bluuebunny/crossref_metadata_2025_split huggingface dataset:
H∞ state estimation for discrete neural networks with random sensor delays under variance constraints
Authors: Du Yanbiao , Chen Cai , Gao Yan 
Published: November 2024

Abstract
This paper primarily investigates the H
∞ state estimation (SE) problem for discrete time-varying uncertain neural networks (NNs) with random occurring sensor delays (ROSDs) under variance constraints. The paper models ROSDs using a random variable that adheres to a Bernoulli distribution with a known probability. The primary purpose is to design a state estimator for time-varying NNs experiencing ROSDs, which proposes sufficient criteria for bounded error variance(EV) and H
∞ performance constraints. This paper introduces a new H
∞ SE algorithm for time-varying NNs with ROSDs, without the need for the augmentation method. In the end, the feasibility and effectiveness are validated through numerical simulation.
Adaptive finite-time command filtered position control for DP ship in level ice
Authors: Liu Caiyun , Shao Chunrui , Chen Xinghua , Liu Qian , Zheng Zhenming 
Published: November 2024

Abstract
This work suggests an adaptive limited-time command filtering control method for moving ships around in the Arctic. It does a good job of solving the thruster dynamics problem while taking into account uneven ice loads, model errors, and unknown changes in the environment. In the DP system, the neural network system is used to get close to the unclear function. A finite-time control law is meant to get the ship to the point and course that the captain wants by using estimates. Lyapunov analysis showed that the closed-loop system as a whole is stable, which means that all signals are still limited in time. Number-based simulations have shown that the suggested control strategy works.
Evaluation of cement ring sealing under cyclic loading conditions
Authors: Zhang Yuhao 
Published: November 2024

Abstract
As exploration and development continue to advance, incidents of cement ring seal failures are occurring more frequently. Such failures can directly impact the safe development of oil and gas wells. To effectively assess the sealing performance of cement rings, this paper explores the mechanisms behind sealing failures at both the cementing interface and the cement ring body under cyclic loading conditions. It also examines the sealing capabilities of the initial cementing interface and the cement ring body by developing a finite element model of the ‘casing-cement ring-facility’ and incorporating the Cohesive cementing unit. The findings indicate that the pressure within the casing is extremely high, and the cement ring is tightly sealed. The results indicate that fluctuations in pressure within the casing can compromise the sealing effectiveness of the cement ring, leading to micro-annular gaps at either the first or second cementing interface. Additionally, damage to the Cohesive unit initiates during the first loading cycle, with increased displacement of the initial cement surface correlating to higher pressure cycles.
Underwater acoustic target recognition model based on transformer and knowledge distillation
Authors: Yu Beiming , Chen Yuechao , Du Shuanping , Bian Zhenwei 
Published: November 2024

Abstract
Traditional methods for feature extraction in underwater acoustics heavily rely on domain expertise and handcrafted features, which are often challenged by the complex and variable ocean environment. In contrast, deep learning models, particularly those based on CNNs and Transformers, offer significant advantages in automatic feature extraction and classification tasks. This study introduces a Spectrogram-Vision Transformer (SViT) model, specifically designed for spectrogram classification, leveraging pre-trained Vision Transformer (ViT) models. The SViT model is further utilized as a teacher model in a knowledge distillation framework, where its robust feature extraction capabilities are transferred to a lightweight ResNet50 student model. Experimental results on the DeepShip dataset demonstrate that the proposed method significantly improves the recognition accuracy and generalization capability of ResNet50, achieving performance close to the more computationally intensive SViT model. This research highlights the effectiveness of Transformers in underwater target recognition and underscores the potential of knowledge distillation in enhancing the performance of lightweight models for real-world applications, particularly in resource-constrained environments.
Improved black-winged kite algorithm based on chaotic mapping and adversarial learning
Authors: Zhao Mingjing , Su Zhongji , Zhao Chenyang , Hua Zexi 
Published: November 2024

Abstract
Aiming at the shortcomings of the black-winged kite algorithm, such as poor uniformity of initial population distribution, subjective search and single direction, this paper proposes an improved black-winged kite algorithm based on chaotic mapping and adversarial learning. It then progresses to adopting Tent chaotic mapping to achieve a uniform initial solution distribution, introducing Beta random distribution and optimizing the nonlinear factor in the attack phase to make the search trend more in line with the demand, and introducing an adversarial learning mechanism to extend the search direction. Twelve benchmark test functions are tested and compared with five other algorithms, and the result shows that the algorithm significantly outperforms the other algorithms in terms of searching ability and optimization-finding accuracy.
A variable step and multi-constraint vehicle’s trajectory generation algorithm based on deep deterministic policy gradient network
Authors: Hu Xingzhi , Li Mingjie , Zhao Bendong , Wang Zhiren , Lai Jianqi 
Published: November 2024

Abstract
Aiming at the problem that the traditional vehicle’s trajectory generation method takes a long time and is difficult to calculate in real time, a variable step and multi-constraint trajectory generation algorithm based on a deep deterministic policy gradient (DDPG) network is proposed. Firstly, the dynamic model and constraint conditions of the vehicle are analyzed. On this basis, the reinforcement learning training model is constructed based on DDPG, and the state, action, and reward design of the training model are defined. At the same time, the variable step is introduced into the action model to improve the generated trajectory’s navigation precision. Finally, the proposed method is verified under different cases, and according to the results, the proposed method can realize quick generation of multi-constraint guidance trajectories.
W-system: A system to understand words in Wordle
Authors: Yang Qiaochu 
Published: November 2024

Abstract
Wordle is a popular puzzle game where players share their daily scores on Twitter. Based on this data, we developed a W-system to analyze and predict Wordle game outcomes, providing insights for further game development. The number of reported results varies daily. To explain this variation, we first cleaned the data reported on Twitter and performed autocorrelation and ADF tests, revealing that the series was not stationary. After performing first-order differencing, we confirmed stationarity. Using the AIC criterion, we determined that the ARIMA (1, 1, 0) model was optimal. We performed residual and LB tests, and then predicted the number of reports for the next 60 days, identifying a decreasing trend. Additionally, we investigated whether any attributes of the words affected the percentage of scores reported in Hard Mode using a random forest model. We found that certain vowels, such as A and E, made words easier, while I and O made words harder. To predict the distribution of reported results for a given future solution word, we designed a 24-5-7 fully connected neural network. The input layer consists of a 24-dimensional vector representing letter occurrences, the hidden layer has five neurons, and the output layer is a 7-dimensional vector representing the distribution of scores. We trained the model using MSE error and the Adam optimizer, achieving a final test set loss of 7.22, indicating good generalization ability. For example, for the word EERIE on March 1, 2023, the model predicted distribution with reasonable confidence, accounting for associated uncertainties. We also developed a model to classify solution words by difficulty. We identified eight attributes of words and used the Analytic Hierarchy Process (AHP) to assign weights to each feature. Applying the k-means clustering algorithm, we classified 355 words into three categories: easy, medium, and hard. Using our model, the word EERIE was classified as medium difficulty. We validated our model by plotting a two-dimensional clustering scatter plot using the two features with the highest weights, showing good separation among the three categories, and indicating the accuracy of our classification model.
Integration of storage in depleted gas reservoirs evaluation of injection and extraction operation
Authors: Ai Xingbo , Zhou Ziyu , ZhaoYang  
Published: November 2024

Abstract
In order to accurately evaluate the operation status of gas injection and production in reservoirs with depleted volcanic reservoirs and the injection and production capacity of a single well, an integrated model of the pore reservoir - wellbore - surface pipeline network was established for the Xushen gas field. Besides, the optimization algorithm of the integrated simulation was used to set up the optimization objective function according to the needs of the actual study, to build the optimization control variables at each level of the system, and to set up single-well production conditions under several additional constraints, including full production capacity, water/gas ratio, fluid-carrying capacity, and peak/valley production changes. The production conditions of a single well are set by the calculation of each sub-system and additional requirements in the field. Through the optimization calculation, the production distribution of a single well is obtained under the conditions of giving full play to the production capacity, taking into account the water/gas ratio, the liquid-carrying capacity, and the production changes during peak and valley periods. The study shows that the water intrusion volume of alternating injection and extraction exhibits cyclic changes of alternating increase and decrease, and the numerical simulation method of the gas reservoir predicts that the water intrusion volume of the scenario at the end of multi-cycle gas injection is 260-340×104 m^3, and that of the scenario at the end of multi-cycle gas extraction is 550-610×104 m^3; and that of the straight well scenario is larger, with a single-cycle volume of 289-338×104 m^3 at the late stage of the five rounds of injection and extraction cycles; The horizontal well program has a larger water intrusion amount, with a single-cycle water intrusion amount of 289-338×104 m^3 at the end of five injection and extraction cycles.
Improved current control strategies for LCL grid-connected inverters for weak grids
Authors: Feng Jianxiang , Zhang Jihong 
Published: November 2024

Abstract
With the continuous progress of new energy technology, the application of LCL-type grid-connected inverters is becoming more and more widespread. However, these inverters have harmonic and stability problems during the grid-connection process. Especially when the grid impedance is large, their quality and safety are easily affected. Therefore, in this study, an improved current control strategy is proposed to solve the above problems. In this study, the first important task is to establish an accurate mathematical model, which takes into account the special characteristics of weak grids and the dynamic behavior of LCL filters. On this basis, the shortcomings of the traditional control strategy are analyzed and targeted improvement measures are proposed, including a control method based on phase compensation and a fuzzy control filter design. The current control strategy reduces the external disturbance and improves the system stability by controlling the current according to the magnitude of the disturbance effect after the disturbance is generated and before the controlled variable changes.
The data acquisition system of phenoPET
Authors: Streun M. , Eguzo C. , Gorke H. , Heil R. , Hinz C. , Keßel D. , Koller R. , Metzner R. , Pflugfelder D. , Scheins J. , Scherer B. , Wüstner P. , van Waasen S. 
Published: November 2024

Abstract
The PET scanner phenoPET is a system dedicated for plant research developed and used for phenotyping studies at the Research Center in Jülich.
The detectors consist of LYSO scintillators and Philips digital silicon photomultipliers.
A central processing board collects their data and transfers them through a USB-3 connection to the data acquisition server.
The USB-3 restricts the maximum output rate to 380 MB/s, which requires controlled data dropping on the board in order to allow measurements with activities that would exceed this limit.
Presently we are developing a new readout system that will eliminate this bottleneck.
We will replace the USB-3 by a fiber optics link with a maximum data rate of 2 GB/s.
In addition, the new central board will provide an embedded operation system (PetaLinux) that has direct access to the hardware interfaces for configuration and monitoring of the detector modules.
This will allow the control software to run directly on the board.
Broad-band, high-gain, low-frequency antennas for radio detection of earth-skimming tau neutrinos
Authors: Huege T. , Krömer O. 
Published: November 2024

Abstract
A promising approach to detect high-energy tau neutrinos is
through the measurement of impulsive radio emission from horizontal
air showers initiated in the Earth's atmosphere. Observations at
frequencies between 30 and 80 MHz seem particularly promising —
if high-gain antennas focused at the horizon and blocking out as
much as possible of the noisy sky are employed. Due to the large
wavelengths, however, designing an antenna with the required
properties is highly non-trivial at such low frequencies. In this
article, we explore suitable antenna designs that provide the
desired high gain, possess a smooth beam, are insensitive to ground
conditions, are easily impedance-matched over the wide band, and are
mechanically simple for deployment in large numbers in inaccessible
terrain. In particular, we consider the “rhombus” antenna design
for both horizontally and vertically polarized radiation a very
attractive option for tau neutrino detection efforts in the
atmosphere with the radio technique.
Sodium alginate based fast swelling nanogels for solubility enhancement of chlorthalidone; synthesis, characterization and biosafety evaluation
Authors: Badshah Syed Faisal , Abdullah Orva , Khan Kifayat Ullah , Hussain Abid , Mukhtiar Muhammad , Barkat Kashif , Jan Nasrullah , Khan Samiullah , Aamir Muhammad , Liaqat Huma , Mehmood Yasir , Jabbar Abdul , Waqar Maham , Khanum Tehreem 
Published: November 2024

Abstract
Purpose of the study was to enhance the solubility of chlorthalidone, poorly soluble diuretic that has been the used for lowering high blood pressure for the past half-century. Solubility is a challenge for approximately 90% of drug candidates. Chlorthalidone is BCS Class IV drug whose poor solubility needs to be improved in order to optimize its efficacy. Using a free radical polymerization technique, sodium alginate-based nanogels were formulated for enhancing solubility of chlorthalidone. The evaluation of various characteristics of nanogels was done by structural characterization, drug loading, swelling, sol–gel transition, in-vitro release, solubility, and toxicity tests. Fourier transform infrared (FT-IR) spectroscopy revealed characteristic peaks of the primary raw materials and polymeric nanogels. The FT-IR spectra of the chlorthalidone-loaded nanogels suggested discrete drug peaks confirming successful drug loading. The system’s amorphous nature and thermal stability were indicated by powder x-ray diffractometry and thermal analysis. The scanning electron microscopy indicated a well-defined porous structure. The size of the nanogels was determined by zeta size analysis to be 189 ± 18.35 n·m. The solubility enhancement factor demonstrated the potential for improved solubility of the poorly soluble drug. The resulting biocompatible nanogels could be used to improve the solubility of hydrophobic drugs.
Interactive effects between extreme temperatures and PM2.5 on cause-specific mortality in thirteen U.S. states
Authors: Castro Edgar , Healy James , Liu Abbie , Wei Yaguang , Kosheleva Anna , Schwartz Joel 
Published: November 2024

Abstract
The extent and robustness of the interaction between exposures to heat and ambient PM2.5 is unclear and little is known of the interaction between exposures to cold and ambient PM2.5. Clarifying these interactions, if any, is crucial due to the omnipresence of PM2.5 in the atmosphere and increasing scope and frequency of extreme temperature events. To investigate both of these interactions, we merged 6 073 575 individual-level mortality records from thirteen states spanning seventeen years with 1 km daily PM2.5 predictions from sophisticated prediction model and 1 km meteorology from Daymet V4. A time-stratified, bidirectional case-crossover design was used to control for confounding by individual-level, long-term and cyclic weekly characteristics. We fitted conditional logistic regressions with an interaction term between PM2.5 and extreme temperature events to investigate the potential interactive effects on mortality. Ambient PM2.5 exposure has the greatest effect on mortality by all internal causes in the 2 d moving average exposure window. Additionally, we found consistently synergistic interactions between a 10 μg m−3 increase in the 2 d moving average of PM2.5 and extreme heat with interaction odds ratios of 1.013 (95% CI: 1.000, 1.026), 1.024 (95% CI: 1.002, 1.046), and 1.033 (95% CI: 0.991, 1.077) for deaths by all internal causes, circulatory causes, and respiratory causes, respectively, which represent 75%, 156%, and 214% increases in the coefficient estimates for PM2.5 on those days. We also found evidence of interactions on the additive scale with corresponding relative excess risks due to interaction (RERIs) of 0.013 (95% CI: 0.003, 0.021), 0.020 (95% CI: 0.008, 0.031), and 0.017 (95% CI: −0.015, 0.036). Interactions with other PM2.5 exposure windows were more pronounced. For extreme cold, our results were suggestive of an antagonistic relationship. These results suggest that ambient PM2.5 interacts synergistically with exposure to extreme heat, yielding greater risks for mortality than only either exposure alone.
Manufacturing of lithium battery toward deep-sea environment
Authors: Zhao Yaohua , Li Nan , Xie Keyu , Wang Chuan , Zhou Sisi , Zhang Xianggong , Ye Cong 
Published: November 2024

Abstract
The operation of deep-sea underwater vehicles relies entirely on onboard batteries. However, the extreme deep-sea conditions, characterized by ultrahigh hydraulic pressure, low temperature, and seawater conductivity, pose significant challenges for battery development. These conditions drive the need for specialized designs in deep-sea batteries, incorporating critical aspects of power generation, protection, distribution, and management. Over time, deep-sea battery technology has evolved through multiple generations, with lithium (Li) batteries emerging in recent decades as the preferred power source due to their high energy and reduced operational risks. Although the rapid progress of Li batteries has notably advanced the capabilities of underwater vehicles, critical technical issues remain unresolved. This review first systematically presents the whole picture of deep-sea battery manufacturing, focusing on Li batteries as the current mainstream solution for underwater power. It examines the key aspects of deep-sea Li battery development, including materials selection informed by electro-chemo-mechanics models, component modification and testing, and battery management systems specialized in software and hardware. Finally, it discusses the main challenges limiting the utilization of deep-sea batteries and outlines promising directions for future development. Based on the systematic reflection on deep-sea batteries and discussion on deep-sea Li batteries, this review aims to provide a research foundation for developing underwater power tailored for extreme environmental exploration.
A data-driven compact drain current model for InGaAs FinFET using TCAD assisted machine learning approach
Authors: Maity Arya , Kumar Maity Subir , Das Himadri Sekhar 
Published: November 2024

Abstract
This article presents a novel machine learning-based framework for modeling the drain current of InGaAs FinFETs. An artificial neural network, trained on a comprehensive dataset generated by 3D numerical device simulations, forms the core of this model. The simulations covered a wide range of device geometries and bias conditions, resulting in diverse current-voltage characteristics. Using TensorFlow in a Python environment, we built and trained a neural network to accurately predict drain current. The model's performance was evaluated on independent test datasets, demonstrating its effectiveness on unseen data. The model accurately captures the effects of varying gate length, drain voltage, and gate voltage. Multiple error metrics, including mean squared error, mean absolute error, root mean squared error, and correlation coefficient, were used to assess its performance. At shorter gate lengths, the calculated relative error in drain current was 2.18% and 1.17% at V

DS
= 50mV and 1V, respectively. After training, the network parameters were extracted to create a Verilog-A model. This model was integrated into the SPICE simulator for circuit simulations. A resistive load inverter circuit was designed to demonstrate the model's capabilities, and voltage transfer characteristics and transient analysis were performed within SPICE. These results validate the effectiveness of the developed machine learning-based InGaAs FinFET model.
DiMA: sequence diversity dynamics analyser for viruses
Authors: Tharanga Shan , Ünlü Eyyüb Selim , Hu Yongli , Sjaugi Muhammad Farhan , Çelik Muhammet A , Hekimoğlu Hilal , Miotto Olivo , Öncel Muhammed Miran , Khan Asif M 
Published: November 2024

Abstract
Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic, and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. DiMA provides a quantitative overview of sequence (DNA/RNA/protein) diversity by use of Shannon’s entropy corrected for size bias, applied via a user-defined k-mer sliding window to an input alignment file, and each k-mer position is dissected to various diversity motifs. The motifs are defined based on the probability of distinct sequences at a given k-mer alignment position, whereby an index is the predominant sequence, while all the others are (total) variants to the index. The total variants are sub-classified into the major (most common) variant, minor variants (occurring more than once and of incidence lower than the major), and the unique (singleton) variants. DiMA allows user-defined, sequence metadata enrichment for analyses of the motifs. The application of DiMA was demonstrated for the alignment data of the relatively conserved Spike protein (2,106,985 sequences) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the relatively highly diverse pol gene (2637) of the human immunodeficiency virus-1 (HIV-1). The tool is publicly available as a web server (https://dima.bezmialem.edu.tr), as a Python library (via PyPi) and as a command line client (via GitHub).