bluuebunny/crossref_metadata_2025 huggingface dataset:
A novel approach of using existing implementations of constitutive material models in any numerical codes interfacing with MFront
Authors: Simo Eric , Helfer Thomas , Mašín David , Nagel Thomas , Mánica Miguel
Published: January 2024

Implementing a constitutive model is a long, tedious and error-prone process, in particular for soils where a wide variety of phenomena must be taken into account. Moreover, the implementation must satisfy the interface requirements of the targeted solver. MFront is a popular code generator based on C++ mostly dedicated to mechanical behaviours which provides interfaces for many academic and industrial solvers. MFront implementations also export metadata which considerably simplifies the behaviour integration in the solver, in particular if the MFrontGenericInterfaceSupport (MGIS) is used by this solver. While MFront greatly reduces the amount of work required to implement a new behaviour, existing legacy implementations are highly valuable and their re-implementation should only be considered with caution considering the trade-offs. In our experience, such a re-implementation increases the maintainability and portability, and generally the numerical performances, but requires significant development effort. In this work, we developed an alternative approach, which consists in using MFront as a wrapper to existing legacy implementations. The MFront wrapper also manages the definition of appropriate metadata and handles the transfer of the data from solver to the legacy implementation on input and output. At this stage, the approach has been used to make available all constitutive models implemented in the UMAT format (written in Fortran) in the OpenGeoSys solver which is linked to MFront via MGIS. The results of a simulation using a UMAT-model in OpenGeoSys verify the approach. The usage of MFront as a wrapper is also shown to have an insignificant/negligible impact on the numerical performance. The proposed approach opens the door to the establishment of a new database of constitutive material models in MFront where legacy implementation of existing models can be made available in all solvers interfaced with MFront.

Thematic collection:
This article is part of the Sustainable geological disposal and containment of radioactive waste collection available at:
https://www.lyellcollection.org/topic/collections/radioactive
Human Emotion Recognition Based on Machine Learning Algorithms with low Resource Environment
Authors: P. Asha , V. Hemamalini , Poongodaia.  , N. Swapna , K. L. S. Soujanya , Gaikwad (Mohite) Vaishali
Published: January 2024

It is difficult to discover significant audio elements and conduct systematic comparison analyses when trying to automatically detect emotions in speech. In situations when it is desirable to reduce memory and processing constraints, this research deals with emotion recognition. One way to achieve this is by reducing the amount of features. In this study, propose "Active Feature Selection" (AFS) method and compares it against different state-of-the-art techniques. According to the results, smaller subsets of features than the complete feature set can produce accuracy that is comparable to or better than the full feature set. The memory and processing requirements of an emotion identification system will be reduced, which can minimise the hurdles to using health monitoring technology. The results show by using 696 characteristics, the AFS technique for emobase yields a Unweighted average recall (UAR) of 75.8%.
Spectral Total-variation Processing of Shapes—Theory and Applications
Authors: Brokman Jonathan , Burger Martin , Gilboa Guy
Published: January 2024

We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al.
2020
] and adaptive anisotropic spectral TV [Biton and Gilboa
2022
]. A new notion of convexity on surfaces is derived by characterizing structures that are stable throughout the TV flow, performed on surfaces. We establish and numerically demonstrate quantitative relationships between TV, area, eigenvalue, and eigenfunctions of the TV operator on surfaces. Moreover, we expand the shape spectral TV toolkit to include zero-homogeneous flows, leading to efficient and versatile shape processing methods. These methods are exemplified through applications in smoothing, enhancement, and exaggeration filters. We introduce a novel method that, for the first time, addresses the shape deformation task using TV. This deformation technique is characterized by the concentration of deformation along geometrical bottlenecks, shown to coincide with the discontinuities of eigenfunctions. Overall, our findings elucidate recent experimental observations in spectral TV, provide a diverse framework for shape filtering, and present the first TV-based approach to shape deformation.
A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines
Authors: Chen Jiale , Van Le Duc , Tan Rui , Ho Daren
Published: January 2024

Visual sensing has been widely adopted for quality inspection in production processes. This article presents the design and implementation of a smart collaborative camera system, called
BubCam
, for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system that leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system that additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction compared with the manual inspection approach.
AdverSPAM: Adversarial SPam Account Manipulation in Online Social Networks
Authors: Concone Federico , Gaglio Salvatore , Giammanco Andrea , Re Giuseppe Lo , Morana Marco
Published: January 2024

In recent years, the widespread adoption of Machine Learning (ML) at the core of complex IT systems has driven researchers to investigate the security and reliability of ML techniques. A very specific kind of threats concerns the
adversary
mechanisms through which an attacker could induce a classification algorithm to provide the desired output. Such strategies, known as Adversarial Machine Learning (AML), have a twofold purpose: to calculate a perturbation to be applied to the classifier’s input such that the outcome is subverted, while maintaining the underlying intent of the original data. Although any manipulation that accomplishes these goals is theoretically acceptable, in real scenarios perturbations must correspond to a set of permissible manipulations of the input, which is rarely considered in the literature. In this article, we present
AdverSPAM
, an AML technique designed to fool the spam account detection system of an Online Social Network (OSN). The proposed black-box evasion attack is formulated as an optimization problem that computes the adversarial sample while maintaining two important properties of the feature space, namely
statistical correlation
and
semantic dependency
. Although being demonstrated in an OSN security scenario, such an approach might be applied in other context where the aim is to perturb data described by mutually related features. Experiments conducted on a public dataset show the effectiveness of
AdverSPAM
compared to five state-of-the-art competitors, even in the presence of adversarial defense mechanisms.
CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge Graph
Authors: Rong Huan , Qian Minfeng , Ma Tinghuai , Jin Di , Sheng Victor S.
Published: January 2024

Object detection
is a widely studied problem in existing works. However, in this paper, we turn to a more challenging problem of “
Covered Object Reasoning
”, aimed at reasoning the category label of target object in the given image particularly when it has been totally
covered
(or
invisible
). To resolve this problem, we propose
CoBjeason
to seize the opportunity when visual reasoning meets the knowledge graph, where “
empirical cognition
” on common visual contexts have been incorporated as knowledge graph to conduct reinforced multi-hop reasoning via two collaborative agents. Such two agents, for one thing, stand at the covered object (or
unknown entity
) to observe the surrounding visual cues in the given image and gradually select
entities
and
relations
from the global
gallery-level
knowledge graph which contains entity-pairs frequently occurring across the entire image-collection, so as to
infer
the main structure of image-level knowledge graph
forward
expanded from the
unknown entity
. In turn, for another, based on the
reasoned
image-level knowledge graph, the semantic context among
entities
will be aggregated backward into
unknown entity
to select an appropriate entity from the global
gallery-level
knowledge graph as the reasoning result. Moreover, such two agents will collaborate with each other, securing that the above
Forward
&
Backward Reasoning
will step towards the same destination of the higher performance on covered object reasoning. To our best knowledge, this is the first work on
Covered Object Reasoning
with Knowledge Graphs and reinforced Multi-Agent collaboration. Particularly, our study on
Covered Object Reasoning
and the proposed model
CoBjeason
could offer novel insights into more basic Computer Vision (CV) tasks, such as
Semantic Segmentation
with better understanding on the current scene when some objects are blurred or covered,
Visual Question Answering
with enhancement on the inference in more complicated visual context when some objects are covered or invisible, and
Image Caption Generation
with the augmentation on the richness of visual context for images containing partially visible objects. The improvement on the above basic CV tasks can further refine more complicated ones involved with nuanced visual interpretation like Autonomous Driving, where the recognition and reasoning on partially visible or covered object are critical. According to the experimental results, our proposed
CoBjeason
can achieve the best overall ranking performance on covered object reasoning compared with other models, meanwhile enjoying the advantage of lower “
exploration cost
”, with the insensitivity against the long-tail covered objects and the acceptable time complexity.
Machine-Learning-Assisted Blood Parameter Sensing Platform for Rapid Next Generation Biomedical and Healthcare Applications
Authors: Palekar Sangeeta , Kalambe Jayu , Patrikar Rajendra M.
Published: January 2024

The pursuit of rapid diagnosis has resulted in considerable advances in blood parameter sensing technologies. As advances in technology, there may be challenges in equitable access for all individuals due to economic constraints, advanced expertise, limited accessibility in particular places, or insufficient infrastructure. Hence, simple, cost efficient, benchtop biochemical blood-sensing platform was developed for detecting crucial blood parameters for multiple disease diagnosis. Colorimetric and image processing techniques is used to evaluate color intensity. CMOS image sensor is utilized to capture images to calculate optical density for sensing. The platform is assessed with blood serum samples, including Albumin, Gamma Glutamyl Transferase, Alpha Amylase, Alkaline Phosphatase, Bilirubin, and Total Protein within clinically relevant limits. The platform had excellent Limits of Detection (LOD) for these parameters, which are critical for diagnosing liver and kidney-related diseases (0.27 g dl−1, 0.86 IU l−1, 1.24 IU l−1, 0.97 IU l−1, 0.24 mg dl−1, 0.35 g dl−1, respectively). Machine learning (ML) algorithms were used to estimate targeted blood parameter concentrations from optical density readings, with 98.48% accuracy and reduced incubation time by nearly 80%. The proposed platform is compared to commercial analyzers, which demonstrate excellent accuracy and reproducibility with remarkable precision (0.03 to 0.71%CV). The platform’s robust stability of 99.84% was shown via stability analysis, indicating its practical applicability.
HPLC Method Development and Validation for the Determination of Apixaban and Clopidogrel in Novel Fixed-Dose Combination Tablets
Authors: Al-Shami Ni’meh , Naseef Hani , Moqadi Ramzi , Kanaze Feras
Published: January 2024

A simple, fast, and accurate high-performance liquid chromatographic (HPLC) method is developed, optimized, and validated for a fixed-dose combination of apixaban (APX) and clopidogrel (CLOP) tablets according to ICH guidelines. Chromatographic separation of the drugs was performed on a BDS Hypersil C18 (4.6 ∗ 150 mm, 5 μm), with acetonitrile (ACN) and trifluoroacetic acid (TFA) in the ratio 48 : 52 (v/v) as a mobile phase, at a flow rate of 0.9 ml/min., injection volume of 5 μL, and column temperature 45°C. The proposed method was linear over the level 25–200% for a concentration of APX 5 μg/ml and CLOP 75 μg/ml (R2 > 0.999). The detection limit for APX and CLOP was found to be 0.3465 and 3.8496 μg/ml, whereas the quantification limit was 1.0499 and 11.6656 μg/ml, respectively. The recovery was more than 99% using the standard addition method. The developed method was found to be specific, accurate, precise, and robust against changes in column temperature (±5°C) and mobile phase composition (±5% ACN); hence, it can be used for the determination of APX and CLOP in the fixed-dose combination tablets.
Alterations of Sexual and Erectile Functions after Brachytherapy for Prostate Cancer Based on Patient-Reported Questionnaires
Authors: Gesztesi László , Kocsis Zsuzsa S. , Jorgo Kliton , Fröhlich Georgina , Polgár Csaba , Ágoston Péter
Published: January 2024

The aim of the study was to compare the side effects of high-dose-rate brachytherapy (HDRBT) and low-dose-rate brachytherapy (LDRBT), with a particular focus on the effects on sexual functions and sexual well-being (PROMOBRA study, NCT02258087). Localized low-risk and low-intermediate-risk prostate cancer patients were treated with mono LDR (N = 123, 145 Gy dose) or mono HDR brachytherapy (N = 117, 19/21 Gy). Prior to the treatment and during follow-up (at 3, 6, 9, 12, 18, and 24 months after treatment, and then annually after two years), patients completed patient-reported outcome measurement (PROM) questionnaires EORTC QLQ-PR-25, International Index of Erectile Function (IIEF), and IIEF-5 (SHIM). We compared the patients in different group breakdowns (HDR vs. LDR, hormone naïve and hormone-receiving HDR vs. LDR, hormone naïve and hormone-receiving patients in general, and 19 Gy HDR vs. 21 Gy HDR). In the hormone-naive LDR group, erectile function, orgasm function, sexual desire, satisfaction with intercourse, and overall satisfaction functions significantly decreased compared to baseline throughout the whole follow-up period. However, there were significant decreases in function at a maximum of three time points after HDR therapy without hormone therapy. In hormone-receiving patients, the orgasm function was significantly better in the HDR group at multiple time points compared to the baseline, and sexual desire improved at four time points. According to our results, both LDRBT and HDRBT can be safely administered to patients with localized prostate cancer. In hormone-naive patients, the HDR group showed only recovering decreases in sexual functions, while the LDR group showed a lasting decline in multiple areas. Thus, HDR appears to be more advantageous to hormone-naive patients.
Data from SPINK1 Status in Colorectal Cancer, Impact on Proliferation, and Role in Colitis-Associated Cancer
Authors: Ida Satoshi , Ozaki Nobuyuki , Araki Kimi , Hirashima Kotaro , Zaitsu Yoko , Taki Katsunobu , Sakamoto Yasuo , Miyamoto Yuji , Oki Eiji , Morita Masaru , Watanabe Masayuki , Maehara Yoshihiko , Yamamura Ken-ichi , Baba Hideo , Ohmuraya Masaki
Published: January 2024

Abstract
Colorectal cancer is a major cause of deaths due to cancer; therefore, research into its etiology is urgently needed. Although it is clear that chronic inflammation is a risk factor for colorectal cancer, the details remain uncertain. Serine protease inhibitor, Kazal type 1 (SPINK1) is mainly produced in pancreatic acinar cells. However, SPINK1 is expressed in various cancers and in inflammatory states, such as colon cancer and inflammatory bowel disease. There are structural similarities between SPINK1 and epidermal growth factor (EGF). Hence, it was hypothesized that SPINK1 functions as a growth factor for tissue repair in inflammatory states, and if prolonged, acts as a promoter for cell proliferation in cancerous tissues. Here, immunohistochemical staining for SPINK1 was observed in a high percentage of colorectal cancer patient specimens and SPINK1 induced proliferation of human colon cancer cell lines. To clarify its role in colon cancer in vivo, a mouse model exposed to the colon carcinogen azoxymethane and nongenotoxic carcinogen dextran sodium sulfate revealed that Spink3 (mouse homolog of SPINK1) is overexpressed in cancerous tissues. In Spink3 heterozygous mice, tumor multiplicity and tumor volume were significantly decreased compared with wild-type mice. These results suggest that SPINK1/Spink3 stimulates the proliferation of colon cancer cells and is involved in colorectal cancer progression.

Implications: Evidence suggests that SPINK1 is an important growth factor that connects chronic inflammation and cancer. Mol Cancer Res; 13(7); 1130–8. ©2015 AACR.
Data from RNA-seq Reveals Aurora Kinase–Driven mTOR Pathway Activation in Patients with Sarcomatoid Metastatic Renal Cell Carcinoma
Authors: Pal Sumanta K. , He Miaoling , Tong Tommy , Wu Huiqing , Liu Xueli , Lau Clayton , Wang Jin-Hui , Warden Charles , Wu Xiwei , Signoretti Sabina , Choueiri Toni K. , Karam Jose A. , Jones Jeremy O.
Published: January 2024

Abstract
Sarcomatoid metastatic renal cell carcinoma (mRCC) is associated with a poor prognosis, and the biology of the disease has been inadequately characterized. RNA sequencing (RNA-seq) was performed on adjacent benign, clear cell, and sarcomatoid components from clinical specimens with sarcomatoid mRCC. M phase and cell-cycle pathways were enriched in sarcomatoid versus adjacent clear cell components, suggesting greater cell proliferation. The expression of aurora kinase A (AURKA) was increased as part of these pathways, and its increased expression was validated by quantitative PCR (qPCR). Immunohistochemical (IHC) analysis revealed that AURKA levels were increased in sarcomatoid tissue compared with their benign or clear cell parts. The increase in AURKA correlated with increased mTOR pathway activity, as evidenced by increased expression of phosphorylated mTOR (S2448) and ribosomal protein S6K (T389). When AURKA was stably expressed in a RCC cell line (Renca), it resulted in increased expression and activity of mTOR, suggesting that overexpression of AURKA can activate the mTOR pathway. These results warrant the analysis of a larger clinical cohort and suggest that targeting AURKA and/or mTOR in patients with sarcomatoid mRCC should be explored.

Implications: Comparative RNA-seq of adjacent sarcomatoid and clear cell histology of RCC indicates a proliferative phenotype and increased AURKA-dependent activation of mTOR signaling in sarcomatoid RCC, which could be targeted by available agents. Mol Cancer Res; 13(1); 130–7. ©2014 AACR.
Data from Loss of Stromal IMP1 Promotes a Tumorigenic Microenvironment in the Colon
Authors: Hamilton Kathryn E. , Chatterji Priya , Lundsmith Emma T. , Andres Sarah F. , Giroux Veronique , Hicks Philip D. , Noubissi Felicite K. , Spiegelman Vladimir S. , Rustgi Anil K.
Published: January 2024

Abstract
The colon tumor microenvironment is becoming increasingly recognized as a complex but central player in the development of many cancers. Previously, we identified an oncogenic role for the mRNA-binding protein IMP1 (IGF2BP1) in the epithelium during colon tumorigenesis. In the current study, we reveal the contribution of stromal IMP1 in the context of colitis-associated colon tumorigenesis. Interestingly, stromal deletion of Imp1 (Dermo1Cre;Imp1LoxP/LoxP, or Imp1ΔMes) in the azoxymethane/dextran sodium sulfate (AOM/DSS) model of colitis-associated cancer resulted in increased tumor numbers of larger size and more advanced histologic grade than controls. In addition, Imp1ΔMes mice exhibited a global increase in protumorigenic microenvironment factors, including enhanced inflammation and stromal components. Evaluation of purified mesenchyme from AOM/DSS-treated Imp1ΔMes mice demonstrated an increase in hepatocyte growth factor (HGF), which has not been associated with regulation via IMP1. Genetic knockdown of Imp1 in human primary fibroblasts confirmed an increase in HGF with Imp1 loss, demonstrating a specific, cell-autonomous role for Imp1 loss to increase HGF expression. Taken together, these data demonstrate a novel tumor-suppressive role for IMP1 in colon stromal cells and underscore an exquisite, context-specific function for mRNA-binding proteins, such as IMP1, in disease states.

Implications: The tumor-suppressive role of stromal IMP1 and its ability to modulate protumorigenic factors suggest that IMP1 status is important for the initiation and growth of epithelial tumors. Mol Cancer Res; 13(11); 1478–86. ©2015 AACR.

See related article by Koltsova and Grivennikov, p. 1452
Data from Defective Myb Function Ablates Cyclin E1 Expression and Perturbs Intestinal Carcinogenesis
Authors: Cheasley Dane , Pereira Lloyd , Sampurno Shienny , Sieber Oliver , Jorissen Robert , Xu Huiling , Germann Markus , Yuqian Yan , Ramsay Robert G. , Malaterre Jordane
Published: January 2024

Abstract
Cyclin E1 is essential for the reentry of quiescent cells into the cell cycle. When hypomorphic mutant Myb mice (MybPlt4) were examined, it was noted that Cyclin E1 (Ccne1) expression was reduced. Furthermore, the induction of Ccne1 in recovering intestinal epithelia following radiation-induced damage was ablated in Myb-mutant mice. These data prompted us to investigate whether Myb directly regulated Ccne1 and to examine whether elevated Myb in colorectal cancer is responsible for Cyclin E1–driven tumor growth. Here, it was found that Myb/MYB and Ccne1/CCNE1 expressions were coupled in both mouse and human adenomas. In addition, the low molecular weight Cyclin E1 was the predominant form in intestinal crypts and adenomatous polyposis coli (Apc)–mutant adenomas. Chromatin immunoprecipitation (ChIP) analysis confirmed that Myb bound directly to the Ccne1 promoter and regulated its endogenous expression. In contrast, MybPlt4 served as a dominant-negative factor that inhibited wild-type Myb and this was not apparently compensated for by the transcription factor E2F1 in intestinal epithelial cells. MybPlt4/Plt4 mice died prematurely on an ApcMin/+ background associated with hematopoietic defects, including a myelodysplasia; nevertheless, ApcMin/+ mice were protected from intestinal tumorigenesis when crossed to MybPlt4/+ mice. Knockdown of CCNE1 transcript in murine colorectal cancer cells stabilized chromosome ploidy and decreased tumor formation. These data suggest that Cyclin E1 expression is Myb dependent in normal and transformed intestinal epithelial cells, consistent with a cell-cycle progression and chromosome instability role in cancer.

Implications: This study demonstrates that Myb regulates Cyclin E1 expression in normal gastrointestinal tract epithelial cells and is required during intestinal tumorigenesis. Mol Cancer Res; 13(8); 1185–96. ©2015 AACR.
Data from Integration of Downstream Signals of Insulin-like Growth Factor-1 Receptor by Endoplasmic Reticulum Stress for Estrogen-Induced Growth or Apoptosis in Breast Cancer Cells
Authors: Fan Ping , Cunliffe Heather E. , Maximov Philipp Y. , Agboke Fadeke A. , McDaniel Russell E. , Zou Xiaojun , Ramos Pilar , Russell Megan L. , Jordan V. Craig
Published: January 2024

Abstract
Estrogen (E2) exerts a dual function on E2-deprived breast cancer cells, with both initial proliferation and subsequent induction of stress responses to cause apoptosis. However, the mechanism by which E2 integrally regulates cell growth or apoptosis-associated pathways remains to be elucidated. Here, E2 deprivation results in many alterations in stress-responsive pathways. For instance, E2-deprived breast cancer cells had higher basal levels of stress-activated protein kinase, c-Jun N-terminal kinase (JNK), compared with wild-type MCF-7 cells. E2 treatment further constitutively activated JNK after 24 hours. However, inhibition of JNK (SP600125) was unable to abolish E2- induced apoptosis, whereas SP600125 alone arrested cells at the G2 phase of the cell cycle and increased apoptosis. Further examination showed that inhibition of JNK increased gene expression of TNFα and did not effectively attenuate expression of apoptosis-related genes induced by E2. A notable finding was that E2 regulated both JNK and Akt as the downstream signals of insulin-like growth factor-1 receptor (IGFIR)/PI3K, but with distinctive modulation patterns: JNK was constitutively activated, whereas Akt and Akt-associated proteins, such as PTEN and mTOR, were selectively degraded. Endoplasmic reticulum–associated degradation (ERAD) was involved in the selective protein degradation. These findings highlight a novel IGFIR/PI3K/JNK axis that plays a proliferative role during the prelude to E2-induced apoptosis and that the endoplasmic reticulum is a key regulatory site to decide cell fate after E2 treatment.

Implications: This study provides a new rationale for further exploration of E2-induced apoptosis to improve clinical benefit. Mol Cancer Res; 13(10); 1367–76. ©2015 AACR.
Pan-Cancer Comparative and Integrative Analyses of Driver Alterations Using Japanese and International Genomic Databases
Authors: Horie Sara , Saito Yuki , Kogure Yasunori , Mizuno Kota , Ito Yuta , Tabata Mariko , Kanai Takanori , Murakami Koichi , Koya Junji , Kataoka Keisuke
Published: January 2024

Using 48,627 samples from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), we present a pan-cancer landscape of driver alterations and their clinical actionability in Japanese patients. Comparison with White patients in Genomics Evidence Neoplasia Information Exchange (GENIE) demonstrates high TP53 mutation frequencies in Asian patients across multiple cancer types. Integration of C-CAT, GENIE, and The Cancer Genome Atlas data reveals many cooccurring and mutually exclusive relationships between driver mutations. At pathway level, mutations in epigenetic regulators frequently cooccur with PI3K pathway molecules. Furthermore, we found significant cooccurring mutations within the epigenetic pathway. Accumulation of mutations in epigenetic regulators causes increased proliferation-related transcriptomic signatures. Loss-of-function of many epigenetic drivers inhibits cell proliferation in their wild-type cell lines, but this effect is attenuated in those harboring mutations of not only the same but also different epigenetic drivers. Our analyses dissect various genetic properties and provide valuable resources for precision medicine in cancer.

Significance:

We present a genetic landscape of 26 principal cancer types/subtypes, including Asian-prevalent ones, in Japanese patients. Multicohort data integration unveils numerous cooccurring and exclusive relationships between driver mutations, identifying cooccurrence of multiple mutations in epigenetic regulators, which coordinately cause transcriptional and phenotypic changes. These findings provide insights into epigenetic regulator–driven oncogenesis.

This article is featured in Selected Articles from This Issue, p. 695
Lymph Node–Targeted Vaccine Boosting of TCR T-cell Therapy Enhances Antitumor Function and Eradicates Solid Tumors
Authors: Drakes Dylan J. , Abbas Abdulraouf M. , Shields Jacqueline , Steinbuck Martin P. , Jakubowski Aniela , Seenappa Lochana M. , Haqq Christopher M. , DeMuth Peter C.
Published: January 2024

T-cell receptor (TCR)–modified T-cell therapies have shown promise against solid tumors, but overall therapeutic benefits have been modest due in part to suboptimal T-cell persistence and activation in vivo, alongside potential tumor antigen escape. In this study, we demonstrate an approach to enhance the in vivo persistence and function of TCR T cells through combination with Amphiphile (AMP) vaccination including cognate TCR T peptides. AMP modification improves lymph node targeting of conjugated tumor immunogens and adjuvants, thereby coordinating a robust T cell–activating endogenous immune response. AMP vaccine combination with TCR T-cell therapy led to complete eradication and durable responses against established murine solid tumors refractory to TCR T-cell monotherapy. Enhanced antitumor efficacy was correlated with simultaneous in vivo invigoration of adoptively transferred TCR T cells and in situ expansion of the endogenous antitumor T-cell repertoire. Long-term protection against tumor recurrence in AMP-vaccinated mice was associated with antigen spreading to additional tumor-associated antigens not targeted by vaccination. AMP vaccination further correlated with pro-inflammatory lymph node transcriptional reprogramming and increased antigen presenting–cell maturation, resulting in TCR T-cell expansion and functional enhancement in lymph nodes and solid tumor parenchyma without lymphodepletion. In vitro evaluation of AMP peptides with matched human TCR T cells targeting NY-ESO-1, mutant KRAS, and HPV16 E7 illustrated the clinical potential of AMP vaccination to enhance human TCR T-cell proliferation, activation, and antitumor activity. Taken together, these studies provide rationale and evidence to support clinical evaluation of combining AMP vaccination with TCR T-cell therapies to augment antitumor activity.
Energy management strategy for photovoltaic powered hybrid energy storage systems in electric vehicles
Authors: Ramanjaneyulu Korada Seetha , Brinda Rajamony , Dhanusu Soubache Irissappane
Published: January 2024

Nowadays, electric vehicles (EVs) using additional energy sources frequently deliver a safe ride without concern about the distance. The energy sources including a battery, an ultra-capacitor (UC), and a photovoltaic (PV) are considered in this research for driving the EV. Vehicles that only use battery-oriented technologies experience problems with charging and quick battery discharge. EVs are used with an ultracapacitor to decrease the quick discharge effects and increase the lifetime of the battery. Furthermore, bidirectional DC-DC converters are a type of power electronics device used to verify the smooth transfer of generated power from energy sources to the motor throughout various stages of the driving cycle. Therefore, this study proposes a perturb and observe (P&O) energy management control technique based on tuna swarm optimization (TSO). The suggested TSO-P&O completely uses UC while regulating the battery because it lowers dynamic battery charging and discharging currents. Due to the aforementioned aspect, the suggested TSO-P&O increases battery life and demonstrates a very dependable, long range power source for an electric car. The TSO-P&O technique achieves the EVs by obtaining the maximum speed of 91.93 km/hr. with a quicker settling time of 4,930 ms when compared with the existing zero-fuel zero-emission (ZFZE) method.
Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method
Authors: Phuphanin Akkachai , Tasakorn Metha , Srivichai Jeerapong
Published: January 2024

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.
Guava fruit disease identification based on improved convolutional neural network
Authors: Mahamudul Hashan Antor , Tariqur Rahman Shaon Md , Avinash Kumar , Ul Islam Rizu Md Rakib , Dey Subhankar
Published: January 2024

Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.
Survey on electrocardiography signal analysis and diabetes mellitus: unraveling the complexities and complications
Authors: Raja Rajeswari Satuluri Venkata Kanaka , Vijayakumar Ponnusamy
Published: January 2024

Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived from an ECG signal. Throughout this study, an instance of diabetes mellitus (DM) is considered for creating concrete awareness and understanding of an ECG signal in DM. This study focuses on finding the correlation between ECG and DM. Detection of DM from ECG signal is also studied. The findings of this survey paper conclude that the correlation between DM individuals with cardiovascular complications has autonomic neuropathy, which may lead to myocardial infarction. It is also found that the QRS complex and its abnormalities are not specific to complications in DM. However, non-invasive detection of diabetes through ECG signals demonstrates future research potential.
An automated system for classifying types of cerebral hemorrhage based on image processing techniques
Authors: Arabiat Areen , Altayeb Muneera
Published: January 2024

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).
Design and analysis of wideband four-port multiple input multiple output antenna using defective ground structure for 5G communication
Authors: Tamminaina Govindarao , Manikonda Ramesh
Published: January 2024

This research work describes a compact four-port multiple input multiple output (MIMO) antenna using defective ground structure (DGS), and it perfectly supports the n77, n78, and n79 frequencies in 5G new radios (NR) bands. It can cover a wideband from 3.4 to 5.4 GHz with good impedance matching. The pair of antenna elements are orientated opposite to another pair with DGS. Due to this technique, it has minimal complexity, is less expensive, and improves isolation. It has also improved the frequency band's reflection coefficient and range using MIMO antenna with different stub lengths. Because it is less expensive, FR-4 substrate is used in the implementation of all antennas. Each antenna element has two identical stubs linked to the primary radiator. On the primary radiating element, a ''HI'' slot is created. The partial ground enhances impedance matching and radiation properties throughout the targeted band. The total dimensions of the four-port MIMO antenna are 46×30×1.6 mm3. The array elements' mutual coupling in the simulation is -14 dB. The ECC value is below 0.01, and the diversity gain (DG) is less than 10 dB. The suggested designs' measured gain ranges from 10 to 11.0 dB, and the radiation efficiency is nearly 91%.
Developing a restaurant recommended system via the Vietnamese food image classification
Authors: Pham Viet Hoang , Nguyen Anh Thai , Phung Bao The , Phan Truong Ho-Viet
Published: January 2024

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.
Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network
Authors: Jdi Hamza , Falih Noureddine
Published: January 2024

The region of Beni Mellal, Morocco is heavily dependent on the agricultural sector as its primary source of income. Accurate temperature prediction in agriculture has many benefits including improved crop planning, reduced crop damage, optimized irrigation systems and more sustainable agricultural practices. By having a better understanding of the expected temperature patterns, farmers can make informed decisions on planting schedules, protect crops from extreme temperature events, and use resources more efficiently. The lack of data-driven studies in agriculture impedes the digitalization of farming and the advancement of accurate long-term temperature prediction models. This underscores the significance of research to identify the optimal machine learning models for that purpose. A 22-year time series dataset (2000-2022) is used in the study. The machine-learning model auto-regressive integrated moving average (ARIMA) and deep learning models simple recurrent neural network (SimpleRNN), gated recurrent unit (GRU), and long short-term memory (LSTM) were applied to the time series. The results are evaluated based on the mean absolute error (MAE). The findings indicate that the deep learning models outperformed the machine-learning model, with the GRU model achieving the lowest MAE.
Segmentation techniques for Arabic handwritten: a review
Authors: Abdalla Sheikh Ahmed , Sanusi Azmi Mohd , Abdel Karim Abuain Waleed , Abd Aziz Maslita
Published: January 2024

Image segmentation refers to the process of partitioning a page into distinct sections. This technique aims to improve and transform the image's representation into a more coherent and user-friendly format. Its common application involves identifying objects and boundaries (such as lines and curves) within images. However, this paper focuses on discussing segmentation methods specifically tailored for Arabic handwritten content. Dealing with the segmentation of Arabic handwritten material poses a significant challenge due to the diverse handwriting styles and the interconnection between Arabic letters. The paper will also touch on the classification of segmentation algorithms originally designed for modern documents, illustrating their adaptation in document processing. Furthermore, the paper will address the difficulties associated with segmenting Arabic handwritten content, including variations in writing style, the connected nature of Arabic characters, the complexity of Arabic cursive writing and as well as the diacritics challenges. Lastly, a concise overview of previously widely used segmentation techniques in various research endeavors will be provided.
Predicting and detecting fires on multispectral images using machine learning methods
Authors: Aitimov Murat , Kaldarova Mira , Kassymova Akmaral , Makulov Kaiyrbek , Muratkhan Raikhan , Nurakynov Serik , Sydyk Nurmakhambet , Bapiyev Ideyat
Published: January 2024

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.
Insights of effectivity analysis of learning-based approaches towards software defect prediction
Authors: Rai Deepti , Arcot Prashant Jyothi
Published: January 2024

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.
An algorithm for decomposing variations of 3D model
Authors: Phuong Tran Thanh , Hien Lam Thanh , Duc Vinh Ngo , Manh Toan Ha , Nang Toan Do
Published: January 2024

In recent times, there has been an increasing number of people who are concerned about the virtual reality field. Parameterization of deformations of 3D models is a meaningful problem in theoretical research and application development of virtual reality. This paper proposes a technique for conditional decomposition of 3D model variations based on a given set of 3D observations of an object, along with a set of input strain weights. The proposed algorithm is conducted through an optimal iterative process with solving the non-negative least squares problem. The output of the technique is a set of base models corresponding to different types of strain. The result of the proposed technique allows the creation of a new 3D model variant of the object in a simple and visually observable way. The algorithm has been tested and proven effective on data that are 3D face models created from the Japanese Female Facial Expression (JAFFE) dataset with labeled expression weights.