Experimental analysis of matrix moveable oil saturation in tight sandstone reservoirs of the south Ordos Basin,China

Tight oil reservoirs in the south Ordos Basin are characterized by fractured,heterogeneous oil-bearing strata(an oil saturation of less than 55%on average),normal pressure(0.8±)and extra-low permeability(less than 0.3 mD).In the Chang 8 tight sandstone reservoir in Honghe oilfield,micro-and nanopores,especially those with a pore-throat radius of less than 1 mm,account for more than 90%.Fluid flow in the matrix is non-linear and crude oil flow rates are very low under normal pressure gradients.An improved understanding of oil mobility in a tight matrix is key to further development of normalpressure tight-oil resources in the continental basin.In this study,constant-velocity and high-pressure mercury injection experiments were conducted using samples of typical tight sandstone cores obtained from the south of Ordos Basin.A new method for reconstructing the full-scale pore-throat distribution characteristics of tight sandstone reservoirs was established successfully,based on which multistage centrifugal tests,tests of low-pressure differential displacement of oil by water,and nuclear magnetic resonance tests were conducted in order to obtain the distribution characteristics of moveable fluid in different pores.The moveable oil saturation(MOS)and degree of oil recovery(i.e.ratio of accumulative oil production to producing geologic reserves)of the core samples under different differential pressures for displacement were determined.As for the tight oil reservoirs in the south Ordos Basin,the moveable fluids are mainly stored in sub-micron(0.10-0.5 mm)pores.For Type I reservoirs(k>0.1 mD),the volume percentage of moveable fluid in pores with a radius larger than 0.5 mm is relatively high(greater than 40%).The degree of oil recovery of water flooding serves as the basis for forecasting recoverable reserves for tight oil reservoirs.Recoverable reserves under water flooding,mainly occur in pores with a radius greater than 0.5 mm.The contribution of Type I reserves to oil production is observed to be greater than 60%,and the degree of oil recovery reaches up to 17.1%.These results help improve our understanding on the evaluation and classification of Chang 8 tight sandstone reservoirs in Honghe oilfield and serve as theoretical basis for pilot tests to explore effective injection media and development methods to improve the matrix-driven pressure differences and displacement efficiency for oil.

Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Research on Site Planning of Mobile Communication Network

In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is proposed.Firstly,based on practical needs,mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives,coverage objectives,and quality objectives.Then,a multi-objective optimization model was established by combining threshold and traffic volume constraints.In order to reduce the time complexity of optimization,a non-dominated sorting genetic algorithm(NSGA)is used to solve the multi-objective optimization problem of site planning.Finally,a strategy for clustering and optimizing weak coverage areas was proposed.In order to avoid redundant neighborhood retrieval during cluster expansion,the Fast Density-Based Spatial Clustering of Applications with Noise(FDBSCAN)clustering method was adopted.With different sub-objectives as the main objectives,this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations,as well as relevant site planning maps,and provided three planning schemes for different main objectives.The simulation results show that the traffic coverage of the three station planning schemes is above 90%.The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.

Patterns and Distribution of Bone Tumors among Patients at Kumi Orthopaedic Center: A Retrospective Study

Introduction: Primary bone tumors, although rare, are an important rising cause of morbidity and mortality in Africa. Late presentation, delayed diagnosis, and failure to obtain proper management are important causes of loss of limbs and lives especially in Sub-Saharan Africa. Existing data on primary bone tumors in Uganda for the various regional levels is limited. This study aimed to determine the patterns and distributions of primary bone tumors especially in rural Uganda. Methods: This was a retrospective study carried out at Kumi Orthopaedic Center from 2012 to 2023. Patients’ information regarding the histological type of bone tumor, age, sex, and tumor site was obtained from their files. Results: A total of 115 bone tumors were seen over the study period. The mean age of patients was 30 years ± 20.1 years (range: 1 to 80 years). The majority of the patients were males 67 (58.3%) and females were 48 (42.6%). Benign tumors accounted for 66 (57.4%) of the tumors while malignant tumors were 49 (42.6%). Most tumors (both benign and malignant) were found in patients between the ages of 11 to 20 years 46 (40.1%) followed by those between 21 to 30 years 15 (13.2%) and the least were in the elderly aged 71 to 80 years 4 (3.5%). The commonest benign tumors were osteochondroma 14 (21.2%), cysts 11 (16.7%), hemangiomas 9 (13.6%), and fibrous dysplasia of the bone 8 (12.1%). The commonest malignant tumors were plasmacytoma 10 (20.4%) followed by metastases 8 (16.3%), osteosarcomas 7 (14.3%), lymphoma 5 (10.2%), and fibrosarcoma 4 (8.2%). Conclusion: Uganda shares some similar epidemiological characteristics of primary bone tumors with other countries;however, this study identified some peculiar differences. Population-based studies are required to obtain more accurate epidemiological data to improve patient diagnosis and treatment.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

Obituary for Donald S.Coffey:Remembering a pioneer in the field of prostate cancer research.(1932-2017)

It is with the deepest sadness that we publish the following obituary for Donald S.Coffey,an Asian Journal of Urology(AJU)respected author.Dr.Coffey was invited to write a paper for the inaugural issue of AJU in 2014.Despite he was 82 years old,he accepted our invitation and contributed a thoughtful perspective article.

Global Strategies to Combat Antimicrobial Resistance: A One Health Perspective

Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies to reduce and combat AMR is critical. Purpose: This study aimed to highlight some global strategies that can be implemented to address AMR using a One Health approach. Methods: This study employed a narrative review design that included studies published from January 2002 to July 2023. The study searched for literature on AMR and antimicrobial stewardship (AMS) in PubMed and Google Scholar using the 2020 PRISMA guidelines. Results: This study reveals that AMR remains a significant global public health problem. Its severity has been markedly exacerbated by inappropriate use of antimicrobials in humans, animals, and the broader ecological environment. Several strategies have been developed to address AMR, including the Global Action Plan (GAP), National Action Plans (NAPs), AMS programs, and implementation of the AWaRe classification of antimicrobials. These strategies also involve strengthening surveillance of antimicrobial consumption and resistance, encouraging the development of new antimicrobials, and enhancing regulations around antimicrobial prescribing, dispensing, and usage. Additional measures include promoting global partnerships, combating substandard and falsified antimicrobials, advocating for vaccinations, sanitation, hygiene and biosecurity, as well as exploring alternatives to antimicrobials. However, the implementation of these strategies faces various challenges. These challenges include low awareness and knowledge of AMR, a shortage of human resources and capacity building for AMR and AMS, in adequate funding for AMR and AMS initiatives, limited laboratory capacities for surveillance, behavioural change issues, and ineffective leadership and multidisciplinary teams. Conclusion: In conclusion, this study established that AMR is prevalent among humans, animals, and the environment. Successfully addressing AMR calls for a collaborative, multifaceted One Health approach. Despite this, some gaps remain effectively implementing strategies currently recommended to combat AMR. As a result, it is essential to reinforce the strategies that are deployed to counter AMR across the human, animal, and environmental sectors.

Corrigendum to “Triple test cross analysis for seed yield and its components in sesame under water stress conditions” [Oil Crop Sci. 7(2) (2022) 71–79]

The author regrets that the affiliated institution of Ibthal S.ElDemardash should be“Department of Genetics and Cytology,National Research Centre,Cairo,Egypt”instead of“Department of Genetics and Cytology,National Research Center,Cairo,Egypt”.The author would like to apologize for any inconvenience caused.

Possibility of Solid Hydrometeor Growth Zone Identification Using Radar Spectrum Width

In this study,the correlation between simulated and measured radar velocity spectrum width(σ_(v))is investigated.The results show that the dendrites growth zones(DGZs)and needles growth zones(NGZs)mostly contain dendrites(DN)and needles(NE),respectively.Clearσ_(v) zones(1.1<σ_(v)(m s^(-1))<1.3 and 0.3<σ_(v)(m s^(-1))<0.7 for the DGZ and NGZ,respectively)could be identified in the case studies(27 and 28 February 2016)near altitudes corresponding to temperatures of–15°C and–5°C,according to the Japan Meteorological Agency and mesoscale model reanalysis data.Oblate particles with diverse particle shapes were observed in the DGZ withσ_(v)>1.2 m s^(-1),a differential reflectivity(ZDR)higher than 0 dB,and a cross-correlation coefficient(ρhv)less than 0.96.In contrast,prolate particles with relatively uniform shapes were observed in the NGZ withσ_(v)<0.6 m s^(-1),a ZDR less than 0 dB,andρhv higher than 0.97.The simulation results show that the DN exhibited a largerσ_(v) compared to the NE,and this observedσ_(v) was strongly dependent on the wind fluctuations(v’)due to turbulence or wind shear.In contrast,the NE exhibited a significantly smallσ_(v)~0.55 m s^(-1),which converges irrespective of v’.In addition,a strong correlation between the measuredσ_(v) values at five radar elevation angles(θ=6.2°,9.1°,13.1°,19°,and 80°)and those simulated in this study confirmed the significance of the analysis results.

Research on Multi-Blockchain Electronic Archives Sharing Model

The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption.Aiming at the problem of dynamic permission management in common access control methods,a new access control method based on smart contract under blockchain is proposed,which improves the intelligence level under blockchain technology.Firstly,the Internet attribute access control model based on smart contract is established.For the dynamic access of heterogeneous devices,the management contract,permission judgment contract and access control contract are designed;Secondly,the access object credit evaluation algorithm based on particle swarm optimization radial basis function(PSO-RBF)neural network is used to dynamically generate the access node credit threshold combined with the access policy,so as to realize the intelligent access right management method.Finally,combined with the abovemodels and algorithms,the workflow of electronic archives sharing and utilization model of multi blockchain is constructed.The experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks,and the secure access control of sharing and utilization is feasible,secure and effective.

Research progress on the effect of oocyte smooth endoplasmic reticulum clusters on early embryo development and pregnancy outcome

With the clinical development and application of intracytoplasmic sperm injection(ICSI)technology in human assisted reproduction,the influence of oocyte quality on embryo development has been paid more and more attention.So far,there have been many reports on oocyte morphology affecting embryo development.It has been found in some works that the appearance of smooth endoplasmic reticulum clusters(SERC)in oocytes may affect the fertilization and embryo development of oocytes.However,with the increasing reports of SERC-containing oocytes obtained by in vitro fertilization and healthy offspring in recent years,there is still some controversy on whether to continue to use SERC-containing oocytes for the following assisted reproductive therapy in clinical practice.Based on this,this review aims to review the research progress of SERC in oocytes in recent years.

Two missense STK11 gene variations impaired LKB1/adenosine monophosphate-activated protein kinase signaling in Peutz-Jeghers syndrome

BACKGROUND Peutz-Jeghers syndrome(PJS)is a rare hereditary neoplastic disorder mainly associated with serine/threonine kinase 11(STK11/LKB1)gene mutations.Preimplantation genetic testing can protect a patient’s offspring from mutated genes;however,some variations in this gene have been interpreted as variants of uncertain significance(VUS),which complicate reproductive decision-making in genetic counseling.AIM To identify the pathogenicity of two missense variants and provide clinical guidance.METHODS Whole exome gene sequencing and Sanger sequencing were performed on the peripheral blood of patients with PJS treated at the Reproductive and Genetic Hospital of Citic-Xiangya.Software was employed to predict the protein structure,conservation,and pathogenicity of the two missense variation sites in patients with PJS.Additionally,plasmids were constructed and transfected into HeLa cells to observe cell growth.The differences in signal pathway expression between the variant group and the wild-type group were compared using western blot and immunohistochemistry.Statistical analysis was performed using one-way analysis of variance.P<0.05 was considered statistically significant.RESULTS We identified two missense STK11 gene VUS[c.889A>G(p.Arg297Gly)and c.733C>T(p.Leu245Phe)]in 9 unrelated PJS families who were seeking reproductive assistance.The two missense VUS were located in the catalytic domain of serine/threonine kinase,which is a key structure of the liver kinase B1(LKB1)protein.In vitro experiments showed that the phosphorylation levels of adenosine monophosphate-activated protein kinase(AMPK)at Thr172 and LKB1 at Ser428 were significantly higher in transfected variation-type cells than in wild-type cells.In addition,the two missense STK11 variants promoted the proliferation of HeLa cells.Subsequent immunohistochemical analysis showed that phosphorylated-AMPK(Thr172)expression was significantly lower in gastric,colonic,and uterine polyps from PJS patients with missense variations than in non-PJS patients.Our findings indicate that these two missense STK11 variants are likely pathogenic and inactivate the STK11 gene,causing it to lose its function of regulating downstream phosphorylated-AMPK(Thr172),which may lead to the development of PJS.The identification of the pathogenic mutations in these two clinically characterized PJS patients has been helpful in guiding them toward the most appropriate mode of pregnancy assistance.CONCLUSION These two missense variants can be interpreted as likely pathogenic variants that mediated the onset of PJS in the two patients.These findings not only offer insights for clinical decision-making,but also serve as a foundation for further research and reanalysis of missense VUS in rare diseases.

Heavy metal pollution of river water and eco-friendly remediation using potent microalgal species

Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities,maintenance of sewage/effluent treatment plants,and dumping of solid waste on river banks.This study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South India.Water samples from 27 locations were collected and analyzed for 12 elements,including Ba,B,and Al,as well as heavy metals such as Pb,Cr,Mn,Fe,Co,Ni,Cu,Zn,and Cd.The samples showed levels of these elements that exceeded World Health Organization recommendations.Pearson correlation analysis revealed the inter-dependency among elements,and the contribution of each element based on factor loadings showed its percentage contribution compared to others.Water samples from six significant locations were chosen for remediation with three algae:Chlorella vulgaris,Scenedesmus dimorphus,and Phormedium sp.The uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d,effectively removing heavy metals from the river water.The increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period,followed by a declining stage after the incubation period.The scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological patterns.This study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure,making the process practicable in the field on a large scale in the near future.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Layered Coded Cache Placement and Cooperative Delivery with Sharing Links in Satellite-Terrestrial Integrated Networks

Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.

Optimization Study of Active-Passive Heating System Parameters in Village Houses in the Southern Xinjiang Province

Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang,a combined active-passive heating system was proposed,and the simulation software was used to optimize the parameters of the system,according to the parameters obtained from the optimization,a test platform was built and winter heating test was carried out.The simulation results showed that the thickness of the air layer of 75 mm,the total area of the vent holes of 0.24 m^(2),and the thickness of the insulation layer of 120 mm were the optimal construction for the passive part;solar collector area of 28 m^(2),hot water storage tank volume of 1.4 m^(3),mass flow rate of 800 kg/h on the collector side,mass flow rate of 400 kg/h on the heat exchanger side,and output power of auxiliary heat source of 5∼9 kWwere the optimal constructions for active heating system.Test results showed that during the heating period,the system could provide sufficient heat to the room under different heating modes,and the indoor temperature reached over 18°C,which met the heating demand.The economic and environmental benefits of the system were analyzed,and the economic benefits of the systemwere better than coal-fired heating,and the CO_(2) emissionswere reduced by 3,292.25 kg compared with coalfiredheating.The results of the study showed that the combinedactive-passiveheating systemcouldeffectively solve the heating problems existing in rural buildings in Southern Xinjiang,and it also laid the theoretical foundation for the popularization of the combined heating systems.

Shock Initiation Experiments with Modeling on a DNAN Based MeltCast Insensitive Explosive

2,4-dinitroanisole(DNAN)is a good replacement for 2,4,6-trinitrotoluene(TNT)in melt-cast explosives due to its superior insensitivity.With the increasing use of DNAN-based melt-cast explosives,the prediction of reaction violence and hazard assessment of the explosives subjected to shock is of great significance.This study investigated the shock initiation characteristics for a DNAN-based melt-cast explosive,DHFA,using the one-dimensional Lagrangian apparatus.The embedded manganin gauges in the apparatus record the pressure histories at four Lagrangian positions and show that shock-todetonation transition in DHFA needs a high input shock pressure.The experimental data are analyzed to calibrate the Ignition and Growth model.The calibration is performed using an objective function based on both pressure history and the arrival time of shock.Good agreement between experimental and calculated pressure histories indicates the high accuracy of the calibrated parameters with the optimization method.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

Glial response in the midcingulate cortex in Huntington’s disease

Huntington’s disease(HD)is a genetic disease characterized by the progressive degeneration of the striatum and cortex.Patients can present with a variety of symptoms that can broadly be classified into motor symptoms,inclusive of choreatic movements and rigidity,mood and psychiatric symptoms,such as depression and apathy,and cognitive symptoms,such as cognitive decline.The causal mutation underlying HD results from an expansion of a CAG repeat sequence on the IT15 gene,resulting in the formation and accumulation of a mutant huntingtin protein.