bluuebunny/crossref_metadata_2025 huggingface dataset:
Single-cell atlas reveals characteristic changes in intrahepatic HBV-specific leukocytes
Authors: Pan Banglun , Wang Zengbin , Chen Rui , Zhang Xiaoxia , Qiu Jiacheng , Wu Xiaoxuan , Yao Yuxin , Luo Yue , Wang Xiaoqian , Tang Nanhong
Published: November 2023

In the progression of liver disease, various metabolic disorders and inflammatory factors cause dysfunction of intrahepatic leukocytes, but there is no research to summarize the changes in the liver microenvironment driven by different pathological factors, especially HBV. Therefore, this study attempted to explore the inherent laws of disease-driven microenvironment changes in diseased livers. Using mass cytometry, flow cytometry, and immunofluorescence, we analyzed the changes in intrahepatic leukocytes from mouse liver diseases and patients with hepatocellular carcinoma (HCC) in this study. We noticed that (i) the inhibitory effect of hepatitis B virus (HBV) on intrahepatic leukocytes did not increase with the surface antigen titers compared with the low hepatitis B surface antigen concentration group, intrahepatic T cells from the high one showed higher expression of PD-1 and lower expression of TIM-3 and CTLA-4, so the proportion of exhausted T cells decreased in the high one, which contributed to the tolerance or adaptation of the liver to HBV; (ii) leukocytes in HBV-associated HCC were in a highly dysfunctional state with highest PD-1/TIM-3/CTLA-4/TOX; (iii) the detailed leukocyte ratio was appropriate to assess the prognosis of HCC, the ratio of cytotoxic/naive/effector/central memory CD8
+
T cells to CD8
+
T cells was positively correlated with the overall survival. In conclusion, our phenotype analysis deeply revealed the function differences in intrahepatic leukocytes caused by different liver disease factors, highlighting their unique changes in HBV related HCC, and helping to discover new HCC targeted immunotherapy strategies.

IMPORTANCE

Hepatitis B virus (HBV)-specific CD8
+
T cells play a central role in the clearance of virus and HBV-related liver injury. Acute infection with HBV induces a vigorous, multifunctional CD8
+
T cell response, whereas chronic one exhibits a weaker response. Our study elucidated HBV-specific T cell responses in terms of viral abundance rather than the timing of infection. We showed that in the premalignant stage, the degree of impaired T cell function was not synchronized with the viral surface antigen, which was attributed the liver’s tolerance to the virus. However, after the development of hepatocellular carcinoma, T cell exhaustion was inevitable, and it was marked by the exhaustion of the signature transcription factor TOX.
Using the network scale-up method to characterise kidney trafficking in Kalai Upazila, Bangladesh
Authors: Li Meng-Hao , Yu Yang , Siddique Abu Bakkar , Lee Narae , Haque Md. Reazul , Rahman Md Lutfay Tariq , Ahmad Manzur , El-Amine Hadi , Koizumi Naoru
Published: November 2023

This study aimed to estimate the prevalence of illegal kidney sales in Kalai Upazila, Bangladesh, using the Network Scale-Up Method (NSUM), an ego-centric network survey-based technique used to estimate the size of hidden populations. The study estimated the size of the kidney seller population, analysed the profiles of kidney sellers and kidney brokers and investigated the characteristics of villagers who are more likely to be connected to kidney sellers to identify possible biases of the NSUM estimate. The study found that the prevalence of kidney trafficking in Kalai Upazila was between 1.98% and 2.84%, which is consistent with the estimates provided by a local leader and reporters, but with much narrower bounds. The study also found that a large proportion of kidney sellers and brokers were men (over 70% and 90%, respectively) and relatively young (mean age of 33 and 39, respectively). Specific reasons for kidney sales included poverty (83%), loan payment (4%), drug addiction (2%) and gambling (2%). While most reported male sellers were farmers (56%) and female sellers were housewives (78%) in need of money, most reported brokers were characterised as rich, well-known individuals.
Evaluation of tissue- and plasma-derived tumor mutational burden (TMB) and genomic alterations of interest in CheckMate 848, a study of nivolumab combined with ipilimumab and nivolumab alone in patients with advanced or metastatic solid tumors with high TMB
Authors: He Jie , Kalinava Natallia , Doshi Parul , Pavlick Dean C , Albacker Lee A , Ebot Ericka M , Tukachinsky Hanna , Pratt James , Fusaro Gina , Oxnard Geoffrey R , Green George , Fabrizio David , Baden Jonathan
Published: November 2023

Background

An accumulation of somatic mutations in tumors leads to increased neoantigen levels and antitumor immune response. Tumor mutational burden (TMB) reflects the rate of somatic mutations in the tumor genome, as determined from tumor tissue (tTMB) or blood (bTMB). While high tTMB is a biomarker of immune checkpoint inhibitor (ICI) treatment efficacy, few studies have explored the clinical utility of bTMB, a less invasive alternative for TMB assessment. Establishing the correlation between tTMB and bTMB would provide insight into whether bTMB is a potential substitute for tTMB. We explored the tumor genomes of patients enrolled in CheckMate 848 with measurable TMB. The correlation between tTMB and bTMB, and the factors affecting it, were evaluated.

Methods

In the phase 2 CheckMate 848 (NCT03668119) study, immuno-oncology-naïve patients with advanced, metastatic, or unresectable solid tumors and tTMB-high or bTMB-high (≥10 mut/Mb) were prospectively randomized 2:1 to receive nivolumab plus ipilimumab or nivolumab monotherapy. Tissue and plasma DNA sequencing was performed using the Foundation Medicine FoundationOne CDx and bTMB Clinical Trial Assays, respectively. tTMB was quantified from coding variants, insertions, and deletions, and bTMB from somatic base substitutions. Correlations between tTMB and bTMB were determined across samples and with respect to maximum somatic allele frequency (MSAF). Assay agreement and variant composition were also evaluated.

Results

A total of 1,438 and 1,720 unique tissue and blood samples, respectively, were obtained from 1,954 patients and included >100 screened disease ontologies, with 1,017 unique pairs of tTMB and bTMB measurements available for assessment. Median tTMB and bTMB were 3.8 and 3.5 mut/Mb, respectively. A significant correlation between tTMB and bTMB (r=0.48, p<0.0001) was observed across all sample pairs, which increased to r=0.54 (p<0.0001) for samples with MSAF≥1%. Assay concordance was highest for samples with MSAF≥10% across multiple disease ontologies and observed for both responders and non-responders to ICI therapy. The variants contributing to tTMB and bTMB were similar.

Conclusions

We observed that tTMB and bTMB had a statistically significant correlation, particularly for samples with high MSAF, and that this correlation applied across disease ontologies. Further investigation into the clinical utility of bTMB is warranted.
Effect of sodium sulfate concentration in drinking water for beef heifers, and the in vitro effect of bismuth subsalicylate on H2S production and fiber disappearance
Authors: Evans M.G. , Ribeiro G.O. , Henry D.H. , Johnson J.A. , Campbell J.R. , Waldner C. , Penner G.B.
Published: November 2023

This study evaluated the effects of ( i) increasing the concentration of sulfate in drinking water on dry matter intake (DMI), water intake, ruminal fermentation, and apparent total-tract digestibility, and ( ii) water sulfate concentration and bismuth subsalicylate (BSS) dose on in vitro ruminal fermentation. Eight ruminally cannulated beef heifers (382 ± 45 kg) were used in a replicated incomplete 3 × 3 Latin square design. Water treatments contained low (342 ± 29 mg/L; LS), medium (2785 ± 72 mg/L; MS), or high (4948 ± 163 mg/L; HS) sulfate. Ruminal digesta was collected to evaluate water sulfate and BSS dose (0.0%, 0.2%, 0.4%, and 0.6% dry matter) on ruminal H2S production in vitro. Water intake increased linearly as water sulfate concentration increased ( p = 0.002), but DMI was not affected. Heifers drinking MS and HS had greater ruminal H2S at 10.5 h after water provision than LS (sulfate × time, p < 0.001). In vitro H2S production (µg and µg/mL of rumen inoculum) increased and plateaued with increasing sulfate ( p < 0.001) and was linearly reduced ( p < 0.001) by increasing BSS dose. Increasing water sulfate concentrations did not negatively affect water or feed intake but increased ruminal H2S concentrations. Bismuth subsalicylate may reduce H2S production.
Impact of quintessence and cloud of strings on self-consistent d-dimensional charged thin-shell wormholes
Authors: Waseem Arfa , Javed Faisal , Gul M. Zeeshan , Mustafa G. , Errehymy Abdelghani
Published: November 2023

This article evaluates the stability constraints of higher-dimensional geometry of thin-shell wormholes developed from the two equivalent copies of inner and outer d-dimensional charged anti-de Sitter black holes bounded by a cloud of strings and quintessence. Such geometrical structures are built using a cut-and-paste method that joins two identical forms of black hole solutions at the hypersurface. We develop the equation of motion for the constructed wormholes and then use the linear radial perturbation approach to examine the stable configuration. The stability constraints depend on the dimensions of the black holes, cloud, and quintessence parameters. It is worth mentioning that the possibility of a stable structure is greatest for the choice of d-dimensional charged anti-de Sitter black holes with quintessence and a cloud of strings.
Magnetic behavior of a ferrimagnetic MXene-like lattice: Monte Carlo study
Authors: Fdil R. El , Fadil Z. , Salmani E. , Haldhar Rajesh , Ez-zahraouy H.
Published: November 2023

In this study, we performed extensive Monte Carlo simulations to comprehensively analyze the magnetic properties of a single-layer MXene-like lattice. Our investigation involved the exploration of transition temperatures and the behavior of hysteresis cycles, all influenced by a series of key physical parameters. Additionally, we carefully mapped the magnetization as a function of temperature and crystal field, introducing variations in the exchange coupling to unveil their impact on the transition temperature. Furthermore, we investigated the behavior of hysteresis loops, complexly dissecting their responses to changes in exchange coupling, temperature, and crystal field. Significantly, our results highlight the central role of the crystal field in the formation of magnetization plateaus, thus providing promising avenues for applications in nanotechnology and advanced memory storage systems.
SHORT-MATURITY ASYMPTOTICS FOR OPTION PRICES WITH INTEREST RATE EFFECTS
Authors: PIRJOL DAN , ZHU LINGJIONG
Published: November 2023

We derive the short-maturity asymptotics for option prices in the local volatility model in a new short-maturity limit [Formula: see text] at fixed [Formula: see text], where r is the interest rate and q is the dividend yield. In the case of practical relevance [Formula: see text] being small, however, our result holds for any fixed [Formula: see text]. The result is a generalization of the Berestycki–Busca–Florent formula ( Berestycki et al. ,  2002 ) [Asymptotics and calibration of local volatility models, Quantitative Finance 2, 61–69] for the short-maturity asymptotics of the implied volatility which includes interest rates and dividend yield effects of [Formula: see text] to all orders in n. We obtain the analytical results for the ATM volatility and skew in this asymptotic limit. Explicit results are derived for the CEV model. The asymptotic result is tested numerically against the exact evaluation in the square-root model [Formula: see text], which demonstrates that the new asymptotic result is in very good agreement with the exact evaluation in a wide range of model parameters relevant for practical applications.
A Geometry-Based Distributed Connectivity Maintenance Algorithm for Discrete-time Multi-Agent Systems with Visual Sensing Constraints
Authors: Li Xiaoli , Fu Jinyun , Liu Mingliang , Xu Yangmengfei , Tan Ying , Xin Yangbin , Pu Ye , Oetomo Denny
Published: November 2023

This paper presents a novel approach to address the challenge of maintaining connectivity within a multi-agent system (MAS) when utilizing directional visual sensors. These sensors have become essential tools for enhancing communication and connectivity in MAS, but their geometric constraints pose unique challenges when designing controllers. Our approach, grounded in geometric principles, leverages a mathematical model of directional visual sensors and employs a gradient-descent optimization method to determine the position and orientation constraints for each sensor based on its geometric configuration. This methodology ensures network connectivity, provided that initial geometric constraints are met. Experimental results validate the efficacy of our approach, highlighting its practical applicability for a range of tasks within MAS.
GAN-Assisted Road Segmentation from Satellite Imagery
Authors: Hu Wenmiao , Yin Yifang , Tan Ying Kiat , Tran An , Kruppa Hannes , Zimmermann Roger
Published: November 2023

Geo-information extraction from satellite imagery has become crucial to carry out large-scale ground surveys in a short amount of time. With the increasing number of commercial satellites launched into orbit in recent years, high-resolution RGB color remote sensing imagery has attracted a lot of attention. However, because of the high cost of image acquisition and even more complicated annotation procedures, there are limited high-resolution satellite datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. Instead of collecting and annotating more data, using synthetic images could be another solution to boost the performance of a model. This article proposes a GAN-assisted training scheme for road segmentation from high-resolution RGB color satellite images, which includes three critical components: (a) synthetic training sample generation, (b) synthetic training sample selection, and (c) assisted training strategy. Apart from the GeoPalette and cSinGAN image generators introduced in our prior work, this article explains in detail how to generate new training pairs using OpenStreetMap (OSM) and introduces a new set of evaluation metrics for selecting synthetic training pairs from a pool of generated samples. We conduct extensive quantitative and qualitative experiments to compare different image generators and training strategies. Our experiments on the downstream road segmentation task show that (1) our proposed metrics are more aligned with the trained model performance compared to commonly used GAN evaluation metrics such as the Fréchet inception distance (FID); and (2) by using synthetic data with the best training strategy, the model performance, mean Intersection over Union (mean IoU), is improved from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model that is standard-trained with 4,000 real images (64.59%), i.e., enabling a 4-fold reduction in real dataset size.
Innovative Self-directed, Problem-oriented, Lifelong learning, Integrated Clinical case Exercise (SPLICE) modules promote critical thinking skills, early clinical exposure, and contextual learning among first professional-year medical students
Authors: Surapaneni Krishna Mohan
Published: November 2023

The very essence of this innovative “Self-directed, Problem-oriented, Lifelong learning, Integrated Clinical case Exercise” (SPLICE) curriculum is the team-based learning of integrated pre-, para-, and clinical learning objectives right from the first professional year of study serving as an early clinical exposure. This unique way of learning creates a holistic educational environment by combining both academic and professional development thereby empowering the next generation of physician leaders to take autonomy of their own learning strategies and emerge as competent lifelong learners.
Análisis Comparado de la Norma Internacional de Contabilidad del Sector Público Nº 1 sobre Estados Financieros y la Normativa del Sistema de Contabilidad General de la Nación: Consecuencia de la Aplicación en Chile
Authors: Medel Garrido John Nelson , Moraga Vásquez Alejandra
Published: November 2023

La Norma Internacional de Contabilidad del Sector Público -NICSP Nº 1-, tiene como objetivo establecer la forma en que se debe garantizar la comprensión y comparabilidad de los estados financieros preparados para aquellos usuarios que no están en condiciones de singularizar sus demandas. Por tanto, la norma define para las entidades públicas en marcha, que disponen de sistemas de contabilidad base devengado o de acumulación, consideraciones de estructura y contenido para la preparación y presentación de sus estados financieros. Este artículo tiene como propósito, determinar si la normativa que rige el Sistema de Contabilidad General de la Nación establecida en el Oficio Nº 60.820, de 2005, de la Contraloría General de la República, actualizado por el Oficio CGR Nº 54.977 de 2010, en el cual se incorpora el Quinto Capítulo sobre Estados Financieros con propósitos de información general demandados por la norma internacional de contabilidad para el Sector Público Nº 1 (NICSP), cumple con las condiciones suficientes para satisfacer las exigencias de información de la NICSP Nº 1.
XTX301, a Tumor-Activated Interleukin-12 Has the Potential to Widen the Therapeutic Index of IL12 Treatment for Solid Tumors as Evidenced by Preclinical Studies
Authors: Patel Ekta , Malkova Natalia V. , Crowe David , Pederzoli-Ribeil Magali , Fantini Damiano , Fanny Manoussa , Madala Hanumantha Rao , Jenkins Kurt A. , Yerov Oleg , Greene Justin , Guzman Wilson , O'Toole Caitlin , Taylor Jacob , O'Donnell Rebekah K. , Johnson Parker , Lanter Bernard B. , Ames Brian , Chen Jia , Vu Sallyann , Wu Hsin-Jung , Cantin Susan , McLaughlin Megan , Hsiao Yu-Shan S. , Tomar Dheeraj S. , Rozenfeld Raphael , Thiruneelakantapillai Lakshmanan , O'Hagan Ronan C. , Nicholson Benjamin , O'Neil Jennifer , Bialucha Carl Uli
Published: November 2023

IL12 is a proinflammatory cytokine, that has shown promising antitumor activity in humans by promoting the recruitment and activation of immune cells in tumors. However, the systemic administration of IL12 has been accompanied by considerable toxicity, prompting interest in researching alternatives to drive preferential IL12 bioactivity in the tumor. Here, we have generated XTX301, a tumor-activated IL12 linked to the human Fc protein via a protease cleavable linker that is pharmacologically inactivated by an IL12 receptor subunit beta 2 masking domain. In vitro characterization demonstrates multiple matrix metalloproteases, as well as human primary tumors cultured as cell suspensions, can effectively activate XTX301. Intravenous administration of a mouse surrogate mXTX301 demonstrated significant tumor growth inhibition (TGI) in inflamed and non-inflamed mouse models without causing systemic toxicities. The superiority of mXTX301 in mediating TGI compared with non-activatable control molecules and the greater percentage of active mXTX301 in tumors versus other organs further confirms activation by the tumor microenvironment–associated proteases in vivo. Pharmacodynamic characterization shows tumor selective increases in inflammation and upregulation of immune-related genes involved in IFNγ cell signaling, antigen processing, presentation, and adaptive immune response. XTX301 was tolerated following four repeat doses up to 2.0 mg/kg in a nonhuman primate study; XTX301 exposures were substantially higher than those at the minimally efficacious dose in mice. Thus, XTX301 has the potential to achieve potent antitumor activity while widening the therapeutic index of IL12 treatment and is currently being evaluated in a phase I clinical trial.
Darolutamide Added to Docetaxel Augments Antitumor Effect in Models of Prostate Cancer through Cell Cycle Arrest at the G1–S Transition
Authors: Buck Stefan A.J. , Van Hemelryk Annelies , de Ridder Corrina , Stuurman Debra , Erkens-Schulze Sigrun , van ’t Geloof Sem , Teubel Wilma J. , Koolen Stijn L.W. , Martens-Uzunova Elena S. , van Royen Martin E. , de Wit Ronald , Mathijssen Ron H.J. , van Weerden Wytske M.
Published: November 2023

Resistance to taxane chemotherapy is frequently observed in metastatic prostate cancer. The androgen receptor (AR) is a major driver of prostate cancer and a key regulator of the G1–S cell-cycle checkpoint, promoting cancer cell proliferation by irreversible passage to the S-phase. We hypothesized that AR signaling inhibitor (ARSi) darolutamide in combination with docetaxel could augment antitumor effect by impeding the proliferation of taxane-resistant cancer cells. We monitored cell viability in organoids, tumor volume, and PSA secretion in patient-derived xenografts (PDX) and analyzed cell cycle and signaling pathway alterations. Combination treatment increased antitumor effect in androgen-sensitive, AR-positive prostate cancer organoids and PDXs. Equally beneficial effects of darolutamide added to docetaxel were observed in a castration-resistant model, progressive on docetaxel, enzalutamide, and cabazitaxel. In vitro studies showed that docetaxel treatment with simultaneous darolutamide resulted in a reduction of cells entering the S-phase in contrast to only docetaxel. Molecular analysis in the prostate cancer cell line LNCaP revealed an upregulation of cyclin-dependent kinase inhibitor p21, supporting blockade of S-phase entry and cell proliferation. Our results provide a preclinical support for combining taxanes and darolutamide as a multimodal treatment strategy in patients with metastatic prostate cancer progressive on ARSi and taxane chemotherapy.
Primary Anterior Medistinal Choriocarcinoma in Male with Lung Metastasis and Pituitary Microadenoma: A Case Report
Authors: Liu Dong , Ye Qingjian , Ye Minjuan , Yang Yuebo
Published: November 2023

The authors report a case of a 29-year-old male presented with bilateral breast enlargement with no significant past medical history or estrogen exposure. Serum β-human chorionic gonadotropin (HCG) was 14,306.60 mIU and positron emission tomography-computed tomography discovered a malignant mass on the right side of anterior superior mediastinum. Magnetic resonance imaging demonstrated pituitary microadenoma. Pathological biopsy showed poorly differential pituitary adenoma and immunohistochemical staining displayed that CK(+), PLAP(−), AFP(−), HCG(+), CD30(−), Oct3/4(−), CK7(+), TTF-1(−), CD117(−), Ki 67(80+), CK5/6(−), EMA(partial+), inhibin(partial+). A diagnosis of primary anterior mediastinal choriocarcinoma metastasis to bilateral lungs accompanied with pituitary microadenoma was confirmed. Then the patient received chemotherapy combined with immunotherapy. But serum β-HCG level was still above the normal, and unfortunately, the patient died 6 months after his diagnosis. This case inspires us to think of the possibility of choriocarcinoma when a man presents gynecomastia or lung metastatic symptoms, adding Opdivo to the chemotherapy might not improve the poor treatment outcomes.
Outcomes of ABO-Incompatible Living Donor Kidney Transplantation Compared to Waiting or Deceased Donor Kidney Transplantation
Authors: Koo Tai Yeon , Lee Juhan , Lee Yonggu , Kim Hyung Woo , Kim Beom Seok , Huh Kyu Ha , Yang Jaeseok
Published: November 2023

Introduction: ABO-incompatible (ABOi) living donor kidney transplantation (LDKT) is considered only for patients who do not have an ABO-compatible (ABOc) LD. Therefore, a clinically practical question is whether to proceed with ABOi LDKT or remain on dialysis while waiting for ABOc deceased donor kidney transplantation (DDKT). However, this issue has not been addressed in Asian countries, where ABOi LDKT programs are more active than DDKT programs. Methods: A total of 426 patients underwent ABOi-LDKT between 2010 and 2020 at Seoul National University Hospital and Severance Hospital, Korea. We compared outcomes between the ABOi-LDKT and the propensity-matched control groups (waiting-list-only group, n = 1,278; waiting-list-or-ABOc-DDKT group, n = 1,278). Results: The ABOi-LDKT group showed a significantly better patient survival rate than the waiting-list-only group (p = 0.001) and the waiting-list-or-ABOc-DDKT group (p = 0.048). When the ABOi-LDKT group was categorized into a high-titer group (peak anti-ABO titer ≥1:128) and a low-titer group (peak anti-ABO titer ≤1:64), the low-titer group showed better patient survival rates than those of the waiting-list-or-ABOc-DDKT group (p = 0.046) or the waiting-list-only group (p = 0.004). In contrast, the high-titer ABOi-LDKT group showed no significant benefit in patient survival compared to the waiting-list-or-ABOc-DDKT group. Death-censored graft survival in the ABOi-LDKT group was not significantly different from that in the ABOc-DDKT group (p = 0.563). Conclusion: The ABOi-LDKT group has better outcomes than the waiting-list-or-ABOc-DDKT group in a country with a long waiting time.
Towards a system for real-time prevention of drowsiness-related accidents
Authors: Khadraoui Abdelhak , Zemmouri Elmoukhtar , Taki Youssef , Douimi Mohammed
Published: November 2023

Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.
Effective modelling of human expressive states from voice by adaptively tuning the neuro-fuzzy inference system
Authors: Panigrahi Surjyo Narayana , Pattanaik Niharika , Palo Hemanta Kumar
Published: November 2023

This paper aims to develop efficient speech-expressive models using the adaptively tuning neuro-fuzzy inference system (ANFIS). The developed models differentiate a high-arousal happiness state from a low-arousal sadness state from the benchmark Berlin (EMODB) database. The proposed low-cost flexible developed algorithms are self-tunable and can address several vivid real-world issues such as home tutoring, banking, and finance sectors, criminal investigations, psychological studies, call centers, cognitive and biomedical sciences. The work develops the proposed structures by formulating several novel feature vectors comprising both time and frequency information. The features considered are pitch (F0), the standard deviation of pitch (SDF0), autocorrelation coefficient (AC), log-energy (E), jitter, shimmer, harmonic to noise ratio (HNR), spectral centroid (SC), spectral roll-off (SR), spectral flux (SF), and zero-crossing rate (ZCR). to alleviate the issues of the curse of dimensionality associated with the frame-level extraction, the features are extracted at the utterance level. Several performance parameters have been computed to validate the individual time and frequency models. Further, the ANFIS models are tested for their efficacy in a combinational platform. The chosen features are complementary and the augmented vectors have indeed shown improved performance with more available information as revealed by our results.
Analyzing the behavior of different classification algorithms in diabetes prediction
Authors: Mahmood Israa Nafea , Abdullah Hasanen S.
Published: November 2023

Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.
Neural network to solve fuzzy constraint satisfaction problems
Authors: Adil Bouhouch , Aicha Er-Rafyg , Abderrahmane Ez-Zahout
Published: November 2023

It has been proven that solving the constraint satisfaction problem (CSP) is an No Polynomial hard combinatorial optimization problem. This holds true even in cases where the constraints are fuzzy, known as fuzzy constraint satisfaction problems (FCSP). Therefore, the continuous Hopfield neural network model can be utilized to resolve it. The original algorithm was developed by Talaavan in 2005. Many practical problems can be represented as a FCSP. In this paper, we expand on a neural network technique that was initially developed for solving CSP and adapt it to tackle problems that involve at least one fuzzy constraint. To validate the enhanced effectiveness and rapid convergence of our proposed approach, a series of numerical experiments are carried out. The results of these experiments demonstrate the superior performance of the new method. Additionally, the experiments confirm its fast convergence. Specifically, our study focuses on binary instances with ordinary constraints to test the proposed resolution model. The results confirm that both the proposed approaches and the original continuous Hopfield neural network approach exhibit similar performance and robustness in solving ordinary constraint satisfaction problems.
Optimization scheme for intelligent master controller with collaboratives energy system
Authors: Jack Kufre Esenowo , Olubiwe Matthew , Chibuzo Obichere Jude-Kenndey , Onyema Akwukwaegbu Isdore , Nosiri Onyebuchi C. , Chijioke Joe-Uzuegbu
Published: November 2023

This paper explores the use of deep learning to optimize the performance of a peer-to-peer energy system with an intelligent master controller. The goal addresses inefficiencies caused by energy seasonality by predicting hourly power consumption through a deep learning algorithm. The intelligent master controller was designed to manage the collaborative energy system, and the deep learning technique was employed as an optimization scheme to forecast power system performance for more efficient utilization. The deep learning algorithm was trained using dataset from American electric power, where consumer load data serves as input, and forecasted power serves as output. The forecasted power was then used as input to the intelligent master controller, which determines suitable power supply for generation and storage based on the predicted demand. The experiment results show promising accuracy with a root mean square error (RMSE) of 0.1819 for hourly energy consumption averaged over a year, 0.2419 for hourly energy consumption averaged over a month, 0.0662 for hourly energy consumption averaged per day, and 0.0217 for hourly energy consumption. These findings demonstrate that the system is well-trained and capable of accurately predicting the energy required by the intelligent master controller, thus enhancing the overall performance of the peer-to-peer energy system.
Cost-effective internet of things privacy-aware data storage and real-time analysis
Authors: Elegbeleye Femi Abiodun , Mbodila Munienge , Esan Omobayo Ayokunle , Elegbeleye Ife
Published: November 2023

It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.
Thai COVID-19 patient clustering for monitoring and prevention: data mining techniques
Authors: Pansayta Sawitree , Chansanam Wirapong
Published: November 2023

This research aims to optimize emerging infectious disease monitoring techniques in Thailand, which will be extremely valuable to the government, doctors, police, and others involved in understanding the seriousness of the spread of novel coronavirus to improve government policies, decisions, medical facilities, treatment. The data mining techniques included cluster analysis using K-means clustering. The infection data were obtained from the open data of the digital government development agency, Thailand. The dataset consisted of 1,893,941 cumulative cases from January 2020 to October 2021 of the outbreak. The results from clustering consisted of 8 groups. Clustering results determined the three largest, three medium-sized, and the two most minor numbers of infected people, respectively. These clusters represent their activities, namely touching an infected person and checking themselves. The components of emerging diseases in Thailand are closely related to waves, gender, age, nationality, career, behavioral risk, and region. The province of onset was mainly in Bangkok and its vicinity or central Thailand, as well as industrial areas. Adult workers aged 19 to 27 years and 43 to 54 years or over were seeds of new infection sources.
Investigating optimal features in log files for anomaly detection using optimization approach
Authors: Ranga Shivaprakash , Mohankumar Nageswara Guptha
Published: November 2023

Logs have been frequently utilised in different software system administration activities. The number of logs has risen dramatically due to the vast scope and complexity of current software systems. A lot of research has been done on log-based anomaly identification using machine learning approach. In this paper, we proposed an optimization approach to select the optimal features from the logs. This will provide the higher classification accuracy on reduced log files. In order to predict the anomalies three phases are used: i) log representation ii) feature selection and iii) Performance evaluation. The efficacy of the proposed model is evaluated using benchmark datasets such as BlueGene/L (BGL), Thunderbird, spirit and hadoop distributed file system (HDFS) in terms of accuracy, converging ability, train and test accuracy, receiver operating characteristic (ROC) measures, precision, recall and F1-score. The results shows that the feature selection on log files outperforms in terms all the evaluation measures.
Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning
Authors: Asian Jelita , Arianti Nunik Destria , Ariefin Ariefin , Muslih Muhamad
Published: November 2023

The low-cost camera for machine vision, such as a webcam, still has a problem with resolution noise. Therefore, it is important to learn strategies to reduce noise from low-cost camera images so that they can be widely used for grading machines in the future. This paper aims to compare three feature extraction methods with auto-preprocessing to classify chili pepper (Capsicum Frutescens) quality using a machine learning algorithm. Three extraction methods were used, including the color feature, oriented FAST and rotated BRIEF (ORB), and the combination color feature and ORB. A total of 525 image data for quality chili pepper were collected using the webcam. The auto-preprocessing strategy to classify chili peppers can improve the performance of machine-learning algorithms for all data generated by the feature extractor. The performance of the chili paper quality classification model with auto-preprocessing of the variable color feature can improve the performance of machine learning algorithms by up to 64.21%. The performance improvement of the classification model using the ORB feature variable and the auto-preprocessing of up to 4.41%. The performance improvement of the classification model using machine learning algorithms is 11.27% when using the combination color feature and ORB feature and auto-preprocessing.
Sampling methods in handling imbalanced data for Indonesia health insurance dataset
Authors: Kurniadi Felix Indra , Purwandari Kartika , Wulandari Ajeng , Permai Syarifah Diana
Published: November 2023

Health insurance fraud is one of the most frequently occurring fraudulent acts and has become a concern for every insurance. According to data from The Indonesian General Insurance Association or Asosiasi Asuransi Umum Indonesia (AAUI), the private insurance industry suffered losses up to billions rupiah throughout 2018 due to the fraudulent acts commited by the perpetrators. The problem in with the number of frauds in Indonesia is that the current system is highly vulnerable and they is still done manually. The other problem from this detection is imbalance data which often occurs in fraudulent cases. In this research, we used a sampling methods using several machine learning as the baseline. The result shows that the instance hardness thresholding algorithm and extreme gradient boosting gives the best performance for all the case. It shows the method can reduced the bias and can achieve better generalization.
Combining convolutional neural networks and spatial-channel “squeeze and excitation” block for multiple-label image classification
Authors: Borvornvitchotikarn Thuvanan , Yooyativong Thongchai
Published: November 2023

In emergency rooms and intensive care units, catheters and tubes are used to keep critically ill patients alive. Appropriate catheter or tube insertion is crucial to avoiding serious complications. Such issues can be rectified if they are identified early. Chest X-rays are commonly used to assess catheter placement. Convolutional neural networks (CNN) have recently been found to enhance multi-label classification tasks on chest X-rays images. Furthermore, attention modules have shown the effect of enhancing spatial encoding on network feature maps. This research analyzed the experiments of each CNN model with different attention blocks. Resnet200D with batch normalization and spatial-channel squeeze and excitation block (BN+scSE) is the best architecture for multiple-label image classification on a chest X-rays dataset from National Institutes of Health Clinical Center (NIH) with multiple catheters and tubes. Then came EfficientNetB5 with BN+scSE and Inception_v3 with spatial squeeze and channel excitation block, respectively.
Intra-class deep learning object detection on embedded computer system
Authors: Santiary Putri Alit Widyastuti , Swardika I Ketut , Dewi Dewa Ayu Indah Cahya , Sugirianta Ida Bagus Ketut
Published: November 2023

Implementation of artificial intelligence tends to be portable, mobile and embeds in embedded computer system (EBD). EBD is a special-purpose computer with limited capacity in a small-form size. Deep learning (DL) had known as cutting edges for object recognition. With DL, object feature extraction analysis is omitted. DL requires large computing resources and capacity. Implement DL algorithm on EBD goal to achieves high detection accuracy and high-efficiency resources. Hence, be able to cope with intra-class variations, and image disturbances. By those challenges and limitations, this study reports the performance of EBD to recognize an object which has high variations in their class, through an optimal raw-input dataset. The raw-input dataset performed optimization process with a supervisor. Yield is the proper optimal input dataset in size. The performance results observed begin from training dataset until evaluation stage of DL. The comparison performs in efficiency resources, loss, validation-loss, timesteps, and detection accuracy by multiclass confusion matrix analysis. This study shows through this purpose method efficient resources are highly archived. Shorter timesteps ensure training stage is successful, and detection accuracy is perfectly archived. In addition, this study proves DL method archived great performances in classifying object that has identical structure.
Word embedding for detecting cyberbullying based on recurrent neural networks
Authors: Shaker Noor Haydar , Dhannoon Ban N.
Published: November 2023

The phenomenon of cyberbullying has spread and has become one of the biggest problems facing users of social media sites and generated significant adverse effects on society and the victim in particular. Finding appropriate solutions to detect and reduce cyberbullying has become necessary to mitigate its negative impacts on society and the victim. Twitter comments on two datasets are used to detect cyberbullying, the first dataset was the Arabic cyberbullying dataset, and the second was the English cyberbullying dataset. Three different pre-trained global vectors (GloVe) corpora with different dimensions were used on the original and preprocessed datasets to represent the words. Recurrent neural networks (RNN), long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and Bidirectional GRU (BiGRU) classifiers utilized, evaluated and compared. The GRU outperform other classifiers on both datasets; its accuracy on the Arabic cyberbullying dataset using the Arabic GloVe corpus of dimension equal to 256D is 87.83%, while the accuracy on the English datasets using 100 D pre-trained GloVe corpus is 93.38%.
Segmentation and yield count of an arecanut bunch using deep learning techniques
Authors: Arekattedoddi Chikkalingaiah Anitha , Dhanesha RudraNaik , Chikkathore Palya Laxmana Shrinivasa Naika , Neelegowda Krishna Alabujanahalli , Mangala Puttaswamy Anirudh , Ayengar Pushkar
Published: November 2023

Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.
Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources
Authors: Trong Hien Chiem , Nguyen Thuan Thanh , Nguyen Thang Trung
Published: November 2023

This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. 
Design of access control framework for big data as a service platform
Authors: Sharma Santosh Kumar , Pratap Ajay , Dev Harsh
Published: November 2023

Big data as a service (BDaaS) platform is widely used by various organizations for handling and processing the high volume of data generated from different internet of things (IoT) devices. Data generated from these IoT devices are kept in the form of big data with the help of cloud computing technology. Researchers are putting efforts into providing a more secure and protected access environment for the data available on the cloud. In order to create a safe, distributed, and decentralised environment in the cloud, blockchain technology has emerged as a useful tool. In this research paper, we have proposed a system that uses blockchain technology as a tool to regulate data access that is provided by BDaaS platforms. We are securing the access policy of data by using a modified form of ciphertext policy-attribute based encryption (CP-ABE) technique with the help of blockchain technology. For secure data access in BDaaS, algorithms have been created using a mix of CP-ABE with blockchain technology. Proposed smart contract algorithms are implemented using Eclipse 7.0 IDE and the cloud environment has been simulated on CloudSim tool. Results of key generation time, encryption time, and decryption time has been calculated and compared with access control mechanism without blockchain technology.