Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach.

The scientific community has raised increasing apprehensions over the transparency and interpretability of machine learning models employed in various domains, particularly in the field of materials science. The intrinsic intricacy of these models frequently results in their characterization as "black boxes", which poses a difficulty in emphasizing the significance of producing lucid and readily understandable model outputs. In addition, the assessment of model performance requires careful deliberation of several essential factors. The objective of this study is to utilize a deep learning framework called TabNet to predict lead zirconate titanate (PZT) ceramics' dielectric constant property by employing their components and processes. By recognizing the crucial importance of predicting PZT properties, this research seeks to enhance the comprehension of the results generated by the model and gain insights into the association between the model and predictor variables using various input parameters. To achieve this, we undertake a thorough analysis with Shapley additive explanations (SHAP). In order to enhance the reliability of the prediction model, a variety of cross-validation procedures are utilized. The study demonstrates that the TabNet model significantly outperforms traditional machine learning models in predicting ceramic characteristics of PZT components, achieving a mean squared error (MSE) of 0.047 and a mean absolute error (MAE) of 0.042. Key contributing factors, such as d33, tangent loss, and chemical formula, are identified using SHAP plots, highlighting their importance in predictive analysis. Interestingly, process time is less effective in predicting the dielectric constant. This research holds considerable potential for advancing materials discovery and predictive systems in PZT ceramics, offering deep insights into the roles of various parameters.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

Enhanced Harmonics Reactive Power Control Strategy Based on Multilevel Inverter Using ML-FFNN for Dynamic Power Load Management in Microgrid.

The shift of the world in the past two decades towards renewable energy (RES), due to the continuously decreasing fossil fuel reserves and their bad impact on the environment, has attracted researchers all around the world to improve the efficiency of RES and eliminate problems that arise at the point of common coupling (PCC). Harmonics and un-balance in 3-phase voltages because of dynamic and nonlinear loads cause a lagging power factor due to inductive load, active power losses, and instability at the point of common coupling. This also happens due to a lack of system inertia in micro-grids. Passive filters are used to eliminate harmonics at both the electrical converter's input and output sides and improve the system's power factor. A Synchronous Reference Frame (SRF) control method is used to overcome the problem related to grid synchronization. The sine pulse width modulation (SPWM) technique provides gating signals to the switches of the multilevel inverter. A multi-layer feed forward neural network (ML-FFNN) is employed at the output of a system to minimize mean square error (MSE) by removing the errors between target voltages and reference voltages produced at the output of a trained model. Simulations were performed using MATLAB Simulink to highlight the significance of the proposed research study. The simulation results show that our proposed intelligent control scheme used for the suppression of harmonics compensated for reactive power more effectively than the SRF-based control methods. The simulation-based results confirm that the proposed ML-FFNN-based harmonic and reactive power control technique performs 0.752 better in terms of MAE, 0.52 for the case of MSE, and 0.222 when evaluating based on the RMSE.

A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simulation Tools Based on AI for Material Analysis and Discovery.

Research has become increasingly more interdisciplinary over the past few years. Artificial intelligence and its sub-fields have proven valuable for interdisciplinary research applications, especially physical sciences. Recently, machine learning-based mechanisms have been adapted for material science applications, meeting traditional experiments' challenges in a time and cost-efficient manner. The scientific community focuses on harnessing varying mechanisms to process big data sets extracted from material databases to derive hidden knowledge that can successfully be employed in technical frameworks of material screening, selection, and recommendation. However, a plethora of underlying aspects of the existing material discovery methods needs to be critically assessed to have a precise and collective analysis that can serve as a baseline for various forthcoming material discovery problems. This study presents a comprehensive survey of state-of-the-art benchmark data sets, detailed pre-processing and analysis, appropriate learning model mechanisms, and simulation techniques for material discovery. We believe that such an in-depth analysis of the mentioned aspects provides promising directions to the young interdisciplinary researchers from computing and material science fields. This study will help devise useful modeling in the materials discovery to positively contribute to the material industry, reducing the manual effort involved in the traditional material discovery. Moreover, we also present a detailed analysis of experimental and computation-based artificial intelligence mechanisms suggested by the existing literature.

Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization.

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.

An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing.

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices' dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device's decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

rs641738C>T near MBOAT7 is associated with liver fat, ALT and fibrosis in NAFLD: A meta-analysis.

BACKGROUND & AIMS: A common genetic variant near MBOAT7 (rs641738C>T) has been previously associated with hepatic fat and advanced histology in NAFLD; however, these findings have not been consistently replicated in the literature. We aimed to establish whether rs641738C>T is a risk factor across the spectrum of NAFLD and to characterise its role in the regulation of related metabolic phenotypes through a meta-analysis. METHODS: We performed a meta-analysis of studies with data on the association between rs641738C>T genotype and liver fat, NAFLD histology, and serum alanine aminotransferase (ALT), lipids or insulin. These included directly genotyped studies and population-level data from genome-wide association studies (GWAS). We performed a random effects meta-analysis using recessive, additive and dominant genetic models. RESULTS: Data from 1,066,175 participants (9,688 with liver biopsies) across 42 studies were included in the meta-analysis. rs641738C>T was associated with higher liver fat on CT/MRI (+0.03 standard deviations [95% CI 0.02-0.05], pz = 4.8×10-5) and diagnosis of NAFLD (odds ratio [OR] 1.17 [95% CI 1.05-1.3], pz = 0.003) in Caucasian adults. The variant was also positively associated with presence of advanced fibrosis (OR 1.22 [95% CI 1.03-1.45], pz = 0.021) in Caucasian adults using a recessive model of inheritance (CC + CT vs. TT). Meta-analysis of data from previous GWAS found the variant to be associated with higher ALT (pz = 0.002) and lower serum triglycerides (pz = 1.5×10-4). rs641738C>T was not associated with fasting insulin and no effect was observed in children with NAFLD. CONCLUSIONS: Our study validates rs641738C>T near MBOAT7 as a risk factor for the presence and severity of NAFLD in individuals of European descent. LAY SUMMARY: Fatty liver disease is a common condition where fat builds up in the liver, which can cause liver inflammation and scarring (including 'cirrhosis'). It is closely linked to obesity and diabetes, but some genes are also thought to be important. We did this study to see whether one specific change ('variant') in one gene ('MBOAT7') was linked to fatty liver disease. We took data from over 40 published studies and found that this variant near MBOAT7 is linked to more severe fatty liver disease. This means that drugs designed to work on MBOAT7 could be useful for treating fatty liver disease.

Genetic variants in CYP7A1 and risk of myocardial infarction and symptomatic gallstone disease.

Aims: Myocardial infarction (MI) and gallstone disease (GSD) are intrinsically linked via cholesterol metabolism. We tested the hypothesis that genetic variants in the gene encoding cholesterol 7 alpha-hydroxylase (CYP7A1), the rate-limiting enzyme in the conversion of cholesterol to bile acids in the liver, are associated with risk of MI and GSD in the general population. Methods and results: We performed tests of association between lipid levels and eight rare non-synonymous mutations and two common variants, rs2081687 and rs3808607, in CYP7A1 in 100 149 individuals from the general population. We further tested whether weighted allele scores for rs2081687 and rs3808607, which were associated with increased plasma levels of low-density lipoprotein (LDL) cholesterol, were associated with an increased risk of both MI and symptomatic GSD. During a mean follow-up of 7 years (0-23 years), MI developed in 2326 individuals and GSD in 2007. For rare mutations, CYP7A1 allele count was associated with an increase in LDL cholesterol of 12% (0.4 mmol/L) for individuals with the highest vs. the lowest allele count (P for trend = 3 × 10-4). For common variants, CYP7A1 weighted allele scores in individuals with a score >0.04 vs. ≤0 were associated with stepwise increases in LDL cholesterol of up to 2.4% (0.08 mmol/L), and with corresponding multifactorially adjusted hazard ratios of 1.25 [95% confidence interval (CI) 1.10-1.41] for MI and 1.39 (95% CI 1.22-1.59) for GSD (P for trend = 5 × 10-4 and 2 × 10-7, respectively). Results were similar in meta-analyses including publicly available data from large consortia. Conclusion: Genetic variants in CYP7A1 which are associated with increased levels of LDL cholesterol, are associated with an increased risk of both MI and GSD.

Comparison of rest and adenosine stress quantitative and semi-quantitative myocardial perfusion using magnetic resonance in patients with ischemic heart disease.

The aim was to compare absolute quantified myocardial perfusion (MP) to semi-quantitative measurements of MP using MRI for detection of ischemia. Twenty-nine patients underwent rest and stress MP imaging obtained by 1.5T MRI and analyzed using own developed software and by commercial available software. Linear regression analysis demonstrated that absolute quantitative data correlated stronger to maxSI (rest: r=0.296, p=.193; stress: r=0.583, p=0.011; myocardial perfusion reserve (MPR): r=0.789, p<0.001; and Δ myocardial blood flow (Δ MBF: r=0.683, p=0.004) than to upslope (rest: r=0.420, p=0.058; stress: r=0.096, p=0.704; MPR: r=0.682, p=0.004; and Δ MBF: r=0.055, p=0.804). Absolute quantified MP was able to distinguish between ischemic and non-ischemic territories at rest (left anterior descending artery (LAD): 103.1±11.3mL/100g/min vs. 206.3±98.5mL/100g/min; p=0.001, right coronary artery (RCA): 124.1±45.2mL/100g/min vs. 241.3±81.7mL/100g/min; p<0.001, and left circumflex artery (LCX): 132.8±53.8mL/100g/min vs. 181.2±56.6mL/100g/min; p=0.060) and at stress (LAD: 148.1±47.2mL/100g/min vs. 296.6±111.6mL/100g/min; p=0.012, RCA: 173.4±63.7mL/100g/min vs. 290.2±100.6mL/100g/min; p=0.008, and LCX: 206.6±105.1mL/100g/min vs. 273.8±78.0mL/100g/min; p=0.186). The correlation between global maxSI and positron emission tomography data was non-significant at rest and borderline significant at stress (r=0.265, p=0.382 and r=0.601, p=0.050, respectively). Quantified MP may be useful in patients for detection of ischemia.

[Dislocation of the ankle without simoustaneously fracture of the bones].

The ankle is a unique modified saddle joint that, together with the subtalar joint, provides range of motion in several physical planes while maintaining stability. The ankle complex functions as a pivoting structure positioned to bear the entire weight of the body which leaves it vulnerable to injuries. Pure dislocation without associated fracture is rare; however, cases of isolated ankle dislocation without fracture have been reported. We report a case of a closed ankle dislocation without an associated fracture in a 17-year-old boy.

Vitamin D deficiency is unrelated to type of atrial fibrillation and its complications.

INTRODUCTION: Vitamin D plays an important role in a broad range of organ functions, including the cardiovascular system. Only one study has tested the association between vitamin D deficiency and arrhythmia and it found no association. The aim of the present study was to evaluate the association between vitamin D deficiency and the type of atrial fibrillation (AF) and complications to AF. MATERIAL AND METHODS: In total, 258 patients were consecutively included from March 2009 to February 2011. All in- and out-patients in the Department of Cardiology at Hvidovre Hospital were invited to participate, provided they had electrocardiographically documented AF. Patients with dementia or terminal illness were excluded. 25 hydroxyvitamin D (25 OHD) was measured with a chemiluminescence assay (Liaison from DiaSorin, Stillwater, Minnesota, USA). RESULTS: No association between vitamin D level and type of AF was found. Furthermore, no association between vitamin D deficiency and ischaemic heart disease, stroke or acute myocardial infarction was found. Vitamin D deficiency was significantly associated with low age (p = 0.02) and gender with a higher proportion of females having the optimal level of 25 OHD (p = 0.0005). CONCLUSION: Other studies have found a beneficial effect of vitamin D on cardiovascular diseases, but we found no association between vitamin D deficiency and the type of AF or complications to AF. Further investigation is necessary to determine whether vitamin D supplementation improves cardiovascular outcomes in patients with AF. FUNDING: The study has received financial support from several private and one public fund. TRIAL REGISTRATION: The study was approved by the National Ethics Committee (Project-ID: H-C-2009-014).