BACKGROUND The increased expression of G3BP1 was positively correlated with the prognosis of liver failure.AIM To investigate the effect of G3BP1 on the prognosis of acute liver failure(ALF)and acute-on-chronic liver ...BACKGROUND The increased expression of G3BP1 was positively correlated with the prognosis of liver failure.AIM To investigate the effect of G3BP1 on the prognosis of acute liver failure(ALF)and acute-on-chronic liver failure(ACLF)after the treatment of artificial liver support system(ALSS).METHODS A total of 244 patients with ALF and ACLF were enrolled in this study.The levels of G3BP1 on admission and at discharge were detected.The validation set of 514 patients was collected to verify the predicted effect of G3BP1 and the viability of prognosis.RESULTS This study was shown that lactate dehydrogenase(LDH),alpha-fetoprotein(AFP)and prothrombin time were closely related to the prognosis of patients.After the ALSS treatment,the patient’amount of decreased G3BP1 index in difference of G3BP1 between the value of discharge and admission(difG3BP1)<0 group had a nearly 10-fold increased risk of progression compared with the amount of increased G3BP1 index.The subgroup analysis showed that the difG3BP1<0 group had a higher risk of progression,regardless of model for end-stage liver disease high-risk or low-risk group.At the same time,compared with the inflam matory marks[tumor necrosis factor-α,interleukin(IL)-1βand IL-18],G3BP1 had higher discrimination and was more stable in the model analysis and validation set.When combined with AFP and LDH,concordance index was respectively 0.84 and 0.8 in training and validation cohorts.CONCLUSION This study indicated that G3BP1 could predict the prognosis of ALF or ACLF patients treated with ALSS.The combination of G3BP1,AFP and LDH could accurately evaluate the disease condition and predict the clinical endpoint of patients.展开更多
In today’s digital era,e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices,computers and the inter-net to provide high-quality healthcare services.E-healthcare decis...In today’s digital era,e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices,computers and the inter-net to provide high-quality healthcare services.E-healthcare decision support sys-tems have been developed to optimize the healthcare services and enhance a patient’s health.These systems enable rapid access to the specialized healthcare services via reliable information,retrieved from the cases or the patient histories.This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions.In the current research work,a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System(SFLODL-DSS)is designed for the diagnosis of the Cardiovascular Diseases(CVD).The aim of the proposed model is to identify and classify the cardiovascular diseases.The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection(SFLO-FS)approach for feature subset election.For the pur-pose of classification,the Autoencoder with Gated Recurrent Unit(AEGRU)model is exploited.Finally,the Bacterial Foraging Optimization(BFO)algorithm is employed tofine-tune the hyperparameters involved in the AEGRU method.To demonstrate the enhanced performance of the proposed SFLODL-DSS technique,a series of simulations was conducted.The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%.Thus,the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.展开更多
Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identi...Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.展开更多
Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more w...Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.展开更多
This paper provides a review of the intrinsic and extrinsic factors affecting the uniaxial compressive strength(UCS)of cemented tailings backfill(CTB).The consideration is that once CTB is poured into underground stop...This paper provides a review of the intrinsic and extrinsic factors affecting the uniaxial compressive strength(UCS)of cemented tailings backfill(CTB).The consideration is that once CTB is poured into underground stopes,its strength is heavily influenced by factors internal to the CTB as well as the surrounding mining environments.Peer-reviewed journal articles,books,and conference papers published between 2000 and 2022 were searched electronically from various databases and reviewed.Additional sources,such as doctoral theses,were obtained from academic repositories.An important finding from the review is that the addition of fibers was reported to improve the UCS of CTB in some studies while decrease in others.This discrepancy was accounted to the different properties of fibers used.Further research is therefore needed to determine the“preferred”fiber to be used in CTB.Diverging findings were also reported on the effects of stope size on the UCS of CTB.Furthermore,the use of fly ash as an alternative binder may be threatened in the future when reliance on the coal power declines.Therefore,an alternative cementitious by-product to be used together with furnace slag may be required in the future.Finally,while most studies on backfill focused on single-layered structures,layered backfill design models should also be investigated.展开更多
Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support Syste...Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support System(BITOLA)to screen out the candidate intermediate molecular(CIM)"Gene or Gene Product”that are related to AAA and DM.The dataset of GSE13760,GSE7084,GSE57691,GSE47472 were used to analyze the differentially expressed genes(DEGs)of AAA and DM compared to the healthy status.We used the online tool ofVenny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs.The Human eFP Browser was applied to examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue.Results There were 86 CIMs suggested by the closed BITOLA system.Among all the DEGs of AAA and DM,8 genes in GSE7084(ISG20,ITGAX,DSTN,CCL5,CCR5,AGTR1,CD19,CD44)and 2 genes in GSE 13760(PSMD12,FAS)were found to be overlapped with the 86 CIMs.By manual checking and comparing with tissuespecific gene data through Human eFP Browser,the gene PSMD12(proteasome 26S subunit,non-ATPase 12)was recognized to be strongly expressed in both the aorta and pancreatic tissue.Conclusion We proposed a hypothesis through text mining that PSMD12 might be involved or potentially involved in the interconnection between AAA and DM,which may provide a new clue for studies on novel therapeutic strategies for the two diseases.展开更多
BACKGROUND The artificial liver support system(ALSS)is an effective treatment method for liver failure,but it requires deep venous intubation and long-term indwelling catheterization.However,the coagulation mechanism ...BACKGROUND The artificial liver support system(ALSS)is an effective treatment method for liver failure,but it requires deep venous intubation and long-term indwelling catheterization.However,the coagulation mechanism disorder of basic liver failure diseases,and deep venous thrombosis(DVT)often occur.AIM To evaluate the risk factors for DVT following use of an ALSS and establish a risk assessment score.METHODS This study was divided into three stages.In the first stage,the risk factors for DVT were screened and the patient data were collected,including ALSS treatment information;biochemical indices;coagulation and hematology indices;complications;procoagulant use therapy status;and a total of 24 indicators.In the second stage,a risk assessment score for DVT after ALSS treatment was developed.In the third stage,the DVT risk assessment score was validated.RESULTS A total of 232 patients with liver failure treated with ALSS were enrolled in the first stage,including 12 with lower limb DVT.Logistic regression analysis showed that age[odds ratio(OR),1.734;P=0.01],successful catheterization time(OR,1.667;P=0.005),activity status(strict bed rest)(OR,3.049;P=0.005),and D-dimer level(≥500 ng/mL)(OR,5.532;P<0.001)were independent risk factors for DVT.We then established a scoring system for risk factors.In the validation group,a total of 213 patients with liver failure were treated with ALSS,including 14 with lower limb DVT.When the cutoff value of risk assessment was 3,the specificity and sensitivity of the risk assessment score were 88.9%and 85.7%,respectively.CONCLUSION A simple risk assessment scoring system was established for DVT patients with liver failure treated with ALSS and was verified to have good sensitivity and specificity.展开更多
In recent years, as there has been a major change in the necessity and importance of sightseeing information, a platform to provide real-time sightseeing information according to the ever-changing circumstances is nec...In recent years, as there has been a major change in the necessity and importance of sightseeing information, a platform to provide real-time sightseeing information according to the ever-changing circumstances is necessary. Additionally, it is effective to adopt gamification to increase users’ motivation to continuously utilize the system in order to provide them with more information. In the present study, in order to support users’ enjoyment in creating efficient and pleasant sightseeing plans, the system that incorporates gamification to increase motivation was developed combining with web-geographic information systems (Web-GIS) and sightseeing planning and sharing system. The system was operated over a period of 2 weeks in Chofu City, Tokyo Metropolis, Japan. Based on the results of a questionnaire survey for 51 users, though the operability of the 3 main functions incorporated with motivation by gamification was rated lower than those of the 2 basic functions, their usefulness was highly rated. Based on the results of the access log analysis, it was effective to design the system so that the same functions can be used regardless of the type of information terminal. Additionally, it was evident that the continuous utilization of the system could increase the number of sightseeing plans created by the users.展开更多
Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-re...Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-regulation and driving cessation in order to suggest development of support systems to prolong older drivers’ safe mobility. Method: Three focus groups were conducted with 19 older active drivers aged 65+ who were divided according to annual mileage driven. Results: A content analysis revealed broad self-regulatory behaviour as already reported in the literature, e.g., avoiding driving at rush hour and at night. The participants also reported difficulty in finding the way to their final destination and an increasing need to plan their travelling. Co-piloting was a behaviour applied by couples to cope with difficulties encountered in traffic. A large part of the discussion was focused on emerging feelings of stress, anxiety and fear when driving in recent years, a feeling induced by external factors e.g., other road users’ behaviour, traffic density or high speed. Apart from health problems, high levels of stress could explain driving cessation, especially for women. An increased feeling of safety and comfort could be achieved by an increased use of support systems specifically designed to respond to older drivers’ needs. Conclusion: Support systems for older drivers should increase comfort and decrease their stress levels. New systems, such as co-pilot function and more developed Global Positioning System (GPS) supporting of the entire travel from door to door, should be developed to respond to the market needs.展开更多
With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage ...With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.展开更多
Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications...Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.展开更多
Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI...Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate OC.In this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.展开更多
The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare se...The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector.Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected.Lately,the testing kits for COVID-19 are not available to deal it with required proficiency,along with-it countries have been widely hit by the COVID-19 disruption.To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19.It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely.According to the apparent clinical research,it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection.The abrupt changes do require a solution so the technology is out there to pace up,Chest X-ray and Computer tomography(CT)scan images could significantly identify the preliminaries of COVID-19 like lungs infection.CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners.The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious,a quick and highly responsive automatic system based on Artificial Intelligence(AI)is always there to aid against the masses to be prone to COVID-19.The proposed Intelligent decision support system for COVID-19 empowered with deep learning(ID2S-COVID19-DL)study suggests Deep learning(DL)based Convolutional neural network(CNN)approaches for effective and accurate detection to the maximum extent it could be,detection of coronavirus is assisted by using X-ray and CT-scan images.The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively,and for validation 94.38 percent and 97.06 percent respectively.The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it ishelpful to enhance the working productivity of radiologists. It could take the curvedown with the downright contribution of radiologists, rapid detection ofCOVID-19, and to overcome this current pandemic with the proven efficacy.展开更多
Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theo...Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.展开更多
A computer aided decision support system (merger and acquisition analyzing and evaluating-decision support system (MAAE-DSS)) for analyzing and evaluating corporate merger and acquisition (M&A) strategies, was pro...A computer aided decision support system (merger and acquisition analyzing and evaluating-decision support system (MAAE-DSS)) for analyzing and evaluating corporate merger and acquisition (M&A) strategies, was proposed. Strategic management tools such as scale index-market growth rate matrix (S-M matrix), industrial attraction-corporate strength matrix (I-S matrix), market growth rate-market occupancy matrix (G-O matrix), and life cycle-competitive position matrix (L-C matrix), were applied in the MAAE-DSS with its own data base (DB), model base (MB), method base (MeB), and knowledge base (KB), in order to support the management bureau in the formulation of M&A strategies.展开更多
The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum ass...The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.展开更多
The paper introduces the present status and development objec-tives of the operational support systems of the three telecommuni-cation operators in China and briefly describes the features of thenew generation telecom...The paper introduces the present status and development objec-tives of the operational support systems of the three telecommuni-cation operators in China and briefly describes the features of thenew generation telecommunication operational support system(NGOSS),such as adopting the TMN/TOM structure,supporting theunified and multiple access processing,conducting effective andcentralized management of data.展开更多
Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r ...Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".展开更多
Aim: In prone breast treatments, a carbon fiber support device resides under the contralateral breast. Tangent beams are designed to encompass the treated breast and these often pass through the board at a shallow ang...Aim: In prone breast treatments, a carbon fiber support device resides under the contralateral breast. Tangent beams are designed to encompass the treated breast and these often pass through the board at a shallow angle, resulting in significant attenuation. Our planners account for this attenuation by adding field-in-field dose to the deep part of the breast, through the board. Concern was raised about how accurate the treatment delivery is when the inherent uncertainties of patients’ positions are accounted for. Furthermore, transmission measurements are usually carried out perpendicular to the board, a non-clinical situation. The goal of this study is to evaluate the dosimetric effect of the board and the robustness of the plan to positional uncertainty. Materials and Methods: Twenty-two breast patients treated on a commercial prone breast board between 2017 and 2020 were selected for this retrospective study. To evaluate the board’s attenuation, we compared the plans with the board removed from the dose calculation. To quantify the robustness of this technique, we moved the beam isocenter with respect to the patient and board. Results: Our results showed that when the breast board is removed from a plan which was designed to account for the board attenuation, the average point dose increases by 7.48%, with a maximum of 22%. Comparing results with a mixed Analysis of Variance (ANOVA) and a least-square means analysis, our robustness evaluation indicates that anterior shifts at every magnitude (1 mm through 5 mm) make a significant difference in all dose statistics (D95, max, 95% prescription coverage and homogeneity index) investigated. In/out and right/left shifts resulted in an insignificant change in dose statistics. Conclusion: Prone breast boards can add significant dosimetric uncertainty into the treatment delivery process. Accounting for plan robustness in the design of the plan is highly recommended. A prone breast board design with support moved away from the beam path is warranted.展开更多
文摘BACKGROUND The increased expression of G3BP1 was positively correlated with the prognosis of liver failure.AIM To investigate the effect of G3BP1 on the prognosis of acute liver failure(ALF)and acute-on-chronic liver failure(ACLF)after the treatment of artificial liver support system(ALSS).METHODS A total of 244 patients with ALF and ACLF were enrolled in this study.The levels of G3BP1 on admission and at discharge were detected.The validation set of 514 patients was collected to verify the predicted effect of G3BP1 and the viability of prognosis.RESULTS This study was shown that lactate dehydrogenase(LDH),alpha-fetoprotein(AFP)and prothrombin time were closely related to the prognosis of patients.After the ALSS treatment,the patient’amount of decreased G3BP1 index in difference of G3BP1 between the value of discharge and admission(difG3BP1)<0 group had a nearly 10-fold increased risk of progression compared with the amount of increased G3BP1 index.The subgroup analysis showed that the difG3BP1<0 group had a higher risk of progression,regardless of model for end-stage liver disease high-risk or low-risk group.At the same time,compared with the inflam matory marks[tumor necrosis factor-α,interleukin(IL)-1βand IL-18],G3BP1 had higher discrimination and was more stable in the model analysis and validation set.When combined with AFP and LDH,concordance index was respectively 0.84 and 0.8 in training and validation cohorts.CONCLUSION This study indicated that G3BP1 could predict the prognosis of ALF or ACLF patients treated with ALSS.The combination of G3BP1,AFP and LDH could accurately evaluate the disease condition and predict the clinical endpoint of patients.
文摘In today’s digital era,e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices,computers and the inter-net to provide high-quality healthcare services.E-healthcare decision support sys-tems have been developed to optimize the healthcare services and enhance a patient’s health.These systems enable rapid access to the specialized healthcare services via reliable information,retrieved from the cases or the patient histories.This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions.In the current research work,a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System(SFLODL-DSS)is designed for the diagnosis of the Cardiovascular Diseases(CVD).The aim of the proposed model is to identify and classify the cardiovascular diseases.The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection(SFLO-FS)approach for feature subset election.For the pur-pose of classification,the Autoencoder with Gated Recurrent Unit(AEGRU)model is exploited.Finally,the Bacterial Foraging Optimization(BFO)algorithm is employed tofine-tune the hyperparameters involved in the AEGRU method.To demonstrate the enhanced performance of the proposed SFLODL-DSS technique,a series of simulations was conducted.The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%.Thus,the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.
基金The authors would like to confirm that this research work was funded by Institutional Fund Projects under Grant No.(IFPIP:646-829-1443)。
文摘Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.
文摘Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.
文摘This paper provides a review of the intrinsic and extrinsic factors affecting the uniaxial compressive strength(UCS)of cemented tailings backfill(CTB).The consideration is that once CTB is poured into underground stopes,its strength is heavily influenced by factors internal to the CTB as well as the surrounding mining environments.Peer-reviewed journal articles,books,and conference papers published between 2000 and 2022 were searched electronically from various databases and reviewed.Additional sources,such as doctoral theses,were obtained from academic repositories.An important finding from the review is that the addition of fibers was reported to improve the UCS of CTB in some studies while decrease in others.This discrepancy was accounted to the different properties of fibers used.Further research is therefore needed to determine the“preferred”fiber to be used in CTB.Diverging findings were also reported on the effects of stope size on the UCS of CTB.Furthermore,the use of fly ash as an alternative binder may be threatened in the future when reliance on the coal power declines.Therefore,an alternative cementitious by-product to be used together with furnace slag may be required in the future.Finally,while most studies on backfill focused on single-layered structures,layered backfill design models should also be investigated.
文摘Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support System(BITOLA)to screen out the candidate intermediate molecular(CIM)"Gene or Gene Product”that are related to AAA and DM.The dataset of GSE13760,GSE7084,GSE57691,GSE47472 were used to analyze the differentially expressed genes(DEGs)of AAA and DM compared to the healthy status.We used the online tool ofVenny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs.The Human eFP Browser was applied to examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue.Results There were 86 CIMs suggested by the closed BITOLA system.Among all the DEGs of AAA and DM,8 genes in GSE7084(ISG20,ITGAX,DSTN,CCL5,CCR5,AGTR1,CD19,CD44)and 2 genes in GSE 13760(PSMD12,FAS)were found to be overlapped with the 86 CIMs.By manual checking and comparing with tissuespecific gene data through Human eFP Browser,the gene PSMD12(proteasome 26S subunit,non-ATPase 12)was recognized to be strongly expressed in both the aorta and pancreatic tissue.Conclusion We proposed a hypothesis through text mining that PSMD12 might be involved or potentially involved in the interconnection between AAA and DM,which may provide a new clue for studies on novel therapeutic strategies for the two diseases.
基金Supported by China Public Health Alliance,No.GWLM202031.
文摘BACKGROUND The artificial liver support system(ALSS)is an effective treatment method for liver failure,but it requires deep venous intubation and long-term indwelling catheterization.However,the coagulation mechanism disorder of basic liver failure diseases,and deep venous thrombosis(DVT)often occur.AIM To evaluate the risk factors for DVT following use of an ALSS and establish a risk assessment score.METHODS This study was divided into three stages.In the first stage,the risk factors for DVT were screened and the patient data were collected,including ALSS treatment information;biochemical indices;coagulation and hematology indices;complications;procoagulant use therapy status;and a total of 24 indicators.In the second stage,a risk assessment score for DVT after ALSS treatment was developed.In the third stage,the DVT risk assessment score was validated.RESULTS A total of 232 patients with liver failure treated with ALSS were enrolled in the first stage,including 12 with lower limb DVT.Logistic regression analysis showed that age[odds ratio(OR),1.734;P=0.01],successful catheterization time(OR,1.667;P=0.005),activity status(strict bed rest)(OR,3.049;P=0.005),and D-dimer level(≥500 ng/mL)(OR,5.532;P<0.001)were independent risk factors for DVT.We then established a scoring system for risk factors.In the validation group,a total of 213 patients with liver failure were treated with ALSS,including 14 with lower limb DVT.When the cutoff value of risk assessment was 3,the specificity and sensitivity of the risk assessment score were 88.9%and 85.7%,respectively.CONCLUSION A simple risk assessment scoring system was established for DVT patients with liver failure treated with ALSS and was verified to have good sensitivity and specificity.
文摘In recent years, as there has been a major change in the necessity and importance of sightseeing information, a platform to provide real-time sightseeing information according to the ever-changing circumstances is necessary. Additionally, it is effective to adopt gamification to increase users’ motivation to continuously utilize the system in order to provide them with more information. In the present study, in order to support users’ enjoyment in creating efficient and pleasant sightseeing plans, the system that incorporates gamification to increase motivation was developed combining with web-geographic information systems (Web-GIS) and sightseeing planning and sharing system. The system was operated over a period of 2 weeks in Chofu City, Tokyo Metropolis, Japan. Based on the results of a questionnaire survey for 51 users, though the operability of the 3 main functions incorporated with motivation by gamification was rated lower than those of the 2 basic functions, their usefulness was highly rated. Based on the results of the access log analysis, it was effective to design the system so that the same functions can be used regardless of the type of information terminal. Additionally, it was evident that the continuous utilization of the system could increase the number of sightseeing plans created by the users.
基金We acknowledge SAFER,Vehicle and Traffic Safety Centre at Chalmers,Gothenburg,Sweden,for funding this researchthe participants from the pensioner or-ganisations PRO and SPF in Jönköping,Sweden.
文摘Background: The number of older people is increasing. Many of them expect to maintain a rich social life and to continue driving at an older age. Objective: The present study investigates the mechanisms behind self-regulation and driving cessation in order to suggest development of support systems to prolong older drivers’ safe mobility. Method: Three focus groups were conducted with 19 older active drivers aged 65+ who were divided according to annual mileage driven. Results: A content analysis revealed broad self-regulatory behaviour as already reported in the literature, e.g., avoiding driving at rush hour and at night. The participants also reported difficulty in finding the way to their final destination and an increasing need to plan their travelling. Co-piloting was a behaviour applied by couples to cope with difficulties encountered in traffic. A large part of the discussion was focused on emerging feelings of stress, anxiety and fear when driving in recent years, a feeling induced by external factors e.g., other road users’ behaviour, traffic density or high speed. Apart from health problems, high levels of stress could explain driving cessation, especially for women. An increased feeling of safety and comfort could be achieved by an increased use of support systems specifically designed to respond to older drivers’ needs. Conclusion: Support systems for older drivers should increase comfort and decrease their stress levels. New systems, such as co-pilot function and more developed Global Positioning System (GPS) supporting of the entire travel from door to door, should be developed to respond to the market needs.
文摘With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.
基金This research was supported by the Ministry of Trade,Industry&Energy(MOTIE),Korea Institute for Advancement of Technology(KIAT)through the Encouragement Program for The Industries of Economic Cooperation Region(P0006082)the Soonchunhyang University Research Fund.
文摘Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.
文摘Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate OC.In this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.
基金Data and Artificial Intelligence Scientific Chair at Umm AlQura University.
文摘The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector.Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected.Lately,the testing kits for COVID-19 are not available to deal it with required proficiency,along with-it countries have been widely hit by the COVID-19 disruption.To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19.It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely.According to the apparent clinical research,it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection.The abrupt changes do require a solution so the technology is out there to pace up,Chest X-ray and Computer tomography(CT)scan images could significantly identify the preliminaries of COVID-19 like lungs infection.CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners.The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious,a quick and highly responsive automatic system based on Artificial Intelligence(AI)is always there to aid against the masses to be prone to COVID-19.The proposed Intelligent decision support system for COVID-19 empowered with deep learning(ID2S-COVID19-DL)study suggests Deep learning(DL)based Convolutional neural network(CNN)approaches for effective and accurate detection to the maximum extent it could be,detection of coronavirus is assisted by using X-ray and CT-scan images.The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively,and for validation 94.38 percent and 97.06 percent respectively.The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it ishelpful to enhance the working productivity of radiologists. It could take the curvedown with the downright contribution of radiologists, rapid detection ofCOVID-19, and to overcome this current pandemic with the proven efficacy.
基金This work is supported in part by the Major Fundamental Research of Natural Science Foundation of Shandong Province under Grant ZR2019ZD05Joint Fund for Smart Computing of Shandong Natural Science Foundation under Grant ZR2020LZH013+1 种基金the Scientific Research Platform and Projects of Department of Education of Guangdong Province under Grant 2019GKQNCX121the Intelligent Perception and Computing Innovation Platform of the Shenzhen Institute of Information Technology under Grant PT2019E001.
文摘Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology.The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis.Background:In recent years,deep learning has been widely used in academia,industry,andmedicine.An increasing number of companies are starting to recruit a large number of professionals in the field of deep learning.Increasing numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis techniques.For now,however,there is no practical training platform that can help radiologists learn automated medical image analysis systematically.ApplicationDesign:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this platform.The platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.
文摘A computer aided decision support system (merger and acquisition analyzing and evaluating-decision support system (MAAE-DSS)) for analyzing and evaluating corporate merger and acquisition (M&A) strategies, was proposed. Strategic management tools such as scale index-market growth rate matrix (S-M matrix), industrial attraction-corporate strength matrix (I-S matrix), market growth rate-market occupancy matrix (G-O matrix), and life cycle-competitive position matrix (L-C matrix), were applied in the MAAE-DSS with its own data base (DB), model base (MB), method base (MeB), and knowledge base (KB), in order to support the management bureau in the formulation of M&A strategies.
基金Supported by the National Science & Technology Pillar Program(2006BAJ07B07)
文摘The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.
文摘The paper introduces the present status and development objec-tives of the operational support systems of the three telecommuni-cation operators in China and briefly describes the features of thenew generation telecommunication operational support system(NGOSS),such as adopting the TMN/TOM structure,supporting theunified and multiple access processing,conducting effective andcentralized management of data.
文摘Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".
文摘Aim: In prone breast treatments, a carbon fiber support device resides under the contralateral breast. Tangent beams are designed to encompass the treated breast and these often pass through the board at a shallow angle, resulting in significant attenuation. Our planners account for this attenuation by adding field-in-field dose to the deep part of the breast, through the board. Concern was raised about how accurate the treatment delivery is when the inherent uncertainties of patients’ positions are accounted for. Furthermore, transmission measurements are usually carried out perpendicular to the board, a non-clinical situation. The goal of this study is to evaluate the dosimetric effect of the board and the robustness of the plan to positional uncertainty. Materials and Methods: Twenty-two breast patients treated on a commercial prone breast board between 2017 and 2020 were selected for this retrospective study. To evaluate the board’s attenuation, we compared the plans with the board removed from the dose calculation. To quantify the robustness of this technique, we moved the beam isocenter with respect to the patient and board. Results: Our results showed that when the breast board is removed from a plan which was designed to account for the board attenuation, the average point dose increases by 7.48%, with a maximum of 22%. Comparing results with a mixed Analysis of Variance (ANOVA) and a least-square means analysis, our robustness evaluation indicates that anterior shifts at every magnitude (1 mm through 5 mm) make a significant difference in all dose statistics (D95, max, 95% prescription coverage and homogeneity index) investigated. In/out and right/left shifts resulted in an insignificant change in dose statistics. Conclusion: Prone breast boards can add significant dosimetric uncertainty into the treatment delivery process. Accounting for plan robustness in the design of the plan is highly recommended. A prone breast board design with support moved away from the beam path is warranted.
