Permeability Logging:A Breakthrough from 0 to1

On March 3,2024,the prototype permeability logging instrument independently developed in China successfully completed its first downhole test in Ren 91 standard well in PetroChina Huabei Oilfield.In the open hole section at a depth of 3925 metres and at a temperature of 148℃,the device collected high-quality permeability logging data.This marks a key technological breakthrough from 0 to 1 in permeability logging,and lays the foundation for the next step in developing a complete set of permeability logging equipment.

Three decades and beyond:Breeding,biotech breakthroughs and future of China's GM insect-resistant cotton

Cotton(Gossypium spp.)is a pivotal crop in the global textile industry,providing essential natural fibers.Additionally,cottonseed offers significant value as a source of oil and as feed for livestock(Huang et al.2021;Wen et al.2023).The sector,dependent on cotton,features a comprehensive value chain extending from the processing of fibers to the production of finished textiles,and it employs tens of millions of individuals(Dorward et al.1970).

Class Imbalanced Problem:Taxonomy,Open Challenges,Applications and State-of-the-Art Solutions

The study of machine learning has revealed that it can unleash new applications in a variety of disciplines.Many limitations limit their expressiveness,and researchers are working to overcome them to fully exploit the power of data-driven machine learning(ML)and deep learning(DL)techniques.The data imbalance presents major hurdles for classification and prediction problems in machine learning,restricting data analytics and acquiring relevant insights in practically all real-world research domains.In visual learning,network information security,failure prediction,digital marketing,healthcare,and a variety of other domains,raw data suffers from a biased data distribution of one class over the other.This article aims to present a taxonomy of the approaches for handling imbalanced data problems and their comparative study on the classification metrics and their application areas.We have explored very recent trends of techniques employed for solutions to class imbalance problems in datasets and have also discussed their limitations.This article has also identified open challenges for further research in the direction of class data imbalance.

Early identification of scientific breakthroughs through outlier analysis based on research entities

Purpose:To address the“anomalies”that occur when scientific breakthroughs emerge,this study focuses on identifying early signs and nascent stages of breakthrough innovations from the perspective of outliers,aiming to achieve early identification of scientific breakthroughs in papers.Design/methodology/approach:This study utilizes semantic technology to extract research entities from the titles and abstracts of papers to represent each paper’s research content.Outlier detection methods are then employed to measure and analyze the anomalies in breakthrough papers during their early stages.The development and evolution process are traced using literature time tags.Finally,a case study is conducted using the key publications of the 2021 Nobel Prize laureates in Physiology or Medicine.Findings:Through manual analysis of all identified outlier papers,the effectiveness of the proposed method for early identifying potential scientific breakthroughs is verified.Research limitations:The study’s applicability has only been empirically tested in the biomedical field.More data from various fields are needed to validate the robustness and generalizability of the method.Practical implications:This study provides a valuable supplement to current methods for early identification of scientific breakthroughs,effectively supporting technological intelligence decision-making and services.Originality/value:The study introduces a novel approach to early identification of scientific breakthroughs by leveraging outlier analysis of research entities,offering a more sensitive,precise,and fine-grained alternative method compared to traditional citation-based evaluations,which enhances the ability to identify nascent breakthrough innovations.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Multifaceted functions of Drp1 in hypoxia/ischemia- induced mitochondrial quality imbalance: from regulatory mechanism to targeted therapeutic strategy

Hypoxic-ischemic injury is a common pathological dysfunction in clinical settings.Mitochondria are sensitive organelles that are readily damaged following ischemia and hypoxia.Dynamin-related protein 1(Drp1)regulates mitochondrial quality and cellular functions via its oligomeric changes and multiple modifications,which plays a role in mediating the induction of multiple organ damage during hypoxic-ischemic injury.However,there is active controversy and gaps in knowledge regarding the modification,protein interaction,and functions of Drp1,which both hinder and promote development of Drp1 as a novel therapeutic target.Here,we summarize recent findings on the oligomeric changes,modification types,and protein interactions of Drp1 in various hypoxic-ischemic diseases,as well as the Drp1-mediated regulation of mitochondrial quality and cell functions following ischemia and hypoxia.Additionally,potential clinical translation prospects for targeting Drp1 are discussed.This review provides new ideas and targets for proactive interventions on multiple organ damage induced by various hypoxic-ischemic diseases.

Big Fish, Big Market Controversies and breakthroughs in salmon farming in China

On August 28,salmon industry insiders and consumers shared insights and analysis on the salmon market and consumption at the 7th Global Salmon Industry Development Summit.An important component of the 18th Shanghai International Fisheries Expo,the event attracted more than100 salmon farming companies from around the globe to exchange views on the current situation and future development of the industry.

Blasts Of Progress Xinjiang Bayi Iron and Steel's carbon reduction breakthroughs

Facing dual challenges of climate change and energy crisis,the global iron and steel industry has been seeking to balance efficiency with energy conservation and environmental protection.Could the realities of blast furnace operation possibly align with low-carbon circular development within China's iron and steel industry?Hydrogen-rich carbon circulating oxygen blast furnace (HyCROF)technology is gaining traction in the Chinese metallurgical industry.

Familial hypercholesterolemia:Current limitations and future breakthroughs

Familial hypercholesterolemia(FH)is characterized by elevated low-density lipoprotein cholesterol levels due to genetic mutations,presenting with xanthomas,corneal arch,and severe cardiovascular diseases.Early identification,diagnosis,and treatment are crucial to prevent severe complications like acute myocardial infarction.Statins are the primary treatment,supplemented by Ezetimibe and proprotein convertase subtilisin/kexin type 9 inhibitors,though their effectiveness can be limited in severe cases.Over 90%of FH cases remain undiagnosed,and current treatments are often inadequate,underscoring the need for improved diagnostic and management systems.Future strategies include advancements in gene testing,precision medicine,and novel drugs,along with gene therapy approaches like AAV-mediated gene therapy and clustered regularly interspaced short palindromic repeats.Lifestyle modifications,including health education,dietary control,and regular exercise,are essential for managing FH and preventing related diseases.Research into FH-related gene mutations,especially LDLR,is critical for accurate diagnosis and effective treatment.

Suggestions on Improving China’s Breakthrough Therapy Designation System

Objective To analyze the characteristics of breakthrough therapy designation(BTD)and its implementation in China,and to provide reference for the optimization of BTD system.Methods A comparative research method was used to study the content and implementation effect of BTD system in China and the relevant policies and implementation of the same procedures of drug regulatory authorities in the United States,Japan and the European Union.Then,the differences in policies and implementation results among these countries were analyzed to provide suggestions for the implementation and optimization of this system in China.Results and Conclusion China’s BTD system is implemented late and a small number of drugs has been approved.At the same time,there are problems such as insufficient guidance and communication from the agency to applicants,a broad application condition,single review mode,and lack of full-time personnel.Both the agencies and the applicants have limited experience due to the short implementation time of BTD system in China.There are still some problems despite we have learned a lot from the experience of other drug regulatory agencies.Therefore,based on our national conditions,we should strengthen the guidance of evaluation agency to applicants,optimize the eligibility criteria of BTD system,introduce the rolling review,and increase the number of professional liaisons,which can accelerate the development and marketing process of drugs with obvious clinical value,and finally to address unmet medical need.

A Review of the Research on the Influencing Factors of Enterprise Key Core Technology Breakthrough

The breakthrough of key core technology is a theoretical and practical strategic issue to realize the rise of the country.This paper analyzes the concept of key core technology and its breakthrough and analyzes its characteristics.Based on the perspective of participants,this paper summarizes the influencing factors of key core technology breakthroughs in enterprises:internal and external enterprises,universities and scientific research institutions,and government.This paper expands the relevant research on key core technology breakthroughs and provides inspiration for enterprises to carry out key core technology research and breakthrough practices.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

Model Development and Adaptive Imbalance Vibration Control of Magnetic Suspended System

A system model is developed to describe the translational and rotational motion of an active-magnetic-bearing-suspended rigid rotor in a single-gimbal control moment gyro onboard a rigid satellite. This model strictly reflects the motion characteristics of the rotor by considering the dynamic and static imbalance as well as the coupling between the gimbal＇s and the rotor＇s motion on a satellite platform. Adaptive auto-centering control is carefully constructed for the rotor with unknown dynamic and static imbalance. The rotor makes its rotation about the principal axis of inertia through identifying the small rotational angles between the geometric axis and the principal axis as well as the displacements from the geometric center to the mass center so as to tune a stabilizing controller composed of a decentralized PD controller with cross-axis proportional gains and high- and low-pass filters. The main disturbance in the wheel spinning can thereby be completely removed and the vibration acting on the satellite attenuated.

Co-effect of Demand-control-support Model and Effort-reward Imbalance Model on Depression Risk Estimation in Humans: Findings from Henan Province of China

Objective To investigate the co-effect of Demand-control-support （DCS） model and Effort-reward Imbalance （ERI） model on the risk estimation of depression in humans in comparison with the effects when they are used respectively. Methods A total of 3 632 males and 1 706 females from 13 factories and companies in Henan province were recruited in this cross-sectional study. Perceived job stress was evaluated with the Job Content Questionnaire and Effort-Reward Imbalance Questionnaire （Chinese version）. Depressive symptoms were assessed by using the Center for Epidemiological Studies Depression Scale （CES-D）. Results DC （demands/job control ratio） and ERI were shown to be independently associated with depressive symptoms. The outcome of low social support and overcommitment were similar. High DC and low social support （SS）, high ERI and high overcommitment, and high DC and high ERI posed greater risks of depressive symptoms than each of them did alone. ERI model and SS model seem to be effective in estimating the risk of depressive symptoms if they are used respectively. Conclusion The DC had better performance when it was used in combination with low SS. The effect on physical demands was better than on psychological demands. The combination of DCS and ERI models could improve the risk estimate of depressive symptoms in humans.

Endocrine milieu and erectile dysfunction： is oestradio-testosterone imbalance, a risk factor in the elderly？

Oestrogens are not exclusive to the female gender but occur in moderate circulating levels of 25-70 pg ml^-1 in men, compared to 44- 153 pg ml^-1 in women. Arising from aromatisation of testosterone （T）, oestrogen is considered to have many opposing physiological functions and the progressive T decline in the aging male is associated with relative and/or absolute increase in serum oestradiol （E2）. Sexual disinterest and erectile dysfunction （ED） in the elderly may well be due to pathophysiological E2-T imbalance; the altered hormonal ratio may also explain the higher incidence of ED in hyperestrogenism or following exposure to environmental/plant oestrogens.

Research on Mechanisms of Steam Breakthrough and Profile Control Design for Steam Soaking Well

Scaled physical model tests for steam breakthrough were conducted based on the analysis of mechanisms and influence factors of steam breakthrough. Physical simulation results showed that at the initial steam breakthrough, preferential flow channels were formed in narrow sand packs and most residual oil left in these channels was immobile. This shortened the steam breakthrough time of follow-up steam flooding and decreased the increment of oil recovery efficiency. Steam breakthrough occurred easily for a smaller producer-injector spacing, and a bigger difference in physical properties between fluids and rock. Steam breakthrough is more likely to occur at a larger formation permeability （k）, greater steam displacement velocity （u） and smaller producer-injector spacing （L）. Steam breakthrough time is a function of the parameter group （uk/L）, i.e. tb=3.2151 （uk/L）^-0.5142. A non piston-like displacement model was built based on steam breakthrough observation for a steam stimulated well in the Jinglou Oilfield, Henan Oilfield Company. The steam volume swept in different directions could be obtained from inter-well permeability capacity and breakthrough angle, and the steam swept pore volume （SSPV） was also determined. Numerical simulation showed that steam sweep efficiency reached its peak value when a slug of profile control agent （slug size 10%-15% SSPV） was set at one half of the inter-well spacing. Field test with 12.5% SSPV of profile control agents in the Jinglou Oilfield achieved success in sealing breakthrough channels and good production performance of adjacent producers.

Revisiting normal perfusion pressure breakthrough in light of hemorrhage-induced vasospasm

Cerebral arteriovenous malformations(AVMs) have abnormally enlarged arteries and veins prone to spontaneous hemorrhage.Immediately following surgical excision of a cerebral AVM,even normal brain tissue surrounding the lesion is subject to hemorrhage,a phenomenon termed normal perfusion pressure breakthrough(NPPB) syndrome.According to this theory,arteries supplying cerebral AVMs become dilated and lose their capacity to dilate or constrict to autoregulate pressure.Acutely after removal of a cerebral AVM,excessive blood pressure in these arterial feeders can cause normal brain tissue to bleed.However,this theory remains controversial.We present a patient with a cerebral AVM that demonstrated cerebrovascular reactivity and argues against an assumption underlying the theory of NPPB syndrome.

Baseline HBV DNA level is the most important factor associated with virologic breakthrough in chronic hepatitis B treated with lamivudine

AIM: To identify the factors associated with virologic breakthrough and to select a subgroup of patients who respond well to lamivudine without developing virologic breakthrough (VBT). METHODS: Of 79 patients who had received lamivudine therapy for 9-57 mo, 34 were HBeAg-positive and 45 were HBeAg-negative, 24 developed virologic breakthrough and 55 did not. Clinical and virologic factors were compared between the two groups. RESULTS: The median duration of therapy was 25 (9-57) mo. Virologic breakthrough was defined as a > 1 log HBV DNA increase following initial suppression. When several factors, including gender, duration of infection, baseline HBV DNA, and baseline ALT in HBeAg-positive chronic hepatitis patients were analyzed by logistic regression, the most important predictor of virologic breakthrough was the baseline HBV DNA (r2 = 0.12, P < 0.05). When HBeAg-postitive chronic hepatitis patients were divided into two groups by a point of 6.6 log HBV DNA, the incidence of virologic breakthough between two groups was significantly different. CONCLUSION: Lamivudine may remain an effective first line therapy for those HBeAg-positive patients with a baseline HBV DNA < 6.6 log10 copies/mL.

High-performance channel estimation and compensation scheme for OFDMreceivers with IQ imbalances

A pilot pattern across two orthogonal frequency division multiplexing OFDM symbols with a special structure is designed for the channel estimation of OFDM systems with inphase and quadrature IQ imbalances at the receiver.A high-efficiency time-domain TD least square LS channel estimator and a low-complexity frequency-domain Gaussian elimination GE equalizer are proposed to eliminate IQ distortion.The former estimator can significantly suppress channel noise by a factor N/L＋1 over the existing frequency-domain FD LS where N and L＋1 are the total number of subcarriers and the length of cyclic prefix and the proposed GE requires only 2N complex multiplications per OFDM symbol.Simulation results show that by exploiting the TD property of the channel the proposed TD-LS channel estimator obtains a significant signal-to-noise ratio gain over the existing FD-LS one whereas the proposed low-complexity GE compensation achieves the same bit error rate BER performance as the existing LS one.