bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photonic probabilistic machine learning using quantum vacuum noise
Authors: Choi Seou , Salamin Yannick , Roques-Carmes Charles , Dangovski Rumen , Luo Di , Chen Zhuo , Horodynski Michael , Sloan Jamison , Uddin Shiekh , Soljačić Marin 
Published: March 2024

Abstract

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~ 1 Gbps and energy consumption of ~ 5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.
Among Men, Testosterone Levels Associated with Increased Risk of IBD: a Two-sample Mendelian Randomization Study
Authors: Chen Dan-na , Zhou Yi-jing , Lu Yu-xuan , Xie Jun-bo , Qiu Yu-qi , Wang Li-qun , Zou Xin , Dai Shi-xue , Zhu Chang-dong 
Published: March 2024

Abstract
Introduction
Although observational studies have shown an association between testosterone (T) and inflammatory bowel disease (IBD), the correlation remains controversial. In this study, we aimed to explore the potential causal relationship between T and IBD among men.
Methods
We utilized two-sample Mendelian randomization (MR) to investigate the causal relationship between testosterone and IBD among men using SNPs as genetic instrumental variables for exposure and outcome. The two-sample Mendelian randomization methods we used included inverse variance weighted (IVW) and MR‒Egger regression. Odds ratios (ORs) were applied to evaluate the results.
Results
A total of 112 SNPs were included in the MR study. The causal relationship between exposure factors and outcomes was derived using the two-sample Mendelian randomization method. The results of MR analysis demonstrated that testosterone among men was associated with an increased risk of inflammatory bowel disease (β=0.146 [OR 1.157 95% CI 1.102, 2.568], p=0.022, IVW), and MR‒Egger tests also supported this conclusion. (0.196[1.218 0.968, 1.534]0.095) The Cochran Q tests indicated that there was heterogeneity but not horizontal pleiotropy of SNPs, so we believe that the results remain robust.
Conclusion
This MR analysis provided evidence that testosterone was causally responsible for IBD among men, indicating that heterogeneous therapeutic measurements should be taken between male and female.
Blockchain-based 6G Task Offloading and Cooperative Computing Resource Allocation Study*
Authors: Tian Shujie , Zhang Yuexia , Bi Yanxian , Yuan Taifu 
Published: March 2024

Abstract

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. The simulation results demonstrate that the WF-CSA algorithm achieves a remarkable reduction in total delay, with improvements of up to 42.58% compared to both the fish swarm algorithm and the wolf pack algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.
Artificial intelligence modeling for power system planning
Authors: Knežević Sonja , Žarković Mileta 
Published: March 2024

Abstract

This paper presents artificial neural network (ANN) models for prediction of energy consumption compared with autoregressive integrated moving average (ARIMA) modeland adaptive neuro fuzzy inference system (ANFIS) models for thermal power plant production, hydro power plant production as well as CO2 emission.These models were developed to streamline power systems planning and minimize errors in planning. The model's accurate functionality is ensured by incorporating the collective expertise of power system planning professionals accumulated over the years. By adhering to these calculations, human errors are eliminated, the benefits of human knowledge are maximized, and a precise framework is established for planning the functionality of large power systems. ANN model for prediction were compared with autoregressive integrated moving average (ARIMA) model. Prediction of power plants production is an effective way to organize power systems for future time intervals so there will be less problems in the growing complexity of power systems. These are problems with a great number of data whose connections are not easy to define. In this paper the solution is presented in the form of ANNas part of supervised machine learning (SML) and ANFISmethodology. Calculations with ANN model was done simultaneously with standard forecasting ARIMA model in order to facilitate a comparison. Databases that are used for training ANN, ANFIS and ARIMA models contain hours of yearly data from Serbian power systems that are related to production of different energy sources, energy consumption and parameters that influence them. Trained ANN models are used to predict energy consumption and ANFIS models are used for prediction of energy production from different energy sources and CO2 emission on yearly or daily basis. Considering the existence of great numbers of data from previous years from which experts made conclusions for production scheduling this data is used to train ANFIS models that will help experts in optimal production scheduling. Using models like these human errors would be minimized and planning will be optimized and improved for experts.
A comparative study on the clinical efficacy of inflatable mediastinoscopy syncrhonized laparoscopic esophagectomy with McKeown's technique
Authors: Wei Hai-Tao , Liu Yang-Yang , Wang Meng-Yao , Zhang Hai-Feng , Hu Bao-Li , Zhang Dong-Hong , Wang Xiao-Long , Fan Lin-Lin , Kang Xia , Li Li 
Published: March 2024

Abstract
Background: Inflatable mediastinoscopy synchronous laparoscopic radical esophagectomy is not widely used domestically and internationally. In order to explore the technological innovation and application effects of inflatable mediastinoscopy synchronous laparoscopic radical esophagectomy, our team summarizes our surgical experience and hopes to further promote its application in clinical practice.
Objective: To explore the technical innovation and application effect of inflatable mediastinoscope synchronous laparoscopic radical resection of esophageal cancer.
Methods: From January 2017 to December 2018, 120 patients with esophageal cancer admitted by the same surgical team were retrospectively analyzed. A total of 64 patients were randomly divided into two groups: the experimental group with inflatable mediastinoscopy and the conventional group with McKeown (control group). Preoperative baseline data, perioperative index, postoperative index, near and long term survival rate and other indexes of patients in 2 groups were recorded, and statistical analysis was performed.
Results: The operation was completed successfully in both groups. In the experimental group, the operation time was (116.26±43.34) min, the intraoperative blood loss was (33.28±19.78) ml, the lymph nodes were removed (32.77±2.23), and the hospital stay was (9.62±3.33) d. Esophagogastric anastomotic leakage occurred in 2 cases, recurrent laryngeal nerve injury in 1 case and chylous leakage in 1 case. All 120 patients were followed up until December 2023. None of the 120 cases had tumor recurrence and metastasis, and no death.
Conclusion: Synchronous laparoscopic radical resection of esophageal cancer with inflatable mediastinoscope has no postoperative chest complications, shorter operation time, less pain and faster recovery. It can be used as a new supplement to the mainstream McKeown surgery for radical resection of esophageal cancer, and has a good development prospect. It is recommended to actively promote its application in clinical practice.
An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination
Authors: Wang Jibin , Guo Xingtian 
Published: March 2024

Abstract
Atrial fibrillation (AF) and atrial flutter (AFL) are the most common and easily confused arrhythmias. However, the existing AF and AFL detection approaches depend on visual examination of electrocardiogram (ECG), which is taxing and time-consuming. Motivated by state refinement module for LSTM (SR-LSTM), we investigate an improved SR-LSTM (ISR-LSTM) that is capable of adaptively refining the current states of sample points in ECG leveraging a sample point-wise attention and a representation gate mechanism for automated AF and AFL signals detection. To confirm the effectiveness, we conducted ablation experiments across two public databases. The results demonstrate the effectiveness of ISR-LSTM while achieving a significant improvement than state-of-the-art algorithms with an accuracy of 99.2% and 98.5%. To our knowledge, ISR-LSTM is the first work that integrates the interaction in ECG into deep network frame providing interpretability.
On the linear stability and super convergence of multi step collocation solutions for functional Volterra integral equations
Authors: Darania Parviz , Sotoudehmaram Fatemeh 
Published: March 2024

Abstract
This paper expands on a topic that was explored in recent research by P. Darania et al. (2020), in which the multi-step collocation method (MSCM) has been studied for solving FIEs along with their convergence analysis. This study focuses on the analysis of the linear stability and super-convergence of MSCM for a general form of delay function Θ(t), which includes discontinuity points where the solutions exhibit low regularity. In addition, we present the stability region and the super-convergence order with various examples, thereby demonstrating the accuracy and efficiency of this method. Moreover, the super-convergence and stability regions of the MSCM for FIEs with delay, Θ(t) = t − τ , that do not exhibit any points of discontinuity have been investigated in [4]. 2010 Mathematics Subject Classification: 45G05; 45D05; 65L05; 65L20.
Human pan-cancer analysis of the predictive biomarker for the CDKN3
Authors: Chen Yingjun , Li Dai , Sha Kaihui , Zhang Xuezhong , Liu Tonggang 
Published: March 2024

Abstract

BACKGROUND
Cell cycle protein-dependent kinase inhibitor protein 3 (CDKN3) is a member of the protein kinase family and has been shown to be oncogenic in several tumors. However, there are no pan-carcinogenic analyses for CDKN3.
METHODS
Using bioinformatics tools such as The Cancer Genome Atlas (TCGA) and the UCSC Xena database, we performed a pan-cancer analysis of CDKN3. We investigated the function of CDKN3 in 33 different kinds of tumor. And we explored the gene expression, survival prognosis status, clinical significance,DNA methylation, immune infiltration, and associated signal pathways of CDKN3.
RESULTS
CDKN3 was significantly upregulated in most of tumors and correlated with overall survival (OS) of patients. Methylation levels of CDKN3 differed significantly between tumors and normal tissues. In addition, infiltration of CD4 + T cells, cancer-associated fibroblasts, macrophages, and endothelial cells were associated with CDKN3 expression in various tumors. Mechanistically, CDKN3 was associated with P53, PI3K-AKT, cell cycle checkpoints, mitotic spindle checkpoint, and chromosome maintenance.
CONCLUSION
Our pan-cancer analysis provides a comprehensive understanding of the role of CDKN3 gene in tumorigenesis. Targeting CDKN3 may provide a new direction for future tumor therapy.
Analysis Of Factors Affecting the Import Demand for Wheat and Corn in Türkiye Using the Seemingly Unrelated Regression Method
Authors: merdan kurtuluş , çomaklı şafak 
Published: March 2024

Abstract

Wheat is the raw material of bread and the essential nutrient for continuity of human life. When there is a decrease in wheat production both in the world and in Türkiye, it affects the prices of not only bread but also food products made from flour, and the impact is directly reflected in society. In this context, it is strategically important for all countries to ensure that there is a sufficient amount of wheat products in their stocks. Corn also has a wide range of use after wheat and barley. Corn is an important plant used in both human nutrition and as animal feed. Since the physical structure of the production areas is not suitable for increasing corn production, the volume of corn production in Türkiye fails to meet the amount of consumption. Factors such as drought, the decrease in yield values, the war between Ukraine and Russia, and the economic collapse caused by COVID-19 in recent years have increased the demand for durable dry food products such as wheat and corn. Excessive demand for grain products has caused a record-breaking increase in wheat and corn prices.
This study analyzed the factors affecting the import demand for wheat and corn in Türkiye between 2006-2021 with the "Seemingly Unrelated Regressions" method. Only the one-year delayed value of wheat imports, which is one of the explanatory variables in the wheat import model, was found to be significant, according to the study's findings. The wheat import model's variables of per capita national income, wheat import price, total wheat consumption, and the one-year delayed value of wheat production were found to have no momentous effect on the amount of wheat imported. In terms of factors influencing corn import demand, similar results were obtained. Only total corn consumption was found to be statistically noteworthy among the explanatory variables in the corn import model. In the corn import model, it was determined that the variables per capita income, corn import price, one-year delayed value of corn production, and one-year delayed value of corn imports did not remarkably affect the volume of corn imports. Based on these findings, wheat imports are influenced by changes in wheat imports from a year ago, while corn imports are influenced by changes in total corn consumption.
JEL Sınıflandırması: Q10, Q17, Q19
Comparison of Perioperative and Functional Outcomes Between Improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy and Standard multi-port Robot-assisted Radical Prostatectomy:A Prospective, Controlled, Nonrandomised Trial
Authors: Fan Shida , Chen Zhengjun , Zhou Fang , Lv Qian , Wang Dong , Ren Shangqing , Tian Xuemei 
Published: March 2024

Abstract

Purpose
To compare perioperative and functional outcomes between Improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy(pf-ssRARP) and Standard multi-port Robot-assisted Radical Prostatectomy༈MPRARP༉.
Methods
A total of 372 consecutive patients underwent RARAP using the da Vinci Si® robotic surgical system .Group I (n = 210) included patients undergoing pf-ssRARP and Group II (n = 162) included patients undergoing MPRARP.Demographics and perioperative data including postoperative recovery outcomes were recorded and compared between the two groups.
Results
Overall mean operative time was significantly shorter with the pf-ssRARP compared to the MPRARP (p < 0.05).The length of hospitalization after the pf-ssRARP was shorter(p < 0.05).In Group I, the positive surgical margin rate was 15.2%, while in Group II, the positive margin rate was 33.3% (p < 0.05).The rate of instant urinary continence was significantly higher in Group I than in Group II(p < 0.05).The percentage of urinary continence was higher in the pf-ssRARP than in the MPRARP,at 6 months post-surgery (p < 0.05) and 9 months post-surgery (p < 0.05).There was no significant difference in the proportion of erectile function in the pf-ssRARP and MPRARP groups at the time of reaching the endpoint of this study(p> 0.05).The two groups were comparable in terms of total hospitalization costs(p < 0.05).
Conclusion
The Improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy is a practical and easy technique to implement in clinical practice.
Impacts of Chicken Manure Biochar on Nitrous Oxide and Methane Emissions from Vegetable Field in Subtropical China
Authors: Alami Mohammad Jawad , Fang Xuejuan , Zhong Dongliang , Zhou Weijun , Gao Bing , Huang Wei , Cui Shenghui 
Published: March 2024

Abstract
Vegetable production in Subtropical China is distinguished by excessive nitrogen (N) fertilization, frequent irrigation, and multiple crop rotations in a single year, making it one of China’s most severely impacted agro-ecosystems. The aforementioned variables are closely related to soil nitrous oxide (N2O) and methane (CH4) emissions. However, not much research has been conducted on the emission characteristics of N2O and CH4 from local farmers’ practices, and the impacts of chicken manure biochar on the emissions of multiple-cropping vegetable fields in south subtropical China. Hence, we conducted a field trial to measure N2O and CH4 emissions using static chamber-gas chromatography. Four treatments were used: control (CK) with no fertilizer, 100% chemical N fertilization (CN), the conventional 30% chicken manure N plus 70%CN (CMN+CN), and 30% chicken manure biochar N plus 70%CN (CMBN+CN). Seven seasons continuous vegetable production showed, the annual cumulative N2O emissions reached to 12.4, 63.5, 111.8 and 44.1 kg N2O-N ha-1 for CK, CN, CMN+CN, and CMBN+CN treatments, respectively. Compared to CN and CMN+CN treatments, CMBN+CN treatment reduced N2O emissions by 35.9% - 65.7%, while it simultaneously increases annual total vegetable yield by 16.1% relative to CN treatment. Seven seasons mean N2O emission factors are 1.3% for CN, 3.8% for CMN+CN, 0.9% for CMBN+CN. The CH4 emission was negligible, fall into 0.07 kg CH4-C ha-1 for CK treatment to 0.8 kg CH4-C ha-1 for CN treatment. Temperature and soil moisture had a strong relationship with N2O emissions. For N2O emissions, the ideal interior chamber temperature was around 31.9 °C and the soil's water-filled pore space (WFPS) was approximately 60%. Our research highlighted that, fertilizer rates and types, soil moisture and temperature were the main controlling factors of N2O emissions in subtropical vegetable field. Future climate change will cause environmental factors to change more dramatically, which will probably result in more N2O emissions from conventional vegetable fields in subtropical areas. Chicken manure biochar can be one of the best substitutes for fresh chicken manure as a soil supplement, because it has a beneficial effect on improving vegetable yield and reducing N2O emissions in subtropical China.
Identification of a Novel Missense Mutation in the LMX1B Gene Associated with Nail-Patella Syndrome in a Chinese Family
Authors: Sun Qian , Zheng Wen , Li Mengxia , Su Guanghao , Wang Xiaodong , Wang Hongying 
Published: March 2024

Abstract

Aim
Nail-patella syndrome is an autosomal dominant disorder caused by mutations in LMX1B gene, affecting different systems including skeleton-muscular, renal and ocular system. The complicated relationship between genotype and phenotype is still unclear. Here, we report a novel missense mutation in a Chinese family.
Method
Clinical information of the family was collected, along with the peripheral blood sample. To investigate the pathogenic mutation, whole exome sequencing was conducted. Subsequently, Sanger sequencing was utilized to confirm the suspicious mutation. Bioinformatic tools was used to predict the 3D structure modification and pathogenicity caused by the mutation.
Results
The proband in our study showed manifestations including nail malformation, patella dysplasia, restricted elbow movement and pes planus Both his mother and sister also displayed skeletal system symptoms. We identified a novel mutation c.812G > C (p.R271T) in this family, which encodes an amino acid within the homeodomain, disturbing its ability to bind DNA, leading to the onset of the syndrome.
Conclusion
Nail-patella syndrome is a rare disorder affecting multiple systems. Manifestations in this family mainly focus on the skeletal system with variations among different members. Our study reports a novel mutation of this disease, previously unreported in literature.
Tikhonov Regularization for Stochastic Non-Smooth Convex Optimization in Hilbert Spaces
Authors: Maulen-Soto Rodrigo , Fadili Jalal , Attouch Hedy 
Published: March 2024

Abstract

To solve non-smooth convex optimization problems with a noisy gradient input, we analyze the global behavior of subgradient-like flows under stochastic errors. The objective function is composite, being equal to the sum of two convex functions, one being differentiable and the other potentially non-smooth. We then use stochastic differential inclusions where the drift term is minus the subgradient of the objective function, and the diffusion term is either bounded or square-integrable. In this context, under Lipschitz's continuity of the differentiable term and a growth condition of the non-smooth term, our first main result shows almost sure weak convergence of the trajectory process towards a minimizer of the objective function. Then, using Tikhonov regularization with a properly tuned vanishing parameter, we can obtain almost sure strong convergence of the trajectory towards the minimum norm solution. We find an explicit tuning of this parameter when our objective function satisfies a local error-bound inequality. We also provide a comprehensive complexity analysis by establishing several new pointwise and ergodic convergence rates in expectation for the convex and strongly convex case.
AMS subject classifications. 37N40, 46N10, 49M99, 65B99, 65K05, 65K10, 90B50, 90C25, 60H10, 49J52.