Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures a...Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.展开更多
Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often resu...Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.展开更多
Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(...Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.展开更多
Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has alway...Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.展开更多
The present work compares microstructures of hot work steels made by different processes, that is, by sprayforming,by casting, and a commercially supplied H13 steel. Material benefits are recognized by sprayforming ho...The present work compares microstructures of hot work steels made by different processes, that is, by sprayforming,by casting, and a commercially supplied H13 steel. Material benefits are recognized by sprayforming hot working tools such as die inserts for hot forging. The sprayformed hot work steels present a fine and homogeneous microstructure,which implies that, at a similar toughness level, the sprayformed steel can be higher alloyed, so that the thermal fatigue and wear resistance at elevated temperatures can be improved. A series of steels with higher vanadium content than commercial hot work steels are developed. There are no segregation and carbide network problems usually encountered in conventional ingot/forging processed high-vanadium steels. Microstructure and hardness of the new sprayformed steels are studied under different heat treatment conditions. It is justified that these sprayformed steels can be directly used for tooling without high temperature hardening. Sprayforming the tool steels onto a precision ceramic mould is demonstrated to extend the technoeconomical benefits, so that a net shape production tool can be rapidly made.Features of the rapid tooling process are also discussed.展开更多
AIM:To study whether selected bacterial 16S ribosomal RNA(rRNA)gene phylotypes are capable of disting- uishing irritable bowel syndrome(IBS). METHODS:The faecal microbiota of twenty volunteers with IBS,subdivided into...AIM:To study whether selected bacterial 16S ribosomal RNA(rRNA)gene phylotypes are capable of disting- uishing irritable bowel syndrome(IBS). METHODS:The faecal microbiota of twenty volunteers with IBS,subdivided into eight diarrhoea-predominant (IBS-D),eight constipation-predominant(IBS-C)and four mixed symptom-subtype(IBS-M)IBS patients,and fifteen control subjects,were analysed at three time-points with a set of fourteen quantitative real-timepolymerase chain reaction assays.All assays targeted 16S rRNA gene phylotypes putatively associated with IBS,based on 16S rRNA gene library sequence analysis. The target phylotypes were affiliated with Actinobac-teria,Bacteroidetes and Firmicutes.Eight of the target phylotypes had less than 95%similarity to cultured bacterial species according to their 16S rRNA gene sequence.The data analyses were made with repeated-measures ANCOVA-type modelling of the data and principle component analysis(PCA)with linear mixed-effects models applied to the principal component scores. RESULTS:Bacterial phylotypes Clostridium cocleatum 88%,Clostridium thermosuccinogenes 85%,Coprobacillus catenaformis 91%,Ruminococcus bromii-like, Ruminococcus torques 91%,and R.torques 93%were detected from all samples analysed.A multivariate analysis of the relative quantities of all 14 bacterial 16S rRNA gene phylotypes suggested that the intestinal microbiota of the IBS-D patients differed from other sample groups.The PCA on the first principal component(PC1),explaining 30.36%of the observed variation in the IBS-D patient group,was significantly altered from all other sample groups(IBS-D vs control, P=0.01;IBS-D vs IBS-M,P=0.00;IBS-D vs IBS-C, P=0.05).Significant differences were also observed in the levels of distinct phylotypes using relative values in proportion to the total amount of bacteria.A phy- lotype with 85%similarity to C.thermosuccinogenes was quantified in significantly different quantities among the IBS-D and control subjects(-4.08±0.90 vs -3.33±1.16,P=0.04)and IBS-D and IBS-M subjects (-4.08±0.90 vs-3.08±1.38,P=0.05).Furthermore,a phylotype with 94%similarity to R.torques was more prevalent in IBS-D patients'intestinal micro- biota than in that of control subjects(-2.43±1.49 vs -4.02±1.63,P=0.01).A phylotype with 93%simi- larity to R.torques was associated with control sam- ples when compared with IBS-M(-2.41±0.53 vs -2.92±0.56,P=0.00).Additionally,a R.bromii-like phylotype was associated with IBS-C patients in com- parison to control subjects(-1.61±1.83 vs-3.69± 2.42,P=0.01).All of the above mentioned phylotype specific alterations were independent of the effect of time. CONCLUSION:Significant phylotype level alterationsin the intestinal microbiotas of IBS patients were observed,further emphasizing the possible contribution of the gastrointestinal microbiota in IBS.展开更多
Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.展开更多
With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of...With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).展开更多
Effects of the weld microstructure and inclusions on brittle fracture initiation are investigated in a thermally aged ferritic high-nickel weld of a reactor pressure vessel head from a decommissioned nuclear power pla...Effects of the weld microstructure and inclusions on brittle fracture initiation are investigated in a thermally aged ferritic high-nickel weld of a reactor pressure vessel head from a decommissioned nuclear power plant.As-welded and reheated regions mainly consist of acicular and polygonal ferrite,respectively.Fractographic examination of Charpy V-notch impact toughness specimens reveals large inclusions(0.5-2.5μm)at the brittle fracture primary initiation sites.High impact energies were measured for the specimens in which brittle fracture was initiated from a small inclusion or an inclusion away from the V-notch.The density,geometry,and chemical composition of the primary initiation inclusions were investigated.A brittle fracture crack initiates as a microcrack either within the multiphase oxide inclusions or from the debonded interfaces between the uncracked inclusions and weld metal matrix.Primary fracture sites can be determined in all the specimens tested in the lower part of the transition curve at and below the 41-J reference impact toughness energy but not above the mentioned value because of the changes in the fracture mechanism and resulting changes in the fracture appearance.展开更多
AIM： To investigate the pathophysiology of irritable bowel syndrome （IBS） by comparing the global mucosal metabolic profiles of IBS patients with those of healthy controls. METHODS： Fifteen IBS patients fulfilling...AIM： To investigate the pathophysiology of irritable bowel syndrome （IBS） by comparing the global mucosal metabolic profiles of IBS patients with those of healthy controls. METHODS： Fifteen IBS patients fulfilling the Rome II criteria, and nine healthy volunteers were included in the study. A combined lipidomics （UPLC/MS） and metabolomics （GC × GC-TOF） approach was used to achieve global metabolic profiles of mucosal biopsies from the ascending colon. RESULTS： Overall, lipid levels were elevated in patients with IBS. The most significant upregulation was seen for pro-inflammatory lysophosphatidylcholines. Other lipid groups that were significantly upregulated in IBS patients were lipotoxic ceramides, glycosphingolipids, and di-and triacylglycerols. Among the meo tabolites, the cyclic ester 2（3H）-furanone was almost 14-fold upregulated in IBS patients compared to healthy subjects （P = 0.03）. CONCLUSION： IBS mucosa is characterised by a distinct pro-inflammatory and lipotoxic metabolic profile. Especially, there was an increase in several lipid species such as lysophospholipids and ceramides.展开更多
In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples fr...In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.展开更多
AIM:To investigate the effects of four probiotic bacteria and their combination on human mast cell gene expression using microarray analysis.METHODS:Human peripheral-blood-derived mast cells were stimulated with Lacto...AIM:To investigate the effects of four probiotic bacteria and their combination on human mast cell gene expression using microarray analysis.METHODS:Human peripheral-blood-derived mast cells were stimulated with Lactobacillus rhamnosus (L.rhamnosus) GG (LGG),L.rhamnosus Lc705 (Lc705),Propionibacterium freudenreichii ssp.shermanii JS (PJS) and Bifidobacterium animalis ssp.lactis Bb12 (Bb12) and their combination for 3 or 24 h,and were subjected to global microarray analysis using an Affymetrix GeneChip Human Genome U133 Plus 2.0 Array.The gene expression differences between unstimulated and bacteria-stimulated samples were further analyzed with GOrilla Gene Enrichment Analysis and Visualization Tool and MeV Multiexperiment Viewer-tool.RESULTS:LGG and Lc705 were observed to suppress genes that encoded allergy-related high-affinity IgE receptor subunits α and γ (FCER1A and FCER1G,respectively) and histamine H4 receptor.LGG,Lc705 and the combination of four probiotics had the strongest effect on the expression of genes involved in mast cell immune system regulation,and on several genes that encoded proteins with a pro-inflammatory impact,such as interleukin (IL)-8 and tumour necrosis factor alpha.Also genes that encoded proteins with anti-inflammatory functions,such as IL-10,were upregulated.CONCLUSION:Certain probiotic bacteria might diminish mast cell allergy-related activation by downregulation of the expression of high-affinity IgE and histamine receptor genes,and by inducing a pro-inflammatory response.展开更多
Mobile edge computing(MEC) deployment in a multi-robot cooperation(MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and commun...Mobile edge computing(MEC) deployment in a multi-robot cooperation(MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot(MR) acts as an edge server to provide services to multiple slave robots(SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs’ energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.展开更多
In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over t...In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.展开更多
基金funded by Sardar Vallabhbhai National Institute of Technology through SEED grant No.Dean(R&C)/SEED Money/2021-22/11153Date:08/02/2022supported by Business Finland EWARE-6G project under 6G Bridge program,and in part by theHorizon Europe(Smart Networks and Services Joint Under taking)program under Grant Agreement No.101096838(6G-XR project).
文摘Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.
基金M.Faheem is supported by VTT Technical Research Center of Finland.
文摘Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.
基金supported by the VTT Technical Research Centre of Finland and the work of Nayef Alqahtani is supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant KFU251882).
文摘Lung cancer(LC)is a major cancer which accounts for higher mortality rates worldwide.Doctors utilise many imaging modalities for identifying lung tumours and their severity in earlier stages.Nowadays,machine learning(ML)and deep learning(DL)methodologies are utilised for the robust detection and prediction of lung tumours.Recently,multi modal imaging emerged as a robust technique for lung tumour detection by combining various imaging features.To cope with that,we propose a novel multi modal imaging technique named versatile scale malleable image integration and patch wise attention network(VSMI2−PANet)which adopts three imaging modalities named computed tomography(CT),magnetic resonance imaging(MRI)and single photon emission computed tomography(SPECT).The designed model accepts input from CT and MRI images and passes it to the VSMI2 module that is composed of three sub-modules named image cropping module,scale malleable convolution layer(SMCL)and PANet module.CT and MRI images are subjected to image cropping module in a parallel manner to crop the meaningful image patches and provide them to the SMCL module.The SMCL module is composed of adaptive convolutional layers that investigate those patches in a parallel manner by preserving the spatial information.The output from the SMCL is then fused and provided to the PANet module.The PANet module examines the fused patches by analysing its height,width and channels of the image patch.As a result,it provides an output as high-resolution spatial attention maps indicating the location of suspicious tumours.The high-resolution spatial attention maps are then provided as an input to the backbone module which uses light wave transformer(LWT)for segmenting the lung tumours into three classes,such as normal,benign and malignant.In addition,the LWT also accepts SPECT image as input for capturing the variations precisely to segment the lung tumours.The performance of the proposed model is validated using several performance metrics,such as accuracy,precision,recall,F1-score and AUC curve,and the results show that the proposed work outperforms the existing approaches.
基金fully supported by the University of Vaasa and VTT Technical Research Centre of Finland.
文摘Secure and automated sharing of medical information among different medical entities/stakeholders like patients,hospitals,doctors,law enforcement agencies,health insurance companies etc.,in a standard format has always been a challenging problem.Current methods for ensuring compliance with medical privacy laws require specialists who are deeply familiar with these laws'complex requirements to verify the lawful exchange of medical information.This article introduces a Smart Medical Data Exchange Engine(SDEE)designed to automate the extracting of logical rules from medical privacy legislation using advanced techniques.These rules facilitate the secure extraction of information,safeguarding patient privacy and confidentiality.In addition,SMDEE can generate standardised clinical documents according to Health Level 7(HL7)standards and also standardise the nomenclature of requested medical data,enabling accurate decision-making when accessing patient data.All access requests to patient information are processed through SMDEE to ensure authorised access.The proposed system's efficacy is evaluated using the Health Insurance Portability and Accountability Act(HIPAA),a fundamental privacy law in the United States.However,SMDEE's flexibility allows its application worldwide,accommodating various medical privacy laws.Beyond facilitating global information exchange,SMDEE aims to enhance international patients'timely and appropriate treatment.
文摘The present work compares microstructures of hot work steels made by different processes, that is, by sprayforming,by casting, and a commercially supplied H13 steel. Material benefits are recognized by sprayforming hot working tools such as die inserts for hot forging. The sprayformed hot work steels present a fine and homogeneous microstructure,which implies that, at a similar toughness level, the sprayformed steel can be higher alloyed, so that the thermal fatigue and wear resistance at elevated temperatures can be improved. A series of steels with higher vanadium content than commercial hot work steels are developed. There are no segregation and carbide network problems usually encountered in conventional ingot/forging processed high-vanadium steels. Microstructure and hardness of the new sprayformed steels are studied under different heat treatment conditions. It is justified that these sprayformed steels can be directly used for tooling without high temperature hardening. Sprayforming the tool steels onto a precision ceramic mould is demonstrated to extend the technoeconomical benefits, so that a net shape production tool can be rapidly made.Features of the rapid tooling process are also discussed.
基金Supported by The Finnish Funding Agency for Technologyand Innovation,Tekes,grants No.945/401/00 and 40160/05the Finnish Graduate School of Applied Biosciences,the Academy of Finland,Grant No.214 157the Centre of Excellence on Microbial Food Safety Research,Academy of Finland
文摘AIM:To study whether selected bacterial 16S ribosomal RNA(rRNA)gene phylotypes are capable of disting- uishing irritable bowel syndrome(IBS). METHODS:The faecal microbiota of twenty volunteers with IBS,subdivided into eight diarrhoea-predominant (IBS-D),eight constipation-predominant(IBS-C)and four mixed symptom-subtype(IBS-M)IBS patients,and fifteen control subjects,were analysed at three time-points with a set of fourteen quantitative real-timepolymerase chain reaction assays.All assays targeted 16S rRNA gene phylotypes putatively associated with IBS,based on 16S rRNA gene library sequence analysis. The target phylotypes were affiliated with Actinobac-teria,Bacteroidetes and Firmicutes.Eight of the target phylotypes had less than 95%similarity to cultured bacterial species according to their 16S rRNA gene sequence.The data analyses were made with repeated-measures ANCOVA-type modelling of the data and principle component analysis(PCA)with linear mixed-effects models applied to the principal component scores. RESULTS:Bacterial phylotypes Clostridium cocleatum 88%,Clostridium thermosuccinogenes 85%,Coprobacillus catenaformis 91%,Ruminococcus bromii-like, Ruminococcus torques 91%,and R.torques 93%were detected from all samples analysed.A multivariate analysis of the relative quantities of all 14 bacterial 16S rRNA gene phylotypes suggested that the intestinal microbiota of the IBS-D patients differed from other sample groups.The PCA on the first principal component(PC1),explaining 30.36%of the observed variation in the IBS-D patient group,was significantly altered from all other sample groups(IBS-D vs control, P=0.01;IBS-D vs IBS-M,P=0.00;IBS-D vs IBS-C, P=0.05).Significant differences were also observed in the levels of distinct phylotypes using relative values in proportion to the total amount of bacteria.A phy- lotype with 85%similarity to C.thermosuccinogenes was quantified in significantly different quantities among the IBS-D and control subjects(-4.08±0.90 vs -3.33±1.16,P=0.04)and IBS-D and IBS-M subjects (-4.08±0.90 vs-3.08±1.38,P=0.05).Furthermore,a phylotype with 94%similarity to R.torques was more prevalent in IBS-D patients'intestinal micro- biota than in that of control subjects(-2.43±1.49 vs -4.02±1.63,P=0.01).A phylotype with 93%simi- larity to R.torques was associated with control sam- ples when compared with IBS-M(-2.41±0.53 vs -2.92±0.56,P=0.00).Additionally,a R.bromii-like phylotype was associated with IBS-C patients in com- parison to control subjects(-1.61±1.83 vs-3.69± 2.42,P=0.01).All of the above mentioned phylotype specific alterations were independent of the effect of time. CONCLUSION:Significant phylotype level alterationsin the intestinal microbiotas of IBS patients were observed,further emphasizing the possible contribution of the gastrointestinal microbiota in IBS.
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
基金supported in part by the National Key Research and Development Program of China(2018YFB1802300)the Science and Technology Commission Foundation of Shanghai(Nos.21511101400 and 22511100600)+2 种基金the Young Elite Scientists Sponsorship Program by CICthe Program of Shanghai Academic/Technology Research Leader(No.21XD1433700)the Shanghai Rising-Star Program(No.21QC1400800)。
文摘With the continuous development of wireless communication technology,the number of access devices continues to soar,which poses a grate challenge to the already scarce spectrum resources.Meanwhile,6G will be an era of air-space-terrestrial-sea integration,and satellite spectrum resources are also very tight in the context of giant constellations.In this paper,we propose a Non-Orthogonal Multiple Access(NOMA)based spectrum sensing scheme for the future satellite-terrestrial communication scenarios,and design the transceiver from uplink and downlink scenarios,respectively.In order to better identify the user's transmission status,we obtain the feature values of each user through feature detection to make decision.We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario.Simulations are performed in each scenario,and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access(OMA).
基金the SAFIR2022 BRUTE project (Barseback RPV material used for true evaluation of embrittlement) for funding the study
文摘Effects of the weld microstructure and inclusions on brittle fracture initiation are investigated in a thermally aged ferritic high-nickel weld of a reactor pressure vessel head from a decommissioned nuclear power plant.As-welded and reheated regions mainly consist of acicular and polygonal ferrite,respectively.Fractographic examination of Charpy V-notch impact toughness specimens reveals large inclusions(0.5-2.5μm)at the brittle fracture primary initiation sites.High impact energies were measured for the specimens in which brittle fracture was initiated from a small inclusion or an inclusion away from the V-notch.The density,geometry,and chemical composition of the primary initiation inclusions were investigated.A brittle fracture crack initiates as a microcrack either within the multiphase oxide inclusions or from the debonded interfaces between the uncracked inclusions and weld metal matrix.Primary fracture sites can be determined in all the specimens tested in the lower part of the transition curve at and below the 41-J reference impact toughness energy but not above the mentioned value because of the changes in the fracture mechanism and resulting changes in the fracture appearance.
基金Supported by Valio Ltd and the Finnish Funding Agency for Technology and Innovation(TEKES)the preparation of this manuscript was funded in part by the Academy of Finland
文摘AIM： To investigate the pathophysiology of irritable bowel syndrome （IBS） by comparing the global mucosal metabolic profiles of IBS patients with those of healthy controls. METHODS： Fifteen IBS patients fulfilling the Rome II criteria, and nine healthy volunteers were included in the study. A combined lipidomics （UPLC/MS） and metabolomics （GC × GC-TOF） approach was used to achieve global metabolic profiles of mucosal biopsies from the ascending colon. RESULTS： Overall, lipid levels were elevated in patients with IBS. The most significant upregulation was seen for pro-inflammatory lysophosphatidylcholines. Other lipid groups that were significantly upregulated in IBS patients were lipotoxic ceramides, glycosphingolipids, and di-and triacylglycerols. Among the meo tabolites, the cyclic ester 2（3H）-furanone was almost 14-fold upregulated in IBS patients compared to healthy subjects （P = 0.03）. CONCLUSION： IBS mucosa is characterised by a distinct pro-inflammatory and lipotoxic metabolic profile. Especially, there was an increase in several lipid species such as lysophospholipids and ceramides.
文摘In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.
基金Supported by Valio Research Centre,the Foundation for Nutrition Research,Academy of Finland Research Council for Biosciences and Environment,Grant No.129954Finnish Funding Agency for Technology and Innovation (TEKES) grant No.2243/31/05
文摘AIM:To investigate the effects of four probiotic bacteria and their combination on human mast cell gene expression using microarray analysis.METHODS:Human peripheral-blood-derived mast cells were stimulated with Lactobacillus rhamnosus (L.rhamnosus) GG (LGG),L.rhamnosus Lc705 (Lc705),Propionibacterium freudenreichii ssp.shermanii JS (PJS) and Bifidobacterium animalis ssp.lactis Bb12 (Bb12) and their combination for 3 or 24 h,and were subjected to global microarray analysis using an Affymetrix GeneChip Human Genome U133 Plus 2.0 Array.The gene expression differences between unstimulated and bacteria-stimulated samples were further analyzed with GOrilla Gene Enrichment Analysis and Visualization Tool and MeV Multiexperiment Viewer-tool.RESULTS:LGG and Lc705 were observed to suppress genes that encoded allergy-related high-affinity IgE receptor subunits α and γ (FCER1A and FCER1G,respectively) and histamine H4 receptor.LGG,Lc705 and the combination of four probiotics had the strongest effect on the expression of genes involved in mast cell immune system regulation,and on several genes that encoded proteins with a pro-inflammatory impact,such as interleukin (IL)-8 and tumour necrosis factor alpha.Also genes that encoded proteins with anti-inflammatory functions,such as IL-10,were upregulated.CONCLUSION:Certain probiotic bacteria might diminish mast cell allergy-related activation by downregulation of the expression of high-affinity IgE and histamine receptor genes,and by inducing a pro-inflammatory response.
基金supported in part by the National Natural Science Foundation of China (Grant No. 61771429)in part by The Okawa Foundation for Information and Telecommunications, in part by G7 Scholarship Foundation+3 种基金in part by the Zhejiang Lab Open Program under Grant 2021LC0AB06in part by the Academy of Finland under Grant 319759, Zhejiang University City College Scientific Research Foundation (No. JZD18002)in part by ROIS NII Open Collaborative Research 21S0601in part by JSPS KAKENHI (Grant No. 18KK0279, 19H04093, 20H00592, and 21H03424)。
文摘Mobile edge computing(MEC) deployment in a multi-robot cooperation(MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot(MR) acts as an edge server to provide services to multiple slave robots(SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs’ energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.
基金This research was supported by Academy of Finland under Grant no.296628.
文摘In this study,we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea.A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis.Backscatter intensity,interferometric coherence magnitude,and interferometric phase have been used as informative features in several classification experiments.Various combinations of classification features were evaluated using Maximum likelihood(ML),Random Forests(RF)and Support Vector Machine(SVM)classifiers to achieve the best possible discrimination between open water and several sea ice types(undeformed ice,ridged ice,moderately deformed ice,brash ice,thick level ice,and new ice).Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification per-formance compared to using only backscatter-intensity.The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies,however,at the expense of somewhat longer processing time.The best overall accuracy(OA)for three methodologies were achieved using combination of all tested features were 71.56,72.93,and 72.91%for ML,RF and SVM classifiers,respectively.Compared to OAs of 62.28,66.51,and 63.05%using only backscatter intensity,this indicates strong benefit of SAR interferometry in discriminating different types of sea ice.In contrast to several earlier studies,we were particularly able to successfully discriminate open water and new ice classes.
