bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating the Impact of Seed‐Borne Mycoflora on Seed Quality and Health of Various Oryza sativa Varieties
Authors: Haider Mohammad Waris , Mishra Mihir Kumar , Fayaz Falak , Ahmad Dilshad , Mehmood Azhar
Published: November 2024

Seed mycoflora poses significant challenges to rice production, leading to substantial economic losses. Fourteen samples of stored and freshly harvested seeds of five rice varieties were collected from the Agricultural farm of Orissa University of Agriculture and Technology, Bhubaneswar, Odisha, to study the morphology of the mycoflora associated with rice seeds and their impact on seed health and quality. Eight fungal species, including Fusarium pallidoroseum, Fusarium fujikuroi, Curvularia lunata, Helminthosporium oryzae, Trichoconis padwickii, Aspergillus flavus, Rhizopus sp., and Penicillium sp., were isolated, identified, and morphologically characterized based on colony growth, mycelial features, conidial structures, and spore measurements. The rice varieties, viz., Hema, Jyotirmayee, Swarna, Lalat, and Annapurna, were artificially inoculated with F. pallidoroseum, F. fujikuroi, T. padwickii, C. lunata, and H. oryzae. Seeds showed that the fungi had a variable effect on seed quality parameters like germination, seedling vigor index I and seedling vigor index II, speed of germination, and seed health. A maximum of 16.33% reduction of germination was observed by T. padwickii, followed by F. fujikuroi (14.80% in Hema). Seedling vigor (1754.81) incited by T. padwickii inoculation was recorded to be the lowest. Seed rot up to 15.8% was observed in the case of F. fujikuroi. The percentage of root discoloration reached 40.0% by F. pallidoroseum and T. padwickii. In Jyotirmayee, C. lunata caused an 18.88% germination reduction and the lowest seedling vigor (1866.03) by T. padwickii. T padwickii also caused the highest reduction of seedling dry weight (36.12%) in this variety. The result reveals the diversity and prevalence of fungal pathogens in rice seeds. Hence, this study underscores the importance of seed sterilization practices in reducing fungal contamination and the requirement of thorough seed examination to reduce the effects of seed mycoflora associated with rice seeds, which could affect rice production.
Supplementary Figure S22 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S22
Supplementary Data from Precise In Situ Delivery of a Photo-Enhanceable Inflammasome-Activating Nanovaccine Activates Anticancer Immunity
Authors: Zhou Yang , Pang Li , Ding Tao , Chen Kang , Liu Jinzhao , Wu Meicen , Wang Weiping , Man Kwan
Published: November 2024

Characterization of nanovaccines and in vivo/in vitro experimental results
Supplementary Figure S13 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S13
Supplementary Methods from PTEN Loss Shapes Macrophage Dynamics in High-Grade Serous Ovarian Carcinoma
Authors: Spear Sarah , Le Saux Olivia , Mirza Hasan B. , Iyer Nayana , Tyson Katie , Grundland Freile Fabio , Walton Josephine B. , Woodman Chloé , Jarvis Sheba , Ennis Darren P. , Aguirre Hernandez Carmen , Xu Yuewei , Spiliopoulou Pavlina , Brenton James D. , Costa-Pereira Ana P. , Cook David P. , Vanderhyden Barbara C. , Keun Hector C. , Triantafyllou Evangelos , Arnold James N. , McNeish Iain A.
Published: November 2024

Description of supplementary methods
Supplementary Figure S12 from Galectin-9 Mediates the Functions of Microglia in the Hypoxic Brain Tumor Microenvironment
Authors: Lee Chanju , Yu Dahee , Kim Hyung-Seok , Kim Ki Sun , Chang Chi Young , Yoon Hee Jung , Won Su Bin , Kim Dae Yeon , Goh Eun Ah , Lee Yong Sun , Park Jong-Bae , Kim Sang Soo , Park Eun Jung
Published: November 2024

Supplementary Fig. S12
Supplementary Table S6 from YAP1 Inhibition Induces Phenotype Switching of Cancer-Associated Fibroblasts to Tumor Suppressive in Prostate Cancer
Authors: Song Hongtao , Lu Tong , Han Donghui , Zhang Jiayu , Gan Lunbiao , Xu Chao , Liu Shaojie , Li Peng , Zhang Keying , Hu Zhihao , Li Hongji , Li Yu , Zhao Xiaolong , Zhang Jingliang , Xing Nianzeng , Shi Changhong , Wen Weihong , Yang Fa , Qin Weijun
Published: November 2024

Supplementary Table 6: The clinical information of scRNA-seq data and the numbers of cells in each sample
Supplementary Table S4 from YAP1 Inhibition Induces Phenotype Switching of Cancer-Associated Fibroblasts to Tumor Suppressive in Prostate Cancer
Authors: Song Hongtao , Lu Tong , Han Donghui , Zhang Jiayu , Gan Lunbiao , Xu Chao , Liu Shaojie , Li Peng , Zhang Keying , Hu Zhihao , Li Hongji , Li Yu , Zhao Xiaolong , Zhang Jingliang , Xing Nianzeng , Shi Changhong , Wen Weihong , Yang Fa , Qin Weijun
Published: November 2024

Supplementary Table 4: Antibodies for Western Blot and immunofluorescence staining
Supplementary Table S2 from YAP1 Inhibition Induces Phenotype Switching of Cancer-Associated Fibroblasts to Tumor Suppressive in Prostate Cancer
Authors: Song Hongtao , Lu Tong , Han Donghui , Zhang Jiayu , Gan Lunbiao , Xu Chao , Liu Shaojie , Li Peng , Zhang Keying , Hu Zhihao , Li Hongji , Li Yu , Zhao Xiaolong , Zhang Jingliang , Xing Nianzeng , Shi Changhong , Wen Weihong , Yang Fa , Qin Weijun
Published: November 2024

Supplementary Table 2: Genotyping Primers and expected band size
Supplementary Figure S1-S5 from YAP1 Inhibition Induces Phenotype Switching of Cancer-Associated Fibroblasts to Tumor Suppressive in Prostate Cancer
Authors: Song Hongtao , Lu Tong , Han Donghui , Zhang Jiayu , Gan Lunbiao , Xu Chao , Liu Shaojie , Li Peng , Zhang Keying , Hu Zhihao , Li Hongji , Li Yu , Zhao Xiaolong , Zhang Jingliang , Xing Nianzeng , Shi Changhong , Wen Weihong , Yang Fa , Qin Weijun
Published: November 2024

Supplementary Figure S1. ScRNA-seq and RNA-seq revealed the ECM-CAF and Lym-CAF. Supplementary Figure S2. Lym-CAF promotes anti-tumor immunity via NF-κB p65. Supplementary Figure S3. ECM-CAF is associated with ECM genes and YAP1. Supplementary Figure S4. YAP1 silencing promotes the secretion of CXCL9/10/11. Supplementary Figure S5. CD248 is a specific biomarker of ECM-CAF.
Supplementary Figure Legends from Live-Cell Invasive Phenotyping Uncovers ALK2 as a Therapeutic Target in LKB1-Mutant Lung Cancer
Authors: Koo Junghui , Seong Chang-Soo , Parker Rebecca E. , Herrera Amy , Dwivedi Bhakti , Arthur Robert A. , Dinasarapu Ashok Reddy , Johnston Henry Richard , Claussen Henry , Tucker-Burden Carol , Ramalingam Suresh S. , Fu Haian , Zhou Wei , Marcus Adam I. , Gilbert-Ross Melissa
Published: November 2024

Legends for Supplementary Figures
Figure S1 from Live-Cell Invasive Phenotyping Uncovers ALK2 as a Therapeutic Target in LKB1-Mutant Lung Cancer
Authors: Koo Junghui , Seong Chang-Soo , Parker Rebecca E. , Herrera Amy , Dwivedi Bhakti , Arthur Robert A. , Dinasarapu Ashok Reddy , Johnston Henry Richard , Claussen Henry , Tucker-Burden Carol , Ramalingam Suresh S. , Fu Haian , Zhou Wei , Marcus Adam I. , Gilbert-Ross Melissa
Published: November 2024

Figure S1
Supplementary Figure 2 from Hypoxia Stimulates PYGB Enzymatic Activity to Promote Glycogen Metabolism and Cholangiocarcinoma Progression
Authors: Pan Yani , Zhou Yue , Shen Yonghua , Xu Lei , Liu Hongwen , Zhang Nannan , Huang Tianlu , Meng Kui , Liu Yu , Wang Lishan , Bai Ge , Chen Qi , Zhu Yun , Zou Xiaoping , Wang Siliang , Wang Zhangding , Wang Lei
Published: November 2024

Hypoxia promotes CCA metabolism and progression through PYGB.
Supplementary Figure 7 from DAZAP1 Phase Separation Regulates Mitochondrial Metabolism to Facilitate Invasion and Metastasis of Oral Squamous Cell Carcinoma
Authors: Zhang Jiayi , Ni Zihui , Zhang Yu , Guo Yan , Zhai Rundong , Wang Mengqi , Gong Zizhen , Wang Mengyao , Zeng Fanrui , Gu Ziyue , Chen Qianming , Liu Laikui , Wang Zhiyong , Zhu Weiwen
Published: November 2024

DAZAP1 promotes OSCC progression via phase separation.
Supplementary Figure 4 from DAZAP1 Phase Separation Regulates Mitochondrial Metabolism to Facilitate Invasion and Metastasis of Oral Squamous Cell Carcinoma
Authors: Zhang Jiayi , Ni Zihui , Zhang Yu , Guo Yan , Zhai Rundong , Wang Mengqi , Gong Zizhen , Wang Mengyao , Zeng Fanrui , Gu Ziyue , Chen Qianming , Liu Laikui , Wang Zhiyong , Zhu Weiwen
Published: November 2024

COX16 enhances invasion and mitochondrial respiration in OSCC cells.
Supplementary Figure S8 from A Dual-Payload Antibody–Drug Conjugate Targeting CD276/B7-H3 Elicits Cytotoxicity and Immune Activation in Triple-Negative Breast Cancer
Authors: Zhou Zhuoxin “Zora” , Si Yingnan , Zhang Jiashuai , Chen Kai , George Ashley , Kim Seulhee , Zhou Lufang , Liu Xiaoguang “Margaret”
Published: November 2024

Supplementary Figure S8
Supplementary Figure S5 from A Dual-Payload Antibody–Drug Conjugate Targeting CD276/B7-H3 Elicits Cytotoxicity and Immune Activation in Triple-Negative Breast Cancer
Authors: Zhou Zhuoxin “Zora” , Si Yingnan , Zhang Jiashuai , Chen Kai , George Ashley , Kim Seulhee , Zhou Lufang , Liu Xiaoguang “Margaret”
Published: November 2024

Supplementary Figure S5
Table 3 from Association of Race and Ethnicity with Genomic Testing at a Comprehensive Cancer Center in North Carolina
Authors: Meernik Clare , Wang Frances , Raveendran Yadurshini , Green Michelle F. , Check Devon K. , Bosworth Hayden B. , Sutton Linda M. , Strickler John H. , Akinyemiju Tomi F.
Published: November 2024

Regression analysis evaluating the association between any genomic testing and race and ethnicity among NH Black and NH White patients with stage IV cancer diagnosed during 2014 to 2019, stratified by cancer type
Table 1 from Association of Race and Ethnicity with Genomic Testing at a Comprehensive Cancer Center in North Carolina
Authors: Meernik Clare , Wang Frances , Raveendran Yadurshini , Green Michelle F. , Check Devon K. , Bosworth Hayden B. , Sutton Linda M. , Strickler John H. , Akinyemiju Tomi F.
Published: November 2024

Sociodemographic and clinical characteristics of patients with stage IV cancer diagnosed during 2014 to 2019 (n = 3,461), overall and stratified by use of genomic testing
Artificial neural networks classification of s-band absorption performance in eco-friendly microwave absorbers
Authors: Ahmad Azizah , Taib Mohd Nasir , Abdullah Hasnain , Ismail Nurlaila , Yassin Ahmad Ihsan Mohd , Mohd Kasim Linda , Mohamad Noor Norhayati
Published: November 2024

Microwave absorbers are essential for applications such as radar stealth and electromagnetic compatibility. Nevertheless, traditional materials encounter obstacles related to cost and sustainability, which has led to the exploration of new options such as materials derived from agricultural waste. This study focuses on the classification challenge of evaluating the absorption performance of eco-friendly microwave absorbers in the S-band (2 to 4 GHz) frequency. Three multilayer perceptron (MLP) algorithms, namely levenberg marquardt (LM), resilient backpropagation (RBP) and scale conjugate gradient (SCG) are assessed for classification accuracy. The dataset consists of 135 absorption performance values of microwave absorbers that were taken from experimental measurements using the naval research laboratory (NRL) arch free. The MLP algorithms will be divided into three divisions, which are training, validation and testing, evaluating important criteria such as accuracy, precision, sensitivity and specificity. The performance of three types of algorithms will be compared using two basic inputs: the absorption values and the single slot sizes. The RBP algorithm achieved 100% accuracy, and a lower mean squared error (MSE) of 0.02500 compared to the LM and SCG. This study provides valuable insights for designing better microwave absorbers and highlights the commercial potential of agricultural waste materials in such applications.
An efficient strategy for optimizing a neuro-fuzzy controller for mobile robot navigation
Authors: Hilali Brahim , Ramdani Mohammed , Naji Abdelwahab
Published: November 2024

Autonomous navigation is one of the key challenges in robotics. In recent years, several research studies have tried to improve the quality of this task by adopting artificial intelligence approaches. Indeed, the neuro-fuzzy approach stands out as one of the most commonly employed methods for developing autonomous navigation systems. Nevertheless, it may encounter problems of accuracy, complexity, and interpretability due to redundancy in the fuzzy rule base, particularly in the fuzzy sets associated with the system’s variables. In this work, a strategy is proposed to optimize an adaptive-network-based fuzzy inference system (ANFIS) controller for reactive navigation by addressing the problem of complexity and accuracy. It consists in combining a suite of methods, namely, data-driven fuzzy modeling, fuzzy sets merging, fuzzy rule base simplification, and parameter training. This process has produced a fuzzy inference system-based controller with high accuracy and low complexity, enabling smooth and near-optimal navigation. This system receives local information from sensors and predicts the appropriate kinematic behavior that enables the robot to avoid obstacles and reach the target in cluttered and previously unknown environments. The performance of the proposed controller and the efficiency of the followed strategy are demonstrated
Optimal task partitioning to minimize failure in heterogeneous computational platform
Authors: Narayana Divyaprabha Kabbal , Babu Sudarshan Tekal Subramanyam
Published: November 2024

The increased energy consumption by heterogeneous cloud platforms surges the carbon emissions and reduces system reliability, thus, making workload scheduling an extremely challenging process. The dynamic voltage- frequency scaling (DVFS) technique provides an efficient mechanism in improving the energy efficiency of cloud platform; however, employing DVFS reduces reliability and increases the failure rate of resource scheduling. Most of the current workload scheduling methods have failed to optimize the energy and reliability together under a central processing unit - graphical processing unit (CPU-GPU) heterogeneous computing platform; As a result, reducing energy consumption and task failure are prime issues this work aims to address. This work introduces task failure minimization (TFM) through optimal task partitioning (OTP) for workload scheduling in the CPU-GPU cloud computational platform. The TFM-OTP introduces a task partitioning model for the CPU-GPU pair; then, it provides a DVFS- based energy consumption model. Finally, the energy-load optimization problem is defined, and the optimal resource allocation design is presented. The experiment is conducted on two standard workloads namely SIPHT and CyberShake workload. The result shows that the proposed TFA-OTP model reduces energy consumption by 30.35%, reduces makespan by 70.78% and reduces task failure energy overhead by 83.7% in comparison with energy minimized scheduling (EMS) approach.
Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication
Authors: Niranjan Pallavi Kanthamangala , Venkatesh Ravikumar
Published: November 2024

The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.
Video conferencing algorithms for enhanced access to mental healthcare services in cloud-powered telepsychiatry
Authors: Senkamalavalli Rajagopalan , Prasad Subramaniyan Nesamony Sheela Evangelin , Shobana Mahalingam , Sri Chellaiyan Bharathi , Sandiri Rajendar , Karthik Jayavarapu , Murugan Subbiah
Published: November 2024

Exploring the video conferencing algorithms for cloud-powered telepsychiatry to improve mental healthcare access. The goal is to evaluate and optimise these algorithms' latency, bandwidth utilisation, packet loss, and jitter across worldwide locations. To provide a smooth and high-quality virtual consultation between patients and mental health providers. Using performance data to identify areas for development, the effort aims to lower technological hurdles and increase telepsychiatry session dependability. Findings will help create strong, efficient algorithms that can handle different network situations, increasing patient outcomes and extending mental healthcare services. In the 1st instance latent analysis in a sample of 5 cities, the average latency (ms) is 45, the peak latency is 120, the off-peak latency is 30, and the packet loss is 0.5. In another instance, bandwidth utilisation in a sample of 5 sessions ranged from 30 to 120 minutes, with data supplied in MB - 150-600 and received in MB - 160-620, with average bandwidth (Mbps) - 5-15 and maximum bandwidth: 10-20.
A new 13N-complexity memory built-in self-test algorithm to balance static random access memory static fault coverage and test time
Authors: Jidin Aiman Zakwan , Hussin Razaidi , Mispan Mohd Syafiq , Fook Lee Weng
Published: November 2024

As memories dominate the system-on-chip (SoC), their quality significantly impacts the chip manufacturing yield. There is a growing need to reduce the chip production time and cost, which mainly depends on the testing phase. Hence, a memory built-in self-test (MBIST) utilizing a low-complexity, high-fault-coverage test algorithm is essential for efficient and thorough memory testing. The March AZ1 algorithm, with 13N complexity, was created earlier to balance the test length and fault coverage. However, poor positioning of a write operation in its test sequence caused the reduction of the transition coupling fault (CFtr) detection. This paper presents the creation of the March AZ algorithm, modified from the March AZ1 algorithm, to increase CFtr coverage while preserving the same complexity. It was accomplished by analyzing the fault coverage offered by the March AZ1 algorithm and then reorganizing its test sequence to address the limitation in detecting CFtr. The newly produced March AZ1 algorithm was successfully implemented in an MBIST controller. The simulation tests validated its functionality and demonstrated that the CFtr coverage was enhanced from 62.5% to 75%, achieving an overall fault coverage of 83.3%. Therefore, with 13N complexity, it offers the best fault coverage among all the existing test algorithms with a complexity below 18N.
An efficient Radix-4 butterfly structure based on the complex binary number system and distributed arithmetic
Authors: Bowlyn Kevin , Hounsinou Sena , Tewell Jordan
Published: November 2024

Complex number arithmetic is pivotal in various applications, requiring the selection of an efficient multiplier for high-performance computations. Fast Fourier transform (FFT)-based multipliers are widely employed for computing complex number products, but their reliance on using dedicated multipliers and treating the real and imaginary parts as two entities significantly add to the cost and complexity of the system. Distributed arithmetic (DA) is a technique that replaces complex multiplications with a bit-level shift and addition mechanism. The complex binary number system (CBNS) utilizes binary arithmetic, which treats the real and imaginary parts as a single entity, which can simplify complex number arithmetic and computations. This paper introduces an approach integrating the CBNS with DA in a Radix-4 decimation in time FFT 8-bit and 16-bit butterfly structure. The proposed design significantly reduces arithmetic computations and eliminates dedicated multipliers, demonstrating a reduction in power consumption, area size, and lookup tables, as well as increasing overall clock performance compared to the conventional FFT architecture on Artix-7, Kintex-7, and Virtex-7 field-programmable gate array chips.
Cascaded AC-DC parallel boost-flyback converter for power factor correction
Authors: Laksmi B. Nur Vidia , Mubarok Muhammad Syahril , Effendi Moh. Zaenal , Aribowo Widi , Liu Tian-Hua
Published: November 2024

A two-stage power factor correction (PFC) topology achieves a higher power factor quality and lower harmonic distortion than a single-stage converter. This paper introduces a two-stage PFC topology using a parallel boost and flyback converter which is employed as a voltage regulator The main boost converter is used for PFC and the other is the active filter circuit. The filter is implemented to improve the quality of phase-current and eliminate the switching loss. Furthermore, a reaction curve of Ziegler-Nichol’s method determines the controller parameter for cascaded PFC converter circuit. Simulated and experimental results are presented to validate the proposed method. The total harmonic distortion (THD) value decreases significantly from 83.35% become 0.98% in the simulation. In addition, experimental results show that the current response has good performances, including less harmonics, higher power factor, and lower THD value compared to without a PFC circuit. The PF increased from 0.43 become 0.96, the THD value decreased from 49.4% become 16.2%, and contains a small number of harmonics. The proposed controller method has better responses than the conventional one, including small steady-state error, fast rise time and settling time. A microcontroller (MCU), type STM32F407VG, produced by STMicroelectronics is used to execute the proposed control in both converters.
Negative-sequence current filter based on inductance coils
Authors: Kletsel Mark , Mashrapov Bauyrzhan , Mashrapova Rizagul , Kislov Alexandr
Published: November 2024

The construction of new relay protection systems without the use of current transformers is a fundamental problem of electro energetics, which has not yet been solved. This works suggests a negative-sequence current filter which receives information from inductance coils (ICs) mounted at a safe distance in the magnetic field of phase currents. This filter does not require current transformers, thus saving high-quality copper, steel, and expensive high-voltage insulation in amount unprecedented for relay protection (a 6 to 110 kV current transformer has 19 to 480 kg in weight). A circuit (including functional diagnostics) and a technique for selecting the parameters of filter components and the points where ICs should be fixed are presented; a structure for IC fastening is described. Computer simulation and experiment were used for data collection. The data show that i) the filter conversion coefficient m= 1.6, and imbalance increases by 7% at the network frequency f= 48–52 Hz; ii) protections based on this filter should have a time delay; iii) the filter is not inferior to well-known well-tested filters with current transformers; and iv) it is functional, but can only be used for single-standing electrical installations.
Optimal cleaning robot on solar panels with time-sequence input based on internet of things
Authors: Fitriyanah Dwi Nur , Saputra Rivaldi Dwi Pramana , Abadi Imam , Musyafa Ali
Published: November 2024

Solar panels are the main component of solar power generation systems, and they function by converting solar energy into electrical energy. Indonesia has great potential for solar energy. Solar panels will work optimally at temperatures of 25 °C to 28 °C. The greater the temperature of the solar panel, the more power generated by the panel. The influence of solar radiation intensity can be caused by dust and animal droppings attached to the surface of the solar panel module. If the surface of a solar panel is covered with dust or dirt, which can block the entry of solar radiation, the resulting power output is not optimal. The aim of this research is to design and implement an automatic cleaning system for solar power plants. The system used is using ESP32 based on the Blynk application and adding internet of things (IoT) devices with a cleaning method using pumped water spraying, then assisted with wipers which have silicon rubber material to clean dust and dirt. Based on the cleaning optimization simulation calculations, we found that the optimal or efficient cleaning condition was once a month, with an efficiency of 75.17%.
Deep learning for skin melanoma classification using dermoscopic images in different color spaces
Authors: Manikandan Sankarakutti Palanichamy , Narani Sandeep Reddy , Karthikeyan Sakthivel , Mohankumar Nagarajan
Published: November 2024

Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%.
Enhancing millimeter-wave communication: a tropical perspective on raindrop size distribution and signal attenuation
Authors: Yusof Nurul Najwa Md , Din Jafri , Yin Lam Hong
Published: November 2024

This study tackles rain attenuation in millimeter-wave (mm-wave) communication, a critical concern for the advancement of 5G wireless technology. It examines the variability of raindrop size distribution (DSD) and its impact on specific attenuation, with a focus on tropical environments such as Malaysia. Using the Joss-Waldvogel disdrometer, this study collected and analyzed extensive DSD data over three years, revealing that the highest DSD concentrations do not necessarily result in the greatest specific attenuation. This study adopted a machine learning approach, specifically supervised learning with linear regression, to enhance the accuracy of attenuation prediction models. A new set of coefficients for the power-law model of specific attenuation was derived and benchmarked against the ITU-R P.838-3 standard and similar studies in comparable climates. The findings emphasize the importance of developing region-specific models that consider local meteorological variations, potentially offering significant improvements to the reliability and design of mm-wave communication systems in the future.
Control of an aquaponic system to improve the yield of gray tilapia and lettuce cultivation
Authors: Gamarra Juan Herber Grados , Jimenez Santiago Linder Rubiños , Olga Rojas Salazar Arcelia , Gallegos Eduardo Nelson Chávez , Jimenez Linett Angélica Velasquez , Rivera Robert Julio Contreras , Perez Mario Alberto Garcia
Published: November 2024

Water quality assessment presents challenges, primarily the paucity of available data and ongoing system maintenance. This research develops an automated monitoring and control of water quality parameters in aquaponic systems with internet of thing (IoT) technology. Proper fish feeding management is important, which is why the fish were fed at 12:00, 16:00 and 07:00. The most significant relative error recorded during the validation of the DS18B20, PH-4502C, SEN0244, SEN0237-A, SEN0189 and DFR0300 sensors is 5.0%. The maximum standard deviation between the mentioned sensors was 1.96, and the highest coefficient of variation reached 7.24%. Before the installation of the aquaponic system, the specific growth rate (SGR) of fish was 4.89±0.17% and after implementing the automated aquaponics system, the SGR of fish increased to 6.21±0.24%. The feed conversion ratio values of the fish, both before and after the installation of the control system, were 1.98±0.14% and 1.53±0.09%, respectively. In addition, an improvement in plant growth was observed, evidenced by the difference in the values of height, number of leaves, leaf length, and weight of the plants before and after the installation of the control system, which was 7.74 cm, 5 leaves, 5.6 cm, and 41.6 g respectively.
Estimation of the required number of nodes of a university cloud virtualization cluster
Authors: Akhmetov Bakhytzhan , Lakhno Valery , Oshanova Nurzhamal , Alimseitova Zhuldyz , Bereke Madina , Izbasova Nurgul
Published: November 2024

When designing a virtual desktop infrastructure (VDI) for a university or inter-university cloud, developers must overcome many complex technical challenges. One of these tasks is estimating the required number of virtualization cluster nodes. Such nodes host virtual machines for users. These virtual machines can be used by students and teachers to complete academic assignments or research work. Another task that arises in the VDI design process is the problem of algorithmizing the placement of virtual machines in a computer network. In this case, optimal placement of virtual machines will reduce the number of computer nodes without affecting functionality. And this, ultimately, helps to reduce the cost of such a solution, which is important for educational institutions. The article proposes a model for estimating the required number of virtualization cluster nodes. The proposed model is based on a combined approach, which involves jointly solving the problem of optimal packaging and finding the configuration of server platforms of a private university cloud using a genetic algorithm. The model introduced in this research is universal. It can be used in the design of university cloud systems for different purposes-for example, educational systems or inter-university scientific laboratory management systems.
Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models
Authors: Tripathy Nrusingha , Mishra Debahuti , Hota Sarbeswara , Mishra Sashikala , Das Gobinda Chandra , Dalai Sasanka Sekhar , Nayak Subrat Kumar
Published: November 2024

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
Hybrid optimization algorithm for analysis of influence propagation in social network
Authors: Bhayyar Akshata Sandeep , Purushotham Kiran
Published: November 2024

Influence maximization(IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence, resulting in an augmented count of influenced nodes following the propagation process. The existing researches mainly concentrated on increasing the spread of influence, but did not consider the running time of the network. In this manuscript, the salp swarm algorithm (SSA) and bi-adaptive strategy particle swarm optimization (BiAS-PSO) algorithms are integrated and named as SS-BiAS-PSO algorithm to increase the spread of influence based on the IM problem to minimize the running time of the network. The datasets utilized for the research are Ego-Facebook, Epinions, Gowalla, and HepTh, while linear threshold (LT) is utilized as a diffusion method. Then, the proposed SS-BiAS-PSO algorithm is deployed for the analysis of influence propagation. The proposed algorithm reaches a high influence spread of 645, 680, 715, and 750 with less running times respectively for 10, 20, 30, and 40 seed set sizes in Ego-Facebook. The proposed algorithm proves more effective than the existing techniques like traditional SSA and particle swarm optimization (PSO).
A novel scheme for enhanced content integrity, authentication, and privacy in information centric network using lightweight blockchain-based homomorphic integrity and authentication
Authors: Chandra Aravinda Thejas , Shivarudraiah Rajashekara Murthy , Gadde Nagaraja , Nagarajappa Ravi Kumar Begur
Published: November 2024

Information-centric networks (ICNs) face challenges in ensuring content integrity and authentication while preserving user privacy. Homomorphic encryption has been widely used to protect content privacy within ICNs. Blockchain technology is widely applied in ICNs to ensure content integrity. Conventional blockchain-based authentication schemes rely on computationally expensive homomorphic encryption to perform secure data transmission. Also, the existing cryptography methods comprise confidentiality, and integrity while sharing content in ICNs. This paper proposes a novel lightweight blockchain-based homomorphic integrity and authentication (Light-BHIA) scheme to increase the integrity and privacy of content within ICNs. This scheme leverages the transparency and immutability of blockchain, the privacy-preserving properties of homomorphic encryption, and decentralized trust mechanisms to achieve secure and verifiable content delivery. The proposed work utilizes a lightweight homomorphic encryption scheme specifically designed for ICNs, reducing computational burdens by using resource-constrained devices. Light-BHIA uses the permissioned blockchain with an efficient consensus mechanism for increasing the trustworthiness of content delivery within ICNs. Authorized recipients can perform integrity and authentication checks on the encrypted content without decryption, maintaining confidentiality. The comparative study reveals proposed scheme achieved a 25% reduction in registration delay and confirms higher integrity, privacy, and confidentiality.
Handling class imbalance in education using data-level and deep learning methods
Authors: Kannan Rithesh , Ng Hu , Yap Timothy Tzen Vun , Wong Lai Kuan , Chua Fang Fang , Goh Vik Tor , Lee Yee Lien , Wong Hwee Ling
Published: November 2024

In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Transmission line sag and magnetic field analysis with sag parabolic equations and Biot-Savart law
Authors: Ahsan Matiullah , Baharom Md Nor Ramdon , Zainal Zainab , Hassan Omar Abu , Hanim Faridah , Kamarudin Saufi , Abd Rahman Rahisham , Yousof Mohd Fairouz Mohd , Jamail Nor Akmal Mohd , Othman Nordiana Azlin
Published: November 2024

This study presents a novel approach to enhance the precision of calculating sag and magnetic fields beneath overhead transmission lines. The Biot-Savart law is integrated with parabolic equations to assign multiple magnetic field sources to each conductor, resulting in improved prediction accuracy. Addressing oversimplifications in traditional models improves the analysis of transmission lines in real-world scenarios. An analysis was performed using MATLAB and Simulink to validate the effectiveness and broad applicability of different configurations. The results demonstrated a significant improvement in precision compared to traditional methods, indicating that this approach has the potential to establish new benchmarks in the field. This methodology makes significant contributions to electromagnetic studies, offering engineers a reliable tool for designing transmission systems that are both safer and more efficient. This advancement in electrical engineering greatly improves transmission network performance by enhancing sag and magnetic field prediction accuracy. This aids in better maintenance planning and reduces outage risks, resulting in more efficient operations and improved overall performance.
Seasonal auto-regressive integrated moving average with bidirectional long short-term memory for coconut yield prediction
Authors: Jayanna Niranjan Shadaksharappa , Lingaraju Raviprakash Madenur
Published: November 2024

Crop yield prediction helps farmers make informed decisions regarding the optimal timing for crop cultivation, taking into account environmental factors to enhance predictive accuracy and maximize yields. The existing methods require a massive amount of data, which is complex to acquire. To overcome this issue, this paper proposed a seasonal auto-regressive integrated moving average-bidirectional long short-term memory (SARIMA-BiLSTM) for coconut yield prediction. The collected dataset is preprocessed through a label encoder and min-max normalization is employed to change non-numeric features into numerical features and enhance model performance. The preprocessed features are selected through an adaptive strategy-based whale optimization algorithm (AS-WOA) to avoid local optima issues. Then, the selected features are given to the SARIMA-BiLSTM to predict the coconut yields. The proposed SARIMA-BiLSTM is adaptable to handling a widespread of various seasonal patterns and captures spatial features. The SARIMA-BiLSTM performance is estimated through the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE). SARIMA-BiLSTM attains 0.84 of R2, 0.056 of MAE, 0.081 of MSE, and 0.907 of RMSE which is better when compared to existing techniques like multilayer stacked ensemble, convolutional neural network and deep neural network (CNN-DNN) and autoregressive moving average (ARIMA).
Predicting personality traits from Arabic text: an investigation of textual and demographic features with feature selection analysis
Authors: Chraibi Khaoula , Chaker Ilham , Zahi Azeddine
Published: November 2024

Automatic personality recognition (APR) utilizes machine learning to predict personality traits from various data sources. This study aims to predict the big five personality traits from modern standard Arabic (MSA) texts, using both textual and demographic features. The “MSAPersonality” dataset is employed to conduct a comprehensive analysis of features and feature selection methods to evaluate their impact on APR model performance. We compared feature selection algorithms from the filter, wrapper, and embedded-based categories through a systematic experimental design that consisted of feature engineering, feature selection, and regression. This study showed that each trait was more accurately predicted using a distinct set of features. However, age and study level were the most common features among the five traits. Moreover, although there were no statistically significant differences in performance between the feature selection techniques, embedded-based methods offered the best compromise between performance, time, and interpretability. These findings contribute to the understanding of APR in general and among Arabic speakers.
O dilema cosmopolita versus nacional nas vanguardas latino-americanas: Uma comparação entre a revista Martín Fierro e a Revista de Antropofagia (1924–1929)
Authors: Queiroz Helaine
Published: November 2024

Este livro, fruto de uma das teses ganhadoras do prêmio “3 VEZES 22: Centenário da Semana de Arte Moderna”, realiza uma análise comparativa entre duas revistas vanguardistas da década de 1920: a argentina Martín Fierro e a brasileira Revista de Antropofagia. Buscando semelhanças e diferenças entre os dois periódicos, a historiadora Helaine Queiroz se depara com o que considera um dilema latino-americano: a tensão entre olhares ao mesmo tempo valorizadores da insularidade nacional e voltados para o estrangeiro. As revistas partem de duas metrópoles latino- -americanas no período: Buenos Aires e São Paulo, cidades em processo vertiginoso de crescimento, com grande número de imigrantes e sobre as quais se projeta um forte imaginário moderno. São mantidas por intelectuais cosmopolitas, que se mantêm atualizados sobre as mais recentes produções culturais, viajando para o exterior ou recebendo informações estrangeiras em seus países de origem. Ambas lidam com a herança deixada pelas antigas metrópoles colonizadoras, com a possibilidade de irmandade latino americana e com a influência dos Estados Unidos sobre o continente. Também voltam seus olhares para dentro da Argentina e do Brasil, examinando a formação nacional, a natureza, a língua e a produção musical. Examinando características como tamanho, número de páginas, cabeçalho, periodicidade, preço, distribuição, tiragem e imagens, a autora traça as relações entre elementos para textuais, contextuais e textuais nas duas revistas, percebendo-as como espaços dinâmicos de circulação e interseção de discursos altamente significativos para o estudo da história latino-americana.
O quarteirão como suporte da transformação urbana (Coleção Caramelo)
Authors:   Universidade de São Paulo. Faculdade Arquitetura e Urbanismo, Noto Felipe de Souza
Published: November 2024

A obra "O quarteirão como suporte da transformação urbana" que integra a Coleção Caramelo da Faculdade de Arquitetura e Urbanismo da Universidade de São Paulo, recoloca a proposição do projeto arquitetônico enquanto projeto de cidade, integrando o campo do desenho urbano. Apresenta a intervenção a partir do quarteirão como possibilidade para experimentação de hipóteses de transformação urbana, entendendo o potencial aglutinador de tal escala e as alternativas de associação coletiva, que poderiam ser estimuladas, inclusive, por vínculos de vizinhança, mas que superam a noção de lote ou mesmo de remembramento de terrenos, já que incorporam as áreas coletivas, as públicas e a de fruição urbana. Interessa apresentar as virtudes de tal ensaio sem que, de forma alguma, seja considerado como o único caminho, já que é incapaz de apontar soluções para todas as dificuldades morfológicas, funcionais e sociais da cidade, mas que possa ser conjugado com outras tantas iniciativas. O ponto de partida da reflexão são as mudanças nos mecanismos de atuação do planejamento urbano na cidade de São Paulo. O autor procura, a partir daí, definir o campo disciplinar do desenho urbano amparando-se na formulação teórica de diversos autores, de modo a criar a argumentação conceitual da investigação e da justificativa da escolha do quarteirão como recorte. Propõe, então, a reconciliação entre o plano e o projeto como perspectiva da atuação contemporânea.
Mapear educar territorializar: a implementação do Núcleo de Arte Educação Ambiental na Ilha do Bororé, Zona Sul de São Paulo (Coleção Caramelo)
Authors:   Universidade de São Paulo. Faculdade Arquitetura e Urbanismo, Bassani Jorge , Juliasz Paula C. Strina ,   Universidade de São Paulo. Faculdade de Filosofia, Letras e Ciências Humanas
Published: November 2024

Na escola, o desenvolvimento do conhecimento pode fornecer instrumentos para a compreensão e a transformação da realidade, extrapolando a simples transmissão de informações e mobilizando a contextualização de assuntos caros para a formação humanizadora. O contexto não é um cenário artificial, pois a realidade é concreta; por isso, um ensino territorializado pode criar meios para uma prática social interdisciplinar e consciente. Com base nesses princípios, esta obra apresenta o processo de implementação do Núcleo de Arte Educação Ambiental (naea), projetado como uma forma de encontro entre sujeitos que pensam o território por meio da paisagem e do patrimônio. Nesse sentido, cabe a pergunta: o que é a prática social no contexto do Bororé? Para começar a desenvolver uma resposta a essa pergunta, é importante saber que a Ilha do Bororé é um conjunto de pequenos bairros situados no extremo sul do município de São Paulo e é integrante da Área de Proteção Ambiental (APA) BororéColônia. Além disso, o território conta com a participação ativa de um grupo de docentes e discentes da Universidade de São Paulo (USP), por meio de trabalhos de extensão universitária com trocas constantes com a comunidade, que progrediram para uma instância de produção comum, com a participação de sujeitos e organizações importantes para o bairro, como a Escola Estadual Adrião Bernardes e o coletivo ambientalista Casa Ecoativa.