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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 104 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

A study utilizing convolutional neural networks (CNN) has been conducted to detect and classify invisible debonding-type defects in reinforced structures. Training data for these defects is collected from the finite element models of honeycomb sandwich panels and skin-stringer systems, commonly employed reinforcement structures in aerospace applications. The excitation frequency is determined based on the amplitude of the reflected wave from the defect, and the optimal sensor array is selected. The constructed two-dimensional training image, created by vertically stacking the measured responses in the time domain, exhibits high classification performance even with a shallow neural network. The neural network undergoes optimization through adjustment to the kernel parameters and initial learning rate. To assess the general performance of the training model, k-fold cross-validation is employed. The CNN-based non-destructive evaluation algorithm demonstrates high classification performance for debonding defects in honeycomb sandwich panels and skin-stringer structures. Moreover, the suggested algorithm is robust against noise, emphasizing its effectiveness in real-world applications.

Abstract In this study, the authors fabricate Sn‐doped 100‐nm thick polycrystalline β‐Ga2O3 synaptic field‐effect transistors (FETs) emulating optical and electrical spike stimulation. When stimulated by deep ultraviolet (UV) optical spikes or electric voltage spikes at the gate, the devices exhibit several essential synaptic functions of excitatory‐postsynaptic currents (EPSCs), inhibitory‐postsynaptic currents (IPSCs), paired‐pulse facilitation (PPF), spike‐number‐dependent plasticity (SNDP), and spike‐timing‐dependent plasticity (STDP). Following UV optical stimulation, the devices mimic synaptic plasticity with a photogate effect, and the gate voltage stimulation emulates the synaptic weights according to the state of the gate dielectric interface. The β‐Ga2O3 synaptic FET demonstrates synergistic functions in various optoelectronic stimulation modes and successfully mimics the visual memory formation in bees with UV photoreceptors. Moreover, to verify the translation of optoelectrical‐derived synaptic behaviors of β‐Ga2O3 synaptic FETs into artificial neuromorphic computing, handwritten digit image recognition of the Modified National Institute of Standards and Technology dataset is performed using a convolutional neural network, and a learning accuracy of 96.92% is achieved. The realization of these fundamental functions of biological synapses suggests the utility of Ga2O3‐based optoelectronic devices for next‐generation neuromorphic computing.

The physics-informed neural network (PINN) has drawn much attention as it can reduce training data size and eliminate the need for physics equation identification. This paper presents the implementation of a PINN with adaptive normalization in the loss function to predict lithium-ion battery cell temperature. In particular, the PINN was trained with the actual battery test data, and a lumped capacitance lithium-ion battery thermal relationship was applied to the loss function with the addition of a pre-layer and connection layer to the neural network architecture. The PINN architecture shows the most accurate battery temperature prediction compared with the fully connected neural network (FCN) and its variants evaluated in this study. The proposed PINN architecture has a mean square prediction error of 0.05 °C with a limited number of training data and without battery thermal model identification.

Background: A job-exposure matrix (JEM) is an important surrogate indicator to evaluate past exposure levels. Although a Korean asbestos JEM has been constructed previously, this JEM includes only a few industrial and occupational groups. This study aimed to reconstruct the JEM by integrating the latest organized data to improve its utility. Methods: We used recent Korean standard industry and occupation codes and extracted 36 articles from a systematic literature review to initiate the reconstruction of the previous Korean asbestos JEM. The resulting data consisted of 141 combinations of industrial and occupational groups. Data from the Netherlands's JEM were also reviewed and categorized into 70 industrial and 117 occupational groups by matching with the Korean data. We also utilized Germany's data, which consisted of 10 industrial and 14 occupational groups. Results: The reconstructed Korean asbestos JEM had 141 combinations of industries and occupations. The time periods are from the 1980s to the 2000s in 10-year intervals. Most of the data were distributed between the 1990s and the 2000s. Occupations with high exposure to asbestos included knitting and weaving machine operators, automobile mechanics or assemblers, ship mechanics or assemblers, mineral ore and stone products processing mechanics, and metal casting machine operators or mold makers. Conclusions: The reconstructed Korean asbestos JEM has expanded the type and duration of the occupational groups of the previous JEM and can serve as an important reference tool for evaluating asbestos exposure and designing compensation and prevention policies in Korea.

This paper proposes a defensive ecological adaptive cruise control (DEco-ACC) algorithm that is capable of reducing an ego vehicle’s dwelling time in the blind spot zones (BSZs) of its neighboring vehicles. To this end, a model predictive control is applied in the use of information such as speed, position, and blind spot zones about preceding and neighboring vehicles. The cost function of the DEco-ACC consists of tracking performance, control effort, and dwelling time in BSZs. Specifically, a continuous and one-time differentiable penalty function is introduced to handle the constraints regarding the BSZs. For optimizing and evaluating the performance of the proposed DEco-ACC, real-world traffic data from Next Generation Simulation (NGSIM) are used to analyze and generate car-following scenarios during highway driving. Especially, in consideration of the most probable case that one neighboring vehicle exists at one adjacent lane, a parametric study is conducted to investigate the impact of the weighting factors on the performance of the DEco-ACC. The simulation results from 100 cases demonstrate that on average, the DEco-ACC with optimized weighting factors can reduce the dwelling time in the neighboring vehicles’ BSZs by 46.3% without significant deterioration of fuel consumption (0.04% increase in average fuel consumption) and drivability, as compared to the Eco-ACC, whose primary objective is the minimization of fuel consumption during safe car-following.

Model predictive control (MPC) has been widely adopted for cooperative adaptive cruise control (CACC) due to its superior performance in achieving fuel-efficient driving while satisfying constraints such as inter-vehicle distance. The core of an MPC-based algorithm is to predict the vehicle’s behavior using a dynamic model, and the space-domain vehicle dynamic model is frequently implemented in recent research along with the time-domain vehicle dynamic model. This paper presents a comparative performance analysis between the space-domain and the time-domain models in the MPC framework for the car-following problem. An MPC design process and analysis method for the high-speed car-following scenario is suggested and presented for equivalent performance comparison between the two approaches. In order to analyze trends between speed tracking and fuel-saving performance, which are conflicting objectives as car-following performance, a bi-objective cost function is proposed and manipulated by various weightings. It is observed that the space-domain model presents stable tracking performance, and the time-domain model shows better fuel efficiency. However, the space-domain model with road information is superior in tracking and fuel efficiency compared to the time-domain model with limited road information. Pareto analysis was implemented to visualize and describe performance differences in various situations regarding tracking error, fuel efficiency, and road grade information levels.

Recent studies suggest that galactose-deficient IgA1 (Gd-IgA1) plays a role in the pathogenesis of primary IgA nephropathy (IgAN) and Henoch–Schönlein purpura nephritis (HSPN). Furthermore, immunostaining of KM55, an antibody that identifies Gd-IgA1, may be helpful to differentiate primary IgAN and HSPN from secondary causes of glomerular IgA deposition. We report sequential kidney biopsies of a malignancy-associated HSPN, showing intense glomerular mesangial IgA deposition at the initial kidney biopsy and dramatic decrease in disappearance of glomerular deposits after tumor removal. We demonstrate that the glomerular IgA deposition contains Gd-IgA1, detected by immunostaining of KM55, with similar distribution and intensity to IgA. This suggests that renal Gd-IgA1 deposition may play a role in the pathogenesis of malignancy-associated HSPN.

Abstract Background There are few published studies on the relationship between occupational lumbar load and facet joint degeneration (FJD). This cross-sectional study was conducted to evaluate the effect of physical lumbar load on FJD by comparing magnetic resonance imaging (MRI) findings of firefighters (FFs) and hospital office workers (HOWs). Methods We randomly sampled 341 male FFs and 80 male HOWs by age stratification. A questionnaire and clinical examination, including MRI of the lumbar spine (T12-S1), were conducted. FJD was diagnosed and graded by using the classification of Pathria et al., and reclassified into two groups as follows: no FJD (grade 0) and FJD (grades 1, 2, and 3). The prevalence of FJD was analyzed according to occupational group. Results The prevalence of FJD ranged from 31% (L1–L2) to 75% (L4–L5) in the FFs, and from 18% (L1–L2) to 69% (L4–L5) in the HOWs. After adjustment for age, body mass index, and frequency of physical exercise, the adjusted odds ratios (OR) for FJD in the FFs were significantly higher than those in the HOWs at all lumbar spinal levels, except for L3–L4 (L1–L2: OR, 2.644; 95% confidence interval [CI], 1.317–5.310; L2–L3: OR, 2.285; 95% CI, 1.304–4.006; L4–L5: OR, 1.918; 95% CI, 1.037–3.544; L5–S1: OR, 1.811; 95% CI, 1.031–3.181). Conclusion This study shows that FFs exhibit a greater likelihood of having FJD than HOWs after controlling for other risk factors of FJD. This suggests that the physical occupational demands of FFs affect their risk of developing FJD.

Public key encryption with keyword search (PEKS) enables users to search over encrypted data outsourced to an untrusted server. Unfortunately, updates to the outsourced data may incur information leakage by exploiting the previously submitted queries. Prior works addressed this issue by means of forward privacy, but most of them suffer from significant performance degradation. In this paper, we present a novel forward private PEKS scheme leveraging Software Guard Extension (SGX), a trusted execution environment provided by Intel. The proposed scheme presents substantial performance improvements over prior work. Specifically, we reduce the query processing cost from O(n) to O(1), where n is the number of encrypted data. According to our performance analysis, the overall computation time is reduced by 80% on average. Lastly, we provide a formal security definition of SGX-based forward private PEKS, as well as a rigorous security proof of the proposed scheme.

The standard treatment of hypoparathyroidism is to control hypocalcemia using calcitriol and calcium supplementation. However, in severe cases this approach is insufficient, and the risks of intravenous (i.v.) calcium administration and prolonged hospitalization must be considered. While the use of recombinant human parathyroid hormone 1-34 [rhPTH(1-34)] for long-term control of hypocalcemia has been established, the benefits of short-term rhPTH(1-34) treatment in children have not been explored. We report two patients with hypoparathyroidism treated with rhPTH(1-34). Patient 1 is a 10-year-old female with polyglandular autoimmune syndrome type 1. Patient 2 is a 12-year-old female with hypoparathyroidism after total thyroidectomy. Both patients showed poor response to i.v. and oral calcium and calcitriol, and patient 1 did not respond to phosphate binders. Patient 1 had rapid increase in serum calcium with a decrease in serum phosphate after a 3-day course of subcutaneous rhPTH(1-34). Patient 2 had normalization of calcium and phosphate levels after a 7-day course of rhPTH(1-34). These cases support a role for rhPTH(1-34) in the acute management of hypoparathyroidism in hospitalized patients to more rapidly correct hypocalcemia and hyperphosphatemia, shorten hospitalization, and reduce the need for frequent i.v. calcium boluses.

The frontal cortex might play an important role in the fear network, and white matter (WM) integrity could be related to the pathophysiology of panic disorder (PD). A few studies have investigated alterations of WM integrity in PD. The aim of this study was to determine frontal WM integrity differences between patients with PD without comorbid conditions and healthy control (HC) subjects by using diffusion tensor imaging. Thirty-six patients with PD who had used medication within 1 week and 27 age- and sex-matched HC subjects participated in this study. Structural brain magnetic resonance imaging was performed on all participants. Panic Disorder Severity Scale and Beck Anxiety Inventory (BAI) scores were assessed. Tract-based spatial statistics (TBSS) was used for image analysis. TBSS analysis showed decreased fractional anisotropy (FA) in frontal WM and WM around the frontal lobe, including the corpus callosum of both hemispheres, in patients with PD compared to HC subjects. Moreover, voxel-wise correlation analysis revealed that the BAI scores for patients with PD were positively correlated with their FA values for regions showing group differences in the FA of frontal WM of both hemispheres. Altered integrity in frontal WM of patients with PD without comorbid conditions might represent the structural pathophysiology in these patients, and these changes could be related to clinical symptoms of PD.