Combined Model Research Articles

Short term power load forecasting system based on improved neural network deep learning model

AbstractThe electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making.

Prediction of water-level variations in an alluvial aquifer surrounded by karstic formations using a combined time series-geostatistical model: a case study from Iran

Prediction of water-level variations in an alluvial aquifer surrounded by karstic formations using a combined time series-geostatistical model: a case study from Iran

Detection and classification of Brandt’s vole burrow clusters utilizing GF-2 satellite imagery and faster R-CNN model

Most small rodent populations worldwide exhibit fascinating population dynamics, capturing the attention of numerous scholars due to their multiyear cyclic fluctuations in population size and the astonishing amplitude of these fluctuations. Hulunbuir steppe stands as a crucial global hub for livestock production, yet in recent decades, the area has faced recurring challenges from steppes rodent invasions, with Brandt’s vole (Lasiopodomys brandtii, BV) being particularly rampant among them. They not only exhibit seasonal reproduction but also strong social behavior, and are generally considered pests, especially during population outbreak years. Prior studies suggest that BV population outbreaks tend to occur across a wider geographic area, and a strong indicator for identifying rodent outbreaks is recognizing their burrow clusters (burrow systems). Hence, this paper conducts target object detection of BV burrow clusters in the typical steppes of Hulunbuir using two GF-2 satellite images from 2021 (the year of the BV outbreak). This task is accomplished by incorporating the Faster R-CNN model in combination with three detection approaches: object-based image classification (OBIC), based on vegetation index classification (BVIC), and based on texture classification (BTC). The results indicate that OBIC demonstrated the highest robustness in BV burrow cluster detection, achieving an average AP of 63.80% and an F1 score of 0.722 across the two images. BTC exhibited the second-highest level of accuracy, achieving an average AP of 55.95% and an F1 score of 0.6660. Moreover, this approach displayed a strong performance in BV burrow clusters localization. In contrast, BVIC achieved the lowest level of accuracy among the three methods, with an average AP of only 29.45% and an F1 score of 0.4370. Overall, this study demonstrates the crucial role of utilizing high-resolution satellite imagery combined with DL-based object detection techniques in effectively monitoring and managing the potential outbreaks of steppe rodent pests across larger spatial extents.

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

Analysis of Tangential Leakage Flow Characteristics in a Variable Diameter Dual Circular Arc Vortex Compressor

(1) To address issues such as a low compression ratio and severe leakage in uniform wall thickness vortex compressors, this paper adopts a new scroll compressor model with a variable diameter double arc combined profile, which has a larger pressure ratio and smaller leakage than the scroll compressor with equal wall thickness that can bring new ideas and methods to the research in the field of scroll compressors. (2) This paper determines the theoretical equation of the variable diameter dual circular arc vortex profile and the combined profile, and infers mathematical models for the leakage line length, working chamber volume, and gas leakage per unit rotation angle of the vortex compressor, followed by simulation. (3) The results show that compared to the uniform wall thickness involute profile, the variable diameter dual circular arc combined profile model reduces the leakage line length by 25%, increases the average total pressure at the working chamber outlet by 2.38%, and decreases the leakage amount by 3.2%, consistent with theoretical analysis. (4) The tangential leakage caused by the radial gap in the variable diameter dual circular arc combined profile model has a significant impact on the uneven distribution of temperature and velocity fields in the working chamber of the vortex compressor, but a smaller impact on the pressure field.

Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

BackgroundPreoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset.MethodsA total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test.ResultsSeven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001).ConclusionsThe combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

A combined score for predicting clinically relevant postoperative pancreatic fistula based on inflammatory parameters and drainage fluid culture results on postoperative day 3.

Clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreatic resection can lead to severe postoperative complications. POPF is defined based on postoperative day (POD) 3 drainage fluid amylase level. POPF correlates with inflammatory parameters as well as drainage fluid bacterial infection. However, a standardized model based on these factors for predicting CR-POPF remains elusive. We aimed to identify inflammatory parameter- and drainage fluid culture-related risk factors for CR-POPF on POD 3 after pancreatoduodenectomy (PD) and distal pancreatectomy (DP). Data from 351 patients who underwent PD or DP between 2013 and 2022 at a single institution were retrospectively analyzed. Risk factors for CR-POPF were investigated using multivariate analyses, and a prediction model combining the risk factors for CR-POPF was developed. Of the 351 patients, 254 and 97 underwent PD and DP, respectively. Multivariate analyses revealed that drainage fluid amylase level ≥722 IU/L, culture positivity, as well as neutrophil count ≥5473/mm3 on POD 3 were independent risk factors for CR-POPF in PD group. Similarly, drainage fluid, amylase level ≥500 IU/L, and culture positivity on POD 3 as well as pancreatic thickness ≥11.1mm were independent risk factors in the DP group. The model for predicting CR-POPF achieved the maximum overall accuracy rate when the number of risk factors was ≥2 in both the PD and DP groups. Inflammatory parameters on POD 3 significantly influence the risk of CR-POPF onset after pancreatectomy. The combined models based on these values can accurately predict the risk of CR-POPF after pancreatectomy.

Quantitative Foveal Structural Metrics as Predictors of Visual Acuity in Human Albinism.

To assess the degree to which quantitative foveal structural measurements account for variation in best-corrected visual acuity (BCVA) in human albinism. BCVA was measured and spectral domain optical coherence tomography (SD-OCT) images were acquired for 74 individuals with albinism. Categorical foveal hypoplasia grades were assessed using the Leicester Grading System for Foveal Hypoplasia. Foveal anatomical specialization (foveal versus parafoveal value) was quantified for inner retinal layer (IRL) thickness, outer segment (OS) length, and outer nuclear layer (ONL) thickness. These metrics, participant sex, and age were used to build a multiple linear regression of BCVA. This combined linear model's predictive properties were compared to those of categorical foveal hypoplasia grading. The cohort included three participants with type 1a foveal hypoplasia, 23 participants with type 1b, 33 with type 2, ten with type 3, and five with type 4. BCVA ranged from 0.08 to 1.00 logMAR (mean ± SD: 0.53 ± 0.21). IRL ratio, OS ratio, and ONL ratio were measured in all participants and decreased with increasing severity of foveal hypoplasia. The best-fit combined linear model included all three quantitative metrics and participant age expressed as a binary variable (divided into 0-18years and 19years or older; adjusted R2 = 0.500). This model predicted BCVA more accurately than a categorical foveal hypoplasia model (adjusted R2 = 0.352). A quantitative model of foveal specialization accounts for more variance in BCVA in albinism than categorical foveal hypoplasia grading. Other factors, such as optical aberrations and eye movements, may account for the remaining unexplained variance.

Scavenging Glyoxal and Methylglyoxal by Synephrine Alone or in Combination with Neohesperidin at High Temperatures.

α-Dicarbonyl compounds, such as glyoxal (GO) and methylglyoxal (MGO), are a series of chemical hazards that exist in vivo and in vitro, posing a threat to human health. We aimed to explore the scavenging effects on GO/MGO by synephrine (SYN) alone or in combination with neohesperidin (NEO). First, through LC-MS/MS, we confirmed that both SYN and NEO could effectively remove GO and form GO adducts, while NEO could also clear MGO by forming MGO adducts, and its ability to clear MGO was stronger than that of GO. Second, a synergistic inhibitory effect on GO was found when SYN and NEO were used in combination by using the Chou-Talalay method; on the other hand, SYN could promote NEO to clear more MGO, although SYN could not capture MGO. Third, after synthesizing four GO/MGO-adducts (SYN-GO-1, SYN-GO-3, NEO-GO-7, and NEO-MGO-2) and identifying their structure through NMR, strict correlations between the GO/MGO-adducts and the GO/MGO-clearance rate were found when using SYN and NEO alone or in combination. Furthermore, it was inferred that the synergistic effect between SYN and NEO stems from their mutual promotion in capturing more GO by the quantitative analysis of the adducts in the combined model. Finally, a study was conducted on flowers of Citrus aurantium L. var. amara Engl. (FCAVA, an edible tea) rich in SYN and NEO, which could serve as an effective GO and MGO scavenger in the presence of both GO and MGO. Therefore, our study provided well-defined evidence that SYN and NEO, alone or in combination, could efficiently scavenge GO/MGO at high temperatures, whether in the pure form or located in FCAVA.

Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.

This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consists of four base classifiers and was evaluated using accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve (AUC). The predictive performance of the combined model combining radiomics and deep learning features outperformed the radiomics and deep learning feature models alone, with accuracy of 0.848 and 0.814 in training and test sets, and AUC of 0.916 and 0.861, respectively. Compared with the base classifiers that make up the multi-model fusion model, the fusion model also exhibited better prediction performance. The fusion model incorporating both radiomics and deep learning features had certain predictive value for the ablation rate of adenomyosis under HIFU therapy and could help select patients with adenomyosis who would benefit from HIFU therapy.

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

Summary The grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment. This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts. Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model. The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Radiomics nomogram based on MRI water imaging identifying symptomatic nerves of patients with primary trigeminal neuralgia: A preliminary study.

The study proposes a combined nomogram based on radiomics features from magnetic resonance neurohydrography and clinical features to identify symptomatic nerves in patients with primary trigeminal neuralgia. We retrospectively analyzed 140 patients with clinically confirmed trigeminal neuralgia. Out of these, 24 patients constituted the external validation set, while the remaining 116 patients contributed a total of 231 nerves, comprising 118 symptomatic nerves, and 113 normal nerves. Radiomics features were extracted from the MRI water imaging (t2-mix3d-tra-spair). Radiomics feature selection was performed using L1 regularization-based regression, while clinical feature selection utilized univariate analysis and multivariate logistic regression. Subsequently, radiomics, clinical, and combined models were developed by using multivariate logistic regression, and a nomogram of the combined model was drawn. The performance of nomogram in discriminating symptomatic nerves was assessed through the area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves. Clinical applications of the nomogram were further evaluated using decision curve analysis. Five clinical factors and 13 radiomics signatures were ultimately selected to establish predictive models. The AUCs in the training and validation cohorts were 0.77 (0.70-0.84) and 0.82 (0.72-0.92) with the radiomics model, 0.69 (0.61-0.77) and 0.66 (0.53-0.79) with the clinical model, 0.80 (0.74-0.87), and 0.85 (0.76-0.94) with the combined model, respectively. In the external validation set, the AUCs for the clinical, radiomics, and combined models were 0.70 (0.60-0.79), 0.78 (0.65-0.91), and 0.81 (0.70-0.93), respectively. The calibration curve demonstrated that the nomogram exhibited good predictive ability. Moreover, The decision curve analysis curve indicated shows that the combined model holds high clinical application value. The integrated model, combines radiomics features from magnetic resonance neurohydrography with clinical factors, proves to be effective in identify symptomatic nerves in trigeminal neuralgia. The diagnostic efficacy of the combined model was notably superior to that of the model constructed solely from conventional clinical features.

Globotriaosylsphingosine improves risk stratification of kidney progression in Fabry disease patients

BackgroundKidney damage is common in patients with Fabry disease (FD), but more accurate information about the risk of progression to kidney failure is needed for clinical decision-making. In particular, FD patients with mild renal involvement often lack timely intervention and treatment. We aimed to utilize a model to predict the risk of renal progression in FD patients. MethodsBetween November 2011 and November 2019, ERT-naive patients with FD were recruited from three medical centers in China. To assess the risk of a 50% decline in the estimated glomerular filtration rate (eGFR) or end-stage kidney disease (ESKD), Cox proportional hazards models were utilized. The performance of these models was assessed using discrimination, calibration, and reclassification. ResultsA total of 117 individuals were enrolled. The mean follow-up time was 4.8 years, during which 35 patients (29.9 %) progressed to the composite renal outcomes. Male sex, baseline proteinuria, eGFR and globotriaosylsphingosine (Lyso-Gb3) were found to be independent risk factors for kidney progression by the Cox model, based on which a combined model containing those clinical variables and Lyso-Gb3 and clinical models including only clinical indicators were constructed. The two prediction models had relatively good performance, with similar model fit measured by R2 (59.8 % vs. 61.1 %) and AIC (51.54 vs. 50.08) and a slight increase in the C statistic (0.949 vs. 0.951). Calibration curves indicated closer alignment between predicted and actual renal outcomes in the combined model. Furthermore, subgroup analysis revealed that Lyso-Gb3 significantly improved the predictive performance of the combined model for kidney prognosis in low-risk patients with a baseline eGFR over 60 ml/min/1.73 m2 or proteinuria levels less than 1 g/d when compared to the clinical model. ConclusionsLyso-Gb3 improves the prediction of kidney outcomes in FD patients with a low risk of progression, suggesting that these patients may benefit from early intervention to assist in clinical management. These findings need to be externally validated.

Numerical simulation for microstructure evolution of magnetorheological fluid based on combined disk DDA-LBM approach

In order to investigate the microstructure evolution characteristic of the magnetorheological fluid (MRF), a novel combined model coupling the disk discontinuous deformation analysis (disk DDA) model with the lattice Boltzmann method (LBM) called the disk DDA-LBM model is developed in this paper, and its acceptability is verified. After that, this new combined model is used to simulate the microstructure evolution process of the MRF. The simulation results clearly demonstrate the chain-process of the MRF, indicating that the proposed method exhibits a satisfactory accuracy in describing the microstructure evolution of the MRF. Simultaneously, a highly effective chain identification technique called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is introduced to quantify the number of particle chains within the MRF. The results demonstrate that this method can accurately identify magnetic chains in the MRF and tracks changes of the chain number throughout the entire chain-process of the MRF.

Cost-effective internet of things privacy-aware data storage and real-time analysis

&lt;span lang="EN-US"&gt;It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.&lt;/span&gt;

Both de-growth and a-growth to achieve strong and weak sustainability: a theoretical model, empirical results, and some ethical insights

This study conceptually characterizes and theoretically represents the four main sustainability paradigms (strong sustainability, weak sustainability, de-growth, and a-growth) in terms of equality and inequality. It then applies these conditions to developed and less-developed countries (OECD and non-OECD countries) and empirically shows that the change in production technology (∆θ) required by a-growth is impossible and the change in consumption preferences (∆α) required by de-growth is unfeasible. Finally, it combines a-growth and de-growth in a theoretical solution for the relationship between ∆α and ∆θ that meets the conditions required by both strong and weak sustainability (parameters are the world’s population, consumption preferences, and production technologies or concerns for nature and future generations in developed and less-developed countries) and empirically demonstrates that this solution is feasible. In particular, sustainability turns out to be an ethical issue more than a technological issue, and the ethical concern for nature turns out to be more favorable than the ethical concern for future generations. Ethical assumptions and implications of the four main sustainability paradigms are highlighted and ethical assumptions and implications of the combined theoretical model are discussed. In particular, intergenerational efficiency is achieved in terms of welfare, and intergenerational equity is achieved in terms of environmental status.

Predictors for early-onset psychotic symptoms in patients newly diagnosed with Parkinson's disease without psychosis at baseline: A 5-year cohort study.

To investigate the risk factors for early-onset psychosis in Parkinson's disease (PD) in a cohort of patients from the Parkinson's Progression Markers Initiative. Longitudinal data on motor and non-motor features, dopamine transporter (DAT) imaging, and cerebrospinal fluid (CSF) measurements were collected. The survival probability of psychotic symptoms, potential risk factors for psychosis development over a 5-year follow-up period, and the performance of the prediction model were evaluated. Among the 338 newly diagnosed patients with PD, 83 developed psychotic symptoms. Gastrointestinal autonomic dysfunction, presence of probable rapid-eye-movement sleep behavior disorder, and the ratio Aβ42: total-tau could independently predict onset of psychosis in PD (hazard ratio (HR) = 1.157, 95% confidence interval (CI) 1.022-1.309, p = 0.021, HR = 2.596, 95% CI 1.287-5.237, p = 0.008, and HR = 0.842, 95% CI 0.723-0.980, p = 0.027, respectively). The combined model integrating baseline clinical predictors, DAT imaging, and CSF measurements achieved better sensitivity than the clinical predictors alone (area under the curve = 0.770 [95% CI 0.672-0.868] vs. 0.714 [95% CI 0.625-0.802], p = 0.098). We identified clinical and CSF predictors of early-onset psychosis in patients with PD. Our study provides evidence and implications for prognostic stratification and therapeutic approaches for PD psychosis.