bluuebunny/crossref_metadata_2025 huggingface dataset:
Diamond cutting and compliant morphable tool polishing of additively manufactured stainless steel
Authors: Nie Qianqian
Published: January 2024

The surfaces of additively manufactured (AMed) workpiece are generally very rough with many large pits and powder particles, which significantly limits their application. In this work, diamond cutting and compliant morphable tools polishing were proposed to finish AMed stainless steel. The surface morphology, indentation hardness and friction coefficient of the as-built, diamond cut and polished workpiece were measured and analyzed. The results show that grinding was able to reduce the roughness of the as-built sample to 2.4 µm. Diamond cutting and polishing significantly reduced the roughness Sa, Sq of the sample from more than 10 µm to 80 nm, 90 nm and 3 nm, 5 nm, respectively. Besides, the sample surface protrusions and valleys were significantly reduced from ~ 50 µm, ~ 30 µm to ~ 0.6 µm, ~ 0.3 µm by diamond cutting and ~ 0.2 µm, 0.1 µm by polishing. The 9 µm abrasive polished surface was flat with some left particles and irregular abrasion marks while the 0.3 µm abrasive polished surface was flatter with no observable protrusions or marks. Polishing made the nanoindentation load-depth curves more identical, and increased the indentation hardness and modulus, and decreased the indentation depth than those on the cut surface. The friction coefficients on the as-built surface were 0.64–0.82. Diamond cutting and polishing greatly reduced the friction coefficients to 0.49 and 0.46.
Production of Iron-enriched Yeast and it’s application in the treatment of iron-deficiency anemia
Authors: Chen Ying , Pang Yuanxiang , Wan Hongbing , Zhou Xinyi , Wan Mingli , Li Shengshuo , Liu Xuelian
Published: January 2024

Iron deficiency anemia (IDA) is one of the most serious forms of malnutrition.Wild type strains of Saccharomyces cerevisiae have higher tolerance to inorganic iron and higher iron conversion and accumulation capacity.The aim of this study was to investigate the effect of yeast iron as a potential organic iron supplement on mice with iron deficiency anemia.60 male KM mice were randomly divided into normal control group and iron deficiency diet model group to establish IDA model. After the model was established, IDA mice were randomly divided into 5 groups: normal control group, IDA group, organic iron (Fe-Gly) group, inorganic iron (FeSO4) group and yeast iron (Fe-F8) group.Mice in the experimental group were given different kinds of iron by intragastric administration once a day for 4w.The results showed that Fe-F8 had an effective recovery function, and the body weight and hematological parameters of IDA mice returned to normal levels. The activities of malondialdehyde, superoxide dismutase, glutathione peroxidase and total antioxidant capacity in serum were increased.In addition, Fe-F8 was more effective in alleviating IDA and improving organ indices with fewer side effects compared to ferrous glycinate and ferrous sulfate groups. This study suggests that the iron-rich strain F8 may play an important role in improving IDA mice and may be developed as a new iron supplement.
Optimizing Transition: Investigating the Influence of Operational Parameters on Production Scheduling Optimization for Mines Transitioning from Open Pit to Block Caving Methods
Authors: Flores Ignacio Ortiz , Anani Angelina , Li Haitao , Jalilzadeh Afrooz
Published: January 2024

Current technologies have made the transition from surface to underground mining methods for mineral extraction feasible and economically viable. Determining the point of transition from one method to the other for deposits that require exploitation with both methods is challenging. The existing research integrates production scheduling optimization with determining the transition depth that maximizes net present value (NPV), making the problem computationally intractable. However, these studies do not consider some realistic operational constraints in the problem setting. Our paper aims to develop an integrated mixed-integer linear programming (MILP) model to investigate the extent to which operational constraints and parameters of transition mines affect the optimal production schedule and NPV of an operation. We developed a computational experiment that evaluates development cost and rate, fleet size, stockpile, production footprint, dilution factor, and crown pillar placement on the model output. A case study is used to validate the model, with a comparative sensitivity analysis to obtain operational insights. The sensitivity of the NPV and computational time to each experimental factor varied significantly. There was no significant difference in NPV (0.15%) when the development cost was incorporated. However, for the fleet size, stockpile, and production footprint, an increase of 4.8%, 10.5 %, and 4.8% in the NPV was observed. We conclude that the extent to which operational parameters and constraints of transition mines are accounted for, has a significant impact on the optimal production schedule and NPV obtained.
Soil microbial community composition and nitrogen enrichment responses to the operation of electric power substation
Authors: Xu Zhi-Xin , Zeng Bo , Chen Sheng , Xiao Sa , Jiang Lin-Gao , Li Xiang , Wu Yun-Fang , Zhao Meng-Xin , Chen Si-Ru , You Le-Xing X.
Published: January 2024

The surge in global energy demand mandates a significant expansion of electric power substations. However, the ecological consequences of electric power substation operation on soil microbial communities and nitrogen enrichment have not been addressed. In this study, we collected soil samples from seven distinct sites at varying distances from an electric power substation in Xintang village, southeastern China, and investigated the microbial diversity and community structures employing metagenomic sequencing technique. Key environmental determinants shaping soil microbial communities at both the phylum and genus levels were identified as soil moisture content, pH and electric conductivity. Prominent taxa identified across all sampled soils included Acidobacteria, Proteobacteria, Actinobacteria, Chloroflexi, Basidiomycota, Ascomycota, and Mucoromycota. While the bacterial community exhibited statistically significant differences across the seven distinct sites, fungal communities did not show such variations. Correlation analysis revealed a diminished nitrogen fixation capacity at the site nearest to the substation, characterized by low moisture content, elevated pH, and robust soil electric conductivity. In contrast, heightened nitrification processes were observed at this site compared to others. These findings were substantiated by the relative abundance of key genes associated with ammonium nitrogen and nitrate nitrogen production. This study provides insights into the relationships between soil microbial communities and the enduring operation of electric power substations, thereby contributing fundamental information essential for the rigorous environmental impact assessments of electric power substations.
Retinoic acid modulation guides human-induced pluripotent stem cell differentiation towards left or right ventricle-like cardiomyocytes
Authors: Zhang Hengliang , Sen Payel , Hamers Jules , Sittig Theresa , Woestenburg Brent , Moretti Allessandra , Dendorfer Andreas , Merkus Daphne
Published: January 2024

Background. Cardiomyocytes (CMs) derived from human induced pluripotent stem cells (hiPSCs) by traditional methods are a mix of atrial and ventricular CMs and many other non-cardiomyocyte cells. Retinoic acid (RA) plays an important role in regulation of the spatiotemporal development of the embryonic heart.
Methods: Engineered heart tissues (EHTs) were generated by assembling CMs derived from hiPSC (hiPSC-CM) at high cell density in a low collagen hydrogel. Different concentrations of RA (Control group without RA, LRA group with 0.05 µM and HRA group with 0.1 µM) were administered during third to sixth days of the differentiation process.
Results: In the HRA group, hiPSC-CMs exhibited highest expression of maturity genes MYH7 and cTnT. The expression of TBX5, NKX2.5 and CORIN, which are the marker genes for left ventricular CMs, was also the highest in the HRA group. In terms of EHT, the HRA group displayed the highest contraction force, the lowest beating frequency, and the highest sensitivity to hypoxia and isoprenaline, which means it was more functionally similar to the left ventricle. RNAsequencing revealed that the heightened contractility of EHT within the HRA group can be attributed to the promotion of augmented extracellular matrix strength by RA.
Conclusion: By interfering with the differentiation process of hiPSC with a specific concentration of RA at a specific time, we were able to successfully induce CMs and EHTs with a phenotype similar to that of the left ventricle or right ventricle.
Enhancing Risk Assessment Models for Heavy Duty and Medium Duty Vehicles through Customization of the EVITA Framework
Authors: Rahimi Narges , Mirhassani Mitra , Schuelke-Leech Beth-Anne
Published: January 2024

As a cornerstone of cybersecurity, risk assessment facilitates identifying and grading potential threats. Despite several risk assessment methodologies tailored for the automotive industry, such as EVITA and HEAVEN, a substantial knowledge gap persists in the context of heavy-duty (HD) and medium-duty (MD) vehicles. This study seeks to bridge this gap by introducing a customized model derived from EVITA, specifically designed for HD/MD vehicles. This model enhances the existing EVITA framework by integrating updated severity and probability weights that reflect the unique applications of HD/MD vehicles. Additionally, the attack tree initially presented in EVITA, is augmented by incorporating Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) standards. This research presents a comprehensive risk assessment methodology for HD/MD vehicles by building upon the EVITA model. The revised severity and likelihood weighting and the integration of CWE and CVE result in a more precise and effective strategy for assessing and mitigating potential risks. The insights gained from this research contribute to the evolution of risk assessment techniques in the automotive industry, with a particular emphasis on heavy-duty and medium-duty vehicles.
Subsurface mixing and ventilation of oxygen minimum zone waters in the southern Bay of Bengal during the summer monsoon
Authors: Roy Rajdeep , Vinayachandran P. N. , George Jenson , Sarkar Amit , Parida Chandanlal
Published: January 2024

During the summer monsoon, the local wind forcings around Sri Lanka causes the formation of a cold dome called the Sri Lanka Dome (SLD), which upwells subsurface waters. To the east of SLD, the summer monsoon current (SMC) flows into the Bay of Bengal (BoB), transporting high-salinity water from the Arabian Sea. We show that the SMC and the upwelled waters of the SLD are ventilated episodically during summer monsoon in the southern BoB, leading to a net exchange of low oxygen subsurface waters with saturated mixed layers. We observed presence of hypoxic boundary < 63 µmol kg− 1 very close to the surface. Within the SLD, it shoaled between 35 to 40 m, with oxygen values reaching as low as 6 µmol kg− 1 at the bottom of the thermocline. Negative fluxes showing the ingassing rates ranged between − 0.33 and − 9.43 µmol m− 2 sec− 1 within the SLD and SMC. We propose that the episodic ventilation seen during this investigation may lead to disequilibrium between mixed layer and below thereby contributing to mid-depth oxygen enrichment. This study possibly illustrates a pathway through which the oxygen minimum zone in BoB may be gaining oxygen, thereby preventing from becoming denitrifying.
General Relativistic Approach to the Vis-viva Equation
Authors: Peng Qi , Yokoyama Shuichiro , Ichiki Kiyotomo
Published: January 2024

A modification to the vis-viva equation that accounts for general relativistic effects is introduced to enhance the accuracy of predictions of orbital motion and precession. The updated equation reduces to the traditional vis-viva equation under Newtonian conditions and is a more accurate tool for astrodynamics than the traditional equation. Preliminary simulation results demonstrate the application potential of the modified vis-viva equation for more complex n-body systems. Spherical symmetry is assumed in this approach; however, this limitation could be removed in future research. This study is a pivotal step toward bridging classical and relativistic mechanics and thus makes an important contribution to the field of celestial dynamics.
A Novel Dual-Channel Single-Polarization PCF: Single-Polarization PCF-Based Plasmonic Biosensor for Multi-Analyte Detection
Authors: Azman Mohd Fahmi Bin , Mashrafi Md. , Haider Firoz , Ahmed Rajib , Aoni Rifat Ahmmed , Junayed Md , Wong Wei Ru , Mahdiraji Ghafour Amouzad , Adikan Faisal Rafiq Mahamd
Published: January 2024

A miniaturized biosensor capable of detecting multiple analytes is proposed with real-time measuring and high sensitivity which are crucial for future sensing devices. We introduce here a dual-channel multi-analyte single polarization (DCSP) photonic crystal fiber (PCF) sensor. The DCSP sensor was fabricated using the stack-and-draw technique to show the sensor practical feasibility. The DCSP offers maximum wavelength and amplitude sensitivities of 11,000 nm/RIU and 807 RIU− 1, respectively in both channels for the analyte refractive index (RI) of na=1.33 to 1.41. The wavelength sensitivities are improved in this case by using (DCSP) sensors, which aid in the detection of multi-analyte, high-sensitivity, and real-time measurements. The DCSP sensor can be a suitable candidate for biosensing where the sample can be in a micro dimension due to having high sensing response. Moreover, the sensor works only at single polarization with lower confinement loss and better wavelength resolution, enhancing the previous works. These results indicate the potential use of the proposed DCSP sensors to improve future sensing technology and sensor's performance in the detection of diseases diagnostic and liquid detection and so on.
Mother trees of common ash (Fraxinus excelsior) disperse different sets of mycobiome through their samaras
Authors: Long Feng , Doonan James Michael , Nielsen Lene Rostgaard , Kjaer Erik Dahl , Kosawang Chatchai
Published: January 2024

Objective
Endophytic mycobiomes are present in all studied plant compartments, including fruits and seeds, but a transmission of the mycobiome between generations is largely unknown. Our objectives were to examine mycobiome transfer via seed wings (samaras) of European ash (Fraxinus excelsior), and to test whether these mycobiomes differ among trees. To achieve this, we used ITS1-based amplicon sequencing and two genotypes of F. excelsior as a model to compare the mycobiome of mother trees and their samaras.
Results
We profiled the mycobiome of 57 seed stalks and seed wings (samaras) collected from two genotypes of F. excelsior using three ramets of each genotype. Alpha diversity indices (Observed OTUs and ACE) suggested a higher richness of the mycobiome associated with seed wing than seed stalk within each genotype. However, there was neither significant differences in diversity between the mycobiomes from the two tissue types nor the two genotypes. PERMANOVA analysis revealed significant differences in the mycobiome composition between seed wings, but not between seed stalks, of the two genotypes. Our results suggest that Fraxinus excelsior mother trees disperse different sets of mycobiomes with their samaras, which may be important for germination and seedling establishment – especially in the light of ash dieback.
Detection of Alzheimer and Mild Cognitive Impairment Patients by Poincare and Entropy Methods based on Electroencephalography Signals
Authors: Aslan Umut , Akşahin Mehmet Feyzi
Published: January 2024

Alzheimer's disease (AD) is characterized by deficits in cognition, behavior, and intellectual functioning, and Mild Cognitive Impairment (MCI) refers to individuals whose cognitive impairment deviates from what is expected for their age but does not significantly interfere with daily activities. Because there is no treatment for AD, early prediction of AD can be helpful to reducing the progression of this disease. This study examines the Electroencephalography (EEG) signal of 3 distinct groups including AD, MCI, and healthy individuals. Recognizing the non-stationary nature of EEG signals, two nonlinear approaches, Poincare and Entropy, are employed for meaningful feature extraction. To extract features from EEG signal, data should segmented into epochs and for each one, feature extraction approaches are implemented. The obtained features are given to machine learning algorithms to classify the subjects. Extensive experiments were conducted to analyze the features comprehensively The results demonstrate that, our proposed method surpasses previous studies in terms of accuracy, sensitivity, and specificity, indicating its effectiveness in classifying individuals with AD, MCI, and those without cognitive impairment.
Identifying causes and associated factors of stillbirths using autopsy of the fetus and placenta
Authors: Sade Eliel Kedar , Lantsberg Daniel , Sar-el Moriel Tagar , Gefen Sheizaf , Gafner Michal , Katorza Eldad
Published: January 2024

Purpose: The study aimed to evaluate the causes of death and associated factors in cases of stillbirth, using post-mortem examination and applying a rigorous, evidence-based holistic approach.
Methods: Our retrospective observational study included cases of autopsy following stillbirth that occurred at our tertiary medical center during a period of 8 years. Detailed up-to-date criteria that incorporate clinical reports, medical history, prenatal imaging, and histopathological findingswere used to evaluate the cause of death and associated factors.
Results: After applying our proposed holistic approach, 138 cases of stillbirth were classified into eight categories based on the causes of death. A definitive cause of death was observed in 100 (72%) cases, while 38 (28%) cases were considered unexplained. The leading cause of death was placental lesions (n=39 ,28%) with maternal vascular malperfusion (MVM) lesions being the most common (54%). Ascending infection was the second most common cause of fetal death (n=24, 17%) and was often seen in the setting of preterm labor and cervical insufficiency.
Conclusion: The largest category of cause of death was attributed to placental pathology. Using rigorous detailed up-to-date criteria that incorporate pathological and clinical factors may help in objectively classifying the cause of death.
Experimental investigation on microstructure fractal characteristics of low-temperature oxidation of gas-bearing coal
Authors: Li Shuaikui , Li Zhenrong , Qin Yujin , Jia Dongxu , Jia Kangteng , Tian Fuchao
Published: January 2024

In order to study the multi-field coupling mechanism of gas and coal spontaneous combustion, low-temperature nitrogen adsorption and SEM were applied to carry out microstructure testing experiments on oxidation of gas-bearing coal in this paper. And a pore fractal calculation model incorporating pore size distribution was established. The effects of low-temperature oxidation and gas ad/de-sorption on the change of coal pore structure were investigated. The results showed that the pore volume of coal oxidized and gas-bearing coal oxidized were decreased by 1.805 and 2.232×10− 3 cm3·g− 1 in all pore size distributions, where the micropores and macropores were converted to mesopores after oxidation of coal and macropores were formed by destruction of micropores and mesopores after oxidization of gas-bearing coal; coal oxidation and gas-bearing coal oxidation specific surface area decreased by 2.264 and 2.320 m2·g− 1, respectively, and the modifying effect of coal pore surface was stronger in the latter than in the former; the irregularity of the surface morphology increased after oxidation of the coal, and a large number of micropores were connected to form microcracks, while the surface morphology of the pores of the oxidized gas-bearing coal tended to be more regular compared with the oxidation of the non-gas-bearing coal; a pore fractal model based on the characteristics of the pore diameter distribution was established, and Dfb is 2.14–2.23, and the coupled action of gas desorption and oxidation on pore modification is stronger than the oxidation. The gas storage state of gas-bearing coal and the distribution of desorbead gas in goaf were affected by change of pore structure, so that the oxidation of gas-bearing coal can be weakened and the risk of spontaneous combustion of gas-bearing coal can be declined, which can provide a theoretical basis for the judgment of the risk of spontaneous combustion of gas-bearing coal in goaf.
PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization
Authors: Bouchene Mohammed Mehdi
Published: January 2024

Univariate Feature Selection (UFS) traditionally involves a labor-intensive process of trial-and-error, necessitating the selection of scoring functions and the determination of feature numbers. These choices can inadvertently affect both the performance and interpretability of the model. To address this challenge, we introduce Particle Swarm Optimization for Univariate Feature Selection (PSO-UFS), an innovative method that automates these crucial decisions. PSO-UFS leverages the power of Particle Swarm Optimization (PSO) to autonomously identify the optimal scoring function and feature subset that maximize a machine learning algorithm's performance metric. Our empirical evaluations across multiple datasets demonstrate that PSO-UFS significantly outperforms traditional UFS in various performance metrics, including accuracy, precision, recall, and F1-score. Importantly, PSO-UFS generates more interpretable feature subsets, thereby enhancing the model's comprehensibility. This advancement paves the way for broader applications in real-world scenarios where feature reduction and interpretability are paramount.
PRMT6-mediated Transcriptional Activation of YTHDF2 Promotes Glioblastoma Migration, Invasion, and EMT via the Wnt–β-Catenin Pathway
Authors: Yu Peng , Xu Tutu , Ma Wenmeng , Fang Xiang , Bao Yue , Xu Chengran , Huang Jinhai , Sun Yongqing , Li Guangyu
Published: January 2024

Background
Protein arginine methyltransferase 6 (PRMT6) plays a crucial role in various pathophysiological processes and diseases. Our current research finds PRMT6 upregulated in glioblastoma multiforme (GBM), enhancing tumor malignancy. Yet, the specifics of PRMT6's regulatory processes and potential molecular mechanisms in GBM remain largely unexplored.
Methods
PRMT6's expression and prognostic significance in GBM were assessed using glioma public databases, immunohistochemistry (IHC), and immunoblotting. Scratch and Transwell assays examined GBM cell migration and invasion. Immunoblotting evaluated the expression of epithelial-mesenchymal transition (EMT) and Wnt-β-catenin pathway-related proteins. Dual-luciferase reporter assays and ChIP-qPCR assessed the regulatory relationship between PRMT6 and YTHDF2. An in situ tumor model in nude mice evaluated in vivo conditions.
Results
Bioinformatics analysis indicates high expression of PRMT6 and YTHDF2 in GBM, correlating with poor prognosis. Functional experiments show PRMT6 and YTHDF2 promote GBM migration, invasion, and EMT. Mechanistic experiments reveal PRMT6 and CDK9 co-regulate YTHDF2 expression. YTHDF2 binds and promotes the degradation of negative regulators APC and GSK3β mRNA of the Wnt-β-catenin pathway, activating it and consequently enhancing GBM malignancy.
Conclusions
Our results demonstrate the PRMT6-YTHDF2-Wnt-β-Catenin axis promotes GBM migration, invasion, and EMT in vitro and in vivo, potentially serving as a therapeutic target for GBM.
Characteristics of Emergency Department Visits Among Older Adults With Hearing Difficultly During the COVID19 Pandemic in the US
Authors: yu juebo
Published: January 2024

Objective
Hearing difficultly(HD) may be associated with an increased risk for emergency department(ED) visits among older adults.The COVID-19 pandemic has negatively affected adult healthy status. However, less is known about the characteristics of ED visits in older adults with hearing difficulty during the COVID-19 pandemic.this study is to assess the association between self-reported HD severity and ED visits during the COVID-19 pandemic.
Methods
This population-based cross-sectional study used self-reported hearing difficulty and characteristics for respondents aged 65 years or older from 3 cycles of National Health Interview Survey from 2020 to 2022. Data were analyzed from February 23, 2023, to March 22, 2023.The main outcome was respondent-reported emergency department visits(including visited hospital emergency room and/or visited urgent care ) in the past 12 months. Generalized linear models were used to adjust for differences between the groups, and the associations between HD status and visit characteristics were expressed as rate ratios.
Results
The COVID19 Pandemic period, common reasons for HD visits included chronic pain (82.8%),fragile (77.9%),trouble falling/staying asleep (73.2%),hypertension (67.4%) ,and arthritis (60.1%)which were 1.5 times as likely compared with non-SRHD visits ( chronic pain: adjusted rate ratio [ARR], 1.64[95% CI, 1.44 to 1.93]; fragile: ARR, 1.57 [95% CI, 1.16 to 1.87]; trouble falling/staying asleep:ARR, 1.51[95% CI, 1.21 to 1.82]; hypertension: ARR, 1.01[95% CI, 0.92 to 1.23]; arthritis:ARR, 1.39[95% CI, 1.31 to 1.57]
Conclusions
In this cross-sectional study,older adults with hearing impairments were more likely to seek emergency visits for chronic pain,fragile,trouble falling/staying asleep, hypertension and arthritis than those without hearing impairments during the COVID-19 pandemic.
Joint Offloading Decision and Trajectory Optimization for Multi-UAV-Assisted Mobile Edge Computing System
Authors: Qiu Bin , Li Xueli , Li Xian
Published: January 2024

Multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computational services to Internet of Thing (IOT) terminal devices (TDs) in the limited or no available infrastructures. However, irrational trajectory of UAVs and offloading decision by TDs contribute to increased delay and energy consumption in the UAV-MEC system. In this paper, we present a two-stage optimization algorithm that jointly considers computation-offloading decision and trajectory to minimize the weighted sum of delay and energy consumption in the multi-UAV-assisted MEC system. Specifically, a Stackelberg Game-based computation offloading (SG-CO) algorithm is designed to obtain the optimal offloading decision for TDs. Furthermore, under the given TDs offloading decision, we develop the Simulated Annealing-Beetle Antennae Search (SA-BAS) algorithm to achieve the optimal trajectory for UAVs, in which SA-BAS algorithm accelerates convergence and enhances global optimization performance. Extensive simulation analysis demonstrates that the proposed scheme outperforms the benchmark schemes, showcasing its effectiveness in minimizing the weight sum of delay and energy consumption.
Collaborative Task Processing for Multiple Edge Nodes Based on Service Placement
Authors: Shi Lei , Feng Shilong , Ji Rui , Xu Juan , Ding Xu
Published: January 2024

With the rapid development of the Internet of Things (IoT), Mobile Edge Com-puting (MEC) technology is shifting computational and storage capabilities fromcentralized clouds to the network edge to meet the low-latency demands ofnumerous emerging applications. However, ensuring quality of service (QoS) formobile users becomes challenging in dense, decentralized wireless communica-tion environments and with limited MEC server storage capacity. Against thisbackground, this paper proposes a collaborative task processing model for mul-tiple ENs based on service placement and formulates a MINLP optimizationproblem aimed at minimizing system latency and cost. To address this problem,we introduce an online optimization algorithm (OPDA) based on the Lyapunovframework which operates in real-time without the need to predict future infor-mation. Subsequently, we decompose the long-term optimization problem into aseries of one-time slot problems and design a two-stage one-time slot optimiza-tion algorithm to obtain an approximate optimal solution. Specifically, we usethe Lagrange multiplier approach to solve the resource allocation problem fortasks and the matching theory to solve the offloading decision and service place-ment problem for tasks. Simulation results show that our algorithm can achievenear-optimal latency performance while satisfying long-term cost constraints.
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
Authors: Chatterjee Soumick , Sarasaen Chompunuch , Rose Georg , Nürnberger Andreas , Speck Oliver
Published: January 2024

Magnetic resonance imaging (MRI) provides high spatial resolution and excellent soft-tissue contrast without using harmful ionising radiation. Dynamic MRI is an essential tool for interventions to visualise movements or changes of the target organ. However, such MRI acquisitions with high temporal resolution suffer from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI. Several approaches, including deep learning based super-resolution approaches by treating each time-point as individual volumes. This research addresses this issue by creating a deep learning model which attempts to learn both spatial and temporal relationships. A modified 3D UNet model, DDoS-UNet, is proposed - which takes the low-resolution volume of the current time-point along with a prior image volume. Initially, the network is supplied with a static high-resolution planning scan as the prior image along with the low-resolution input to super-resolve the first time-point. Then it continues step-wise by using the super-resolved time-points as the prior image while super-resolving the subsequent time-points. The model performance was tested with 3D dynamic data that was undersampled to different in-plane levels and achieved an average SSIM value of 0.951±0.017 while reconstructing only 4% of the k-space - which could result in a theoretical acceleration factor of 25. The proposed approach can be used to reduce the required scan-time while achieving high spatial resolution - consequently alleviating the spatio-temporal trade-off of dynamic MRI, by incorporating prior knowledge of spatio-temporal information from the available high resolution planning scan and the existing temporal redundancy of time-series images into the network model.
Biodegradation of Kitchen Waste mediated by Fungal Enzyme cocktail
Authors: Singh Muskaan , Dutta Upma
Published: January 2024

The non-edible portions of food that are wasted during handling, processing, or after consumption are referred to as "kitchen waste." Due to its high moisture concentration, kitchen waste is difficult to handle using conventional methods, such as incineration. However, it is crucial to find an alternative to securely dispose of organic waste since it is a major source of pollution. Efficient decomposition of food waste may be achieved by a variety of microorganisms utilizing cellulose, proteins, lipids, and carbohydrates to fulfil their energy needs. Therefore, it is possible to accelerate the biodegradation of kitchen trash by establishing consortia of these microbes. The present study adopted a novel approach relating to hydrolysis of food waste by biological treatment using hydrolytic enzymes produced by fungal isolates Aspergillus niger MA6, Fusarium solani MA7 and Trichoderma asperellum MA8. The screening of the isolates was done by qualitative assays using specific media viz. tributyrin agar, skim milk agar, cellulose congo red agar, and starch agar as substrates. Futhermore, compatibility of selected isolates was checked followed by production of consortia by submerged fermentation. A 10mL of consortium of MA6, MA7 and MA8 was tested in 450 g of kitchen waste blended with sawdust in different trials with varied pH (5.7, 6.5 and 7.5) and temperature (28ºC, 30°C and 40ºC) to estimate the weight reduction (%) and the study showed that approximately 90.80% weight reduction could be achieved at pH 5.7 and at 30ºC using enzymes produced by these potential fungi. The physiochemical properties of the untreated and biologically treated kitchen waste were compared to ascertain the changes occurring due to degradation. The current approach is anticipated to become an economically attractive and environmental friendly solution for the disposal of substantial kitchen waste causing various environmental issues.
Sublinear Algorithms for Several Geometric Optimization (With Outliers) Problems
Authors: DING HU
Published: January 2024

In this paper, we study several important geometric optimization problems arising in machine learning. First, we revisit the Minimum Enclosing Ball (MEB) problem in Rd. The problem has been extensively studied before, but real-world machine learning tasks often need to handle large-scale datasets so that we cannot even afford linear time algorithms. We introduce the notion of stability for MEB, which is natural and easy to understand. Roughly speaking, an instance of MEB is stable, if the radius of the resulting ball cannot be significantly reduced by removing a small fraction of the input points. Under the stability assumption, we present two sampling algorithms for computing radius-approximate MEB with sample complexities independent of the number of input points n. In particular, the second algorithm has the sample complexity even independent of the dimensionality d. We also consider the general case without the stability assumption. We present a hybrid algorithm that can output either a radius-approximate MEB or a covering-approximate MEB. Our algorithm improves the running time and the number of passes for the previous sublinear MEB algorithms. Our method relies on two novel techniques, the Uniform-Adaptive Sampling method and Sandwich Lemma. Furthermore, we observe that these two techniques can be generalized to design sublinear time algorithms for a range of geometric optimization problems with outliers in high dimensions, including MEB with outliers, one-class and two-class linear SVMs with outliers, k-center clustering with outliers, and flat fitting with outliers.
Multi-View Multi-Label Personalized Classification via Generalized Exclusive Sparse Tensor Factorization
Authors: Fei Luhuan , Lin Weijia , Wang Jiankun , Sun Lu , Kudo Mineichi , Kimura Keigo
Published: January 2024

Multi-Label Classification (MLC) assigns multiple relevant labels to each sample simultaneously,while multi-view MLC aims to apply MLC to handle heterogeneous data represented by multiple feature subsets.In recent years, a variety of methods have been proposed to handle these problems and have achieved great success in a wide range of applications. MLC saves global label correlation by building a single model shared by all samples but ignores sample-specific local structures, while Personalized Learning (PL) is able to preserve sample-specific information by learning local models but ignores the global structure. Integrating PL with MLC is a straightforward way to overcome the limitations, but it still faces three key challenges. 1) capture both local and global structures in a unified model; 2) efficiently preserve full-order interactions between labels, samples and features or multi-view features in heterogeneous data; 3) learn a concise and interpretable model where only a fraction of interactions are associated with multiple labels. In this paper, we propose a novel Multi-Label Personalized Classification (MLPC) method and its multi-view extension to handle these challenges. For 1), it integrates local and global components to preserve sample-specific information and global structure shared across samples, respectively. For 2), a multilinear model is developed to capture full-order label-feature-sample interactions, and over-parameterization is avoided by tensor factorization. For 3), exclusive sparsity regularization penalizes factorization by promoting intra-group competition, thereby eliminating irrelevant and redundant interactions during Exclusive Sparse Tensor Factorization (ESTF). Moreover, theoretical analysis generalizes the proposed ESTF and reveals the equivalence between MLPC and a family of jointly regularized counterparts. We develop an alternating algorithm to solve the optimization problem, and demonstrate its effectiveness based on comprehensive experiments on both synthetic and real-world benchmark datasets.
Gröbner-shirshov Bases and Linear Bases for Free Differential type Algebras Over Algebras
Authors: Qi Zihao , Qin Yufei , Zhou Guodong
Published: January 2024

We study a question which can be roughly stated as follows: Given a (unital or nonunital) algebra A together with a Gröbner-Shirshov basis G, consider the free operated algebra B over A, such that the operator satisfies some polynomial identities Φ which are Gröbner-Shirshov in the sense of Guo et al., when does the union Φ ∪ G will be an operated Gröbner-Shirshov basis for B? We answer this question in the affirmative under a mild condition in our previous work with Wang. When this condition is satisfied, Φ ∪ G is an operated Gröbner-Shirshov basis for B and as a consequence, we also get a linear basis of B. However, the condition could not be applied directly to differential type algebras introduced by Guo, Sit and Zhang, including usual differential algebras.
This paper solves completely this problem for differential type algebras. Some new monomial orders are introduced which, together with some known ones, permit the application of the previous result to most of differential type algebras, thus providing new operated GS bases and linear bases for these differential type algebras. Versions are presented both for unital and nonunital algebras.However, a class of examples are also presented, for which the natural expectation in the question is wrong and these examples are dealt with by direct inspection.
2000 Mathematics Subject Classification. 13P10 03C05 08B20 12H05 16S10 .
Exploring the active components and potential mechanisms of Qingre Liangxue Decoction in the treatment of psoriasis vulgaris with blood-heat syndrome based on UHPLC-Q-exactive Orbitrap/MS and network pharmacology
Authors: Liu Jiayi , Zhou Xiangnan , Cai Rui , Wu Doudou , Bai Yanping
Published: January 2024

Background
Psoriasis is a common, chronic, inflammatory papulosquamous skin disease which is difficult to be cured. Qingre Liangxue Decoction(QRLXD), originated from the classic formula Xijiao Dihuang Decoction, as a famous classical prescription for the treatment of psoriasis vulgaris (PV) with blood-heat syndrome in the traditional Chinese medicine (TCM), shows good efficacy in clinical practice. Since the mechanism of action is still unclear, this article was aimed to profile the absorbed prototype constituents in rat serum after its oral administration and explore the molecular mechanism of QRLXD using network pharmacology and molecular docking technology.
Methods
First, chemical components of QRLXD absorbed in vivo were comprehensively analyzed by Ultra high performance liquid chromatography-high resolution mass spectrometry (UPLC-Q-Exactive-Orbitrap-MS) method. The Swiss Target Prediction was used to predict corresponding potential target genes of active compounds. Simultaneously, 5 disease databases were used to search and filter psoriasis vulgaris (PV) with blood-heat syndrome targets, and then we constructed a protein-protein interaction (PPI) network. Metascape were utilized for GO and KEGG enrichment analysis. After that, we constructed a network of "QRLXD-compounds-target-pathway-PV" by Cytoscape. Molecular docking simulation was conducted to further refine the drug-target interaction by AutoDock software.
Results
A total of 68 ingredients were identified in vitro and 18 ingredients in vivo, targeting 523 proteins, among which 129 were related to psoriasis. The protein with a higher degree in the PPI network is TNF, ALB, AKT1, STAT3 and EGFR. 1488 GO biological process items and 162 KEGG signal pathways were enriched. GO and KEGG indicated that QRLXD treatment of psoriasis mainly involves protein kinase activity, protein tyrosine kinase activity, phosphotransferase activity, alcohol group as acceptor and other biological processes.The signaling pathways mainly include the pathways in cancer,PI3K-Akt ,MAPK and IL-17 signaling pathways. The main compounds and the target protein had a good binding ability in molecular docking. The molecular docking results revealed that Dibutyl phthalate, Ethyl benzoylformate, Scopoletin, Albiflorin R1, 3-Hydroxycoumarin have higher affinity with TNF, ALB and AKT1.
Conclusion
This study suggests that QRLXD against PV with blood-heat syndrome through multiple components, multiple targets and multiple pathways. This study identified effective compounds of QRLXD in vivo and predicted potential molecular mechanisms of QRLXD in psoriasis treatment, serving as an important basis for the modernization of QRLXD.
Determination of stability and activity of immobilized lipase for transesterification reaction in fluorous solvent and deducing the reaction mechanism by molecular docking study
Authors: Jawale Priyanka , Bhanage Bhalchandra
Published: January 2024

The objective of the present study was to immobilize lipase and use it for transesterification reaction in a fluorous solvent. Polymeric substance tylose (Tyl) was chosen as immobilization support. Tylose immobilized lipase from Candida rugosa (CRL) was subjected to fluorous solvents treatment and was found most stable in perfluorooctane (PFO). The immobilized Tyl-CRL was characterized for protein loading, solvent stability, surface morphology, restoration of secondary structure, and thermal stability. Immobilized CRL was further applied for the synthesis of phenethyl propionate in a fluorous solvent. All reaction parameters were optimized using the response surface method (RSM). Optimized reaction parameters were substrate ratio 1:3, immobilized lipase quantity 30 mg, and temperature 45oC. Prepared biocatalyst was evaluated for recyclability, and it was found that catalytic activity decreased by 77% up to the fifth recycle run. The optimized synthesis protocol scope was checked to synthesize various flavoring esters. The molecular-level interactions of substrate and lipase were studied using docking.
A Physical Organic Strategy to Predict and Interpret Stabilities of Chemical Bonds in Energetic Compounds for the Discovery of Thermal-Resistant Properties
Authors: Liu Haitao , Chen Peng , Huang Xin , Wei Xianfeng
Published: January 2024

The in-depth understanding about the stability of chemical bonds in energetic compounds plays a central role for molecular design and safety-related evaluations. Most energetic compounds contain nitro as explosophores, and nitro cleavage is fundamental for thermal and mechanical stability. However, the quantum chemistry approach to accurately predict energy and temperature properties related to bond stability is challenging, due to the tradeoff between computational costs and deviations. Herein, the bond orders are proposed as accurate and computational-cost efficient descriptors for predicting the chemical bond stability and thermal-resistant properties. The intrinsic bond strength index (IBSI) demonstrates the best prediction for experimental homolytic bond dissociation energies (R2 > 0.996), which is on par with the results from high-precision quantum chemistry methods. The effects from bond connectivity and steric hindrance hierarchy were analyzed to reveal underlying mechanisms. Additionally, the IBSI descriptors are successfully applied to predict the thermal decomposition temperatures of 24 heat-resistant energetic compounds (R2 = 0.995), thus validating the effectiveness for the prediction and interpretation of chemical bond stability in energetic compounds via a physical organic approach.
WITHDRAWN: Object Extraction and Detection UsingU2-Net and YOLOv7
Authors: Bhatt Pooja , Makwana Dhruv , Joshi Akshit , Raol Krishnaraj
Published: January 2024

Object extraction, detection and mattingof background are constitute foundational pursuits incomputer vision across diverse application domains.For serving this purpose we delves into the usageof U2-Net, a deep learning model, and YOLOv7, anadvanced object detection framework. This attempt ofinvestigation of YOLOv7, an advanced object recogni-tion framework, and U2-Net, a deep learning networkwith a focus on noticeable object extraction. In ourstudy, U2-Net and YOLOv7 are trained on mentioneddata-sets, with a focus on their unique contributionsto object extraction and detection and matting. Theprimary focus of this study is the practical exami-nation of their application, with particular attentionto their real-world implementation along with associ-ated challenges. Notably, we emphasize our intentionto reinforce this effort by providing these modelswith domain-specific information through training onpertinent data-sets. We carefully examine how U2-Net and YOLOv7 could improve the object detectionand recognition performance of our study. The mainobjective of this research is to boost imagination inapplications involving computer vision by providingreal-world examples of how these models might beused.
Label Distribution Learning by Utilizing Common and Label-Specific Feature Fusion Space
Authors: Zhang Ziyun , Wang Jing , Geng Xin
Published: January 2024

Label Distribution Learning (LDL) is a novel machine learning paradigm that focuses on the description degrees of labels to a particular instance. Existing LDL algorithms generally learn with the original input space, that is, all features are simply employed in the discrimination processes of all class labels. However, this common-used data representation strategy ignores that each label is supposed to possess some specific characteristics of its own and therefore might have sub-optimal performance. We propose in this paper Label Distribution Learning by utilizing Common and Label-Specific Feature fusion Space. It first partitions all instances by label-value ranking. Second, it constructs label-specific features of each label by conducting clustering analysis on different instance categories. Third, it performs training and testing by querying the clustering results. Comprehensive experiments on several real-world label distribution data sets validate the superiority of our method against other LDL algorithms as well as the effectiveness of label-specific features.