bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Nonfunctional Requirement Violations in Autonomous Systems
Authors: Fang Xinwei , Yaman Sinem Getir , Calinescu Radu , Wilson Julie , Paterson Colin
Published: November 2023

Autonomous systems are often used in applications where environmental and internal changes may lead to requirement violations. Adapting to these changes proactively, i.e., before the violations occur, is preferable to recovering from the failures that may be caused by such violations. However, proactive adaptation needs methods for predicting requirement violations timely, accurately, and with acceptable overheads. To address this need, we present a method that allows autonomous systems to predict violations of performance, dependability and other nonfunctional requirements, and therefore take preventative measures to avoid or otherwise mitigate them. Our method for
pre
dicting these autonomou
s
sys
t
em disrupti
o
ns (PRESTO) comprises a design time stage and a run-time stage. At design-time, we use parametric model checking to obtain algebraic expressions that formalise the relationships between the nonfunctional properties of the requirements of interest (e.g., reliability, response time, and energy use) and the parameters of the system and its environment. At run-time, we predict future changes in these parameters by applying piece-wise linear regression to online data obtained through monitoring, and we use the algebraic expressions to predict the impact of these changes on the system requirements. We demonstrate the application of PRESTO through simulation in case studies from two different domains.
Approximating Sparsest Cut in Low-treewidth Graphs via Combinatorial Diameter
Authors: Chalermsook Parinya , Kaul Matthias , Mnich Matthias , Spoerhase Joachim , Uniyal Sumedha , Vaz Daniel
Published: November 2023

The fundamental Sparsest Cut problem takes as input a graph
G
together with edge capacities and demands and seeks a cut that minimizes the ratio between the capacities and demands across the cuts. For
n
-vertex graphs 
G
of treewidth 
k
, Chlamtáč, Krauthgamer, and Raghavendra (APPROX’10) presented an algorithm that yields a factor-

\(2^{2^k}\)

approximation in time

\(2^{O(k)} \cdot n^{O(1)}\)

. Later, Gupta, Talwar, and Witmer (STOC’13) showed how to obtain a 2-approximation algorithm with a blown-up runtime of

\(n^{O(k)}\)

. An intriguing open question is whether one can simultaneously achieve the best out of the aforementioned results, that is, a factor-2 approximation in time

\(2^{O(k)} \cdot n^{O(1)}\)

.

In this article, we make significant progress towards this goal via the following results:

(i)

A factor-

\(O(k^2)\)

approximation that runs in time

\(2^{O(k)} \cdot n^{O(1)}\)

, directly improving the work of Chlamtáč et al. while keeping the runtime single-exponential in
k
.

(ii)

For any

\(\varepsilon \in (0,1]\)

, a factor-

\(O(1/\varepsilon ^2)\)

approximation whose runtime is

\(2^{O(k^{1+\varepsilon }/\varepsilon)} \cdot n^{O(1)}\)

, implying a constant-factor approximation whose runtime is nearly single-exponential in
k
and a factor-

\(O(\log ^2 k)\)

approximation in time

\(k^{O(k)} \cdot n^{O(1)}\)

.

Key to these results is a new measure of a tree decomposition that we call
combinatorial diameter
, which may be of independent interest.
First-Principles Approaches to Magnetoelectric Multiferroics
Authors: Xu Changsong , Yu Hongyu , Wang Junling , Xiang Hongjun
Published: November 2023

Magnetoelectric multiferroics, which display both ferroelectric and magnetic orders, are appealing because of their rich fundamental physics and promising technological applications. The revival of multiferroics since 2003 led to a comprehensive understanding of the mechanisms that facilitate the coexistence of electric and magnetic orders and conceptually new design strategies for device architectures, which brought us an important step closer to multiferroic-based technology. In the past thirty years, first-principles calculations based on the laws of quantum mechanics played a crucial role in understanding the electronic, magnetic, and structural properties of multiferroics and guided the design of new multiferroics with improved properties. In this review, we provide a comprehensive overview of first-principles approaches to magnetoelectric multiferroics, especially in low-dimensional forms. In particular, we discuss methods to build an effective Hamiltonian from first principles for magnets, ferroelectrics, and multiferroics. The recently developed machine learning potential approach for multiferroics is also outlined. Furthermore, we present the unified model for spin-induced ferroelectricity and methods for computing the linear magnetoelectric coupling tensor. Finally, recent progress in multiferroic systems and the applications of first-principles approaches to these systems are reviewed.
Instinctive Negotiation by Autonomous Agents in Dense, Unstructured Traffic: A Controls Perspective
Authors: Jankovic Mrdjan
Published: November 2023

Operating autonomous agents in unstructured space presents a difficult problem. The complexity of making decisions such as when to yield and when to go ahead increases exponentially with the number of agents. This is true for humans as well as for software that controls autonomous agents. With some practice, however, human operators are able to move efficiently in a maze of interacting agents in dense traffic. One recent result correlates the instability of equilibria in a multiagent system with an absence of gridlocks. These control barrier function–based algorithms do not include a decision-making component—the action is continuous, and negotiation happens through instability. This mechanism, referred to as instinctive negotiation, is contrasted with discontinuity-induced decisions arising from nonconvex optimization. Based on observed behavioral similarities and insights into human implicit and explicit learning, this article proposes a connection with human driving and suggests that humans may employ a mechanism similar to instinctive negotiation to navigate dense traffic.
Person-Centered Modeling: Techniques for Studying Associations Between People Rather than Variables
Authors: Woo Sang Eun , Hofmans Joeri , Wille Bart , Tay Louis
Published: November 2023

The goal of person-centered methods is to identify subpopulations of individuals based on within-group similarity of data relative to between-group variability. In this article, we provide an overview of specific person-centered methods, thus shifting the attention from studying relations between variables to studying relations between people or entities of interest. Next, we present a selective and critical review of recent research utilizing person-centered modeling approaches, highlighting key trends in the organizational psychology and organizational behavior literature from both the methodological and the conceptual perspectives. Lastly, we conclude with reflections and recommendations, highlighting several areas that need careful consideration when conducting person-centered research.
Driving the Extra Mile in the Gig Economy: The Motivational Foundations of Gig Worker Citizenship
Authors: Moorman Robert H. , Lyons Brian D. , Mercado Brittany K. , Klotz Anthony C.
Published: November 2023

The emergence of gig work (e.g., freelancing, rideshare driving, food and parcel delivery, travel nursing, virtual assistantship) and the gig economy challenges organizational researchers to consider how they should revise traditional theories of work behavior to consider the dynamics of new work arrangements. As a prime example that is central to this review, organizational citizenship behavior (OCB) is a form of job performance whereby motives stem from the quality of work relationships with direct supervisors, coworkers, and other organizational agents. However, gig workers experience very different work relationships and may perform OCB for different reasons (if at all). In this review, we address the question of how OCB theory should evolve to be relevant to gig workers. We summarize traditional motives for OCB performance and review current research describing and classifying gig work. We conclude by ( a) identifying gig worker citizenship (GWC) as a form of citizenship behavior that better fits the reality of gig work and ( b) offering a revised model of how OCB motives may help predict GWC performance.
Effect of Ar-N2 Ratio on Morphology and Electrochemical Properties of Lithium Electrodes Manufactured by Magnetron Sputtering
Authors: Mochalov Sergey E. , Kuzmina Elena V. , Kamalova Guzel B. , Karaseva Elena V. , Kolosnitsyn Vladimir S.
Published: November 2023

This article discusses the possibility of regulating the morphology of lithium electrodes manufactured by magnetron sputtering from a liquid-phase target on stainless steel substrates by a composition of argon-nitrogen gas mixture. Lithium electrodes produced by magnetron sputtering in an impurities-free argon gas, have a polycrystalline structure. The sizes of lithium crystals depend on the thickness of fabricated electrodes and vary from units to tens of microns. An increase in the content of nitrogen in a mixture with argon leads to a decrease in the size of lithium crystals and, in the limiting case, to the formation of a microcrystalline structure of lithium electrodes. The effect of nitrogen on the morphology of lithium electrodes is explained by the formation of lithium nitride on the surface of growing lithium crystals, which disrupts the regularity of condensing lithium layers and hinders the growth of crystals. Lithium electrodes fabricated by magnetron sputtering of lithium in argon-nitrogen mixtures have better electrochemical properties compared to electrodes produced in pure argon.
Microglia in Central Control of Metabolism
Authors: Kim Jung Dae , Copperi Francesca , Diano Sabrina
Published: November 2023

Beyond their role as brain immune cells, microglia act as metabolic sensors in response to changes in nutrient availability, thus playing a role in energy homeostasis. This review highlights the evidence and challenges of studying the role of microglia in metabolism regulation.
Survey of Tick-Borne Zoonotic Agents in Ixodes Ticks Carried by Wild Passerines during Postbreeding Migration through Italy
Authors: Grassi Laura , Franzo Giovanni , Grillo Sofia , Mondin Alessandra , Drigo Michele , Barbarino Fulvio , Comuzzo Cristina , Legnardi Matteo , Bertola Michela , Montarsi Fabrizio , Menandro Maria Luisa
Published: November 2023

Recently, increasing attention has been posed on the role of migrating birds in the spread of ticks and indirectly of tick-borne pathogens (TBPs). Despite, Italy is considered a bridge between continental Europe and North Africa and a necessary path to connect Mediterranean countries, few studies have been conducted on ticks collected from birds migrating through this country and associated TBPs. The aims of this research were to estimate the infestation burden and identify tick species feeding on migratory birds, and perform a molecular screening for TBPs. During autumn migration (2019–2020), birds were inspected for ticks in a ringing station located in north-east Italy. Ticks were identified and screened for tick-borne encephalitis virus (TBEV), Borrelia burgdorferi sensu latu, Rickettsia spp., Ehrlichia spp., Neoehrlichia spp., Anaplasma phagocytophilum, and Bartonella spp. Ixodes ticks (n = 209) were feeding on 2.6% of passerines (88/3411). Most of these (208/209) were Ixodes ricinus, except one Ixodes frontalis. Eight bird species were infested: common blackbird, redwing, brambling, song thrush, common chaffinch, European robin, water pipit, and coal tit. Rickettsiales showed a low prevalence, from 1.4% of Ehrlichia spp., 4.3% of A. phagocytophilum, up to 7.2% of Rickettsia spp.. Borrelia burgdorferi s.l. had the highest prevalence, 54.6%, and several zoonotic genotypes were identified: B. garinii, B. valaisiana, B. afzeli, B. burgdorferi sensu stricto, and B. miyamotoi. All specimens were negative for TBEV and Bartonella spp.. Although the tick burden was generally low, most of the vectors (>60%) were positive for at least one pathogen, highlighting the relevance of a continuous monitoring of migrating birds as potential sources of pathogen dispersal.
Blockchain-Based Authentication Scheme with an Adaptive Multi-Factor Authentication Strategy
Authors: Xu Yanbin , Jian Xinya , Li Tao , Zou Shuang , Li Beibei
Published: November 2023

Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.
Quantifying the Dynamic Factors Influencing New-Age Users’ Adoption of 5G Using TAM and UTAUT Models in Emerging Country: A Multistage PLS-SEM Approach
Authors: Dadhich Manish , Rathore Sumangla , Gyamfi Bright Akwasi , Ajibade Samuel-Soma M. , Agozie Divine Q.
Published: November 2023

Objectives. The 5G has ushered in a new age of life-changing breakthroughs and advancements due to faster speeds, greater bandwidth, and ultra-high expectancy. The study proposes a multistage approach for quantifying the dynamic factors affecting users’ adoption of 5G in emerging countries. Method. This study integrated the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) to recommend a comprehensive model that the industry–academia can adopt. In the proposed model, various core hypotheses and subhypotheses were tested by employing 510 5G users of the metro cities of India. An online questionnaire was used to collect the facts, and the data were framed in the conceptual model to test the validation using partial least squares structural equation modeling (PLS-SEM). Results. The findings suggest that users’ perceptions of adopting 5G are overwhelming in that perceived trust was discovered as a mediating enabler between behavioral intention (BI) and selected manifest. Performance expectancy (PE), effort expectancy (EE), social factors (SF), facilitating factors (FF), hedonic motivation (HM), perceived benefits (PB), price value (PV), and habit (HB). Contribution. By identifying key enablers in the suggested model, service providers may better evaluate these aspects, particularly in ensuring reliable infrastructure for 5G service stands. The study is undoubtedly a novel attempt to assist the telecom industry and policymakers in accelerating the adoption of 5G in emerging economies of Asian continents.
Battery Voltage Prediction Technology Using Machine Learning Model with High Extrapolation Accuracy
Authors: Kawahara Takuma , Sato Koji , Sato Yuki
Published: November 2023

Battery performance prediction techniques based on machine learning (ML) models and lithium-ion battery (LIB) data collected in the real world have received much attention recently. However, poor extrapolation accuracy is a major challenge for ML models using real-world data, as the data frequency distribution can be uneven. Here, we have investigated the extrapolation accuracy of the ML models by using artificial data generated with an electrochemical simulation model. Specifically, we set a lower open circuit voltage (OCV) limit for the training data and generated data limited to the higher state of charge (SOC) region to train the voltage prediction model. We have validated the root mean squared error (RMSE) of the voltage for the test data at several lower OCV limit settings and defined the

average
+
3

standard deviations of them as an evaluation metric. Eight representative ML models were evaluated, and it was found that the multilayer perceptron (MLP) showed an accuracy of 92.7 mV, which was the best extrapolation accuracy. We also evaluated models with published experimental data and found that the MLP had an accuracy of 102.4 mV, reconfirming that it had the best extrapolation accuracy. We also found that MLP was robust to changes in the data of interest since the accuracy degradation when changing from simulation to experimental data was as small as a factor of 1.1. This result shows that MLP can achieve higher voltage prediction accuracy even when collecting data for comprehensive SOC conditions is difficult.
Biology of the Endemic Endangered Swallowtail Butterfly, Papilio desmondi teita (Lepidoptera: Papilionidae), on Wild Citrus Species in Taita Hills, Kenya
Authors: Genga Oliver C. , Samita Namikoye E. , Kahuthia-Gathu Ruth , Kioko Esther N.
Published: November 2023

The Taita Hills swallowtail butterfly, Papilio desmondi teita, was reared in captivity on wild citrus (Rutaceae), Toddalia asiatica and Clausena anisata, in Ngangao Forest, Taita Hills, Kenya. The butterflies were exposed to the host plants for oviposition inside the laying buckets under ambient laboratory conditions. Results revealed that P. desmondi teita could only complete development on T. asiatica and but not on C. anisata in both seasons. The egg incubation period on T. asiatica was significantly shorter (

P

 < 0.001) compared to C. anisata during the first season (

P

 = 0.595) but not in the second season. The development period of the butterfly in 1st and 2nd seasons on T. asiatica was 81.13 ± 0.44 days and 112.15 ± 1.20 days, respectively, while a female-biased sex ratio was observed in the first and second seasons. Findings from this study indicate that T. asiatica was the most suitable host plant for development of P. desmondi teita.
Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration
Authors: Bai Congcong , Jing Jun , Liu Bokun , Yao Wenbin , Yang Chengcheng , Alagbé Adjé Jérémie , Jin Sheng
Published: November 2023

To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver’s visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers’ visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model’s remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers’ visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems.
Postoperative Outcome of Combined Phacovitrectomy in Eyes with Excessive Myopia (>-30 D)
Authors: Fan Hua , Zhang Mingming , Tzekov Radouil , Qian Zhuyun , Yang Jiasong , Xie XiaoLin , Li Wensheng
Published: November 2023

Background. To report the outcomes of phacoemulsification combined with vitrectomy in eyes with extreme myopia (-30 diopters or more). Case Presentation. Three patients with cataract, vitreous opacities, and extreme myopia of more than -30 diopters underwent a combined surgical procedure of cataract extraction combined with vitrectomy. Postoperative refractive correction of the three cases ranged from -1.0 D to -2.5 D spherical equivalent. There was an obvious hyperopic shift of all cases. All patients noted a significant improvement in uncorrected and best-corrected visual acuity from 0.4 to 0.8 in case 1, from CF/70 cm to 1.0 in case 2, and from 0.12 to 0.5 in the right eye and 0.15 to 0.2 in the left eye in case 3. Vitreous floaters disappeared in all cases. No complications were noted during follow-up. Conclusions. To the best of the authors’ knowledge, these represent the first reported clinical cases of combined cataract extraction+vitrectomy surgery in eyes with extreme (>-30 D) myopia. Our results support the notion that phacoemulsification combined with vitrectomy may be a good therapeutic option for cataracts and vitreous floaters in cases with extreme myopia.
Toxic Epidermal Necrolysis after COVID-19 mRNA-1237 Vaccination
Authors: Hehr Nicole , Davis Benjamin P.
Published: November 2023

This letter illustrates a case of toxic epidermal necrolysis (TEN) after COVID-19 mRNA-1273 vaccination, which corresponds with the existing published data and contributes detailed knowledge of TEN reaction after vaccination. Interestingly, the reaction started at the site of vaccination and the patient went on to tolerate a major excipient of the vaccine suggesting the reaction may be associated with the mRNA itself or is triggered by the immunostimulatory action of the vaccine.
Umbilical Cord Mesenchymal Stem Cells Overexpressing Heme Oxygenase-1 Promotes Symptoms Recovery in Cystitis Rats by Alleviating Neuroinflammation
Authors: Gao Qiongqiong , Gao Zhentao , Su Minzhi , Huang Yong , Zhang Chi , Li Cuiping , Zhan Hailun , Liu Bolong , Zhou Xiangfu
Published: November 2023

Interstitial cystitis/bladder pain syndrome (IC/BPS) seriously reduces the patient’s quality of life, yet current therapies only provide partial relief. In the spinal dorsal horn (SDH), neuroinflammation plays a pivotal role in the development of IC. Injection of human umbilical cord mesenchymal stem cells (hUMSCs) to reduce inflammation is an effective strategy, and heme oxygenase-1 (HO-1) exhibits anti-nociceptive effect in neuroinflammatory pain. This study aimed to test the therapeutic effects of hUMSCs overexpressing HO-1 on cyclophosphamide-induced cystitis rat model. Cystitis rats were transplanted with altered cells and then assessed for 3 weeks. A series of behavioral measurements would be trial including suprapubic mechanical allodynia, depressive-like behaviors, micturition frequency, and short-term memory function. Additionally, western blot, immunofluorescence staining, and ELISA kit test for anti-inflammation effect. HUMSCs were capable of being transduced to overexpress HO-1. Injection of hUMSCs overexpressing HO-1 was more effective than hUMSCs alone in alleviating behavioral symptoms in rats. Furthermore, hUMSCs overexpressing HO-1 inhibited the activation of glial and TLR4/p65/NLRP3 pathway, decreased the levels of pro-inflammatory cytokines in the SDH region. Surprisingly, it markedly increased anti-inflammatory cytokine IL-10, reduced MDA content, and protected GSH concentrations in local environment. Our results suggest that injecting hUMSCs overexpressing HO-1 intrathecally can significantly promote functional outcomes in cystitis rats by reducing neuroinflammation, at least, partly through downregulating TLR4/p65/NLRP3 signaling pathway in the SDH region. This cell therapy affords a new strategy for IC/BPS treatment.
Detection and Prediction of Neurodegenerative Disorders by Long Noncoding RNA Signature Analysis among Bangladeshi Population
Authors: Rahman Tania , Rozario Patricia , Naim Mustafizur Rahman , Seraj Md Ferdous , Chowdhury Ahmed Hossain
Published: November 2023

Long noncoding RNAs (lncRNAs) have appeared lately as a new class of genes that may control and mediate cellular processes. It plays a critical role in many diseases, including neurodegenerative disorders. HOTAIR and MKRN2‐42:1 are two lncRNAs that have been seen upregulated in various diseases including in the peripheral blood of the patients suffering from Parkinson’s disease (PD) in different countries. It has also been seen that Meg3v1 and NEAT1 lncRNAs are expressed highly in patients suffering from motor neuron disease (MND) from various countries, though the association between the reason for the onset of this disease and the expression of these specific lncRNAs is not clear. The primary aim of this study was to observe expression variations for specific lncRNAs in the peripheral blood of PD and MND patients compared with healthy individuals and to look for lncRNAs that might be related to the pathogenesis of PD and MND. The aim of this study was also to investigate the association of lncRNA sequence as biomarkers with the risk of having neurodegenerative disorders (PD and MND) in the population of Bangladesh. The purpose of this research also included the possibility of prediction of these neurodegenerative disorders by analyzing and comparing the data which was acquainted from the study where 20 PD patients and 20 healthy subjects and 25 MND patients and 25 healthy subjects of Bangladesh were examined. Their peripheral blood was taken, and plasma was separated. Later, RT‐PCR was performed to observe the changes in the expression of specific lncRNAs. Results showed that expression of HOTAIR and MKRN2‐42:1 was positively correlated (p < 0.05) with the symptoms (5) of Parkinson’s suspects in comparison to the healthy subjects. Similarly, Meg3v1 and NEAT1 lncRNAs were expressed significantly (p < 0.05) in MND patients rather than that in healthy controls. Data were analyzed using GraphPad Prism 7.0, where p < 0.05 was considered statistically significant. Successful prediction of the possibility of neurodegenerative disorder by analyzing data regarding the expression of lncRNAs as biomarkers may help in advance clinicians to stratify high‐risk patients and prepare for appropriate medical intervention.
Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Authors: Hagos Yeman Brhane , Lecat Catherine S.Y. , Patel Dominic , Mikolajczak Anna , Castillo Simon P. , Lyon Emma J. , Foster Kane , Tran Thien-An , Lee Lydia S.H. , Rodriguez-Justo Manuel , Yong Kwee L. , Yuan Yinyin
Published: November 2023

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning–based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.

Significance:

Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.
Supplementary Figure S4 from Effective Reprogramming of Patient-Derived M2-Polarized Glioblastoma-Associated Microglia/Macrophages by Treatment with GW2580
Authors: Fermi Valentina , Warta Rolf , Wöllner Amélie , Lotsch Catharina , Jassowicz Lena , Rapp Carmen , Knoll Maximilian , Jungwirth Gerhard , Jungk Christine , Dao Trong Philip , von Deimling Andreas , Abdollahi Amir , Unterberg Andreas , Herold-Mende Christel
Published: November 2023

Live-cell imaging demonstrates accumulation of autologous T cells around tumor cells and the induction of tumor cell apoptosis.
Supplementary Figure S1 from Oncogenic Drivers and Therapeutic Vulnerabilities in KRAS Wild-Type Pancreatic Cancer
Authors: Singh Harshabad , Keller Rachel B. , Kapner Kevin S. , Dilly Julien , Raghavan Srivatsan , Yuan Chen , Cohen Elizabeth F. , Tolstorukov Michael , Andrews Elizabeth , Brais Lauren K. , da Silva Annacarolina , Perez Kimberly , Rubinson Douglas A. , Surana Rishi , Giannakis Marios , Ng Kimmie , Clancy Thomas E. , Yurgelun Matthew B. , Schlechter Benjamin L. , Clark Jeffrey W. , Shapiro Geoffrey I. , Rosenthal Michael H. , Hornick Jason L. , Nardi Valentina , Li Yvonne Y. , Gupta Hersh , Cherniack Andrew D. , Meyerson Matthew , Cleary James M. , Nowak Jonathan A. , Wolpin Brian M. , Aguirre Andrew J.
Published: November 2023

KRAS WT pancreatic cancer cohort curation and variant filtering
Supplementary Table S2 from Survivin Dendritic Cell Vaccine Safely Induces Immune Responses and Is Associated with Durable Disease Control after Autologous Transplant in Patients with Myeloma
Authors: Freeman Ciara L. , Atkins Reginald , Varadarajan Indumathy , Menges Meghan , Edelman Jeffrey , Baz Rachid , Brayer Jason , Castaneda Puglianini Omar , Ochoa-Bayona Jose Leonel , Nishihori Taiga , Shain Kenneth H. , Shah Bijal , Chen Dung Tsa , Kelley Linda , Coppola Domenico , Alsina Melissa , Antonia Scott , Anasetti Claudio , Locke Frederick L.
Published: November 2023

Patient Allred score
Data from Clinical and Genomic Landscape of FGFR3-Altered Urothelial Carcinoma and Treatment Outcomes with Erdafitinib: A Real-World Experience
Authors: Guercio Brendan J. , Sarfaty Michal , Teo Min Yuen , Ratna Neha , Duzgol Cihan , Funt Samuel A. , Lee Chung-Han , Aggen David H. , Regazzi Ashley M. , Chen Ziyu , Lattanzi Michael , Al-Ahmadie Hikmat A. , Brannon A. Rose , Shah Ronak , Chu Carissa , Lenis Andrew T. , Pietzak Eugene , Bochner Bernard H. , Berger Michael F. , Solit David B. , Rosenberg Jonathan E. , Bajorin Dean F. , Iyer Gopa
Published: November 2023

AbstractPurpose:
Erdafitinib is the only FDA-approved targeted therapy for FGFR2/3-altered metastatic urothelial cancer. We characterized the genetic landscape of FGFR-altered urothelial carcinoma and real-world clinical outcomes with erdafitinib, including on-treatment genomic evolution.
Experimental Design:
Prospectively collected clinical data were integrated with institutional genomic data to define the landscape of FGFR2/3-altered urothelial carcinoma. To identify mechanisms of erdafitinib resistance, a subset of patients underwent prospective cell-free (cf) DNA assessment.
Results:
FGFR3 alterations predictive of erdafitinib sensitivity were identified in 39% (199/504) of patients with non-muscle invasive, 14% (75/526) with muscle-invasive, 43% (81/187) with localized upper tract, and 26% (59/228) with metastatic specimens. One patient had a potentially sensitizing FGFR2 fusion. Among 27 FGFR3-altered cases with a primary tumor and metachronous metastasis, 7 paired specimens (26%) displayed discordant FGFR3 status. Erdafitinib achieved a response rate of 40% but median progression-free and overall survival of only 2.8 and 6.6 months, respectively (n = 32). Dose reductions (38%, 12/32) and interruptions (50%, 16/32) were common. Putative resistance mutations detected in cfDNA involved TP53 (n = 5), AKT1 (n = 1), and second-site FGFR3 mutations (n = 2).
Conclusions:
FGFR3 mutations are common in urothelial carcinoma, whereas FGFR2 alterations are rare. Discordance of FGFR3 mutational status between primary and metastatic tumors occurs frequently and raises concern over sequencing archival primary tumors to guide patient selection for erdafitinib therapy. Erdafitinib responses were typically brief and dosing was limited by toxicity. FGFR3, AKT1, and TP53 mutations detected in cfDNA represent putative mechanisms of acquired erdafitinib resistance.
CD15 Is a Risk Predictor and a Novel Target in Clear Cell Renal Cell Carcinoma
Authors: Stenzel Philipp Joachim , Schindeldecker Mario , Seidmann Larissa , Herpel Esther , Hohenfellner Markus , Hatiboglu Gencay , Foersch Sebastian , Porubsky Stefan , Macher-Goeppinger Stephan , Roth Wilfried , Tagscherer Katrin Elisabeth
Published: November 2023

Introduction: Tumor cells use adhesion molecules like CD15 or sialylCD15 (sCD15) for metastatic spreading. We analyzed the expression of CD15 and sCD15 in clear cell renal cell carcinoma (ccRCC) regarding prognosis. Methods: A tissue microarray containing tissue specimens of 763 patients with ccRCC was immunohistochemically stained for CD15 and sCD15, their expression quantified using digital image analysis, and the impact on patients’ survival analyzed. The cell lines 769p and 786o were stimulated with CD15 or control antibody in vitro and the effects on pathways activating AP-1 and tumor cell migration were examined. Results: ccRCC showed a broad range of CD15 and sCD15 expression. A high CD15 expression was significantly associated with favorable outcome (p < 0.01) and low-grade tumor differentiation (p < 0.001), whereas sCD15 had no significant prognostic value. Tumors with synchronous distant metastasis had a significantly lower CD15 expression compared to tumors without any (p < 0.001) or with metachronous metastasis (p < 0.01). Tumor cell migration was significantly reduced after CD15 stimulation in vitro, but there were no major effects on the activating pathways of AP-1. Conclusion: CD15, but not sCD15, qualifies as a biomarker for risk stratification and as an interesting novel target in ccRCC. Moreover, the data indicate a contribution of CD15 to metachronous metastasis. Further research is warranted to decipher the intracellular pathways of CD15 signaling in ccRCC in order to characterize the CD15 effects on ccRCC more precisely.
Breast cancer detection using ensemble of convolutional neural networks
Authors: Nadkarni Swati , Noronha Kevin
Published: November 2023

Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
Development of an internet of things-based weather station device embedded with O2, CO2, and CO sensor readings
Authors: Megantoro Prisma , Saud Al-Humairi Safaa Najah , Kustiawan Arya Dwi , Arsalan Muhammad Rafi Nabil , Prastio Rizki Putra , Awalin Lilik Jamilatul , Vigneshwaran Pandi
Published: November 2023

Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O2), carbon dioxide (CO2), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server.
A checkpointing mechanism for virtual clusters using memory-bound time-multiplexed data transfers
Authors: Yaothanee Jumpol , Chanchio Kasidit
Published: November 2023

Transparent hypervisor-level checkpoint-restart mechanisms for virtual clusters (VCs) or clusters of virtual machines (VMs) offer an attractive fault tolerance capability for cloud data centers. However, existing mechanisms have suffered from high checkpoint downtimes and overheads. This paper introduces Mekha, a novel hypervisor-level, in-memory coordinated checkpoint-restart mechanism for VCs that leverages precopy live migration. During a VC checkpoint event, Mekha creates a shadow VM for each VM and employs a novel memory-bound timed-multiplex data (MTD) transfer mechanism to replicate the state of each VM to its corresponding shadow VM. We also propose a global ending condition that enables the checkpoint coordinator to control the termination of the MTD algorithm for every VM in a VC, thereby reducing overall checkpoint latency. Furthermore, the checkpoint protocols of Mekha are designed based on barrier synchronizations and virtual time, ensuring the global consistency of checkpoints and utilizing existing data retransmission capabilities to handle message loss. We conducted several experiments to evaluate Mekha using a message passing interface (MPI) application from the NASA advanced supercomputing (NAS) parallel benchmark. The results demonstrate that Mekha significantly reduces checkpoint downtime compared to traditional checkpoint mechanisms. Consequently, Mekha effectively decreases checkpoint overheads while offering efficiency and practicality, making it a viable solution for cloud computing environments.
Performance analysis of a three-stage quadrature RC generator with operational amplifiers
Authors: Karapenev Boyan , Sadinov Stanimir
Published: November 2023

This paper presents the special features of RC harmonic oscillation generators and their widespread use and in particular the quadrature generators which provide two output signals dephased at 90º or 270º. Quadrature generators can be classified as those with an aperiodic frequency-determining circuit or with a phase inverter group which are used to generate oscillations of one or more fixed frequencies. Studies of a three-stage quadrature RC generator circuit with operational amplifiers have been performed. The results obtained from the simulation and experimental studies performed are presented for the selected circuit. It can be assumed that the experimental and simulation results completely coincide to an accuracy of up to 20% for the amplitude of the generated signals and to the total accuracy for the generated frequency. Quadrature generators are very widely used in communication technology and, most importantly, in the structure of digital frequency, phase and quadrature-amplitude modulators and demodulators, in vector RLC meters and many other electronic circuits and devices in practice.
Ultraviolet-C lamp control system designed to estimate deactivation of the coronavirus disease
Authors: Rinanda Saputri Fahmy , Radithya Linus Gregorius
Published: November 2023

To prevent the transmission of the coronavirus disease (COVID-19), one approach involves the application of disinfectants containing specific chemical compounds. Nonetheless, an overabundance of chemicals may yield adverse effects on both humans and the environment. Therefore, alternative methods are needed to prevent the spread of the virus without endangering humans and the environment. One method that minimizes the use of chemicals is ultraviolet-C (UV-C) light. The method used in this study is to make a UV-C lamp control system based on the internet of things (IoT). Then conduct experiments on the spread of UV-C radiation using a system that has already been built. Based on the research that has been done, a disinfectant system has been successfully designed using two Philips 30 W UV-C lamps with Wemos D1 mini microcontroller and Blynk application. The results of data collection show that the highest ultraviolet-C radiation irradiation on the intended object is 0.017 mW/cm2 with a distance between the two is 1.5 m.
Novel control strategy for the global model of wind turbine
Authors: El Fadili Yattou , Berrada Youssef , Boumhidi Ismail
Published: November 2023

This paper presents a new nonlinear control for the overall model of a three-blade horizontal axis variable speed wind turbine (VSWT) including mechanical and electrical parts, with the aim of improving its performance and making it more profitable. The proposed control is an extension of the classical sliding mode control (SMC) by converting its sliding surface into a sliding sector. The classical SMC approach is widely used for nonlinear systems due to its stability against parameter variation, it is robustness against modeling uncertainties, its good results against external disturbances, and its ease of implementation in real time. Unfortunately, the SMC has a major drawback related to the chattering phenomenon. This phenomenon is due to the utility of a higher switching gain in the case of large uncertainties, it causes high-frequency oscillations once the sliding regime is reached, and it can cause a loss of accuracy by influencing the input control variables. This is the reason that aims to develop a new control law to eliminate the chattering and to guarantee stability, which is demonstrated by the Lyapunov theory. The effectiveness of the developed control is compared with the SMC and is illustrated by numerical simulations using MATLAB toolboxes.
Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network
Authors: Hor Yan Tan , Mohd Azam Sazuan Nazrah , Md. Sani Zamani , Azizan Azizul
Published: November 2023

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.
Monitoring climate change effects on coral reefs using edge-based image segmentation
Authors: Afreen Awalludin Ezmahamrul , Wan Yussof Wan Nural Jawahir Hj , Bachok Zainudin , Firdaus Aminudin Muhammad Afiq , Che Din Mohd Safuan , Suzuri Hitam Muhammad
Published: November 2023

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors.
Pilot based channel estimation improvement in orthogonal frequency-division multiplexing systems using linear predictive coding
Authors: Saleh Idan Sarah , Al-Haddad Mohammed Kasim
Published: November 2023

Pilot based least square (LS) channel estimation is a commonly used channel estimation technique in orthogonal frequency-division multiplexing based systems due to its simplicity. However, LS estimation does not handle the noise effect and hence suffers from performance degradation. Since the channel coefficients are correlated in time and hence show a slower variation than the noise, it is possible to encode the channel using linear predictive coding (LPC) without the noise. In this work, the channel is estimated from the pilots using LS estimation and in a second step the channel’s LS estimated is encoded as LPC coefficients to produce an improved channel estimation. The estimation technique is simulated for space-time block coding (STBC) based orthogonal frequency-division multiplexing (OFDM) system and the bit error rate (BER) curves show improvement of the LPC estimation over the LS estimation of the channel.