological innovation to maintain their global advantages, while Chinese auction houses attract emerging consumer groups through specialization in traditional art fields and digital tools. However, the rapid development of technology may invalidate some of the research conclusions, and it is recommended to conduct long-term research in the future to reveal the continued impact of technological changes on the auction industry.
Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset
Authors: Zhou Yunpeng , Wu Jinyu , Liu Rentao , Yu Zixuan
Published: January 2025

The GDP and unemployment rates of two geographic units are influenced by their population density and economic characteristics, as well as their distance and spatial relationship. This work aim to use Geographically Weighted Regression (GWR), a classic and widely used method for modeling spatial heterogeneity, to analyze the correlation of these multiple factors. However, GWR does not precisely express its weighting kernel, making it insufficient to estimate complex geographic processes. Therefore, the work employed the Geographically Weighted Neural Network Regression (GNNWR) model, which combines Ordinary Least Squares (OLS) and neural networks, to estimate spatial heterogeneity based on GWR. This work collected various indicators, such as population density and GDP of first-level administrative units of the top 20 countries by GDP at the end of 2022, along with the Euclidean distance and geographic topology between these units. Using GNNWR, the work analyzed the correlation of their GDP growth. The results show that GNNWR outperforms OLS and GWR in fitting accuracy and provides more accurate predictions of the economic correlation between two geographic units.
Adaptive Iterative Control Optimization ICP Algorithm for Robust Point Cloud Registration in Urban Environments
Authors: Zhu Tianhang , Mao Yuxiang , Zhang Junzhi
Published: January 2025

This paper puts forward an improved ICP algorithm to improve the robustness of point cloud registration in complex urban environment, and adopts adaptive iterative control to solve the limitations of traditional ICP algorithm such as premature convergence or over-fitting caused by static iteration. Sobel convolution enhances the response ability of the algorithm to the complexity of the environment, and dynamically adjusts the iteration limit according to the feature difference of the point cloud, thus improving the registration accuracy and calculation efficiency. According to a large number of experiments on KITTI dataset, compared with the traditional ICP method, the algorithm can effectively reduce the root mean square error and improve the registration accuracy. These results verify the effectiveness and robustness of the algorithm in autonomous driving, robot navigation and urban mapping.
Sentiment Analysis and Rating Video Game Dimensions via NLP
Authors: Yuan Liwei , Ding Maoxiang , Meng Feiyang , Tian Yumin
Published: January 2025

Online gaming platforms generate vast amounts of user comments, which serve as valuable information for potential users and purchasers. Extracting meaningful guiding information from this data is crucial. As a Natural Language Processing technique (referred to as NLP), sentiment analysis technology demonstrates a high efficiency in accurately discerning the emotional tones within the comments, thereby objectively evaluating the advantages and disadvantages of gaming products based on user comments. In this paper, the comments of two games are analyzed, Grand Theft Auto V (referred to as GTAV) and Cyberpunk 2077 (referred to as 2077) on the Steam platform. Based on the self-built sentiment dictionary, keywords representing different sentiments were extracted by applying two different sentiment analysis models which are VADER and TEXTBLOB. Then we apply two models to classify the sentiments that the keywords express. Finally, the ratings of five dimensions (community, gameplay, storyline, sound and graphic) were obtained by transforming the sentiment analyze score, and the differences in the training results of different models were compared. The result analysis shows the performance difference between the VADER model and TEXTBLOB model through analyzing the standardized scores and the Pearson correlation coefficient. And it was particularly noted that the VADER model is better at capturing emotional changes in game reviews, while the TEXTBLOB model may not fully represent the users emotional inclination towards various aspects related to the game. The results reveal significant differences in model performance, providing insights into their effectiveness for gaming comment analysis.
A Machine Learning-Enhanced Chat Application for the Identification of Mental Disorders
Authors: Cao Qian , Yan Zhihao , Gong Zihang , Huang Jin
Published: January 2025

The prevalence of mental disorders is increasing, but they continue to be underdiagnosed and under addressed. Social media platforms offer novel opportunities for detecting potential mental health issues through the analysis of user-generated content. This paper presents a chat-based program developed using machine learning models trained on a dataset of comments from Reddit users. The program is capable of predicting the type of mental illness based on user input. This study provides a detailed comparison of various classification algorithms, including Nave Bayes, Logistic Regression (LR), Support Vector Machines (SVM), and Random Forests (RF). Additionally, the paper discusses relevant machine learning techniques from previous studies. The results indicate that LR model, particularly with a uni-gram feature representation, outperforms other models with an accuracy of 0.81 and demonstrates the fastest processing speed. Future research directions include the integration of Large Language Models and the development of a multilingual chat interface.
Path Planning for Robots in Fire Scenarios based on Dijkstra Algorithm and Genetic Algorithm
Authors: Song Yingwei , Duan Qihong , Liu Juntian
Published: January 2025

Robots have emerged as a pivotal element in enhancing firefighting operations, offering a blend of efficiency and safety to human responders. This paper delves into the development of a path planning strategy for robots navigating through fire scenarios. Particularly we fused the Dijkstra Algorithm and Genetic Algorithm (GA). Our methodology commences with a simplified yet comprehensive definition of the fire environment, incorporating factors such as obstacle height, surface roughness, and fire sources. The environment and surroundings are represented by a 2.5-dimensional grid map. On the other hand, the robots traversal and ascent capabilities modeled to reflect varying velocities across different terrains. The Dijkstra Algorithm is subsequently utilized to identify the optimal path from the starting point to the destination, ensuring a balance between minimal traversal time and reduced thermal exposure. Our results, demonstrated through MATLAB simulations, reveal a marked improvement in path planning when GA optimization is applied. The comparative analysis across three scenarios underscores the versatility and effectiveness of our approach, showcasing a significant reduction in both traversal time and thermal exposure. Index TermsRobots, Firefighting, Path planning, Dijkstra, GA
Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction
Authors: Dai Peixin , Zhang Jingsi , Shu Zhitao
Published: January 2025

Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity. Convolutional Neural Networks (CNNs), particularly ResNet and Unet, have made significant progress in medical image processing, offering robust capabilities for image segmentation. However, limited research has explored their integration with human-computer interaction (HCI) to enhance usability, interpretability, and clinical applicability. This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++, designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems. ResUnet++ integrates residual blocks in both the downsampling and upsampling phases, ensuring critical image features are preserved. By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively. This fosters informed decision-making and supports workflow efficiency in clinical settings. We evaluated ResUnet++ on the LGG Segmentation Dataset, achieving a Jaccard Loss of 98.17%. The results demonstrate its strong segmentation performance and potential for real-world applications. By bridging advanced medical imaging techniques with HCI, ResUnet++ offers a foundation for developing interactive diagnostic tools, improving clinician trust, decision accuracy, and patient outcomes, and advancing the integration of AI in healthcare workflows.
A Review of Robots in Caring for the Elderly
Authors: Zhang Jiaming
Published: January 2025

This literature review mainly talks about robots in caring for the elderly. With the intensification of global ageing, the number of elderly people is constantly increasing. The growing elderly population and the escalation of low fertility have led to certain nursing challenges. In order to deal with these problems and reduce the pressure on nursing staff, many caring robots are put in use. The elderly are the primary target of care provided by caring robots, both emotionally and medically. They have the ability to provide emotional support to the elderly as well as take care of the day-to-day activities of the aged. When it comes to providing care for elderly people, robots too face a few challenges. However, as caring robots are used more and more, people have a more positive understanding of them. In the future, caring robots will be more intelligent. Not only are they useful for personal care, but they also play a significant role in the delivery of community and social services.
A Review of Research Literature on the Impact of Industrial Digitization on the Integrated Development of Logistics and Modern Agriculture
Authors: Wang Erqi
Published: January 2025

With the continuous progress of science and technology, industrial digitization has become a key force to promote the development of all walks of life. Especially in the two fields of logistics industry and modern agriculture, the application of digital technology not only improves the efficiency, but also promotes the deep integration of the two. This paper will be from the perspective of research at home and abroad, and from the digital industry, logistics industry and the agricultural industry integration development, the influence of the development of logistics industry, digital industry influence on the development of modern agriculture, the influence of the logistics industry and modern agricultural integration development five aspects on the main view of the existing literature.