bluuebunny/crossref_metadata_2025 huggingface dataset:
Ozone and its precursors dynamics in the megapolis atmospheric surface layer at radiation fog
Authors: Andreev Viktor V , Konyukhov Vadim K , Stepanov Eugene V
Published: August 2023

A significant (many times) increase in the concentrations of ozone precursors (NO, NO2, CO, CH4 and CH
x
) in the surface atmospheric layer due to their accumulation under calm conditions was recorded during an episode of the radiation fog in Moscow early November 2021. At the same time the partial pressure of H2O vapor decreased by ∼30% due to water condensation during temperature drop. A strong correlation had been observed between the O3 content and the ratio of nitrogen oxides’ concentrations NO2/NO at fog. In particular, the O3 content dropped to zero values at fog. The latter can be attributed to the simultaneous action of several factors: the absence of photochemical ozone production at high NO concentrations, the absence of horizontal and vertical ozone transfer under calm, and the additional ozone runoff due to dissolution in fog microdroplets.
The seizure severity score: a quantitative tool for comparing seizures and their response to therapy
Authors: Pattnaik Akash R , Ghosn Nina J , Ong Ian Z , Revell Andrew Y , Ojemann William K S , Scheid Brittany H , Georgostathi Georgia , Bernabei John M , Conrad Erin C , Sinha Saurabh R , Davis Kathryn A , Sinha Nishant , Litt Brian
Published: August 2023

Objective. Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies. Approach. We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features. Main results. The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p = 0.018, 97.5% confidence interval = [−1.242, −0.116]) and lower pre-surgical severity was associated with better surgical outcome (p = 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity. Significance. We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool’s utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.
Evoked compound action potentials during spinal cord stimulation: effects of posture and pulse width on signal features and neural activation within the spinal cord
Authors: Brucker-Hahn Meagan K , Zander Hans J , Will Andrew J , Vallabh Jayesh C , Wolff Jason S , Dinsmoor David A , Lempka Scott F
Published: August 2023

Objective. Evoked compound action potential (ECAP) recordings have emerged as a quantitative measure of the neural response during spinal cord stimulation (SCS) to treat pain. However, utilization of ECAP recordings to optimize stimulation efficacy requires an understanding of the factors influencing these recordings and their relationship to the underlying neural activation. Approach. We acquired a library of ECAP recordings from 56 patients over a wide assortment of postures and stimulation parameters, and then processed these signals to quantify several aspects of these recordings (e.g., ECAP threshold (ET), amplitude, latency, growth rate). We compared our experimental findings against a computational model that examined the effect of variable distances between the spinal cord and the SCS electrodes. Main results. Postural shifts strongly influenced the experimental ECAP recordings, with a 65.7% lower ET and 178.5% higher growth rate when supine versus seated. The computational model exhibited similar trends, with a 71.9% lower ET and 231.5% higher growth rate for a 2.0 mm cerebrospinal fluid (CSF) layer (representing a supine posture) versus a 4.4 mm CSF layer (representing a prone posture). Furthermore, the computational model demonstrated that constant ECAP amplitudes may not equate to a constant degree of neural activation. Significance. These results demonstrate large variability across all ECAP metrics and the inability of a constant ECAP amplitude to provide constant neural activation. These results are critical to improve the delivery, efficacy, and robustness of clinical SCS technologies utilizing these ECAP recordings to provide closed-loop stimulation.
Classical uncertainty relations and entropy production in non-equilibrium statistical mechanics
Authors: Muratore-Ginanneschi Paolo , Peliti Luca
Published: August 2023

We analyze Fürth’s 1933 classical uncertainty relations in the modern language of stochastic differential equations. Our interest is motivated by their application to non-equilibrium classical statistical mechanics. We show that Fürth’s uncertainty relations are a property inherent in martingales within the framework of a diffusion process. This result implies a lower bound on the fluctuations in current velocities of entropic quantifiers associated with transitions in stochastic thermodynamics. In cases of particular interest, we recover a well-known inequality for optimal mass transport relating the mean kinetic energy of the current velocity and the squared quadratic Wasserstein distance between the probability distributions of the entropy. We take advantage in particular of an unpublished suggestion by Krzysztof Gawȩdzki to derive a lower bound to the entropy production by a transition described by a Langevin–Kramers process in terms of the squared quadratic Wasserstein distance between the initial and final states of the transition. Finally, we illustrate how Fürth’s relations admit a straightforward extension to piecewise deterministic processes. We show that the results presented in this paper pertain to the characteristics exhibited by general Markov processes.
CMOS Scaling Challenges for High Performance and Low Power applications facing Reliability Criteria towards the Decananometer range
Authors: Bravaix A , Hamparsoumian G , Sonzogni J , Pitard H , Garba-Seybou T , Kussener E , Federspiel X , Cacho F
Published: August 2023

The huge improvements in integrated circuits manufacturing has faced great challenges between process optimization, performance requirements and the trade-off between low power operation and reliability for long term use. Both the variability at time zero and the time variability due to external constraints and aging phenomena make mandatory the validation of the CMOS technology nodes from device to circuits and products under operation. While High-K Metal-Gate (HKMG) offered good compromises down to 28nm gate-length, the move to Fully Depleted Silicon on Insulator (FDSOI) allows to further scale the dimension down to 14nm effective gate-length with ultra-thin equivalent gate-oxide thickness (EOT) of 1.35 nm. This has been obtained guarantying a small subthreshold slope for switching, small drain-induced barrier lowering (DIBL) for limited short-channel effect (SCE) and excellent current drivability thanks to a proper gate-stack with HfO2/SiON optimization and adapted rapid thermal processing and annealing. We give new insights to determine first the performance with temperature, the process variability impact at time zero and the robustness of CMOS nodes submitted to interface traps, oxide charge and recoverable traps. Their role is analysed using accelerated DC and AC experiments in devices to SRAM cell and array, focusing on the balance between hot-carrier and bias temperature damage. This allows to guaranty the technology robustness, speed performance and limited power consumption for product qualification.
Piezoresistive properties of strain-hardening cement composite with liquid-type multi-walled carbon nanotubes dispersed by ultrasonicator
Authors: Wang Q , Jeong W J , Yun H D , Seo S Y
Published: August 2023

Multi-walled carbon nanotubes (MWCNTs) are a new type of nanomaterial with superior performance. They are frequently used in cement matrices to increase electrical conductivity and as a sensor for monitoring the health of building structures. However, MWCNTs agglomerate easily and are difficult to disperse, which makes their homogeneous dispersion in the matrix material the focus of exploration. In this study, the dispersion method of MWCNTs in strain-hardening cement composites (SHCCs) with corresponding piezoresistance is investigated along with the effects of dispersed and nondispersed MWCNTs on the resistive sensitivity of the composites under monotonic loading. The experimental results indicate that MWCNTs in cement matrix composites can be effectively dispersed by applying ultrasonic energy. In addition, they can significantly increase the piezoresistive sensitivity of the composite.
Performance analysis of vortex symmetric Airy beam as optical communication link through turbulence channel
Authors: Zhang Yalin , Wang Junfeng , Fu Jiqiang , Ye Guoyong
Published: August 2023

The orbital angular momentum of the vortex beam can be used for information coding will help to improve the communication rate. The autofocusing phenomenon of the symmetric Airy beam (SAB) can help improve the light intensity in the receiving aperture. The vortex symmetric Airy beam (VSAB) which couples the autofocusing SAB and topological charge can provide new possibilities for the optical carrier of free space optical communication (FSOC). In this paper, the performance analysis of VSAB as the optical carrier propagation in the turbulence is studied. The evaluation parameters mean signal-to-noise ratio (SNR) and mean bit error rate (BER) for the VSAB propagating in turbulent atmosphere are calculated, using numerical calculations. The influences law of the parameters of the VSAB on evaluation parameters have been obtained.
Proposal for New Development Environment Based on Digital Materials and a Case Study of Gasoline Particulate Filter
Authors: Yamamoto K
Published: August 2023

Carbon dioxide emissions must be reduced as a countermeasure against global warming. A gasoline direct injection (GDI) engine is known for its high fuel economy, but there are more particulate emissions. To trap these particulates, a gasoline particulate filter (GPF) is needed. In this study, numerical simulations of the flow field in the GPF have been performed using the lattice Boltzmann method (LBM). Three types of filters were tested by our proposed approach, named as a digital material technique, where virtual materials are created by the simulation. Focusing on the pore characteristics of the filters, we used filters with the same pore size but different porosity. For comparison, the real internal structures of the actual GPF were detected using an X-ray CT method. The simulated flow field and the pressure drop across the filter wall were investigated, comparing with those of the actual GPF. As a result, it was found that the larger the porosity, the smaller the pressure drop. By setting the pore size and porosity to the same values as the actual GPF, a similar flow field was obtained, and the value of the pressure drop was close to that of the actual GPF.
Space Relative Position and Attitude Measurement Method Based on Solid State Lidar and High Precision Cooperative Target
Authors: Yuan Gao , Yifeng Yang , Di Wu , Lihui Jiang , Xuwei Zhang
Published: August 2023

With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement.

This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.
An Ultrasensitive Photoluminescent Immunosensing of Lipovitellin by Using N-doped Graphene Quantum Dots
Authors: Wang H D , Yang A L , Qi C , Zhang Z Z , Wang J , Ru S G
Published: August 2023

The increasing levels of environmental estrogens (EEs) have caused harmful effects on wildlife and human beings. Constructing a sensitive detection technique for EEs is meaningful. For intrinsic high specificity and sensitivity, immunosensing technique is a good choose for estrogeneous chemicals test in complex environment. As the main cleavage composition of vitellogenin (Vtg), a biomarker of EEs, lipovitellin (Lv) was proved to have same immunogenicity as Vtg, while better thermal stability than Vtg, thus Lv is more suitable than Vtg to be used as a standard matter to establish standard test curve. Upon the fluorescence resonance energy transfer (FRET) between N-doped graphene quantum dots (N-GQDs) coupled with anti-lipovitellin monoclonal antibody (anti-Lv-mAb) and reduced graphene oxide (rGO), a free-labelled photoluminescent immunosensing of Lv was developed and validated to quantify Lv of Paralichthys olivaceus. The assay obtained a large linear measuring range of 1 pg/mL- 2 µg/mL, high sensitivity of 9910.67 CPS/(ng/mL), low limit of detection (LOD) of 9.4 pg/mL and relative standard deviation of 1.19%. For 1 ng/mL Lv dispersed in drink water and sea water, the error of the recovery ratio is 1.65% and 10. 27% respectively. Our results demonstrated that the N-GQDs prepared by ultrasonic-assisted hydrothermal method could be effectively used to construct a nano photoluminescent immunosensor, which possesses the advantages of simplicity, rapidity, high specificity and anti-interference.
A Spatiotemporal Traffic Speed Prediction Method Combining Attention and Multivariate Graph Convolution Fusion
Authors: Chen Hongwei , Han Hui , Liu Donghua
Published: August 2023

Spatiotemporal prediction is widely used in the fields of neuroscience, climate, and transportation, and traffic speed prediction is one of the typical research areas. Since traffic networks are irregular grid structures with complex nonlinear spatiotemporal dependencies between nodes, traditional single-feature prediction methods are difficult to adapt to complex road conditions. In order to improve the prediction accuracy of traffic speed, this paper proposes a spatiotemporal traffic speed prediction method combining attention and multivariate graph convolution fusion (CAMGCF). It constructs a spatiotemporal model, specifically, the network uses multivariate graph convolution to fuse external factor features and traffic speed features and uses an attention mechanism to adaptively fuse feature information from different time series to achieve stability in long-term prediction. Finally, the framework is evaluated on three real-world large-scale road network traffic datasets, and the experimental results show that the prediction accuracy of this paper has more accurate results compared with the benchmark method.
Regional Soil Nutrient Content Prediction Model Based on Big Data
Authors: Cui Yaqi , Yue Qin
Published: August 2023

Soil nutrients play a decision-making role in environmental management, and the prediction of soil nutrients can be used to achieve precise fertilization and regulate production. In response to the problem of low accuracy of soil nutrient content prediction by traditional prediction models, this paper designs a prediction model of soil nutrient content based on big data statistics. Soil nutrient content data are collected using a spectral collector, and the data are smoothed, standardized, and orthogonalized to eliminate the data that affect the prediction accuracy. The processed spectrograms are analyzed to summarize the big data law of soil nutrients and clarify that different bands at the abrupt change of reflection curve correspond to different contents of soil nutrients. After repeating several times, the calibration of model accuracy is completed, and the design of the soil nutrient content prediction model based on big data statistics is realized. Through comparison experiments with the traditional model, it was verified that the designed model can improve the prediction accuracy by about 3 times and is more suitable for the prediction of soil nutrient content.
A Clustering Algorithm for Tunnel Boring Machine Data Based on Ridge Regression and Fuzzy C-Means
Authors: Li Yiyang , Pang Yong , Wang Yitang , Wang Suhang , Song Xueguan
Published: August 2023

A fuzzy clustering data partitioning method based on ridge regression is proposed to address the high correlation between the data attributes of the tunnel boring machine. The method utilizes the fuzzy partition matrix as the weight for ridge regression analysis and then employs the resulting regression equation as the clustering model to iteratively approach the target minimum value. This process enables data clustering based on the relevant characteristics of the data attributes. Experimental results using functional data sets demonstrate that this method achieves higher accuracy in clustering functional correlation data compared to traditional methods. Additionally, the proposed algorithm is evaluated using measured data from a bid section of a city subway. Results indicate that the algorithm effectively addresses the problem of data classification and key performance index prediction, with a misclassification rate and prediction error of 23.8% and 2.11%, respectively. These findings meet the engineering requirements and provide support for the subsequent data analysis of the tunnel boring machine.
A Fine-grained Chinese Short Text Classification Method Based on Capsule Networks
Authors: Xu Yangshuyi , Zhang Lin
Published: August 2023

Chinese short-text classification is an essential primary research direction in natural language processing. The main problem facing the current Chinese short text classification task is how to effectively extract critical semantic feature information and determine the text category within a short text length. Aiming at the problem of feature information extraction of Chinese short texts, this paper introduces a novel sparse attention mechanism. It proposes a Chinese short text classification model RGSC combined with capsule networks. After the model generates word vectors through the RoBERTa pre-training model, the Bi-GRU model initially extracts text feature information. The profound text feature extraction module SC, composed of sparse attention mechanism and capsule networks, is used to further extract critical semantic feature information. The results of ablation experiments on the TouTiao Chinese short text dataset and performance comparison experiments with various models show that the RGSC model can effectively extract the essential text feature information while effectively reducing the irrelevant noise information contained in the text features, and obtain fine-grained text feature information.
The Interval Estimation and Hypothesis Test of the Parameter from Inverse Topp-Leone Distribution under Type-II Censored Sample
Authors: Xi Changxin , Liu Hua
Published: August 2023

The maximum likelihood estimation of the parameters of inverse Topp-Leone distribution under Type-II Censored Sample is studied, the pivot value of the parameter is constructed, the accurate confidence interval of the parameter and the rejection region of the hypothesis test are obtained, and the statistical analysis is carried out by numerical simulation.
Investigation of bionic composite laminates inspired by the natural impact-resistant helicoidal structure in the mandibles of trap-jaw ants
Authors: Zhao Shicai , Zhang Deyuan , Yan Yupu , Yin Xiaoming
Published: August 2023

Many living organisms exhibit exceptional capabilities and have evolved effective strategies to synthesize impact-resistant and damage-tolerant structures. One such example can be observed in the rapid mandible strikes of Odontomachus monticola, a species of trap-jaw ants from the ponerine subfamily. During trap-jaw strikes, the mandibles can achieve peak speeds of 35.42 m s−1, and the maximum acceleration can reach 71 729 g within an average duration of 0.18 ms. The extreme acceleration results in instantaneous mandible strike forces that can exceed 330 times the ant’s body weight, withstanding thousands of impacts. A natural impact-resistant fibrous helicoidal structure is found in the mandibles of trap-jaw ants. This microstructure is characterized by periodic modulus oscillations that increase energy absorption and improve stress redistribution, offering added protection against damage from impact loading. A carbon fiber reinforced helicoidal composite is fabricated based on the microstructure of the trap-jaw ant’s mandibles. The results show that the helicoidal composite with a 12° helical-fiber exhibits higher residual strength, making it more capable of withstanding strong collisions. The catastrophic propagation of damage along the thickness direction is prevented by in-plane spreading and redirection of cracks. This research provides useful references for fabricating bionic impact-resistant composites.
Assessing non-synthetic crosslinkers in biomaterial inks based on polymers of marine origin to increase the shape fidelity in 3D extrusion printing
Authors: Carvalho Duarte Nuno , Dani Sophie , Sotelo Carmen G , Pérez-Martín Ricardo I , Reis Rui L , Silva Tiago H , Gelinsky Michael
Published: August 2023

In the past decade, there has been significant progress in 3D printing research for tissue engineering (TE) using biomaterial inks made from natural and synthetic compounds. These constructs can aid in the regeneration process after tissue loss or injury, but achieving high shape fidelity is a challenge as it affects the construct’s physical and biological performance with cells. In parallel with the growth of 3D bioprinting approaches, some marine-origin polymers have been studied due to their biocompatibility, biodegradability, low immunogenicity, and similarities to human extracellular matrix components, making them an excellent alternative to land mammal-origin polymers with reduced disease transmission risk and ethical concerns. In this research, collagen from shark skin, chitosan from squid pens, and fucoidan from brown algae were effectively blended for the manufacturing of an adequate biomaterial ink to achieve a printable, reproducible material with a high shape fidelity and reticulated using four different approaches (phosphate-buffered saline, cell culture medium, 6% CaCl2, and 5 mM Genipin). Materials characterization was composed by filament collapse, fusion behavior, swelling behavior, and rheological and compressive tests, which demonstrated favorable shape fidelity resulting in a stable structure without deformations, and interesting shear recovery properties around the 80% mark. Additionally, live/dead assays were conducted in order to assess the cell viability of an immortalized human mesenchymal stem cell line, seeded directly on the 3D printed constructs, which showed over 90% viable cells. Overall, the Roswell Park Memorial Institute cell culture medium promoted the adequate crosslinking of this biopolymer blend to serve the TE approach, taking advantage of its capacity to hamper pH decrease coming from the acidic biomaterial ink. While the crosslinking occurs, the pH can be easily monitored by the presence of the indicator phenol red in the cell culture medium, which reduces costs and time.
Factors influencing smallholder farmers’ climate change risk perceptions: A behavioral approach in semi-arid regions of Zimbabwe
Authors: Mugari Ephias , Vambe Arthur T , Tatsvarei Simbarashe , Nethengwe Nthaduleni Samuel , Mutambara Jackqeline
Published: August 2023

The resilience of the agricultural sector is increasingly threatened by climate change while rainfed agriculture has become more susceptible to climate and weather-related stressors. However, farmers’ perceptions of climate change risk which vary by niche, greatly influence their adaptation strategies. This study used a questionnaire survey to solicit factors that inform and shape climate change risk perceptions of smallholder farmers in semi-arid areas in Zimbabwe. A sample of 180 farming households was randomly selected from six wards of Beitbridge District, Matabeleland South Province. Descriptive statistics, chi-square tests, and the ordered probit regression model were used to assess the determinants of smallholder farmers’ perceptions of climate change risk. Results show that perceptions of climate change risk are significantly enhanced by educational level, participation in social groups, household experience in agriculture, access to climate information and extension services, as well as age, gender, and education level of the household head. However, resource endowments reduced the climate change risk perception of households. However, the weak influence of these factors points towards the existence of other issues that influence smallholder farmer climate risk perceptions that warrant additional research to understand how these inform and shape smallholder farmer decision-making. Nonetheless, there is a continuous need to improve smallholder farmers’ access to climate information and build their capacity to address emerging climate risks. This is critical to shaping their climate change risk perceptions and influencing their decisions to adopt resilience-building technologies.
Magnetocaloric properties in (La,R)(Fe,Mn,Si)13H (R = Ce and Pr)—toward a better alloy design that results in a reduction in volume of permanent magnets and the establishment of long-term reliability in cooling systems
Authors: Fujita Asaya , Imaizumi Kaoru
Published: August 2023

The magnetocaloric effect (MCE) in La1−z
R
z
(Fe0.89−x
Mn
x
Si0.11)13H
y

max (R = Ce and Pr) is verified in view of correlation between alloying recipes such as selection of doping elements and fundamental physics that governs MCE. The Ce-doped specimen with z = 0.3 & x = 0.017 exhibits a peaky isothermal entropy change ΔS
M profile with a maximum value of 20 J kg−1 K under a field change of 0.8 T at the Curie temperature of 285 K. In contrast, the enlarged field dependence of the Curie temperature and diminished hysteresis results in the adiabatic temperature change ΔT
ad of 2.7 K under a field change of 0.8 T at the Curie temperature of 289 K for the Pr-doped specimen.
Micromixing optimization of non-newtonian fluids with heterogeneous zeta potential
Authors: Bansal Anshul Kumar , Nhaichaniya Gajendra Kumar , Bhardwaj Mayank , Chitnis Swapnil
Published: August 2023

This paper aims to improve micromixing in T-shaped microchannels using heterogeneous zeta potential, which is essential for analyzing biochemical fluids in various industrial applications. We investigate the impact of different parameters, including zeta potential, applied electric field, and a number of heterogeneous surface patterns, on microchannel mixing performance. Our results demonstrate that increasing the zeta potential on the heterogeneous pair patches amplifies the recirculation zone, improving mixing performance within the microchannel. Therefore, we recommend increasing the value of heterogeneous zeta potential on the patches to enhance the mixing performance further. Additionally, we examine the non-Newtonian behavior of the fluid using the power law model and find that pseudoplastic fluids (n < 1) have lower mixing performance compared to dilatant fluids (n > 1) due to higher velocity. We evaluate the effects of various parameters on mixing performance to design an efficient micromixer that achieves rapid and homogeneous mixing of sample fluids. Finally, we optimize the micromixing efficiency using Taguchi to achieve the optimum value of mixing. The optimized value of micromixing efficiency for the corresponding non-Newtonian fluid is also determined.
Improving the power quality and hydrogen production from renewable energy sources based microgrid
Authors: Balu Vankudothu , Krishnaveni K , Malla Priyanka , Malla Siva Ganesh
Published: August 2023

The world is looking for utilization of renewable energy sources to reduce global warming as well as the consumption of fossil fuels. In this scenario, solar and wind energy are widely used at many places worldwide. However, both solar irradiance and wind speed are depending on nature. Hence, an energy storage device must be required to operate at their best utilization level by converting them to electricity. One of the best energy storage devices for medium power range is the battery. However, batteries require high maintenance and suffering from self-discharge as well as storage capacity will be decreased day by day. Hence, storing hydrogen can be an economically feasible solution instead of using batteries for high power range. Usually an aqua electrolyzer can easily convert water to hydrogen and oxygen through electricity. However, due to slow dynamics of heat transfer from electricity, the generation of hydrogen cannot meet the fast response like electrical devices. Therefore, a novel controlling technique is required to increase the production quality of hydrogen during random changes in both solar irradiance and wind speed. In order to achieve the best utilization, both photovoltaic panels and wind turbines are operated at their maximum power point levels. In this research boost converters are used to operate as maximum power point tracking devices. The whale optimization technique is integrated to respective controllers of all the converters to achieve stable production of hydrogen during rapid changes in irradiance and wind speed. The Whale Optimization Algorithm (WOA) technique is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to show the benefits of tracking response of the system on improving production of hydrogen from hybrid renewable energy sources based Microgrid. Hardware—in the—Loop (HIL) is developed to analyze the results with the help of OPAL-RT modules. Along with HIL results, a Real Time Digital Simulator (RTDS) based results are also presented to evaluate the performance of the proposed method.
A new perspective on the Sullivan dictionary via Assouad type dimensions and spectra
Authors: Fraser Jonathan , Stuart Liam
Published: August 2023

The Sullivan dictionary provides a beautiful correspondence between Kleinian groups acting on hyperbolic space and rational maps of the extended complex plane. We focus on the setting of geometrically finite Kleinian groups with parabolic elements and parabolic rational maps. In this context an especially direct correspondence exists concerning the dimension theory of the associated limit sets and Julia sets. In recent work we established formulae for the Assouad type dimensions and spectra for these fractal sets and certain conformal measures they support. This allows a rather more nuanced comparison of the two families in the context of dimension. In this expository article we discuss how these results provide new entries in the Sullivan dictionary, as well as revealing striking differences between the two families.
A stroll around the critical Potts model
Authors: Hairer Martin
Published: August 2023

Over the past decade or so, a broad research programme spearheaded by H. Duminil-Copin and his collaborators has vastly increased our understanding of a number of critical or near-critical statistical mechanics models. Most prominently, these include the

q
q

-state Potts models and, essentially equivalently, the FK cluster models. In this short review, we present a small selection of recent results from this research area.
A Ramanujan integral and its derivatives: computation and analysis
Authors: Gautschi Walter , Milovanović Gradimir
Published: August 2023

The principal tool of computation used in this paper is classical Gaussian quadrature on the interval [0,1], which happens to be particularly effective here. Explicit expressions are found for the derivatives of the Ramanujan integral in question, and it is proved that the latter is completely monotone on

(
0
,
∞
)

(0,\infty )

. As a byproduct, known series expansions for incomplete gamma functions are examined with regard to their convergence properties.

The paper also pays attention to another famous integral, the Euler integral — better known as the gamma function — revitalizing a largely neglected part of the function, the part corresponding to negative values of the argument, which plays a prominent role in our work.
The Slice Spectral Sequence of a 𝐶₄-Equivariant Height-4 Lubin–Tate Theory
Authors: Hill Michael , Shi XiaoLin , Wang Guozhen , Xu Zhouli
Published: August 2023

We completely compute the slice spectral sequence of the

C
4

C_4

-spectrum

B

P

(

(

C
4

)

)

⟨
2
⟩

BP^{(\!(C_4)\!)}\langle 2 \rangle

. This spectrum provides a model for a height-4 Lubin–Tate theory with a

C
4

C_4

-action induced from the Goerss–Hopkins–Miller theorem. In particular, our computation shows that

E
4

h

C

12

E_4^{hC_{12}}

is 384-periodic.
Stability and weak KAM solutions of contact Hamilton-Jacobi equation
Authors: Xu Yang , Yan Jun , Zhao Kai
Published: August 2023

We are concerned with the stability of viscosity solutions to contact Hamilton-Jacobi equation
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="StartLayout 1st Row upper H left-parenthesis x comma partial-differential Subscript x Baseline u left-parenthesis x right-parenthesis comma u left-parenthesis x right-parenthesis right-parenthesis equals 0 comma x element-of upper M comma EndLayout">

H
(
x
,

∂
x

u
(
x
)
,
u
(
x
)
)
=
0
,

x
∈
M
,

\begin{align*} H(x,\partial _x u(x),u(x))=0, \quad x\in M, \end{align*}

where

H
=
H
(
x
,
p
,
u
)

H=H(x,p,u)

satisfies Tonelli conditions. We study the relationship between Lyapunov stability of viscosity solutions and the structure of the set of weak KAM solutions to the contact Hamilton-Jacobi equation.
Operator algebras with the partial factorization property
Authors: Ji Guoxing
Published: August 2023

A subalgebra

A

\mathfrak A

in a

σ
\sigma

-finite von Neumann algebra

M

\mathcal M

has the left (resp. right) partial factorization property if for any invertible operator

S
∈

M

S\in \mathcal M

, there exists an isometry (resp. a co-isometry)

U
∈

M

U\in \mathcal M

such that

U
∗

S
,

S

−
1

U
∈

A

U^*S, S^{-1}U\in \mathfrak A

. We show that the relative invariant subspace lattice

L
a
t

M

A

{Lat}_{\mathcal M}\mathfrak A

consisting of those projections in

M

\mathcal M

whose ranges are invariant under

A

\mathfrak A

is commutative. Moreover if

M

\mathcal M

has a separable predual, then

L
a
t

M

A

{Lat}_{\mathcal M}\mathfrak A

is generated by a nest together with the lattice of projections in the center of

M

\mathcal M

. In particular, if

A

\mathfrak A

is a von Neumann subalgebra, then

A

=

M

\mathfrak A=\mathcal M

.
Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data
Authors: Lu Na , Qiao Yi , An Pengfei , Luo Jiajian , Bi Changwei , Li Musheng , Lu Zuhong , Tu Jing
Published: August 2023

Motivation

Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown.

Results

We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras.

Availability and implementation

The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.
Genesis-DB: a database for autonomous laboratory systems
Authors: Reder Gabriel K , Gower Alexander H , Kronström Filip , Halle Rushikesh , Mahamuni Vinay , Patel Amit , Hayatnagarkar Harshal , Soldatova Larisa N , King Ross D
Published: August 2023

Artificial intelligence (AI)-driven laboratory automation—combining robotic labware and autonomous software agents—is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.

Availability and implementation

Genesis-DB code and installation instructions are available at the GitHub repository https://github.com/TW-Genesis/genesis-database-system.git. The database use case demo code and data are also available through GitHub (https://github.com/TW-Genesis/genesis-database-demo.git). The ontology can be downloaded here: https://github.com/TW-Genesis/genesis-ontology/releases/download/v0.0.23/genesis.owl. The ontology term descriptions (including mappings to existing ontologies) and maintenance standard operating procedures can be found at: https://github.com/TW-Genesis/genesis-ontology.
BERTrand—peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing
Authors: Myronov Alexander , Mazzocco Giovanni , Król Paulina , Plewczynski Dariusz
Published: August 2023

Motivation

The advent of T-cell receptor (TCR) sequencing experiments allowed for a significant increase in the amount of peptide:TCR binding data available and a number of machine-learning models appeared in recent years. High-quality prediction models for a fixed epitope sequence are feasible, provided enough known binding TCR sequences are available. However, their performance drops significantly for previously unseen peptides.

Results

We prepare the dataset of known peptide:TCR binders and augment it with negative decoys created using healthy donors’ T-cell repertoires. We employ deep learning methods commonly applied in Natural Language Processing to train part a peptide:TCR binding model with a degree of cross-peptide generalization (0.69 AUROC). We demonstrate that BERTrand outperforms the published methods when evaluated on peptide sequences not used during model training.

Availability and implementation

The datasets and the code for model training are available at https://github.com/SFGLab/bertrand.
Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations
Authors: Rustand Denis , van Niekerk Janet , Krainski Elias Teixeira , Rue Håvard , Proust-Lima Cécile
Published: August 2023

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.
Behavior of higher trophic levels associated with an invasive plant varies among populations
Authors: Ismail Mohannad , Siemann Evan , Ding Jianqing
Published: August 2023

Invasive plants from their native and introduced ranges differ in their interactions with herbivores but it is not known whether they also vary in their interactions with herbivore natural enemies. Here, we used olfactometer bioassays and cage experiments to investigate how foraging behaviors of 2 parasitoid and 1 hyperparasitoid species depended on plant population origin. Triadica sebifera (Euphorbiaceae) is native to China but invasive in the United States. In China, it is fed on by a specialist noctuid Gadirtha fusca (Lepidoptera: Nolidae), which hosts a parasitoid Apanteles sp. (Hymenoptera: Microgastinae) and hyperparasitoid (Hymenoptera: Eurytomidae) plus a generalist aphid Toxoptera odinae (Homoptera: Aphidiidae) parasitized by Lysiphlebus confusus (Hymenoptera: Aphidiinae). Both parasitoids preferred plants infested by their host over herbivore-free plants in olfactometer bioassays. Apanteles sp. and Eurytomid wasps preferred G. fusca infested plants from China populations over those from US populations in olfactometer bioassays but L. confusus wasps did not discriminate between T. odinae infested plants from China vs. US populations. Similarly, G. fusca caterpillars on China population plants were more likely to be parasitized than ones on US population plants when they were in the same cage but odds of parasitism for T. odinae did not differ for those on China vs. US population plants. These results suggest that populations from the native and introduced ranges may differ in traits that impact higher trophic levels. This may have implications for successful control of invasive plants as biocontrol agents are introduced or herbivores begin to feed on them in their introduced ranges.
Genetic determinants of thyroid function in children
Authors: Mulder Tessa A , Campbell Purdey J , Taylor Peter N , Peeters Robin P , Wilson Scott G , Medici Marco , Dayan Colin , Jaddoe Vincent V W , Walsh John P , Martin Nicholas G , Tiemeier Henning , Korevaar Tim I M
Published: August 2023

Objective

Genome-wide association studies in adults have identified 42 loci associated with thyroid stimulating hormone (TSH) and 21 loci associated with free thyroxine (FT4) concentrations. While biologically plausible, age-dependent effects have not been assessed. We aimed to study the association of previously identified genetic determinants of TSH and FT4 with TSH and FT4 concentrations in newborns and (pre)school children.

Methods

We selected participants from three population-based prospective cohorts with data on genetic variants and thyroid function: Generation R (N = 2169 children, mean age 6 years; N = 2388 neonates, the Netherlands), the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 3382, age 7.5 years, United Kingdom), and the Brisbane Longitudinal Twin Study (BLTS; N = 1680, age 12.1 years, Australia). The association of single nucleotide polymorphisms (SNPs) with TSH and FT4 concentrations was studied with multivariable linear regression models. Weighted polygenic risk scores (PRSs) were defined to combine SNP effects.

Results

In childhood, 30/60 SNPs were associated with TSH and 11/31 SNPs with FT4 after multiple testing correction. The effect sizes for AADAT, GLIS3, TM4SF4, and VEGFA were notably larger than in adults. The TSH PRS explained 5.3%-8.4% of the variability in TSH concentrations; the FT4 PRS explained 1.5%-4.2% of the variability in FT4 concentrations. Five TSH SNPs and no FT4 SNPs were associated with thyroid function in neonates.

Conclusions

The effects of many known thyroid function SNPs are already apparent in childhood and some might be notably larger in children as compared to adults. These findings provide new knowledge about genetic regulation of thyroid function in early life.