bluuebunny/crossref_metadata_2025 huggingface dataset:
Gigaxonin Suppresses Epithelial-to-Mesenchymal Transition of Human Cancer Through Downregulation of Snail
Authors: Veena Mysore S. , Gahng Jungmo J. , Alani Mustafa , Ko Albert Y. , Basak Saroj K. , Liu Isabelle Y. , Hwang Kimberly J. , Chatoff Jenna R. , Venkatesan Natarajan , Morselli Marco , Yan Weihong , Ali Ibraheem , Kaczor-Urbanowicz Karolina Elżbieta , Gowda Bhavani Shankara , Frost Patrick , Pellegrini Matteo , Moatamed Neda A. , Wilczynski Sharon P. , Bomont Pascale , Wang Marilene B. , Shin Daniel Sanghoon , Srivatsan Eri S.
Published: February 2024

Gigaxonin is an E3 ubiquitin ligase that plays a role in cytoskeletal stability. Its role in cancer is not yet clearly understood. Our previous studies of head and neck cancer had identified gigaxonin interacting with p16 for NFκB ubiquitination. To explore its role in cancer cell growth suppression, we analyzed normal and tumor DNA from cervical and head and neck cancers. There was a higher frequency of exon 8 SNP (c.1293 C>T, rs2608555) in the tumor (46% vs. 25% normal, P = 0.011) pointing to a relationship to cancer. Comparison of primary tumor with recurrence and metastasis did not reveal a statistical significance. Two cervical cancer cell lines, ME180 and HT3 harboring exon 8 SNP and showing T allele expression correlated with higher gigaxonin expression, reduced in vitro cell growth and enhanced cisplatin sensitivity in comparison with C allele expressing cancer cell lines. Loss of gigaxonin expression in ME180 cells through CRISPR-Cas9 or siRNA led to aggressive cancer cell growth including increased migration and Matrigel invasion. The in vitro cell growth phenotypes were reversed with re-expression of gigaxonin. Suppression of cell growth correlated with reduced Snail and increased e-cadherin expression. Mouse tail vein injection studies showed increased lung metastasis of cells with low gigaxonin expression and reduced metastasis with reexpression of gigaxonin. We have found an association between C allele expression and RNA instability and absence of multimeric protein formation. From our results, we conclude that gigaxonin expression is associated with suppression of epithelial–mesenchymal transition through inhibition of Snail.

Significance:

Our results suggest that GAN gene exon 8 SNP T allele expression correlates with higher gigaxonin expression and suppression of aggressive cancer cell growth. There is downregulation of Snail and upregulation of e-cadherin through NFκB ubiquitination. We hypothesize that exon 8 T allele and gigaxonin expression could serve as diagnostic markers of suppression of aggressive growth of head and neck cancer.
Dabrafenib Alters MDSC Differentiation and Function by Activation of GCN2
Authors: Ciudad M. Teresa , Quevedo Rene , Lamorte Sara , Jin Robbie , Nzirorera Nadine , Koritzinsky Marianne , McGaha Tracy L.
Published: February 2024

The effect of targeted therapeutics on anticancer immune responses is poorly understood. The BRAF inhibitor dabrafenib has been reported to activate the integrated stress response (ISR) kinase GCN2, and the therapeutic effect has been partially attributed to GCN2 activation. Because ISR signaling is a key component of myeloid-derived suppressor cell (MDSC) development and function, we measured the effect of dabrafenib on MDSC differentiation and suppressive activity. Our data showed that dabrafenib attenuated MDSC ability to suppress T-cell activity, which was associated with a GCN2-dependent block of the transition from monocytic progenitor to polymorphonuclear (PMN)-MDSCs and proliferative arrest resulting in PMN-MDSC loss. Transcriptional profiling revealed that dabrafenib-driven GCN2 activation altered metabolic features in MDSCs enhancing oxidative respiration, and attenuated transcriptional programs required for PMN development. Moreover, we observed a broad downregulation of transcriptional networks associated with PMN developmental pathways, and increased activity of transcriptional regulons driven by Atf5, Mafg, and Zbtb7a. This transcriptional program alteration underlies the basis for PMN-MDSC developmental arrest, skewing immature MDSC development toward monocytic lineage cells. In vivo, we observed a pronounced reduction in PMN-MDSCs in dabrafenib-treated tumor-bearing mice suggesting that dabrafenib impacts MDSC populations systemically and locally, in the tumor immune infiltrate. Thus, our data reveal transcriptional networks that govern MDSC developmental programs, and the impact of GCN2 stress signaling on the innate immune landscape in tumors, providing novel insight into potentially beneficial off-target effects of dabrafenib.

Significance:

An important, but poorly understood, aspect of targeted therapeutics for cancer is the effect on antitumor immune responses. This article shows that off-target effects of dabrafenib activating the kinase GCN2 impact MDSC development and function reducing PMN-MDSCs in vitro and in vivo. This has important implications for our understanding of how this BRAF inhibitor impacts tumor growth and provides novel therapeutic target and combination possibilities.
Frühes NSCLC: Die präoperative Milzgröße ist ein Prognosefaktor für das Überleben
Authors: Lesser Thomas
Published: February 2024

Background: The correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear. Methods: A retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS. Results: The discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52–66) years) and 608 patients (403 (66.28%) males; age 69 (62–76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02). Conclusion: Preoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.
An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion
Authors: Cai Fangqi , Cheng Liwei , Liao Xiaoling , Xie Yuping , Wang Wu , Zhang Haofeng , Lu Jinhua , Chen Ru , Chen Chunxia , Zhou Xing , Mo Xiaoyun , Hu Guoping , Huang Luying
Published: February 2024

Introduction: Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate between MPE and BPE. Methods: A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection processes were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest were employed to construct radiomic models. LR analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results: Out of the total 1,834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomic model. Among the radiomic models, the SVM model demonstrated the highest predictive performance [area under the curve (AUC), training cohort: 0.876, test cohort: 0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin, carcinoembryonic antigen, carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC: 0.932, 0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC: 0.850, 0.820) and the radiomic model (AUC: 0.876, 0.774). The calibration curves and DCA further confirmed the practicality of the combined model. Conclusion: This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool for assisting in the clinical diagnosis of PE patients.
Chronic Kidney Disease in Brazil: Current Status and Recommended Improvements
Authors: Bauer Andrea Carla , Elias Rosilene M. , Abensur Hugo , Batista Marcelo Costa , Jansen Angela Marie , Riella Miguel Carlos
Published: February 2024

Background: Over the last 3 decades, over 700 million individuals worldwide have been diagnosed with chronic kidney disease (CKD). In a 2017 survey in southern Brazil, 11.4% of those surveyed had CKD. Early identification and effective therapy in Brazil may reduce CKD’s impact. This panel discusses the early diagnosis and treatment of CKD and the barriers and actions needed to improve the management of CKD in Brazil. A panel of Brazilian nephrologists was provided with relevant questions to address before a multiday conference. During this meeting, each narrative was discussed and edited through several rounds until agreement on the relevant topics and recommendations was achieved. Summary: Panelists highlighted hurdles to early diagnosis and treatment of CKD. These include, but are not limited to, a lack of public and patient education, updated recommendations, multidisciplinary CKD treatment, and a national CKD database. People-centered, physician-centered, and healthcare institution-centered actions can be taken to improve outcomes. Patient empowerment is needed via multiple channels of CKD education and access to health-monitoring wearables and apps. Primary care clinicians and nonspecialists must be trained to screen and manage CKD-causing illnesses, including diabetes and hypertension. The healthcare system may implement a national health data gathering system, more screening tests, automated test result reporting, and telehealth. Key Messages: Increasing access to early diagnosis can provide a path to improving care for patients with CKD. Concerted efforts from all stakeholders are needed to overcome the barriers.
Evaluation of the Renal and Cardiovascular Effects of Long-Term Tolvaptan Treatment in Autosomal Dominant Polycystic Kidney Disease
Authors: Demiray Alparslan , Ozan Ramazan , Özaytürk Salih Güntuğ , İmamoğlu Hakan , Zararsız Gökmen , Sipahioğlu Murat Hayri , Tokgöz Bülent , Elçik Deniz , Koçyiğit İsmail
Published: February 2024

Background/Aims: Cardiovascular diseases constitute a significant cause of morbidity and mortality in individuals with autosomal dominant polycystic kidney disease (ADPKD). This study aims to assess the long-term effects of tolvaptan on the kidneys and heart in rapidly progressing ADPKD.
Methods: Among 354 patients diagnosed with ADPKD, 58 meeting the eligibility criteria for tolvaptan were included in the study. The study comprised two groups with similar demographic and clinical characteristics: 29 patients receiving tolvaptan treatment and 29 in the control group. Several included genetic analysis, magnetic resonance imaging (MRI), and echocardiography. Clinical and cardiac changes were recorded in both groups after a three year follow-up.
Results: Tolvaptan treatment demonstrated a significant reduction in the rate of eGFR decline compared to the control group. Furthermore, it was observed that tolvaptan could prevent the development of cardiac arrhythmias by inhibiting an increase in QTc interval and heart rate.
Conclusion: These findings suggest that, in addition to slowing kidney progression in ADPKD management, tolvaptan may potentially benefit in preventing cardiac complications.
Coherence-based sufficient condition for support recovery using block generalized orthogonal matching pursuit
Authors: Madhavan Aravindan , Govindarajan Yamuna
Published: February 2024

Challenge is to find the support vectors of the unknown block sparse vector with compressed measurements in an underdetermined system where the number of unknowns is more than that of measurements. To recover unknown block sparse vector, restricted isometry property (RIP) is a sufficient condition need to be satisfied. Finding the restricted isometric constant is a non-polynomial hard problem for large values of n. In this paper coherence-based recovery guarantee has been proposed to recover the support vectors using block generalized orthogonal matching pursuit (BGOMP). It is proved that BGOMP can able to recover the support vectors with lesser number of iteration than block orthogonal matching pursuit (BOMP) by selecting multiple block support elements per iteration. Simulation results show detection performance of BGOMP is better than BOMP, block subspace pursuit (BSP) and block compressive sampling matching pursuit (BCoSaMP) for different block sparsity and block length. In most of the cases for different block sparsity and block length computation time for BGOMP is lesser than BCoSaMP, BSP and BOMP due to the multiple selection of elements in each iteration.
Modelling and estimating trajectory points from RTK-GNSS based on an integrated modelling approach
Authors: Nahar Ravenny Sandin , Ng Kok Mun , Kamaruzaman Fadhlan Hafizhelmi , Razak Noorfadzli Abdul , Johari Juliana
Published: February 2024

The sparse Gaussian process regression (GPR) has been used to model trajectory data from Real time kinematics-global navigation satellite system (RTK-GNSS). However, upon scrutinizing the model residuals; the sparse GPR model poorly fits the data and exhibits presence of correlated noise. This work attempts to address these issues by proposing an integrated modeling approach called GPR-LR-ARIMA where the sparse GPR was integrated with the linear regression with autoregressive integrated moving average errors (LR-ARIMA) to further enhance the description of the trajectory data. In this integrated approach, the predicted trajectory points from the GPR were further described by the LR-ARIMA. Simulation of the GPR-LR-ARIMA on three sets of trajectory data indicated better model fit, revealed in the normally distributed model residuals and symmetrically distributed scatter plots. Correlated noise was also successfully eliminated by the model. The GPR-LR-ARIMA outperformed both the GPR and LRARIMA by its ability to improve mean-absolute-error in 2-dimension positioning by up to 86%. The GPR-LR-ARIMA contributes to enhancement of positioning accuracy of dynamic GNSS measurements in localization and navigation system with good model fit.
Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods
Authors: Rahma Osmalina Nur , Ain Khusnul , Putra Alfian Pramudita , Rulaningtyas Riries , Zalda Khouliya , Lutfiyah Nita , Laili Alami Nafisa Rahmatul , Chai Rifai
Published: February 2024

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
The development of color histogram method to identify air quality index based on sky images
Authors: Enggari Sofika , Sumijan Sumijan , Tajuddin Muhammad
Published: February 2024

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.
Two-stage HOG/SVM for license plate detection and recognition
Authors: Mazouz Lakhdar Djelloul , Meche Abdelkrim , Ouamri Abdelaziz , Ait Darna Abdel Wahab
Published: February 2024

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.
A novel fuzzy logic-based approach for textual documents indexing
Authors: Rassam Latifa , Ettahiri Imane , Zellou Ahmed , Doumi Karim
Published: February 2024

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.
Masked facial recognition using ensemble convolutional neural network and grey-level co-occurrence matrix
Authors: Gupta Om Pradyumana , Agrawal Arun Prakash , Pal Om
Published: February 2024

The COVID-19 pandemic proved how face masks became necessary to stop the spread of infection. Due to this, effective identification of people wearing face mask became challenging. Masked facial recognition has significantly increased in accuracy because of developments in convolutional neural networks (CNNs). Small size of the dataset of masked facial images has been a problem in earlier research. As would be expected, this results in poorer accuracy when the model tries to identify faces. In this study, a novel model is proposed with textural feature extraction using grey-level co-occurrence matrix (GLCM) and an ensemble of two pre-trained CNNs DenseNet-121 and VGG-16. Using the minimum redundancy and maximum relevance, the model has improved accuracy by choosing the most important features of the image. The model was trained using in-house dataset that included 38,290 photos of 2,500 people with approximately equal distribution of properly masked, partially masked, and unmasked images. In this, we evaluated the performance of the model on different classifiers multi-class logistic regression (LR) and support vector machine (SVM) with one-vs-rest (OvR) classification and artificial neural network (ANN) and applied a soft voting scheme. The model achieved the highest accuracy of 98.56% at a learning rate of 0.001 on the ANN classifier.
Syntactic analysis of complex sentences containing Arabic psychological verbs
Authors: Amzali Asmaa , Kourtin Asmaa , Mourchid Mohammed , Mouloudi Abdelaziz , Mbarki Samir
Published: February 2024

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.
Implementation of an Arabic spell checker
Authors: Kassmi Rafik , Mbarki Samir , Mouloudi Abdelaziz
Published: February 2024

This paper outlines the implementation of a spell checker for the Arabic language, leveraging the capabilities of NooJ and its functionality, specifically noojapply. In this paper, we shall proceed to provide clear definitions and comprehensive descriptions of several categories of spelling errors. Next, we will provide a comprehensive introduction to the NooJ platform and its command-line utility, noojapply. In the subsequent section, we shall outline the four main phases of our spell checker prototype. We intend to develop a local grammar in NooJ for the purpose of error detection. Afterwards, a morphological grammar and a local grammar will be created in NooJ with the aim of providing an exhaustive list of possible corrections. Following that, a revised algorithm will be employed to arrange these candidates in descending order of ranking. Subsequently, a web user interface will be developed to visually represent our research efforts. Finally, we will proceed to showcase a series of tests and evaluations conducted on our prototype, Al Mudaqiq.
Uplink-downlink resource optimization for provisioning 5G application considering SUI fading channel models
Authors: Kaba Vanita , Patil Rajendra R.
Published: February 2024

Recently, superposition coding (SC), Stanford University Interim (SUI) fading channel, user clustering, successive interference cancellation (SIC) mechanism has been incorporated into the power allocation design for attaining good performance. Nonetheless, the existing model works with cluster size up to 4 user per cluster and induce higher control channel overhead. In addressing this paper introduce an effective resource allocation (ERA) design for power domain non-orthogonal multiple access (PD-NOMA) system. The ERA, first employ Zadoff Chu (ZC) coding, then power is allocated according to the distance considering 5 user per cluster. Finally, multi-level SIC is done at the receiver end. The ERA performance is studied by varying signal-to-noise ratio (SNR) in terms of bit error rate (BER) considering a maximum of five users per cluster. Further, the ERA performance in terms of BER is tested under different scenario defined in SUI propagation scenario such as higher pathloss, moderate path loss, and low path loss. The throughput performance under varied density and speed is also studied; the ERA achieves much better performance in terms of ERA, SNR and throughput than standard multi-user downlink resource allocation (MUDRA) model.
Mutual coupling reduction between antennas array for 5G mobile applications
Authors: Chahboun Noha , Bellekhiri Abderrahim , Zbitou Jamal , Laaziz Yassin
Published: February 2024

This paper introduces the design of a multiple-input-multiple-output (MIMO) antenna optimized for low-profile applications supporting sub-6 GHz fifth-generation (5G) wireless applications. We have started the design from a single antenna with a square patch shape, each antenna array is composed from 4-element radiators fed by using power dividers and quarter microstrip lines. Mounted on a single Rogers RT5880 substrate, the MIMO antenna functions at 3.5 GHz. In order to miniature and to decrease the mutual coupling between the both antennas array we have optimised a magnetic wall based on periodic structures permitting to decrease the mutual coupling between the both antenna array. The unit element from the wall was optimised, studied and validated in order to absorb the surface current and to enhance the isolation between the different radiating elements. The dimensions of the proposed MIMO antenna are 154×220×0.578 mm³. The MIMO antenna final circuit achieves a peak gain of 9 dBi and an isolation around -30 dB. The introduction of the magnetic wall permits to enhance the isolation between the antenna array from -20 dB to -30 dB at 3.5 GHz band. This advancement contributes to the overall performance improvement of the MIMO antenna system.
For wireless LAN application, microstrip patch antenna design in S-band
Authors: Rana Md. Sohel , Islam Sourav Md. Soriful , Al Mamun Md. Abdulla , Faruk Omer , Rahaman Md. Mominur , Shahriar Md. Shehab Uddin , Halder Sukanto , Ahmed Md. Toukir , Chowdhury Imran , Faruq Omar , Mondal Saikat , Islam Md. Hasibul , Sinha Shuva Shubhra Kanti
Published: February 2024

This article presents a 3.5 GHz rectangular microstrip patch antenna (RMPA) designed, studied, and analyzed for wireless LAN applications. Using Fr-4 as substrate material, whose dielectric permittivity is 4.3, patch thickness is 1.65 mm, and loss tangent is 0.025. A feeding line with an impedance of 50 Ω is utilized to supply the antenna with power. Computer simulation technology (CST) software has been used to design the antenna and origin pro software has been used to display the resulting figures from the simulation. The antenna simulation showed that the return loss is -56.82 dB; the directivity gain is 6.02 dBi, the bandwidth is 0.148 GHz, and the voltage standing wave ratio (VSWR) is 1.0028. The paper aims to increase the return loss, develop a standard VSWR, increase the directivity gain of the antenna, and improve the antenna bandwidth. The results of the proposed antenna were much better than previously published papers, which were suitable for wireless applications. This proposed antenna can be used for future wireless LAN applications.
Big data clustering based on spark chaotic improved particle swarm optimization
Authors: Boushaki Saida Ishak , Mahammed Brahim Hadj , Bendjeghaba Omar , Mosbah Messaoud
Published: February 2024

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.
Machine learning approach for intrusion detection system using dimensionality reduction
Authors: Manikandan Deepa , Dhilipan Jayaseelan
Published: February 2024

As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
Education and awareness: keys to solid waste reduction
Authors: Andrade-Arenas Laberiano , Espinoza Elizabeth Liñan
Published: February 2024

In the research, education, and awareness were focused on as essential pillars to successfully address the problem of reducing solid waste. The objective is to implement educational and awareness solutions within the community to encourage a substantial change in behavior toward the reduction of solid waste. The design thinking methodology was applied to develop effective solutions. To measure the level of public awareness, we conducted interviews using the Atlas ti22 which allowed us to triangulate with the surveys that revealed that 60% of those surveyed agreed with the policies regarding environmental impact, 55% agreed that the authorities take preventive measures regarding public health and 58% stated that the participation of the citizens in recycling programs. Then, innovative prototypes were developed that satisfied the real needs of users and experts in their evaluation, thus laying a solid foundation. It was concluded that citizens as well as authorities must be aware that by working collaboratively, we can contribute to society.
A new hybrid parallel genetic algorithm for multi-destination path planning problem
Authors: Yusuf Luthfiansyah Ilhamnanda , Musdholifah Aina
Published: February 2024

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.
An intelligent time aware food recommender system using support vector machine
Authors: Panwar Minakshi , Sharma Ashish , Mahela Om Prakash , Khan Baseem , Ali Ahmed
Published: February 2024

This paper formulated a support vector machine powered time-aware food recommender system (SVMTAFRS) to recommend healthy food for the customers. The rated food item incorporates the user preference (UP) in terms of calories, nutrition factor, and all food contents required for a healthy diet. This also takes into account the user age, time of day and week day while predicting the food rating. The SVMTAFRS involves two steps for computation of user identity document (UID) and predicted food rating (PFR). UID is computed considering the customer age (CA), UP in terms of calories and suitable weight factors. PFR is computed considering the UID and time of day (TOD). PFR for week end day is computed by multiplying the PFR by week end multiplying factor (WEMF). Support vector machine (SVM) is used for recommending the suitable healthy food for customer in terms of correct values of PFR. Efficacy of PFR is tested in terms of mean absolute error (MAE) and root mean squared error (RMSE). This is established that performance of the SVMTAFRS is superior compared to the rule-based food recommender system (RBFRS).
Effects of hammer configurations on pearl millet grinding system with a hammer mill: theory and experiment
Authors: Diop Moustapha , Thiam Mouhamadou , Kebe Abdoulaye , Gueye Ibrahima
Published: February 2024

In grinding processes using hammer mills, the configuration, number and speed of hammers are some of the main factors that can affect system performance. This paper aims to investigate the effects of hammer configurations in terms of specific energy consumption (SEC), grinding mass efficiency, and productivity. These effects were studied theoretically on the basis of classical grinding laws and experimentally with four different hammer configurations. From theoretical studies, a decreasing power model of SEC versus hammer configurations was developed, which was then validated with a determination coefficient of 0.99 in experiments using a 2 HP-DC hammer mill. The good agreement between theoretical and experimental results confirms that the specific energy consumption and the productivity are directly dependent on hammer configurations, but the effects are not significant for grinding mass efficiency.
A flamethrower mounted on UAV for kite litter clearing on high voltage transmission line
Authors: Pratama Angela Widiya , Al Haqqi Fauzan , Sati’at Nur Anisa , Yohandri Yohandri
Published: February 2024

The high voltage transmission line (HVTL) is a part of the electric power transmission system that distributes high-capacity electricity. There were numerous interruptions to the transmission line, one of which was caused by the kite getting stuck in the conductor. In the past, interference from kites on the conductor wire has been removed by crawling over it. This conventional method poses safety risks, is high-cost, and is time-consuming. This article describes the development of a flamethrower mounted on an unmanned aerial vehicle (UAV) for kite litter clearing on a high-voltage transmission line is presented. The flamethrower is fitted on the UAV to achieve high-mounted wire. The UAV was controlled using a transmitter and a receiver based on an Arduino. The flamethrower was tested for clearing a kite on the transmission line. The effect of nozzle diameter on flame burst length and the time it takes to burn a kite has been investigated. According to the experiment results, the performance of the flamethrower is highly satisfactory. Based on the component prices and manufacturing costs, the flamethrower has been successfully assembled at a low cost for a total of below $55.
Development and Validation of Micro-Lecture Videos as Learning Support Material for Grade 7 Science Competencies
Authors: De Oca Phillip Raymund R. , Villaceran Lyndon G. , Deoma Mark Enrique B. , Linaugo Joji D. , Abao Grace M. , De Oca Marielle Grace A.
Published: February 2024

As students opted for modular distance learning during the pandemic, they were forced to self-regulated learning with little assistance from teachers. Video lectures are materials commonly designed for active learning methods in distance learning. However, studies found that the length of videos has a strong influence on students’ control over their learning. Longer videos have issues sustaining students’ focused attention to content. This study aimed to develop, validate and evaluate the quality and effectiveness of micro-lecture videos in Physics. Using the developmental research design anchored on the ADDIE model, 7 videos were developed. Videos were quality assured and administered to grade 7 students for evaluation, and feedbacking. Using a developed instrument, an LRMDS standard evaluation tool from the Department of Education, and thematic analysis, quality assurance results, test scores, and interview transcripts were gathered and analyzed statistically and thematically. Results show that despite limited engagement, video lectures are vital so students are guided to self–regulate their learning at their own pace. They also brought positive outcomes, particularly on ownership of learning where students manage and control the pace of their video view, allowing them to replay anytime. On the quality of materials, micro-lecture videos have relevant, logically arranged information, with appropriate content and language that are free of bias. They also have a well-defined purpose, are content-friendly, and can engage viewers for a better understanding of concepts. Moreover, end-users responded that the material was easy to use, can be viewed in messenger, and can be downloaded anytime since it has a smaller file size.
Mecânica dos sólidos: volume I
Authors:   Universidade de São Paulo. Escola de Engenharia de São Carlos, Coda Humberto Breve
Published: February 2024

Este é o primeiro volume da obra Mecânica dos sólidos. A mecânica dos Sólidos é entendida neste livro como a combinação de duas disciplinas muito importantes na formação da maioria dos engenheiros, a saber, a Estática das Estruturas e a Resistência dos Materiais. Em geral, textos associados à Estática das Estruturas abordam diversas técnicas de solução estrutural, fazendo muito pouca menção à análise de tensões internas, enquanto textos de Resistência dos Materiais se limitam ao estudo dos níveis de tensão em estruturas muito simples, que dispensam o uso de técnicas mais elaboradas de solução da estática. Assim, o presente texto conjuga os conhecimentos dessas disciplinas de forma natural e breve, fazendo a ligação entre algumas das principais técnicas de solução da estática e o cálculo dos níveis de tensão em estruturas. O enfoque principal do livro é fornecer subsídios ao entendimento dos processos de determinação dos níveis de tensão e deformação em estruturas, bem como das suas consequências na integridade e utilidade das mesmas. Os assuntos são abordados de forma prática, procurando proporcionar ao aluno um aprendizado agradável e interessante. Assim, alguns conteúdos são propostos em nível introdutório e depois revisitados com grau de complexidade mais elevado.
Myosin Light Chain Phosphatase Plays an Important Role in Cardiac Fibrosis in a Model of Mineralocorticoid Receptor‐Associated Hypertension
Authors: Ye Zhe , Okamoto Ryuji , Ito Hiromasa , Ito Rie , Moriwaki Keishi , Ichikawa Mizuki , Kimena Lupiya , Ali Yusuf , Ito Masaaki , Gomez‐Sanchez Celso E. , Dohi Kaoru
Published: February 2024

Background

Myosin phosphatase targeting subunit 2 (MYPT2) is an important subunit of cardiac MLC (myosin light chain) phosphatase, which plays a crucial role in regulating the phosphorylation of MLC to phospho‐MLC (p‐MLC). A recent study demonstrated mineralocorticoid receptor‐related hypertension is associated with RhoA/Rho‐associated kinase/MYPT1 signaling upregulation in smooth muscle cells. Our purpose is to investigate the effect of MYPT2 on cardiac function and fibrosis in mineralocorticoid receptor‐related hypertension.

Methods and Results

HL‐1 murine cardiomyocytes were incubated with different concentrations or durations of aldosterone. After 24‐hour stimulation, aldosterone increased CTGF (connective tissue growth factor) and MYPT2 and decreased p‐MLC in a dose‐dependent manner. MYPT2 knockdown decreased CTGF. Cardiac‐specific MYPT2‐knockout (c‐MYPT2
−/−
) mice exhibited decreased type 1 phosphatase catalytic subunit β and increased p‐MLC. A disease model of mouse was induced by subcutaneous aldosterone and 8% NaCl food for 4 weeks after uninephrectomy. Blood pressure elevation and left ventricular hypertrophy were observed in both c‐MYPT2
−/−
and MYPT2
+/+
mice, with no difference in heart weights or nuclear localization of mineralocorticoid receptor in cardiomyocytes. However, c‐MYPT2
−/−
mice had higher ejection fraction and fractional shortening on echocardiography after aldosterone treatment. Histopathology revealed less fibrosis, reduced CTGF, and increased p‐MLC in c‐MYPT2
−/−
mice. Basal global radial strain and global longitudinal strain were higher in c‐MYPT2
−/−
than in MYPT2
+/+
mice. After aldosterone treatment, both global radial strain and global longitudinal strain remained higher in c‐MYPT2
−/−
mice compared with MYPT2
+/+
mice.

Conclusions

Cardiac‐specific MYPT2 knockout leads to decreased myosin light chain phosphatase and increased p‐MLC. MYPT2 deletion prevented cardiac fibrosis and dysfunction in a model of mineralocorticoid receptor‐associated hypertension.
Association Between ECG Abnormalities and Mortality in a Low‐Risk Population
Authors: Lee Sung Ho , Lee Mi Yeon , Kang Jeonggyu , Choi Hyo‐In , Lee Seung‐Jae , Lee Jong‐Young , Kim Byung Jin , Sung Ki‐Chul , Park Kyoung‐Min
Published: February 2024

Background

The ECG is a simple, noninvasive screening method for cardiovascular disease and arrhythmia. The impact of ECG abnormality on mortality is not certain in low‐risk populations. To address this, we evaluated the association between ECG abnormality and mortality.

Methods and Results

We retrospectively assessed baseline ECG and all‐cause mortality and cardiovascular mortality in 660 383 patients presenting for medical check‐ups. Baseline ECG abnormalities were classified according to the Minnesota Code. Among the total 660 383 participants, 23 609 (3.6%) had major and 110 038 (16.7%) had minor ECG abnormalities. All‐cause mortality occurred in 7751 patients (1.1%) and cardiovascular mortality in 1180 (0.18%) over a median follow‐up period of 8.8 years. Major ECG abnormalities were associated with all‐cause mortality (hazard ratio [HR], 1.11 [95%, 1.03–1.2]) and cardiovascular mortality (HR, 1.92 [95% CI, 1.63–2.27]) compared with no ECG abnormalities. All‐cause mortality was associated with right atrial enlargement (HR, 2.11 [95% CI, 1.1–4.07]), left atrial enlargement (HR, 1.76 [95% CI, 1.1–2.84]), sinus tachycardia (HR, 1.52 [95% CI, 1.15–2.01]), complete atrioventricular block (HR, 2.1 [95% CI, 1.05–4.2]), atrial fibrillation (HR, 1.52 [95% CI, 1.26–1.84]), and left ventricular hypertrophy (HR, 1.15 [95% CI, 1.02–1.3]). Cardiovascular mortality was associated with left atrial enlargement (HR, 4.52 [95% CI, 2.15–9.5]), atrial fibrillation (HR, 3.22 [95% CI, 2.33–4.46]), left ventricular hypertrophy (HR, 1.72 [95% CI, 1.35–2.19]), major Q‐wave abnormality (HR, 1.6 [95% CI, 1.08–2.39]), and major ST‐T abnormality (HR, 1.76 [95% CI, 1.01–3.04]).

Conclusions

ECG abnormalities, including left atrial enlargement, left ventricular hypertrophy, atrial fibrillation, and major Q‐wave and ST‐T abnormalities, were associated with cardiovascular mortality in a low‐risk population.