bluuebunny/crossref_metadata_2025 huggingface dataset:
THE Priesthood and Administrartion of Abydos during the Saite Period (based on the Data from Private Monuments)
Authors: Izosimov Denis
Published: October 2023

The article deals with the main composition of the Abydos’ elite during the Saite Period (664–525 B.C.). The aim of the article is to analyze the changes in Abydos’ elite after the unification of Egypt under the rule of Psamtik I. The author analyzes the data from private inscriptions, mostly the lists of different positions of the monuments’ owners. The vast majority of private stelae and statues belonged to local priests, associated with cults of the VIII (Thinite) nome of Upper Egypt. The data shows the existence of other sacerdotal ranks and titles that can be regarded as superior or inferior in the sacerdotal hierarchy. However, the exact position of the most common priestly titles cannot be determined due to the lack of data on their specific functions. A small part of private monuments, dating back to the reign of last Saite kings, preserved information about the non-priestly positions of their owners that held different military or administrative offices. The absence of data on any sacerdotal posts of these nobles can be explained by the gradual isolation of priesthood from other social groups of Ancient Egypt. The author suggests that this disproportion between priestly and civil offices reflects the development of the royal administration during the Saite Period. Due to Psamtik I’ policy, local priests retained their power over their nomes in exchange for swearing their loyalty to the Saite king. The emergence of civil offices in private inscriptions of the VI century B.C. denotes the strengthening of royal power.
Stylistics of Amaravati and Poetics of “Lalitavistara”: Comparative Analysis of Visual and Textual Narrative
Authors: Aleksandrova Natalia
Published: October 2023

The Ancient Indian Buddhist hagiographic narrative developed both in the form of text and in the form of images created in abundance in places of worship. These two forms of tradition were interrelated in certain ways, although the variability of versions was reflected both in the text and in the pictorial lines of development. Comparative analysis of these two forms of Buddhist narrative can be carried out in different ways. Above all, the comparison is focused on the events of the Buddha’s life as reflected in the narratives, as well as attributes and other external features of the characters. The author pays specific attention to the stylistic side and tries to match the style of pictures to the texts. Comparing “Lalitavistara”, the most important Buddhist hagiographic text, to the stupa at Amaravati with its abundance of images, one can detect a certain closeness between the poetics of the text and the style of the images. This includes multi-figure composition, associated with the expression of the idea of cosmic spheres, as well as special dynamism of characters’ movement, extreme rhythmicity of constructions, abundance of formulas and symbols. These common features in the imagery and stylistics of Amaravati and “Lalitavistara” obviously stem from a certain affiliation of the monastic environment that gave rise to them: the design of the stupa was associated with the chaityaka sect (dominant in the region) which claimed the “transcendental” nature of the Buddha.
Drone Untuk Keperluan Penyemprotan Tanaman Padi
Authors: Habriansyah Imran , Destinirenza Adesia , Rizky Elga Amira , Taufik Akhmad , Mukhtar Mukhtar , Hamzah Firman , Paisal Paisal
Published: October 2023

Penelitian yang dilakukan bertujuan untuk mengembangkan drone untuk keperluan penyemprotan pestisida atau pupuk cair pada tanaman pertanian dengan daya angkat dengan daya angkat yang cukup besar (payload >300 gr) dan mengembangkan mekanisme drone yang dapat melakukan penyemprotan pestisida/pupuk cair pada satu areal sawah secara otomatis. Adapun komponen utama yang diperlukan pada penelitian ini terdiri dari sistem mekanik, sistem elektronik. Sistem mekanik terdiri dariÂ frame drone, tangki penyimpanan pestisida,Â propeller,Â nozzle, dudukan komponen elektronik agro-drone dan peredam getaran. Adapun untuk sistem elektronik agro-drone terdiri dari baterai, modul penurun tegangan, GPS,Â transmitterÂ &Â receiver,Â remoteÂ kontrol,Â pixhawk,Â relayÂ modul, ESC danÂ safety switch. Penelitian ini melewati beberapa tahapan, yakni studi literatur, perancangan alat yang terdiri dari perancangan sistem mekanik dan elektronik, pembuatan dan perakitan agro-drone, pengujian agro- drone dan pengolahan data serta penyusunan laporan akhir.Dari hasil penelitian, agro-drone dapat terbang dengan normal, baik dengan kendali manual maupun dengan kontrol otomatis dengan daya angkat cairan yang lebih dari sebelumnya yakni 800 mL dan telah mampu melakukan penyemprotan cairan secara otomatis di area sawah petani dengan luas Â±29 m2Â dengan menghabiskanÂ volumeÂ cairan sebanyak 750 mL dengan waktu 137 detik dengan jarak antarÂ lineÂ 1 m.
Entre Foucault e Brown: a escrita da história e o papel do intelectual na análise dos processos de subjetivação neoliberais
Authors: Fortes Ribas Thiago
Published: October 2023

O artigo propõe uma comparação entre as pesquisas de Michel Foucault e de Wendy Brown a partir das críticas e das complementariedades propostas pela pensadora norte-americana derivadas de sua leitura de Nascimento da biopolítica. Inicialmente, a explicação sobre o modo como Foucault escreve suas pesquisas históricas no contexto da sua crítica ao humanismo tem a função de auxiliar a compreensão das diferenças nos papeis intelectuais adotados. A recusa da história dialética aparece ligada, então, às críticas a figura do intelectual universal supostamente capaz de revelar um destino antropológico. É, justamente, no desenvolvimento de um modo arqueológico e genealógico de fazer história que a análise de processos de subjetivação ganha sentido para Foucault. Assim, a interpretação crítica de Brown é contextualizada em relação a metodologia foucaultiana. Além da leitura de Brown da análise foucaultiana dos processos de subjetivação neoliberais, no desenvolvimento do artigo, são analisados os argumentos próprios da autora sobre os impactos da racionalidade neoliberal nas forças políticas do presente. Por fim, busca-se apontar os ganhos e os perigos das duas perspectivas em relação ao papel intelectual que sustentam.
Transepistemes devinientes de las rupturas asignificantes: decolonialidad planetaria-complejidad
Authors: Rodríguez Milagros Elena
Published: October 2023

Asumiendo que la decolonialidad planetaria es apodíctica de la complejidad y de la transdisciplinariedad, bajo rupturas asignificantes, en  condiciones abiertas para conformar al ser humano a pensar complejamente, sustentamos transepistemes devinientes de las rupturas asignificantes de la decolonialidad planetaria-complejidad como objetivo complejo de indagación; en las líneas de investigación: educación-transepistemologías transcomplejas, Decolonialidad planetaria-complejidad en re-ligaje y transepistemologías de los conocimientos-saberes y transmetodologías transcomplejas. Lo realizamos con el transmétodo la hermenéutica comprensiva, ecosófica y diatópica; en los momentos analíticos, empíricos y propositivos. En el momento propositivo los transepistemes decoloniales planetarios – complejos van desmitificando des-ligando los epistemes fijos con pretendidas y vendidas aseveraciones hacia el colonizador. Los transepistemes por su esencia decolonial planetaria ponen en igual grado de importancia todos los saberes ancestrales, y desde el patrimonio histórico de las civilizaciones aportan al conocer desde transmetodologías otras, abiertas, inclusivas; transdisciplinares fuera de los impositores de la destrucción de la naturaleza en una falsa conquista; por ello salvaguardar los saberes ancestrales como transepistemes ricos en amor por la humanidad es aceptar que somos naturaleza en la Tierra como patria. Transepistemes transdisciplinares como ejercicios de liberación, conocedores y promotores de la complejidad de la vida; es una esencia característica de los transepistemes que nos incitan a una nueva civilización de hacer ciencia, de conocer rompiendo las fronteras de las ciencias.
Analisis Ketepatan Pengkodean Diagnosis Hipertensi Di Rumah Sakit Arjawinangun
Authors: Rahmawati Fitria Dewi
Published: October 2023

Abstrak Salah satu target global penyakit tidak menular adalah menurunkan prevalensi Hipertensi sebesar 33% antara tahun 2010 dan 2030. Hipertensi merupakan salah satu standar pelayanan minimal (SPM) kesehatan di Indonesia. Angka Cakupan pelayanan kesehatan penderita hipertensi di Jawa barat tahun 2020 sebesar 34,7 % berdasarkan hasil pengukuran tekanan darah. Cakupan pelayanan kesehatan penderita hipertensi tertinggi di Jawa Barat terjadi di kota Cirebon. Pelaporan morbiditas kasus hipertensi di Rumah Sakit dibagi menjadi kode I10  dan I11-I15 pada ICD-10. Ketidakakuratan kode diagnosis akan mempengaruhi data dan informasi laporan, ketepatan tarif INA-CBG’s yang pada saat ini digunakan sebagai metode pembayaran untuk pelayanan pasien dengan asuransi atau jaminan. Dampak yang terjadi ketika terdapat ketidaktepatan dalam kode hasil verifikasi dari BPJS akan terjadi kelebihan pembayaran atau kekurangan pembayaran. Tujuan penelitian ini untuk mengetahui pelaksanaan kodifikasi pasien Hipertensi rawat jalan di Rumah Sakit Arjawinangun. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan desain penelitian analisis univariat. Pengumpulan data dengan menggunakan instrumen ceklist. Sampel penelitian sebanyak 73 pasien dari populasi kunjungan
Implementation of the Prolanis Online Health Service Management Information System Application (SIMPELPRO) at the Semarang City Public Health Center , Case Study at Padangsari Public Health Center Semarang City
Authors: Wijayanta Setya - , Masrochah Siti , Subinarto Subinarto , Naryanti Indah
Published: October 2023

The government is working to reduce the prevalence of chronic diseases and prevent people with chronic diseases from entering the complication stage. One form of effort in this regard is through the chronic disease management program (Prolanis). So that the participants do not know the types of activities and schedules of Prolanis activities so that participants do not take advantage of them. The purpose of this study was to examine the implementation of the online prolanis service management information system application (SIMPELPRO) at the Semarang City Public Health Center, a case study at the Padangsari Public Health Center. This research method is descriptive, with primary data sources from interviews, documentation studies and literature. System design using the prototype method. Prototypes were made and evaluated, and continued to the implementation stage. The result of this study Has been developed using SIMPELPRO web based application for service management of Prolanis activities. The focus of the application in this research is still on the presentation of the health education materials. The development uses the prototype method with UML as a design tool. The application has received positives feedbacks from Prolanis participants, Although some participants have complainedabout limited access to smartphones and the need for assistance for elderly participants.
Analysis Of The External Cause Code Accuracy Case Of Injury In General Hospital PKU Muhammadiyah Bantul
Authors: Ningsih Kori Puspita
Published: October 2023

The assignment of diagnosis codes and procedures must be done correctly to ensure the quality of medical records is met. What is often forgotten when giving a diagnosis code is the secondary code or external cause. This research aims to determine the recording, implementation and level of accuracy of external cause codes. Descriptive research method with a cross sectional design. The research sample was 48 injury medical record files. The research results showed that external cause information was documented on triage forms, emergency department assessments, initial adult inpatient nursing assessments, integrated patient progress notes, and discharge summaries. External cause coding is carried out by a coder with a medical record background in accordance with the SOP for coding implementation using electronic ICD-10 contained in SIMRS. The level of accuracy of the external cause code up to the 5th character (Category A) is 0%, the 4th character (Category B) is 38%, the 3rd character (Category C) is 27%, and the 1st character (chapter) (Category D) of 35%. The reason for the inaccuracy is because SIMRS does not accommodate coding up to the fifth character and the SPO does not yet indicate the officer's activity steps to determine up to the 5th character sub-category. It can be concluded that the accuracy of external cause coding at RSU PKU Muhammadiyah Bantul falls into the Fair criteria with an accuracy range of 20-40%.
Ethnomedicinal application, phytochemistry and therapeutic effects of genus clerodendrum
Authors: Ogunlakin Akingbolabo Daniel , Akinwumi Idayat Adeola , Ambali Owoola Azeezat
Published: October 2023

The genus Clerodendrum consist of flowering plants, which was once classified as a member of the Verbenaceae family, was recently classified as a member of the Lamiaceae family. Various species of this genus have been generally used for treating several ailments and disorders as well as ornamental plants. Secondary metabolites found in abundance in these species include terpenoids, saponins, carbohydrates, and glucosides as well as alkaloids, flavonoids, and phenolic compounds. Studies conducted so far have shown that the extracts and compounds of the species in this genus exert diverse physiological activities, including anti-inflammatory, anti-diabetic, antihypertensive, anti-allergic, analgesic, hepatoprotective, post-coital antifertility, antimicrobial, anticholinesterase, membrane stabilizing, antihelmitic, hypolipidemic, antitumor, and antimicrobial properties. Medicinal plants in this genus are beneficial, therefore they should be considered as lead in drug discovery and formulation.Keywords: Clerodendrum species, Forkloric uses, Pharmacological application, Phytoconstituents, Therapeutics
Honor Endorsement in Male Youth: A General Risk Factor for Aggressive Behavior?
Authors: Kollek Marie , Soellner Renate
Published: October 2023

Honor refers to socially conferred self-worth, strong norms of reciprocity, and direct retaliation for transgressions. Honor norms have been discussed as relevant in explaining aggressive behavior, particularly for immigrant groups. In this study we examined the endorsement of honor norms and their association with aggressive behavior in a culturally diverse sample in Germany. Online questionnaire data were used from male adolescents aged 11–18 years (N = 774; Mage = 15.37, 34.2% with a migration background). Multiple linear regression analyses indicated that students of Turkish and Russian origin endorsed honor more than students without a migration background, whereas students of Polish origin did not differ. Within negative binomial regression models, honor endorsement was significantly positively related to aggressive behavior, while this effect did not differ between students from different migration backgrounds and students without migration background respectively. Thus, the internalization of honor norms emerged as a general risk for aggressive behavior among male adolescents.
A Meta-Analysis on the Link Between Young People’s Social Environment, Socioeconomic Status, and Political Violence Outcomes
Authors: Jahnke Sara , Abad Borger Katharina , Burgsmüller Lena , Hoppe Catalina , Beelmann Andreas
Published: October 2023

Young people are particularly likely to engage in political violence, hold positive attitudes towards political violence, and show willingness to engage in political violence. The social environment in which young people are immersed is characterized by factors increasing and protecting against the risk of such outcomes. The present meta-analysis systematically summarizes the body of studies on the link between political violence outcomes and risk and protective variables in the following domains: a) parents and family (familial support, familial conflict, parental control, importance of family, parental violence), b) radical networks and peers (having friends with racist or violent attitudes, membership in political groups that oppose mainstream politics, general membership in a peer group), c) school (school attachment, school achievement), and d) socioeconomic status. A total of 288 effect sizes from 44 reference samples met the selection criteria. Findings were combined using two- and three-level meta-analytic models. Average effect sizes ranged between very small to small (|r| = .03 to |r| = .26), with the largest effect sizes detected for membership in a political group that opposes mainstream politics and having friends with racist or violent attitudes. The results are constrained by the low number of eligible samples and the significant level of heterogeneity for many of the meta-analyses.
String Covering: A Survey
Authors: Mhaskar Neerja , Smyth W.F.
Published: October 2023

The study of strings is an important combinatorial field that precedes the digital computer. Strings can be very long, trillions of letters, so it is important to find compact representations. Here we first survey various forms of one potential compaction methodology, the cover of a given string x, initially proposed in a simple form in 1990, but increasingly of interest as more sophisticated variants have been discovered. We then consider covering by a seed; that is, a cover of a superstring of x. We conclude with many proposals for research directions that could make significant contributions to string processing in future.
Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework
Authors: La Barbera David , Roitero Kevin , Mizzaro Stefano
Published: October 2023

Online misinformation is posing a serious threat for the modern society. Assessing the veracity of online information is a complex problem which nowadays is addressed by heavily relying on trained fact-checking experts. This solution is not scalable, and due to the importance of the problem the issue gained the attention of the scientific community, which proposed many based on Artificial Intelligence and Machine Learning methods. Despite the efforts made, the effectiveness of such approaches is not yet enough to allow them to be used without supervision. In this position paper, we propose a hybrid human-in-the-loop framework for fact-checking: we address the misinformation issue by relying on a combination of automatic Artificial Intelligence methods, crowdsourcing ones, and experts. We study the single components of the framework as well as their interactions, and we propose an interleaving of the different components which we believe will serve as a useful starting point for the future research towards effective and scalable fact-checking.
Grounding End-to-End Pre-trained architectures for Semantic Role Labeling in multiple languages
Authors: Hromei Claudiu D. , Croce Danilo , Basili Roberto
Published: October 2023

Situated natural language interactions between humans and robots are strictly necessary for complex applications: communication here implies the reference to the environment shared between a user and the robot. This paper proposes a transformer-based architecture that supports the integration of spatial information (as logical representation) about a semantic map of the environment and the input utterances. The generated interpretation is a logical form of the command that makes references to the state of the world through a single end-to-end process, stimulated at each interaction by an explicit linguistic description of the environment. In this specific work, the end-to-end capability of the targeted transformer is studied in light of its multilingual applications where the robot can be queried in different natural languages. The obtained experimental results confirm the applicability of transformers to grounded human-robotic interaction, with benefits in terms of both portability of the approach across domains and effectiveness in terms of reachable accuracy. Moreover, language-specific processing chains are shown to be preferable to large-scale multilingual models for their better trade-off between accuracy and complexity. Overall, the proposed architecture outperforms previous approaches and paves the way for sustainable multilingual architectures.
Multi-Target Neuroprotection of Thiazolidinediones on Alzheimer’s Disease via Neuroinflammation and Ferroptosis
Authors: Yang Jiahui , Shi Xiaohua , Wang Yingying , Ma Ming , Liu Hongyu , Wang Jiaoqi , Xu Zhongxin
Published: October 2023

Alzheimer’s disease (AD) is the main cause of dementia in older age. The prevalence of AD is growing worldwide, causing a tremendous burden to societies and families. Due to the complexity of its pathogenesis, the current treatment of AD is not satisfactory, and drugs acting on a single target may not prevent AD progression. This review summarizes the multi-target pharmacological effects of thiazolidinediones (TZDs) on AD. TZDs act as peroxisome proliferator-activated receptor gamma (PPAR γ) agonists and long-chain acyl-CoA synthetase family member 4 (ACSL4) inhibitors. TZDs ameliorated neuroinflammation and ferroptosis in preclinical models of AD. Here, we discussed recent findings from clinical trials of pioglitazone in the treatment of AD, ischemic stroke, and atherosclerosis. We also dissected the major limitations in the clinical application of pioglitazone and explained the potential benefit of pioglitazone in AD. We recommend the use of pioglitazone to prevent cognitive decline and lower AD risk in a specific group of patients.
Modelling a machine learning based multivariate content grading system for YouTube Tamil-post analysis
Authors: Srivatsun G. , Thivaharan S.
Published: October 2023

Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv - NB) where the comments are acquired from an online social network and performed training using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation.
Xception model for disease detection in rice plant
Authors: Meena Rakesh , Joshi Sunil , Raghuwanshi Sandeep
Published: October 2023

Rice is a staple meal that helps people worldwide access sufficient food. However, this crop has several illnesses, significantly lowering its production and quality. Because of this, it is imperative to conduct early disease detection to halt the spread of infections. Because of this, it is desirable to develop an automatic system that will help agronomists, pathologists, and indeed growers in directly diagnosing rice diseases. This would allow for preventative measures to be done as quickly as feasible. In this day and age of artificial intelligence, researchers have experimented with various learning approaches to discover diseases that can affect rice plants. Deep learning has recently seen considerable use in many computer vision and image analysis fields, becoming one of the most prominent machine learning algorithms. Deep learning has also recently found substantial usage in many computer vision and picture analysis fields. On the other hand, deep learning methods have seen very little application in plant disease recognition, except for some ongoing research centered on the problem and using a public dataset of pictures magnified to show plant leaves. Because of their high computational complexity, which requires a huge memory cost, and the complexity of experimental materials’ backgrounds, which makes it difficult to train an efficient model, deep learning methods have only seen limited use in plant disease recognition. This is due to several factors, including the following: The Inception module was improved to recognise and detect rice plant illnesses in this research by substituting the original convolutions with architecture based on modified-Xception (M-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach can achieve the specified level of performance, with an average recognition fineness of 99.73% on the public dataset and 98.05% on the domestic dataset, respectively. Our proposed work is better as per existing methods and models.
Valued-inverse dombi fuzzy graphs
Authors: Jahanpanah Sirus , Hamidi Mohammad
Published: October 2023

Fuzzy graphs as labeled graphs (fuzzy vertex labeling and fuzzy edge labeling) have many applications in real life such as complex networks, coding theory, medical sciences, communication networks, and management sciences. Also, triangular norms as a special class of functions, have many applications in fuzzy set theory, probability and statistics, and other areas. This paper considers the notations of an inverse fuzzy graph and triangular norms to introduce the new type of graphs as valued-inverse Dombi fuzzy graphs. The valued-inverse Dombi fuzzy graphs are a generalization of inverse fuzzy graphs and are dual to Dombi fuzzy graphs. For any given greater than or equal to one real number, we construct a type of Dombi inverse fuzzy graph and investigate some conditions such that the product and union of Dombi inverse fuzzy graphs be a Dombi inverse fuzzy graph.
An algorithm for solving a system of linear equations with Z-numbers based on the neural network approach
Authors: Moosavi Seyyed Mohammad Reza Hashemi , Araghi Mohammad Ali Fariborzi , Ziari Shokrollah
Published: October 2023

Mathematical modeling of many natural and physical phenomena in industry, engineering sciences and basic sciences lead to linear and non-linear devices. In many cases, the coefficients of these devices, taking into account qualitative or linguistic concepts, show their complexity in the form of Z-numbers. Since Z-number involves both fuzziness and reliability or probabilistic uncertainty, it is difficult to obtain the exact solution to the problems with Z-number. In this work, a method and an algorithm are proposed for the approximate solution of a Z-number linear system of equations as an important case of such problems. The paper is devoted to solving linear systems where the coefficients of the variables and right hand side values are Z-numbers. An algorithm is presented based on a ranking scheme and the neural network technique to solve the obtained system. Moreover, two examples are included to describe the procedure of the method and results.
Construction of ecological security evaluation model of healing landscape based on deep learning
Authors: Wang Hao , Xu Yanyan , Han Yue , Zhang Kejia
Published: October 2023

With the rapid growth of the global population and the increasing urbanization, the urban landscape in China is gradually enriched, and the scale of the landscape that plays a healing role is expanding. However, curing the problem of landscape ecological security is an important part of Homeland security, economic and social sustainable development. We must deal with the relationship between high-quality social development and ecological environment protection on the basis of scientific evaluation. To address this issue, research has provided better data support for feature extraction through image preprocessing. Then the Convolutional neural network in deep learning is trained through a large number of collected measured data. Finally, the pressure state response model is used to evaluate the ecological security of the healing landscape. The results show that the average error of the ground class in 2010 was 13.65%, and the fitting accuracy reached 86.35%, indicating that this method has high accuracy and can be effectively applied in evaluation. Meanwhile, in 2010 and 2019, the average landscape ecological security levels of City A were 7.27 and 6.65, both at a “safe” level, but the overall security level showed a downward trend. It is recommended to optimize the land use pattern in future urban planning and construction, improve the urban landscape ecological security index value, and maintain consistency with the actual situation of the city. This can provide reference for the evaluation model of urban landscape ecological security, and further provide scientific basis and guidance for the ecological civilization construction of urban agglomerations. In subsequent research, the evolution trend of urban landscape ecological security can be taken as the research goal, and finally, guidance on optimizing urban landscape ecological security can be provided.
Classifying drivers of deforestation by using the deep learning based poly-highway forest convolution network
Authors: Abdus Subhahan D. , Vinoth Kumar C.N.S.
Published: October 2023

The worldwide deforestation rate worsens year after year, ultimately resulting in a variety of severe implications for both mankind and the environment. In order to track the success of forest preservation activities, it is crucial to establish a reliable forest monitoring system. Changes in forest status are extremely difficult to manually annotate due to the tiny size and subtlety of the borders involved, particularly in regions abutting residential areas. Previous forest monitoring systems failed because they relied on low-resolution satellite images and drone-based data, both of which have inherent limitations. Most government organizations still use manual annotation, which is a slow, laborious, and costly way to keep tabs on data. The purpose of this research is to find a solution to these problems by building a poly-highway forest convolution network using deep learning to automatically detect forest borders so that changes over time may be monitored. Here initially the data was curated using the dynamic decomposed kalman filter. Then the data can be augmented. Afterward the augmented image features can be fused using the multimodal discriminant centroid feature clustering. Then the selected area can be segmented using the iterative initial seeded algorithm (IISA). Finally, the level and the driver of deforestation can be classified using the poly-highway forest convolution network (PHFCN). The whole experimentation was carried out in a dataset of 6048 Landsat-8 satellite sub-images under MATLAB environment. From the result obtained the suggested methodology express satisfied performance than other existing mechanisms.
Optimized deep learning-based intrusion detection framework for vehicular network
Authors: Vishnukumar Ravula , Ramaiah Mangayarkarasi
Published: October 2023

The Internet’s evolution resulted in a massive amount of data. As a result, the internet has become more sophisticated and vulnerable to massive attacks. The attack detection system is a key feature for system security in modern networks. The IDS might be signature-based or detect anomalous behavior. Researchers recently created several detection algorithms for identifying network intrusions in vehicular network security, but they failed to detect intrusions effectively. For this reason, the optimal Deep Learning approach, namely Political Fractional Dingo Optimizer (PFDOX)-based Deep belief network is introduced for attack detection in network security for vehicles. The Internet of Vehicle simulation is done initially, and then the input data is passed into the pre-processing phase, which removes noise present in the data. Then, the feature extraction module receives the pre-processed data. The Deep Maxout Network is trained using the Fractional Dingo optimizer (FDOX)is utilized to detect normal and abnormal behavior. Fractional calculus and Dingo optimizer (DOX) are combined to create the proposed FDOX. Finally, intruder/attack types are classified using the Deep Belief Network, which is tuned using the PFDOX. The PFDOX is created by the assimilation of the DOX, Fractional Calculus, and Political Optimizer (PO). The experimental result shows that the designed PFDOX_DBN for attack type classification offers a better result based on f-measure, precision, and recall with the values of 0.924, 0.916, and 0.932, for the CIC-IDS2017 dataset.
Identification of wheat plant disease using hybrid model of pre-processing with CNN
Authors: Rajesh Kanna R. , Ulagamuthalvi V.
Published: October 2023

Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy.
Construction of small confusion component based on logarithmic permutation for hybrid information hiding scheme
Authors: Khan Majid , Batool Syeda Iram , Munir Noor , Alshammari Fahad Sameer
Published: October 2023

The design and development of secure nonlinear cryptographic Boolean function plays an unavoidable measure for modern information confidentiality schemes. This ensure the importance and applicability of nonlinear cryptographic Boolean functions. The current communication is about to suggest an innovative and energy efficient lightweight nonlinear multivalued cryptographic Boolean function of modern block ciphers. The proposed nonlinear confusion element is used in image encryption of secret images and information hiding techniques. We have suggested a robust LSB steganography structure for the secret hiding in the cover image. The suggested approach provides an effective and efficient storage security mechanism for digital image protection. The technique is evaluated against various cryptographic analyses which authenticated our proposed mechanism.
Medical image segmentation using hybrid Contrast Limited Adaptive Histogram Equalization (CLAHE) and expectation maximization algorithm
Authors: Prabu Shankar K.C. , Shyry S. Prayla
Published: October 2023

Early detection of diseases in men and women can improve treatment and reduce the risk involved in human life. Nowadays techniques which are non-invasive in nature are popularly used to detect the various types of diseases. Histopathological analysis plays a major role in finding the nature of the disease through medical images. Manual interpretation of these medical imaging takes time, is tedious, subjective, and can have human errors. It has also been discovered that the interpretation of these images varies amongst diagnostic labs. As computer power and memory capacity have increased, methodologies and medical image processing techniques have been developed to interpret and analyse these images as a substitute for human involvement. The challenge lies in devising an efficient pre-processing technique that helps in analysing, processing and preparing the medical image for further diagnostics. This research provides a hybrid technique that reduces noise in the NITFI medical image by using a 2D adaptive median filter at level 1. The edges of the filtered medical image are preserved using the modified CLAHE algorithm which preserves the local contrast of the image. Expectation Maximization (EM) algorithm extracts the ROI part of the image which helps in easy and accurate identification of the disease. All the three steps are run over the 3D image slices of a NIFTI image. The proposed method proves that it achieves close to ideal RMSE, PSNR and UQI values as well as achieves an average runtime of 37.193 seconds for EM per slice.
Fuzzy difference operators derived from overlap functions
Authors: Dai Songsong , Song Haifeng , Xu Yingying , Du Lei
Published: October 2023

This paper introduces the concept of (O, N)-difference, for an overlap function O and a fuzzy negation N. (O, N)-differences are weaker than fuzzy difference constructed from positive and continuous t-norms and fuzzy negations, in the sense that (O, N)-differences do not necessarily satisfy certain properties, as the right neutrality principle, but only weaker versions of these properties. This paper analyzes the main properties satisfied by (O, N)-differences, and provides a characterization of (O, N)-difference.
RETRACTED: A comprehensive MAGDM-based approach using EDAS and CRITIC as an auxiliary tool for quality evaluation of ceramic product modeling design
Authors: Wang Yashao
Published: October 2023

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Impaired Neurodevelopment in Children with 5q-SMA - 2 Years After Newborn Screening
Authors: Kölbel Heike , Kopka Marius , Modler Laura , Blaschek Astrid , Schara-Schmidt Ulrike , Vill Katharina , Schwartz Oliver , Müller-Felber Wolfgang
Published: October 2023

Objective:

Numerous studies have consistently found that reduced SMN protein expression does not severely affect cognitive function in SMA patients. However, the average intelligence quotient of SMA patients has ranged above to below average in different studies. The cognitive development of SMA patients identified through newborn screening remains largely unknown.

Methods:

40 of 47 eligible SMA patients (23 females/17 males) from 39 families identified through newborn screening between January 2018 and December 2020 underwent developmental testing using Bayley III (BSID) after the 2 years of age. The mean age was 29.25 months (23–42 months). 17 patients had 2, 11 patients had 3 and 12 patients had ≥4 copies of SMN2.

Results:

cognitive scale: mean 94.55 (SD 24.01); language scale: mean 86.09 (SD 26.41); motor scale: 81.28 (SD 28.07). Overall, the cognitive scales show that 14 children were below average, 20 children were average and 6 children were above average. 10/14 children with below average scores had 2 SMN2 copies. The post-hoc pairwise comparisons showed that the cognition main scale was significantly more sensitive to the number of SMN2 copies than the motor main scale of the BSID (M Δ= 10.27, p = 0.014). There is also evidence that cognition scored higher than the language main scale (M Δ= 7.11, p = 0.090).

Conclusion:

The impaired cognitive development of SMA children with 2 SMN2 copies, despite early initiation of therapy, underscores the critical role of the SMN protein in the early stages of brain development.
Ankle rehabilitation robot training for stroke patients with foot drop: Optimizing intensity and frequency
Authors: Zhang Qingfang , Wang Yulong , Zhou Mingchao , Li Dongxia , Yan Jie , Liu Quanquan , Wang Chunbao , Duan Lihong , Hou Dianrui , Long Jianjun
Published: October 2023

BACKGROUND: Robotic solutions for ankle joint physical therapy have extensively been researched. The optimal frequency and intensity of training for patients when using the ankle robot is not known which can affect rehabilitation outcome. OBJECTIVE: To explore the optimal ankle robot training protocol on foot drop in stroke subjects. METHODS: Subjects were randomly divided into four groups, with 9 in each group. The subjects received different intensities (low or high intensity) with frequencies (1 session/day or 2 sessions/day) of robot combination training. Each session lasted 20 minutes and all subjects were trained 5 days a week for 3 weeks. RESULTS: After 3 weeks of treatment, all groups showed an improvement in passive and active ankle dorsiflexion range of motion (PROM and AROM) and Fugl-Meyer Assessment for lower extremity (FMA-LE) compared to pre-treatment. When training at the same level of intensity, patients who received 2 sessions/day of training had better improvement in ankle dorsiflexion PROM than those who received 1 session/day. In terms of the improvement in dorsiflexion AROM and FMA-LE, patients who received 2 sessions/day with high intensity training improved better than other protocols. CONCLUSION: High frequency and high intensity robot training can be more effective in improving ankle dysfunction.
An empirical study of various detection based techniques with divergent learning’s
Authors: Bendale Bhagyashree Pramod , Swati Dattatraya Shirke Swati
Published: October 2023

The prevalence of violence against women and children is concerning, and the initial step is to raise awareness of this issue. Certain forms of detection based techniques are not frequently regarded both socially and culturally permissible. Designing and implementing effective approaches in secondary and supplementary avoidance simultaneously depends on the characterization and assessment. Given the greater incidence of instances and mortalities resulting developing an early detection system is essential. Consequently, violence against women and children is a problem of human health of pandemic proportions. As a result, the focus of this survey is to analyze the existing methods used to identify violence in photos or films. Here, 50 research papers are reviewed and their techniques employed, dataset, evaluation metrics, and publication year are analyzed. The study reviews the potential future research areas by examining the difficulties in identifying violence against women and children in literary works for researchers to overcome in order to produce better results.
Aquaponics: System Layout and Components
Authors: Ngo-Hoang Dai-Long
Published: October 2023

Utilizing alternative agricultural techniques for household sustenance or income generation has garnered attention, driven by contemporary concerns over the safety of commercially grown produce, the reduced space requirements, and the manageable regulation of production variables. Among these innovative methods, aquaponic production has gained significant traction, offering both fresh vegetable yield and nutritious fish protein. This publication delves into the intricate facets of aquaponic systems, elucidating their constituent elements, their roles, and the interconnecting mechanisms pivotal for seamless operation. The ensuing information furnishes a comprehensive understanding of aquaponic system components, elucidating their functionality and profound significance in ensuring the efficiency of the entire system.
The Influence of Social Media Marketing and Financial Literacy on Purchase Decisions of Gold Savings in Pegadaian Unit Boja
Authors: Maharani Marsaa Putri , Supaya Sandi , Nugroho Jati
Published: October 2023

To specify the influence of Social Media Marketing and Financial Literacy on Purchase Decisions is the goals of this research. The issue in this research is due to the expansion of internet users in Indonesia, yet the level of financial literacy in Indonesia is still relatively low and many people don’t know the benefits of Pegadaian’s gold savings product. This study uses quantitive research methods with non-probability sampling techniques (purposive sampling). This study determines the quantity of samples taken are 95 respondents. Multiple linear regression analysis techniques are used to analyze the data in this research, this was held to measure the influence of the independent variables on the dependent variable. The results found that Social Media Marketing and Financial Literacy have significant effect towards Purchase Decisions. The amount of the coefficient of determination is 90,5% on purchase decisions and the remaining 9,5% is influenced by additional factors not considered in this study. This shows that the Social Media Marketing variable and Financial Literacy variable have an important role in increasing the Purchase Decision of gold savings. Based on these data, it is hoped that PT Pegadaian UPC Boja would improve social media marketing and financial literacy so that the decision to purchase gold savings can be achieved.
QUALITY OF FINANCIAL REPORTS IN PAPUA PROVINCE: WHAT FACTORS INFLUENCE IT?
Authors: Asmiati Wulan , Ponto Sahrul , Pertiwi Dian , Sejati Fajar Rina
Published: October 2023

Government accounting standards, internal control systems, regional government financial accounting systems, and resource competencies were tested to see whether they had an effect or not on the quality of local government financial reports in Papua Province. A total of 57 questionnaires from 27 OPDs were analyzed using multiple linear regression analysis. The results of this research indicate that government accounting standards and human resource competency respectively have an influence on the quality of local government financial reports. In other hand, internal control system and regional government financial accounting system respectively has no effect on the quality of regional government financial reports. It is hoped that the implications of this research can be input for the government of Papua Province to improve the quality of its financial reports.
INFLUENCE OF THE TRIPLE BOTTOM LINE CONCEPT ON SUSTAINABILITY AND SUCCESS OF TOFU SMEs IN MAGELANG
Authors: Kurnia Isnu Niki Adi , Hasanudin Mohamad , Putri Alvianita Gunawan
Published: October 2023

Business sustainability is essential for enterprises to endure in the future and provide economic, social, and environmental opportunities for future generations. By establishing business sustainability in economic, social, and environmental aspects, a business can enhance its success. This study seeks to analyse business sustainability and success in tofu SMEs in Magelang Regency, with a focus on sustainable practices. The study was conducted using purposive sampling technique with specific criteria and considerations to determine the sample. A total of 60 participants from the tofu industry hub in Candimulyo District were included in the sample. The analysis employed quantitative methods and a descriptive analysis approach, using variables and indicators. The research findings indicate that the sustainability of tofu SMEs is influenced by economic, social, and environmental variables within the triple bottom line concept. Meanwhile, the success of small and medium-sized enterprises producing tofu is determined by economic and social factors, operating within the framework of the triple bottom line concept.