bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resource constrained neural network training
Authors: Pietrołaj Mariusz , Blok Marek 
Published: January 2024

AbstractModern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental, computational, and power constraints are inevitable consequences of such a shift. Limiting the bit count of neural network parameters proved to be a valid technique for speeding up and increasing efficiency of the inference process. Hence, it is understandable that a similar approach is gaining momentum in the field of neural network training. In the face of growing complexity of neural network architectures, reducing resources required for preparation of new models would not only improve cost efficiency but also enable a variety of new AI applications on modern personal devices. In this work, we present a deep refinement of neural network parameters limitation with the use of the asymmetric exponent method. In addition to the previous research, we study new techniques of floating-point variables limitation, representation, and rounding. Moreover, by leveraging exponent offset, we present floating-point precision adjustments without an increase in variables’ bit count. The proposed method allowed us to train LeNet, AlexNet and ResNet-18 convolutional neural networks with a custom 8-bit floating-point representation achieving minimal or no results degradation in comparison to baseline 32-bit floating-point variables.
Predicting malaria risk considering vector control interventions under climate change scenarios
Authors: Sadoine Margaux L. , Zinszer Kate , Liu Ying , Gachon Philippe , Fournier Michel , Dueymes Guillaume , Dorsey Grant , Llerena Ana , Namuganga Jane Frances , Nasri Bouchra , Smargiassi Audrey 
Published: January 2024

AbstractMany studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.
Deciphering the role of alternative splicing as a potential regulator in fat-tail development of sheep: a comprehensive RNA-seq based study
Authors: Bakhtiarizadeh Mohammad Reza 
Published: January 2024

AbstractAlthough research on alternative splicing (AS) has been widely conducted in mammals, no study has investigated the splicing profiles of genes involved in fat-tail formation in sheep. Here, for the first time, a comprehensive study was designed to investigate the profile of AS events and their involvement in fat-tail development of sheep. In total, 45 RNA-Seq samples related to seven different studies, which have compared the fat-tailed vs thin-tailed sheep breeds, were analyzed. Two independent tools, rMATS and Whippet, along with a set of stringent filters were applied to identify differential AS (DAS) events between the breeds per each study. Only DAS events that were detected by both tools as well as in at least three datasets with the same ΔPSI trend (percent spliced in), were considered as the final high-confidence set of DAS genes. Final results revealed 130 DAS skipped exon events (69 negative and 61 positive ΔPSI) belonged to 124 genes. Functional enrichment analysis highlighted the importance of the genes in the underlying molecular mechanisms of fat metabolism. Moreover, protein–protein interaction network analysis revealed that DAS genes are significantly connected. Of DAS genes, five transcription factors were found that were enriched in the biological process associated with lipid metabolism like “Fat Cell Differentiation”. Further investigations of the findings along with a comprehensive literature review provided a reliable list of candidate genes that may potentially contribute to fat-tail formation including HSD11B1, SIRT2, STRN3 and TCF7L2. Based on the results, it can be stated that the AS patterns may have evolved, during the evolution of sheep breeds, as another layer of regulation to contribute to biological complexity by reprogramming the gene regulatory networks. This study provided the theoretical basis of the molecular mechanisms behind the sheep fat-tail development in terms of AS.
Effects of iodine intake on gut microbiota and gut metabolites in Hashimoto thyroiditis-diseased humans and mice
Authors: Gong Boshen , Meng Fanrui , Wang Xichang , Han Yutong , Yang Wanyu , Wang Chuyuan , Shan Zhongyan 
Published: January 2024

AbstractHashimoto thyroiditis (HT) is an organ-specific autoimmune disease linked to iodine intake. Emerging evidence highlights the gut microbiota’s role in HT pathogenesis via the microbiota-gut-thyroid axis. However, the process through which iodine intake modifies the microbiota and triggers HT remains unclear. This study examines how iodine affects gut dysbiosis and HT, recruiting 23 patients with HT and 25 healthy individuals to assess gut microbiota composition and metabolic features. Furthermore, we establish a spontaneously developed thyroiditis mouse model using NOD.H-2h4 mice highlighting the influence of iodine intake on HT progression. The butanoate metabolism significantly differs between these two groups according to the enrichment results, and butyric acid is significantly decreased in patients with HT compared with those in healthy individuals. Gut dysbiosis, driven by excessive iodine intake, disrupts TH17/Treg balance by reducing butyric acid. In summary, iodine intake alters intestinal microbiota composition and metabolic changes influencing the microbiota-gut-thyroid axis.
Obesity causes mitochondrial fragmentation and dysfunction in white adipocytes due to RalA activation
Authors: Xia Wenmin , Veeragandham Preethi , Cao Yu , Xu Yayun , Rhyne Torrey E. , Qian Jiaxin , Hung Chao-Wei , Zhao Peng , Jones Ying , Gao Hui , Liddle Christopher , Yu Ruth T. , Downes Michael , Evans Ronald M. , Rydén Mikael , Wabitsch Martin , Wang Zichen , Hakozaki Hiroyuki , Schöneberg Johannes , Reilly Shannon M. , Huang Jianfeng , Saltiel Alan R. 
Published: January 2024

AbstractMitochondrial dysfunction is a characteristic trait of human and rodent obesity, insulin resistance and fatty liver disease. Here we show that high-fat diet (HFD) feeding causes mitochondrial fragmentation in inguinal white adipocytes from male mice, leading to reduced oxidative capacity by a process dependent on the small GTPase RalA. RalA expression and activity are increased in white adipocytes after HFD. Targeted deletion of RalA in white adipocytes prevents fragmentation of mitochondria and diminishes HFD-induced weight gain by increasing fatty acid oxidation. Mechanistically, RalA increases fission in adipocytes by reversing the inhibitory Ser637 phosphorylation of the fission protein Drp1, leading to more mitochondrial fragmentation. Adipose tissue expression of the human homolog of Drp1, DNM1L, is positively correlated with obesity and insulin resistance. Thus, chronic activation of RalA plays a key role in repressing energy expenditure in obese adipose tissue by shifting the balance of mitochondrial dynamics toward excessive fission, contributing to weight gain and metabolic dysfunction.
Mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade response in melanoma
Authors: Mahmood Mahnoor , Liu Eric Minwei , Shergold Amy L. , Tolla Elisabetta , Tait-Mulder Jacqueline , Huerta-Uribe Alejandro , Shokry Engy , Young Alex L. , Lilla Sergio , Kim Minsoo , Park Tricia , Boscenco Sonia , Manchon Javier L. , Rodríguez-Antona Crístina , Walters Rowan C. , Springett Roger J. , Blaza James N. , Mitchell Louise , Blyth Karen , Zanivan Sara , Sumpton David , Roberts Edward W. , Reznik Ed , Gammage Payam A. 
Published: January 2024

AbstractThe mitochondrial genome (mtDNA) encodes essential machinery for oxidative phosphorylation and metabolic homeostasis. Tumor mtDNA is among the most somatically mutated regions of the cancer genome, but whether these mutations impact tumor biology is debated. We engineered truncating mutations of the mtDNA-encoded complex I gene, Mt-Nd5, into several murine models of melanoma. These mutations promoted a Warburg-like metabolic shift that reshaped tumor microenvironments in both mice and humans, consistently eliciting an anti-tumor immune response characterized by loss of resident neutrophils. Tumors bearing mtDNA mutations were sensitized to checkpoint blockade in a neutrophil-dependent manner, with induction of redox imbalance being sufficient to induce this effect in mtDNA wild-type tumors. Patient lesions bearing >50% mtDNA mutation heteroplasmy demonstrated a response rate to checkpoint blockade that was improved by ~2.5-fold over mtDNA wild-type cancer. These data nominate mtDNA mutations as functional regulators of cancer metabolism and tumor biology, with potential for therapeutic exploitation and treatment stratification.
Evolution of chirped cosh-Gaussian vortex beam through uniaxial crystal orthogonal to the optical axis
Authors: Bayraktar Mert , Chatzinotas Symeon 
Published: January 2024

Abstract
This paper introduces the chirped cosh-Gaussian vortex beam(CCGVB) and investigates its propagation in uniaxial crystal. The received field is obtained benefiting from the Huygens–Fresnel integral. In addition, analysis of the received field is carried out considering the variations in displacement parameter, chirped parameter, decay factor, and topological charge. Our results show that chirped parameter and decay factors place the beam in an off-axis position. These results can be beneficial for the design of optical communication systems, laser weapons, laser designators, and optical tracking while applying beam shaping.
Machine learning analysis of data obtained by finite element method: a new approach in structural design
Authors: Demircioğlu Ufuk , Bakır Halit , Çakır Mutlu Tarık 
Published: January 2024

Abstract
This study investigates the impact of cutout and added masses on the natural frequencies of a beam structure and employs machine-learning algorithms to predict optimal locations for added masses, achieving desired natural frequency ranges. The evaluation utilizes COMSOL MULTIPHYSICS to analyze a beam structure with cutouts and added mass locations, generating a dataset of original natural frequencies. This dataset is utilized to train machine-learning algorithms, and subsequently tested with desired natural frequencies and cutout locations for forecasting optimal added mass positions. Various machine learning methods are explored, and regression metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared are employed to assess performance. Results indicate that the Extra Trees Regressor demonstrates superior performance, yielding RMSE, MSE, and R-squared values of 0.000579, 3.35537e-07, and 0.999948, respectively. Additionally, the study explores the influence of employing different natural frequencies (modes) as inputs for machine-learning algorithms. Findings reveal that increasing the number of utilized modes enhances machine-learning performance, albeit at the expense of computational time. Overall, this research establishes a novel approach, leveraging machine learning to optimize the placement of added masses for achieving desired natural frequency characteristics in beam structures.
Nonlinear dust-ion acoustic solitary waves for collisional electronegative dusty plasma in the presence of trapped electron distribution
Authors: Acharya Num Prasad , Basnet Suresh , Khanal Raju 
Published: January 2024

Abstract
We have investigated the characteristics of nonlinear propagation of dust-ion acoustic solitary waves in collisional electronegative unmagnetized dusty plasma, which consists of trapped electrons, Boltzmann negative ions, mobile positive ions, mobile negative dust particulates, and a uniform background of neutral particles. In account of ion-neutral collisions, the modified Korteweg–de Vries relation has been derived by employing the standard reductive perturbation method. Analytical and numerical solutions of the damped Korteweg–de Vries equation has been presented in which finite difference method is used for numerical solution. On the other hand, the dust charging equation has been solved by using Newton’s Raphson method. It is found that the temperature ratio of free to trapped electrons, ion-neutral collision, concentration of negative ions, dust number density, and dust density perturbation modify the basic properties of the dust-ion acoustic solitary waves. The temporal evolution of dust-ion acoustic solitary waves is crucial as it affects the amplitude and width of wave structure. In addition, the analytical and numerical solutions are compared, and their deviation is graphically illustrated.
Small attenuation negative group delay of spoof surface plasmon polaritons
Authors: Li Weiwen , Qiao Qian , Li Wanli 
Published: January 2024

Abstract
Spoof surface plasmon polariton (SSPP) waveguides can be used to effectively construct on-chip microwave systems due to the single-side conductor structure. SSPP waveguides usually exhibit normal dispersion, which can introduce large group delays to transmission signal and impair waveform shapes. However, the negative group velocity (NGV) effect may occur in the split modes of coupled surface plasmon polariton waveguides. In this regard, the synthesis wave model of forward and backward transmission waves is applied, which generates a quadrupole vortex wave transmission. In this paper, the SSPP unit structure with folded multi-split rings is proposed to achieve anomalous dispersion with NGV feature in the fundamental mode. The SSPP unit can be equivalent to an epsilon-negative medium, and a maximum negative group delay (NGD) can be reached at the frequency where the NGV begins to appear. For the corresponding SSPP waveguide, a NGD band with lower attenuation can be achieved at the stopband edge. While the SSPP unit structures are loaded as dispersive materials in a microstip line, the similar NGD performance can be obtained. Clearly, the SSPP NGD with lower attenuation provides a new delay equalization method for ultra reliable and low latency communications (URLLC), and can also be used as an effective dispersion compensation means.
Modeling the dynamical behavior of the interaction of T-cells and human immunodeficiency virus with saturated incidence
Authors: Boulaaras Salah , Jan Rashid , Khan Amin , Allahem Ali , Ahmad Imtiaz , Bahramand Salma 
Published: January 2024

Abstract
In the last forty years, the rise of HIV has undoubtedly become a major concern in the field of public health, imposing significant economic burdens on affected regions. Consequently, it becomes imperative to undertake comprehensive investigations into the mechanisms governing the dissemination of HIV within the human body. In this work, we have devised a mathematical model that elucidates the intricate interplay between CD4+ T-cells and viruses of HIV, employing the principles of fractional calculus. The production rate of CD4+ T-cells, like other immune cells depends on certain factors such as age, health status, and the presence of infections or diseases. Therefore, we incorporate a variable source term in the dynamics of HIV infection with a saturated incidence rate to enhance the precision of our findings. We introduce the fundamental concepts of fractional operators as a means of scrutinizing the proposed HIV model. To facilitate a deeper understanding of our system, we present an iterative scheme that elucidates the trajectories of the solution pathways of the system. We show the time series analysis of our model through numerical findings to conceptualize and understand the key factors of the system. In addition to this, we present the phase portrait and the oscillatory behavior of the system with the variation of different input parameters. This information can be utilized to predict the long-term behavior of the system, including whether it will converge to a steady state or exhibit periodic or chaotic oscillations.
Design and verification of a parallel elastic robotic leg
Authors: Tanfener Emre , Karagöz Osman Kaan , Candan Sinan Şahin , Turgut Ali Emre , Yazıcıoğlu Yiğit , Ankaralı Mustafa Mert , Saranlı Uluç 
Published: January 2024

Abstract
This paper presents the design and experimental verification of a parallel elastic robotic leg mechanism that aims to capture the dynamics of the linear mass-spring-damper model. The mechanism utilizes a wrapping cam mechanism to linearize the non-linear force resulting from the elongation of the parallel elastic element. Firstly, we explain the desired dynamics of the mass-spring-damper model, including the impact transitions, and the design of the wrapping cam mechanism. We then introduce a system identification procedure to estimate the parameters of the leg mechanism corresponding to the dynamic model. The estimated parameters are tested with a cross-validation approach to evaluate the mechanism’s performance in tracking the desired model. The experimental results show that the passive dynamics of the mechanism resemble the linear model as intended. Thus, the robot provides a basis for using parallel elastic actuation while using model-based controllers that benefit the analytic solutions of the linear model.
Duality for a boundary driven asymmetric model of energy transport
Authors: Carinci Gioia , Casini Francesco , Franceschini Chiara 
Published: January 2024

Abstract
We study the asymmetric brownian energy, a model of heat conduction defined on the one-dimensional finite lattice with open boundaries. The system is shown to be dual to the symmetric inclusion process with absorbing boundaries. The proof relies on a non-local map transformation procedure relating the model to its symmetric version. As an application, we show how the duality relation can be used to analytically compute suitable exponential moments with respect to the stationary measure.
Failure analysis steam turbine in sugar factory thermal power plant: a Review
Authors: Balaso Farande Bahubali , Jagtap Hanumant P. 
Published: January 2024

Abstract
Generating electricity from thermal energy is one of the main ways of generating electricity. However, it must be generated in the most efficient and effective way. Due to the urgency of electricity generation, it is essential that it is produced in the most efficient way to minimize losses, maximise output, optimise resource utilisation and reduce overall costs of the power plant. These outputs are even more critical in the current era where sustainability has become the top priority. A thermal power plant is a converter of fossil fuels to electricity in which steam is used during a cycle to spin a turbine driving generator to produce electricity. It is important to maintain a power plant if it runs without failure. Reliability analysis is a standard tool for designing, scheduling and maintaining any system. Reliability of a power plant refers to the ability to generate electricity efficiently and more cost-effectively with an acceptable quality assurance. This paper provides an in-depth analysis of power generation in a sugar factory by steam turbine and the reasons for steam turbine failure. It also analyzes the maintenance models employed to enhance steam turbine availability in thermal power plants.
Recent Trends and advances in deep learning techniques for the classification of landslides using satellite images: comprehensive survey
Authors: Sharma A , Chopra S R , Sapate S G , Bhagawati P B 
Published: January 2024

Abstract
A landslide is a geographical catastrophe that occurs frequently in monsoon season and has a formidable impact over a wide range to pose risks to human lives and infrastructure worldwide. Traditional methods to classify and identify landslides are more time-consuming and less reliable. In the past few years artificial intelligence algorithms mainly, deep learning algorithms were used in many fields to detect and identify automatic disasters like landslides and earthquakes. Numerous research and classification approaches have been implemented in satellite image processing for the detection and prediction of landslides. The most challenging task in the classification and prediction of landslides from satellite imagery is to train the model with appropriate techniques and datasets which predict “accurately”. Limited work has been done on high-resolution satellite images using convolution techniques. This article presents a comprehensive study of recent deep-learning approaches based on convolutional neural networks to achieve efficient classification of landslide satellite images. A few selected research articles on deep learning approaches based on CNN for automatic detection of landside from peer reviews journals etc. are considered for this study. “The performance of all surveyed articles is evaluated using accuracy recall precision and F 1 score parameters”. This study illustrates the viability of deep learning approaches in learning complex and high-resolution satellite images for the classification and prediction of landslides.