bluuebunny/crossref_metadata_2025 huggingface dataset:
Semantic Data Integration and Querying: A Survey and Challenges
Authors: Masmoudi Maroua , Ben Abdallah Ben Lamine Sana , Karray Mohamed Hedi , Archimede Bernard , Baazaoui Zghal Hajer
Published: March 2024

Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.
Efficient Brain Tumor Segmentation with Lightweight Separable Spatial Convolutional Network
Authors: Zhang Hao , Liu Meng , Qi Yuan , Yang Ning , Hu Shunbo , Nie Liqiang , Zhang Wenyin
Published: March 2024

Accurate and automated segmentation of lesions in brain MRI scans is crucial in diagnostics and treatment planning. Despite the significant achievements of existing approaches, they often require substantial computational resources and fail to fully exploit the synergy between low-level and high-level features. To address these challenges, we introduce the Separable Spatial Convolutional Network (SSCN), an innovative model that refines the U-Net architecture to achieve efficient brain tumor segmentation with minimal computational cost. SSCN integrates the PocketNet paradigm and replaces standard convolutions with depthwise separable convolutions, resulting in a significant reduction in parameters and computational load. Additionally, our feature complementary module enhances the interaction between features across the encoder-decoder structure, facilitating the integration of multi-scale features while maintaining low computational demands. The model also incorporates a separable spatial attention mechanism, enhancing its capability to discern spatial details. Empirical validations on standard datasets demonstrate the effectiveness of our proposed model, especially in segmenting small and medium-sized tumors, with only 0.27M parameters and 3.68 GFlops. Our code is available at
https://github.com/zzpr/SSCN
.
Silver Nanoparticles Decorated in In Situ Reduced Graphene Oxide Nanohybrids Improved Properties in Poly(vinylidene fluoride)/Poly(methyl methacrylate) Blends
Authors: Khorramshokouh Afifeh , Ramezani Hesam , Sahami Mehdi , Sharif Mehdi , Vaferi Behzad
Published: March 2024

In this paper, reduced graphene oxide decorated with silver nanoparticle (rGO‐Ag) nanohybrids were prepared using an environmentally friendly approach and incorporated as reinforcement in poly(vinylidene fluoride)‐poly(methyl methacrylate) blends via a melt mixing process. The microstructure of rGO‐Ag nanohybrids and its effect on the microstructure, mechanical, thermal, and electrical properties of the PVDF/PMM/rGO‐Ag was studied using Fourier transform infrared (FTIR), Raman spectroscopy, X‐ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), tensile, thermogravimetric analysis (TGA), and impedance spectroscopy methods. FTIR and TEM analysis confirmed that rGO‐Ag successfully synthesized and Ag nanoparticles are located on the rGO surface. The tensile analysis demonstrated that incorporating 1 wt.% of rGO‐Ag in PVDF/PMMA blend increases Young’s modulus and strength of nanocomposite up to 31% and 35%, respectively. The Halpin‐Tsai model was also used for PVDF/PMMA/rGO‐Ag nanocomposites, and the results confirmed that this model works well to predict the tensile modulus. Impedance spectroscopy analysis showed that the presence of rGO‐Ag nanohybrids in PVDF/PMMA blend effectively enhanced the conductivity of PVDF/PMMA blend. TGA results demonstrated that the presence of rGO‐Ag nanohybrids enhanced the thermal stability of nanocomposites and increased the degradation temperature of PVDF/PMMA/rGO‐Ag nanocomposites in the range of 20°C compared to PVDF/PMMA blend.
Effects of Sitting and Supine Positions on Tongue Color as Measured by Tongue Image Analyzing System and Its Relation to Biometric Information
Authors: Murakami Aya , Morita Akira , Watanabe Yuki , Ishikawa Takaya , Nakaguchi Toshiya , Ochi Sadayuki , Namiki Takao
Published: March 2024

Tongue diagnosis is one of the important diagnostic methods in Kampo (traditional Japanese) medicine, in which the color and shape of the tongue are used to determine the patient’s constitution and systemic symptoms. Tongue diagnosis is performed with the patient in the sitting or supine positions; however, the differences in tongue color in these two different positions have not been analyzed. We developed tongue image analyzing system (TIAS), which can quantify tongue color by capturing tongue images in the sitting and supine positions. We analyzed the effects on tongue color in two different body positions. Tongue color was quantified as L∗a∗b∗ from tongue images of 18 patients in two different body positions by taking images with TIAS. The CIEDE 2000 color difference equation (ΔE00) was used to assess the difference in tongue color in two different body positions. Correlations were also determined between ΔE00, physical characteristics, and laboratory test values. The mean and median ΔE00 for 18 patients were 2.85 and 2.34, respectively. Of these patients, 77.8% had a ΔE00 < 4.1. A weak positive correlation was obtained between ΔE00 and systolic blood pressure and fasting plasma glucose. Approximately 80% of patients’ tongue color did not change between the sitting and supine positions. This indicates that the diagnostic results of tongue color are trustworthy even if medical professionals perform tongue diagnosis in two different body positions.
Efficient Numerical Method for Solving a Quadratic Riccati Differential Equation
Authors: Yirga Wendafrash Seyid , Gelu Fasika Wondimu , Melesse Wondwosen Gebeyaw , Duressa Gemechis File
Published: March 2024

This study presents families of the fourth‐order Runge–Kutta methods for solving a quadratic Riccati differential equation. From these families, the England version is more efficient than other fourth‐order Runge–Kutta methods and practically well‐suited for solving initial value problems in general and quadratic Riccati differential equation in particular. The stability analysis of the present method is well‐established. In order to verify the accuracy, we compared the numerical solutions obtained using the England version of fourth‐order Runge–Kutta method with the recently published works reported in the literature. Several counter examples are solved using the present methods to demonstrate their reliability and efficiency.
Black Soldier Fly Larvae Meal vs. Soy Protein Concentrate Meal: A Comparative Digestibility Study in Barramundi (Lates calcarifer)
Authors: Le Boucher Richard , Chung Weiqiang , Ng Kai Lin Jessalin , Tan Lydia Shun En , Lee Co Sin
Published: March 2024

Black soldier fly larvae meal (BSFM) from Hermetia illucens has emerged as a dependable protein source in aquaculture. This study aimed to assess BSFM’s digestibility in barramundi juveniles and compare it to soy protein concentrate meal (SPCM). Four diets (control, 30% BSFM; 30% SPCM; and commercial feed control) were tested on 1,800 barramundi juveniles (weight: 71.1 g) over 51 days in a recirculating aquaculture system (RAS). The final body weight (FBW) of fish fed with BSFM reached 222.2 (± 8.7), with a thermal‐unit growth coefficient (TGC) of 4.33 (± 0.15) and a feed conversion ratio (FCR) of 1.04 (± 0.01). While BSFM and SPCM inclusion did not significantly impact FBW, body weight gain (BWG), TGC, or survival rates (P  > 0.05), FCR increased. BSFM significantly raised total feed intake (P < 0.05) but did not affect daily feed intake (P  > 0.05). Importantly, BSFM and SPCM inclusion did not alter diet apparent digestibility coefficient (ADC) for any nutrient groups (P  > 0.05), with BSFM showing high ADC for dry matter (76.8%), crude protein (93.2%), and gross energy (83.9%). No significant difference (P  > 0.05) was observed in these ADCs between BSFM and SPCM. The high digestibility of BSFM in warm seawater RAS (29.4°C) under high stocking density (33.7 kg m−3) supports its efficacy in contemporary barramundi farming.
Gas‐to‐Biochar Byproducts of Pyrolysis and Gasification of Star Anise Biomass
Authors: Cao Hanlin , Luo Qingbao , He Yao , Liang Jiayu , Lin Sen , Evrendilek Fatih , Qi Jianying , Yang Zuoyi , Zhong Sheng , Liu Jingyong , Yang Chunxiao , Huang Wenxiao
Published: March 2024

In a transition to a circular economy, second‐generation biomass energy has come to the forefront. The present study is aimed at characterizing biochar and byproducts of the pyrolysis of star anise residue (ANI) in the N2 and CO2 atmospheres as well as the kinetics and optimal reaction mechanisms based on the Flynn–Wall–Ozawa and Coats‐Redfern methods. The ANI pyrolysis involved three stages, with the first one (161.5–559.1°C) as the main phase. The activation energy was lower in the N2 atmosphere than in the CO2 atmosphere (179.44–190.17 kJ/mol). The primary volatile products generated during the ANI pyrolysis were small molecule products (H2O, CO2, CO, and CH4), organic acids, alcohols, and ketones. The atmosphere type exerted a minimal impact on the types of gases released, with the CO2 atmosphere increasing CO and CH4 emissions. The pyrolytic oil of ANI contained a variety of organic compounds, including alcohols, phenols, ketones, acids, sugars, and other nitrogen‐ and oxygen‐containing cyclic compounds, with its predominant compounds being acids, esters, ketones, and sugars. The elevated temperature range of 300–700°C enhanced the charring degree of the ANI biochar. The biochar showed stronger aromaticity in the CO2 atmosphere but better granularity in the N2 atmosphere. This study introduced an innovative perspective by showcasing the potential of ANI as a promising biomass source for energy generation and underscored its abundance, sustainability, and applicability as a raw material in fragrance production. It also emphasized the significance of CO2‐reuse technology as a means to mitigate CO2 emissions. The findings of this work offer a theoretical and practical basis for the comprehensive utilization and efficient disposal of star anise residues.
Bilateral Glaucoma as Possible Additional Feature for PGAP3-Associated Hyperphosphatasia
Authors: Obaid Osama , Batawi Reem , Alqurashi Heba , Ewis Thana , Obaid Ahmad A.
Published: March 2024

Hyperphosphatasia with mental disorder (HPMRS) is a rare autosomal recessive disease caused by gene mutations in enzymes involved in the synthesis and remodeling of lipids. Seven-month-old boy diagnosed with bilateral glaucoma had a cleft palate, facial dysmorphism, hypertelorism, a broad nasal bridge, and large fleshy earlobes. A brain MRI scan also revealed brain abnormalities. The observed phenotype in a seven-month-old boy is in agreement with the phenotypic features of HPRMS type-4. Whole exome sequencing revealed a possible pathogenic variant of PGAP3 in a homozygous state (c.320C > T, p.Ser107Leu) which supported the diagnosis of HPRMS type-4. We report an unusual presentation for HPMRS and suggest adding this syndrome to the list of differential diagnoses of syndromic congenital glaucoma.
Isolation and Genetic Characterization of the Human Relapsing Fever Spirochete Borrelia persica from a Dog with Improved Cultivation Techniques
Authors: Shwartz Dor , Nachum-Biala Yaarit , Ben-Shitrit Bar , Straubinger Reinhard K. , Baneth Gad
Published: March 2024

Borrelia persica is the causative agent of tick‐borne relapsing fever in Israel and is prevalent in the Eastern Mediterranean basin and parts of Asia. Infection with B. persica causes severe illness and potentially life‐threatening complications in humans and companion animals. Isolation and in vitro cultivation of B. persica in culture medium is difficult and there are only two previous isolates of this spirochete. Here, we describe the first cultivation of B. persica from an ill dog. Isolation from the dog’s blood was performed with the Pettenkofer‐LMU Bp medium and spirochetes with vibrant motility and cell density of 2 × 106/ml were observed in culture by dark‐field microscopy 3 days after sampling. The isolate was passaged every 3–4 days with cell densities reaching up to 3 × 107/ml achieved over 30 passages. Cryopreservation was made at −80°C without any cryoprotectant additive, and successful growth from thawed culture samples was observed up to 3 months after freezing with repeated freeze and thaw cycles. Generation time during the exponential growth phase was 14.6 hr. Genetic characterization by polymerase chain reaction amplification and DNA sequencing of the flagellin, glycerophosphodiester phosphodiesterase, and 16S rRNA genes indicated that the isolate is a B. persica genotype I strain associated with human relapsing fever. The new canine isolate would be helpful for research on the pathogenesis of relapsing fever and the new modifications in cultivation and preservation methods may assist in future studies of relapsing fever by simplifying previously reported in vitro methods.
Development of Live Vaccine Candidates for Canine Influenza H3N2 Using Naturally Truncated NS1 Gene
Authors: Hwang Jaehyun , Yoon Sun-Woo , Ga Eulhae , Moon Suyun , Choi Jaeseok , Bae Eunseo , Kang Jung-Ah , Kim Hye Kwon , Jeong Dae Gwin , Song Daesub , Na Woonsung
Published: March 2024

The NS1 influenza protein of influenza A virus is a viral nonstructural protein encoded by the NS gene segment that has multiple accessory functions during viral infection. In recent years, the major role ascribed to NS1 has been its inhibition of host immune responses, especially the limitation of both interferon (IFN) production and the antiviral effects of IFN‐induced protein. We isolated an equine influenza virus with a naturally truncated NS1 gene in our previous study. In this current research, we inserted this partially truncated NS gene into the H3N2 canine influenza virus using reverse genetics to develop a live attenuated vaccine strain. To evaluate whether the developed strain is suitable as a live vaccine candidate, we compared its replication kinetics with wild‐type virus in MDCK cells and specific pathogen‐free eggs. Additionally, we investigated host antiviral gene expression, viral replication in the respiratory system, and associated lung tissue damage in mice experiments. To confirm the efficacy of the vaccine candidate, we evaluated the immunogenicity and protectivity of the developed vaccine strain against canine influenza H3N2, compared with a commercial inactivated vaccine. Through these experiments, it was confirmed that the naturally truncated NS1 inserted virus has sufficient potential as a live vaccine candidate, and we hopefully expect that this study would make a great contribution to the development of a live vaccine for canine influenza H3N2.
A Case in Which HLA-DR4 is Involved in the Development of Complex Immune-Related Endocrinological Adverse Events following Combination Therapy with Nivolumab and Ipilimumab
Authors: Kidawara Yonekazu , Kadoya Manabu , Kakutani-Hatayama Miki , Koyama Hidenori
Published: March 2024

Immune checkpoint inhibitors (ICIs) have become a focal point in cancer immunotherapy, though their utilization is also linked to the occurrence of diverse immune-related adverse events (irAEs). Herein, we present details of a 42-year-old woman diagnosed with a malignant vaginal melanoma who underwent ICI therapy with the combination of nivolumab and ipilimumab. Approximately two months after initiating therapy, the patient manifested destructive thyroiditis and fulminant type 1 diabetes mellitus, thus necessitating intensive insulin therapy. Following the onset of adrenocorticotropic hormone deficiency, frequent hypoglycemic episodes prompted the initiation of replacement therapy with hydrocortisone. Human leukocyte antigen (HLA)-DNA typing revealed the presence of HLA-DRB1∗04 : 05 and DQB1∗04 : 01. HLA-DR4 has been suggested to be associated with the development of multiple endocrine irAEs. This is the first reported case of three endocrine irAEs occurring within a short period, in which the presence of HLA-DR4 may have contributed to the pathogenesis.
Geochemical Characteristic and Source Correlation of Biodegraded Oils from the Western Halaalate Area of Junggar Basin
Authors: Ma Zunjing , Zhang Yingchun , Wang Youjing , Liu Yancheng
Published: March 2024

The relationship between biodegraded oil and its source has long been a complex and contentious topic. The Western Halaalate area is located in the Piedmont area on the northwest margin of the Junggar Basin. Source rocks of the Fengcheng Formation exist both locally and in the nearby Mahu Depression. In order to determine the source of biodegraded crude oil in this area, the molecular marker characteristics of biodegraded crude oil were analyzed by gas chromatography-mass spectrometry. The results showed that the vitrinite reflectance (Ro) of source rocks of the Permian Fengcheng Formation in the nearby Mahu Depression is greater than 1.3% and has entered a high mature stage of condensate oil and moisture gas; the source rock of Permian Fengcheng Formation in Western Halaalate area is in a mature stage with Ro of 0.79%~1.13%. The ascending configuration of tricyclic terpenes C20-C21-C23 for the crude oil samples found in the Carboniferous strata of the Western Halaalate Area is consistent with the characteristics of the Fengcheng Formation source rocks, which are present in both the Western Halaalate Area and the nearby Mahu Depression. Chromatography spectrometry examination shows that crude oils have undergone a varying degree of biodegradation. The Carboniferous oil was originated from the in situ Fengcheng Formation source rocks based on the application of molecular markers resistant to biodegradation, such as maturity parameters, salinity parameters, the new gammacerane index, and aromatic hydrocarbon parameters, combined with the analysis of hydrocarbon migration pathway. In addition, the oil biodegradation alteration rules in the Western Halaalate area were clarified, which advances regional knowledge of the relationship between biodegraded oil and source rocks.
A Dual-Band Low SAR Microstrip Patch Antenna with Jean Substrate for WBAN Applications
Authors: Thaiwirot Wanwisa , Hengroemyat Yotrawee , Kaewthai Thamonwan , Akkaraekthalin Prayoot , Chalermwisutkul Suramate
Published: March 2024

This paper presents a low-profile dual-band microstrip patch antenna operated at the 2.45/5.8 GHz ISM bands, targeting wireless body area network (WBAN) applications. The proposed antenna is fabricated on a jean substrate measuring 74×82 mm2. The four corners of the conventional rectangular patch are cut to activate an additional operating frequency and to enhance the radiation pattern. The circular slot is added to the radiating patch to fine-tune the desired frequencies. The measured impedance bandwidth of the proposed antenna at 2.45 GHz and 5.8 GHz bands is 3.68% (2.40 GHz–2.49 GHz) and 3.81% (5.67 GHz–5.89 GHz), respectively. The measured maximum gains are 3.08 dBi at 2.45 GHz and 2.15 dBi at 5.8 GHz. Moreover, the antenna’s performance when placed on flat and rounded body surfaces is also investigated. The proposed antenna achieves stable radiation pattern and low specific absorption rate (SAR) value with low complexity in design. The results indicate that the proposed antenna can be a promising candidate for WBAN applications.
Impact of Aqueous Extract Artemisia Herba‐Alba Leaves as a Green Inhibitor against Acid Activation of 2024 Aluminum Alloy
Authors: Hechiche Nacer , Culioli Gérald , Kadri Abdelaziz , Boughrara Dalila , Saal Amar , Perrin F. Xavier
Published: March 2024

This work is part of the development of new bio‐sourced corrosion inhibitors from an abundant resource that can replace conventional synthetic inhibitors that are harmful to both human health and the environment. The corrosion inhibition performance of an aqueous extract of Artemisia herba-alba on the corrosion of 2024 aluminum alloy in a 1 M hydrochloric acid solution is investigated by weight loss method, electrochemical (linear polarization, potentiodynamic polarization, and electrochemical impedance spectroscopy) and SEM techniques. The extract shows excellent corrosion‐inhibiting properties on aluminium alloy with a maximum inhibition efficiency of 93% at 0.6 g/L. The adsorption of the natural extract obeys the extended Langmuir isotherm equation adsorption model for multicomponent systems. Temperature studies show that the efficiency of the extract decreases with increasing temperature and that the corrosion activation energies increase in the presence of the extract. Liquid chromatography/high resolution mass spectrometry is used to identify the chemical constituents of the natural extract, and the most abundant phytochemicals for each subclass of metabolite are investigated using density functional theory (DFT) calculations. This study paves the way for further development of a plant that is particularly abundant in the desert regions of North Africa and has until now been used mainly for food for livestock and for pharmaceutical applications.
A Multiobjective Model for a Multilevel Blood Supply Chain to Improve the Attractiveness of Blood Centers during the COVID-19 Pandemic
Authors: Esfandabadi Abolfazl Moghimi , Shishebori Davood , Fakhrzad Mohammad-Bagher , Zare Hassan Khademi
Published: March 2024

Failure to control crisis conditions leads to irreparable damage to many supply chains around the world, including blood supply chains (BSCs) as critical networks in the health system. Consequently, it significantly reduces the supply of blood and its products, as vital materials, and exerts detrimental effects on the activities of blood organizations and facilities as well as the health of individuals in society. In the present study, the proposed model seeks to simultaneously minimize the operating costs and the shortage of blood products with the aim of improving the attractiveness of blood centers during the COVID-19 pandemic. Accordingly, by optimizing the overall cost and the attractiveness of blood donation centers, an attractive efficient environment is provided. It can help to remove barriers to blood donation and improve blood health. To this end, the model takes certain strategies into account for the proper establishment of new local blood collection centers (BCCs) and mobile BCCs. It also arranges suitable transportation vehicles for the efficient transfer of blood products to the provincial centers of the candidate country and sets various incentive policies for blood donation. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the BCCs, a two-objective mathematical model is developed. It produces Pareto solutions using the ε-constraint method. Finally, the efficiency of the proposed approach and the sensitivity of the corresponding parameters are analyzed through a practical case study. The obtained results represent that a growth in the attractiveness of blood centers induces a raise in the number of donors, and, consequently, the amount of the donated blood grows. This depends on more investment at all levels of the supply chain, including collection, production, storage, and transportation. Moreover, the performance and attractiveness of a BSC can be enhanced significantly if the number of collection centers and the amount of blood sent from the receiving centers to the demand nodes are increased.
Temperature Effects on Physiochemical Characteristics of Sugar-Based Natural Deep Eutectic Solvents
Authors: Tarikuzzaman Mohammad , Sagar Viral , Wong Mark James , Lynam Joan G.
Published: March 2024

Phase behavior, density, viscosity, conductivity, pH, and surface tension were measured, and FTIR was performed for a series of mixtures of sugar (glucose, fructose, xylose, and sucrose), water, and choline chloride (ChCl) at specific molar ratios. These mixtures, called sugar-based natural deep eutectic solvents (NADESs), were investigated as a function of temperature. Contact angle measurements indicated that NADES exhibited slightly lower wettability but higher surface tension than water. Temperature was found to greatly impact density, viscosity, and conductivity. The optimum water molar ratio for lower viscosity was found at ratios higher than those reported in the literature, indicating that the NADES investigated may have industrial process applications.
A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants
Authors: Gao Zhihu , Sun Bo , Wu Zeyu , Xi Yang , Feng Qiang , Wang Zili , Zhou Yeli , Pan Junlin , Xu Jiankun
Published: March 2024

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin‐based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic‐jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.
Actividades de Gobierno Corporativo y sostenibilidad: Efectos financieros en algunas empresas pertenecientes al IPSA en Chile
Authors: Rodríguez Rojas Ignacia , Torres Ramírez Nicolás
Published: March 2024

La investigación buscó establecer la relación entre la aplicación de actividades de Gobierno Corporativo, actividades de sostenibilidad y el efecto de estas en los resultados financieros de las empresas seleccionadas. Se aplicó un análisis estadístico para estimar la eficiencia del estudio y comprobar si existe algún efecto en la rentabilidad empresarial. Mediante la recopilación de la información relacionada con sostenibilidad, gobernanza y finanzas de las organizaciones analizadas se construyó una base de datos que permitió aplicar un modelo econométrico a las variables analizadas. La evidencia muestra que la emisión de informes de sostenibilidad tiene una relación positiva con la rentabilidad de las empresas, por el lado contrario no demuestra lo mismo respecto a las variables analizadas de Gobierno Corporativo. Además, estos hallazgos pueden proporcionar evidencia sobre cómo tomar la decisión de divulgar información financiera y no financiera puede mejorar la rentabilidad corporativa.
La relevancia perentoria de la sostenibilidad empresarial en la contabilidad: un enfoque holístico para la perpetuidad de la prosperidad
Authors: Paredes Carrasco Rafael
Published: March 2024

Este ensayo aborda la importancia de la sostenibilidad empresarial en la contabilidad. Se presenta una revisión de la literatura sobre la conjugación sostenibilidad-contabilidad, donde se observa un aumento en la publicación de artículos relacionados con la materia. Además, se presenta la teoría del triple impacto como una herramienta útil para comprender la interconexión entre la sostenibilidad económica, social y ambiental en el contexto empresarial. El documento también incluye propuestas para calcular y medir el impacto sostenible de las empresas y cómo la contabilidad de gestión puede ayudar a las empresas a tomar decisiones informadas y responsables. En cuanto a la metodología, se realizó una revisión bruta de la data de literatura concerniente a la conjugación sostenibilidad-contabilidad a través de Scopus, arrojando un total de 3.303 artículos publicados desde el año 2017 hasta el año 2022. También se presentó un marco conceptual de la sostenibilidad desde la óptica de la contabilidad y se propuso un Modelo de ecuaciones estructurales para relacionar variables latentes sostenibles. Se destaca la necesidad de un enfoque multidisciplinario para abordar adecuadamente los desafíos y oportunidades de integrar la sostenibilidad en la contabilidad y la gestión empresarial.
Ramón Ramos Arriagada: Un Auténtico Académico en el área de la Administración Financiera
Authors: Caiceo Escudero Jaime , Retamal Peñaloza Myriam
Published: March 2024

En este artículo se presenta la faceta académica de un prestigioso académico chileno, natural de Punta Arenas, pero que desarrolló su labor principalmente en Santiago y en el extranjero. Se ha destacado especialmente por su experticia en el ámbito de la administración financiera de las empresas con enfoque latinoamericano y por iniciar en sus clases el uso del idioma inglés. Su ligazón por cerca de 50 años ha estado con la Universidad Técnica del Estado y que desde 1981 la dictadura militar le restringió las sedes de provincia y se transformó en la Universidad de Santiago de Chile. En ella ha sido académico en el amplio sentido de la palabra: docencia, investigación y relación con el medio y ha ocupado importantes cargos: Director del Departamento de Contabilidad y Auditoría, Vicerrector de Administración y Finanzas y Contralor Universitario. Ha establecido una importante red, tanto en Chile como en el extranjero de académicos ligados a la Contabilidad y a la Auditoría.
Data from RETRACTED: Methylation Mediated Silencing of MicroRNA-1 Gene and Its Role in Hepatocellular Carcinogenesis
Authors: Datta Jharna , Kutay Huban , Nasser Mohd W. , Nuovo Gerard J. , Wang Bo , Majumder Sarmila , Liu Chang-Gong , Volinia Stefano , Croce Carlo M. , Schmittgen Thomas D. , Ghoshal Kalpana , Jacob Samson T.
Published: March 2024

Abstract
MicroRNAs (miR) are a class of small (∼21 nucleotide) noncoding RNAs that, in general, negatively regulate gene expression. Some miRs harboring CGIs undergo methylation-mediated silencing, a characteristic of many tumor suppressor genes. To identify such miRs in liver cancer, the miRNA expression profile was analyzed in hepatocellular carcinoma (HCC) cell lines treated with 5-azacytidine (DNA hypomethylating agent) and/or trichostatin A (histone deacetylase inhibitor). The results showed that these epigenetic drugs differentially regulate expression of a few miRs, particularly miR-1-1, in HCC cells. The CGI spanning exon 1 and intron 1 of miR-1-1 was methylated in HCC cell lines and in primary human HCCs but not in matching liver tissues. The miR-1-1 gene was hypomethylated and activated in DNMT1−/− HCT 116 cells but not in DNMT3B null cells, indicating a key role for DNMT1 in its methylation. miR-1 expression was also markedly reduced in primary human hepatocellular carcinomas compared with matching normal liver tissues. Ectopic expression of miR-1 in HCC cells inhibited cell growth and reduced replication potential and clonogenic survival. The expression of FoxP1 and MET harboring three and two miR-1 cognate sites, respectively, in their respective 3′-untranslated regions, was markedly reduced by ectopic miR-1. Up-regulation of several miR-1 targets including FoxP1, MET, and HDAC4 in primary human HCCs and down-regulation of their expression in 5-AzaC–treated HCC cells suggest their role in hepatocarcinogenesis. The inhibition of cell cycle progression and induction of apoptosis after re-expression of miR-1 are some of the mechanisms by which DNA hypomethylating agents suppress hepatocarcinoma cell growth. [Cancer Res 2008;68(13):5049–58]
A comparative analysis of cervical cancer diagnosis using machine learning techniques
Authors: Elmi Abdikadir Hussein , Abdullahi Abdijalil , Ali Bare Mohamed
Published: March 2024

This study undertakes a comprehensive analysis of cervical cancer diagnosis using machine learning (ML) techniques. We start by introducing the critical importance of early and accurate diagnosis of cervical cancer, a significant health issue globally. The objective of this research is to compare the effectiveness of three ML algorithms: K-nearest neighbors (KNN), linear support vector machine (SVM), and Naive Bayes classifier, in predicting biopsy results for cervical cancer. Our methodology involves utilizing a substantial dataset to train and test these algorithms, focusing on performance measures like accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). The findings reveal that KNN demonstrates superior performance, with high precision, recall, accuracy, and F1 score, alongside a notable AUC. This suggests KNN's potential utility in clinical applications for cervical cancer prognosis. Meanwhile, linear SVM and Naive Bayes exhibit certain limitations, indicating a need for further optimization. This study highlights the promising role of ML in enhancing medical diagnostic processes, particularly in oncology.
Fast Naïve Bayes classifiers for COVID-19 news in social networks
Authors: Cahyono Hasan Dwi , Mahadewa Atara , Wijayanto Ardhi , Wardani Dewi Wisnu , Setiadi Haryono
Published: March 2024

The growth of fake news has emerged as a substantial societal concern, particularly in the context of the COVID-19 pandemic. Fake news can lead to unwarranted panic, misinformed decisions, and a general state of confusion among the public. Existing methods to detect and filter out fake news have accuracy, speed, and data distribution limitations. This study explores a fast and reliable approach based on Naïve Bayes algorithms for fake news detection on COVID-19 news in social networks. The study used a dataset of 10,700 tweets and applied text pre-processing, term-weighting, document frequency thresholding (DFT), and synthetic minority oversampling techniques (SMOTE) to prepare the data for classification. The study assessed the performance and runtime of four models: gradient boosting (GDBT), decision tree (DT), multinomial Naïve Bayes (MNB), and complement Naïve Bayes (CNB). The testing results showed that the CNB model reaches the highest accuracy, precision, recall, and F1-score of approximately 92% each, with the shortest runtime of 0.55 seconds. This study highlights the potential of the CNB model as an effective tool for detecting online fake news about COVID-19, given its superior performance and rapid processing time.
Water quality monitoring with an early warning system for enhancing the shrimp aquaculture production
Authors: Aryotejo Guruh , Adi Prajanto Wahyu , Sarwoko Eko Adi
Published: March 2024

Aquaculture provided 43% of the aquatic animal food consumed by humans in 2007, and it is anticipated to expand even more to meet the increasing future demand. Marine Science Techno Park (MSTP) is one of the Techno Parks in Indonesia and is located in Teluk Awur, Jepara. MSTP has an intensive system of Vannamei shrimp farming activities. The challenge that has been faced annually is the organic matter of the remaining shrimp feed that accumulates in the waters can cause a decrease in pond water quality. Ammonia-N anions derived from the decomposition of feed residues can cause toxins in shrimp culture ponds which can further interfere with shrimp survival. The objective of this study is to design a self-controlled water quality monitoring system that is equipped with an early warning system based on internet of things (IoT) and to implement the design for analyzing the water quality including dissolved oxygen (DO), pH, and temperature in the Vannamei farming pond of MSTP UNDIP through integrating IoT which is equipped with an early warning system. If the water quality reaches a threshold value, the monitoring system will send a sound signal to the buzzer followed by sending an alert notification in real-time conditions automatically to a smartphone.
Exploring corpus linguistics via computational tool analysis: key finding review
Authors: Wan Jusoh Wan Nur Aida Sakinah , Jobar Norfaizah Abdul , Md Yusoff Md Zahril Nizam , Mahat Hanifah
Published: March 2024

Corpus linguistics investigates language using extensive text databases. Tools assist researchers in analyzing, extracting, and interpreting linguistic information efficiently. Furthermore, if researchers only use traditional tools in corpus linguistic analysis, they will lack the comprehensiveness and efficiency required to effectively navigate and derive valuable insights from language data. This paper employed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to find the primary data based on a few keywords in corpus linguistic, corpus analysis, computational linguistic, text corpora and tool support. Based on this method, we used advanced searching techniques on Scopus and Web of Science (WoS) and discovered (N=28) data pertinent to the study. Expert scholars decide on a theme based on the problem, which is (i) types of corpus tools and their uses; (ii) their contributions and their capabilities (iii) limitations of corpus tools. All the tools were used in interdisciplinary studies. In summary, this systematic review uncovers pivotal key findings at the intersection of computational tools and corpus analysis, enriching linguistic knowledge. It highlights the interdisciplinary potential of corpus-based analysis in advancing linguistic tools and, their applications, as well as language analysis.
Classifying flexible pavement defects using hybrid machine learning approach
Authors: Soni Jaykumar , Gujar Rajesh
Published: March 2024

The transportation infrastructure sector significantly impacts a country’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.
Empowering health data protection: machine learning-enabled diabetes classification in a secure cloud-based IoT framework
Authors: Hamid Dalia Ebrahim , Amer Hanan M. , Salah Moustafa Hossam El-Din , Marie Hanaa Salem
Published: March 2024

Smart medical devices and the internet of things (IoT) have enhanced healthcare systems by allowing remote monitoring of patient's health. Because of the unexpected increase in the number of diabetes patients, it is critical to regularly evaluate patients' health conditions before any significant illness occurs. As a result of transmitting a large volume of sensitive medical data, dealing with IoT data security issues remains a difficult challenge. This paper presents a secure remote diabetes monitoring (SR-DM) model that uses hybrid encryption, combining the advanced encryption standard and elliptic curve cryptography (AES-ECC), to ensure the patients' sensitive data is protected in IoT platforms based on the cloud. The health statuses of patients are determined in this model by predicting critical situations using machine learning (ML) algorithms for analyzing medical data sensed by smart health IoT devices. The results reveal that the AES-ECC approach has a significant influence on cloud-based IoT systems and the random forest (RF) classification method outperforms with a high accuracy of 91.4%. As a consequence of the outcomes obtained, the proposed model effectively establishes a secure and efficient system for remote health monitoring.
Heart disease prediction using ML through enhanced feature engineering with association and correlation analysis
Authors: Lakshmanarao Annemneedi , Sai Krishna Thotakura Venkata , Ravi Kiran Tummala Srinivasa , Murali krishna Chinta Venkata , Ushanag Samsani , Supriya Nandikolla
Published: March 2024

Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset.
Enabling low-latency IoT communication for resource-constrained devices with the led cipher and decipher protocol
Authors: Naik Mahendra Shridhar , Sreekantha Desai Karanam , Sairam Kanduri VSSSS
Published: March 2024

Block cipher algorithms are crucial for securing applications on resource-constrained devices. This paper introduces the modified light encryption device (MLED) cipher-decipher architecture, specifically designed to accommodate both 64-bit and 128-bit key sizes while maintaining a consistent 64-bit block and data size. MLED comprises 8-step and 12-step processes for MLED-64 and MLED-128 modules, respectively. Each stage involves a four-round operation followed by an add-round key operation. The add constant module (ACM) and mixed column modules (MCMs) within the round operation have been optimized for improved latency and throughput. Performance analysis reveals that MLED-64/128 requires less than 1% of the available slices and operates at 125 MHz on the Artix-7 FPGA. It achieves delays of 7.5 and 12.5 clock cycles for MLED-64 and MLED-128, respectively, translating to throughputs of 1366.5 Mbps and 819.89 Mbps. Additionally, MLED-64/128 exhibits hardware efficiencies of 2.373 and 0.986 Mbps/slice, respectively. Comparative evaluations with existing LED and other block ciphers (BCs) demonstrate that MLED-64/128 achieves significant improvements in latency, throughput, and efficiency, making it a compelling choice for securing resource-constrained IoT applications.
Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization
Authors: Silvester Sinzy , Kurain Shaji
Published: March 2024

E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.
Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning
Authors: Swain Satyaprakash , Pattnayak Binod Kumar , Mohanty Mihir Narayan , Jayasingh Suvendra Kumar , Patra Kumar Janardan , Panda Chittaranjan
Published: March 2024

Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, partial tree (PART), random forest (RF), and support vector machine (SVM). The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.