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Abstract Objectives Epilepsy and migraine are common chronic neurological disease. Epidemiologic studies and shared pathophysiology and treatment suggest that these two diseases overlap. However, migraine is often underestimated among patients with epilepsy. This study aimed to evaluate the prevalence of migraine and identify the related influencing factors among adult patients with epilepsy. Methods Adult patients with epilepsy were recruited at the outpatient epilepsy clinic of 13 tertiary hospitals in China from February to September 2022. ID Migraine questionnaire was applied to evaluate for migraine. Both univariable and multivariable logistic regression models were used to explore the influencing factors of migraine. Results A total of 1326 patients with epilepsy were enrolled in this study. The prevalence of migraine among patients with epilepsy was 19.2% (254/1326). In the multivariable analysis, being female (OR = 1.451, 95% CI: 1.068–1.975; p = 0.018), focal and focal to bilateral tonic–clonic seizures (OR = 1.583, 95% CI: 1.090–2.281; p = 0.015), and current seizure attack in the last 3 months (OR = 1.967, 95% CI: 1.282–3.063; p = 0.002) were the influencing factors for migraine. However,

Abstract Background The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) patients who undergo continuous renal replacement therapy (CRRT) are different. Setting up risk stratification tools for both short-term and long-term deaths is highly important for clinicians. Method A total of 1535 AKI patients receiving CRRT were included in this study, with 1144 from the training set (the Dryad database) and 391 from the validation set (MIMIC IV database). A model for predicting mortality within 10 and 90 days was built using nine different machine learning (ML) algorithms. AUROC, F1-score, accuracy, sensitivity, specificity, precision, and calibration curves were used to assess the predictive performance of various ML models. Results A total of 420 (31.1%) deaths occurred within 10 days, and 1080 (68.8%) deaths occurred within 90 days. The random forest (RF) model performed best in both predicting 10-day (AUROC: 0.80, 95% CI: 0.74–0.84; accuracy: 0.72, 95% CI: 0.67–0.76; F1-score: 0.59) and 90-day mortality (AUROC: 0.78, 95% CI: 0.73–0.83; accuracy: 0.73, 95% CI: 0.69–0.78; F1-score: 0.80). The importance of the feature shows that SOFA scores are rated as the most important risk factor for both 10-day and 90-day mortality. Conclusion Our study, utilizing multiple machine learning models, estimates the risk of short-term and long-term mortality among AKI patients who commence CRRT. The results demonstrated that the prognostic factors for short-term and long-term mortality are different. The RF model has the best prediction performance and has valuable potential for clinical application.

Abstract There is growing consensus that gas-fired generators will play a crucial role during the transition to net-zero energy systems, both as an alternative to coal-fired generators and as a flexibility service provider for power systems. However, malfunctions of gas networks have caused several large-scale power blackouts. The transition from coal and oil to gas fuels significantly increases the interdependence between gas networks and electric power systems, raising the risks of more frequent and widespread power blackouts due to the malfunction of gas networks. In a coupled gas–electricity system, the identification and transmission of gas network malfunction information, followed by the redispatch of electric power generation, occur notably faster than the propagation and escalation of the malfunction itself, e.g., significantly diminished pressure. On this basis, we propose a gas-electric early warning system that can reduce the negative impacts of gas network malfunctions on the power system. A proactive control strategy of the power system is also formulated based on the early warning indicators. The effectiveness of this method is demonstrated via case studies of a real coupled gas–electricity system in China.

Abstract A low‐cost W‐band leaky‐wave antenna with two‐dimensional scanning capability is presented. By using the low‐loss transversal long‐slot array structure with a single‐layer parabolic reflector, the antenna realizes a passband from 85 to 102.8 GHz with Voltage Standing Wave Ratio

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arbitrage value. However, this price-smoothing effect can result in significant external welfare changes by reducing consumer costs and producer revenues, which is not negligible for the community with energy storage systems. As such, we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare. To incorporate market interaction into the SDP format, we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices. Then we present an analytical SDP algorithm that does not require state discretization. Apart from computational efficiency, another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value. Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage. The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.

While price schedules can help improve the economic efficiency of renewable energy-powered microgrids, time-of-use (TOU) pricing has been identified as an effective way for microgrid development, which is presently limited by its high costs. In this study, we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system, that comprises a government agent, local utility company agent, and different types of consumer agents. In the proposed model, we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction. Two pricing strategies, namely, the TOU seasonal pricing and TOU monthly pricing, are evaluated and compared with traditional fixed pricing. The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing. Additionally, TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.

Abstract Internet of Things devices generate a large number of heterogeneous workloads in real-time that require specific application to tackle, and the inability to communicate between devices and communication base stations due to complex scenarios is a thorny issue. Service caching play a key role in managing specific-request workload from devices, and unmanned aerial vehicles with computation and communication functions can effectively solve communication barrier between devices and ground base stations. In addition, the joint optimization of workload offloading and service cache placement is a key issue. Accordingly, we design an unmanned aerial vehicle-enabled mobile edge computing system with multiple devices, unmanned aerial vehicles and edge servers. The proposed framework takes into account the randomness of workload arrival, the time-varying nature of channel states, the limitations of the hosting service caching, and wireless communication blocking. Furthermore, we designed workload offloading and service caching hosting decision-making optimization problems to minimize the long-term weighted average latency and energy consumption costs. To tackle this joint optimization problem, we propose a request-specific workload offloading and service caching decision-making scheme based on the medley deep reinforcement learning scheme. To this end, the proposed scheme is decomposed into two-stage optimization subproblems: the workload offloading decision-making problem and the service caching hosting selection problem. In terms of the first subproblem, we model each device as a learning agent and propose the workloads offloading decision-making scheme based on multi-agent deep deterministic policy gradient. For the second subproblem, we present the decentralized double deep Q-learning scheme to tackle the service caching hosting policy. According to the comprehensive experimental results, the proposed scheme is able to converge rapidly on various parameter configurations and whose performance surpasses the other four baseline learning algorithms.

Abstract Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.

In an attempt to improve the utilization efficiency of multi-energy coupling in park-level integrated energy system (PIES), promote wind power consumption and reduce carbon emissions, a low-carbon economic operation optimization model of PIES integrating flexible load and carbon trading mechanism is constructed. Firstly, according to the characteristics of load response, the demand response is divided into four types: which can be shifted, transferred, reduced and replaced. Secondly, the PIES basic architecture is given by considering the combined heat and power generation coupling equipment, new energy and flexible load in the park. Finally, introducing the ladder-type carbon trading mechanism into the system and minimize the total operating cost, the low-carbon economic operation optimization model of PIES is established. The YALMIP toolbox and CPLEX solver are used to solve the example, the simulation results show that the participation of electrical and thermal coupled scheduling and flexible electric or thermal loads can significantly reduce the system operating cost, reduce the load peak-to-valley difference and relieve peak power consumption pressure.

With the continuous growth of electricity demand, the safety and stability of transmission lines have become increasingly important. To ensure the reliability of power supply, it is essential to promptly detect and address foreign object intrusions on transmission lines, such as tree branches, kites, and balloons. Addressing the issues where foreign objects can cause power outages and severe safety accidents, as well as the inefficiency, time consumption, and labor-intensiveness of traditional manual inspection methods, especially in large-scale power transmission lines, we propose an enhanced YOLOv8-based model for detecting foreign objects. This model incorporates the Swin Transformer, AFPN (Asymptotic Feature Pyramid Network), and a novel loss function, Focal SIoU, to improve both the accuracy and real-time detection of hazards. The integration of the Swin Transformer into the YOLOv8 backbone network significantly improves feature extraction capabilities. The AFPN enhances the multi-scale feature fusion process, effectively integrating information from different levels and improving detection accuracy, especially for small and occluded objects. The introduction of the Focal SIoU loss function optimizes the model’s training process, enhancing its ability to handle hard-to-classify samples and uncertain predictions. This method achieves efficient automatic detection of foreign objects by comprehensively utilizing multi-level feature information and optimized label matching strategies. The dataset used in this study consists of images of foreign objects on power transmission lines provided by a power supply company in Jilin, China. These images were captured by drones, offering a comprehensive view of the transmission lines and enabling the collection of detailed data on various foreign objects. Experimental results show that the improved YOLOv8 network has high accuracy and recall rates in detecting foreign objects such as balloons, kites, and bird nests, while also possessing good real-time processing capabilities.

ObjectivesTo explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) in patients with prostate cancer (PCa).MethodsIn this retrospective study, all patients underwent 68Ga-PSMA-11 PET/CT, transrectal ultrasound (TRUS)-guided systematic biopsy, and RP for PCa sequentially between January 2017 and December 2022. Two DL models (three-dimensional [3D] ResNet-18 and 3D DenseNet-121) based on 68Ga-PSMA-11 PET and support vector machine (SVM) models integrating clinical data with DL signature were constructed. The model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.ResultsOf 109 patients, 87 (44 upgrading, 43 non-upgrading) were included in the training set and 22 (11 upgrading, 11 non-upgrading) in the test set. The combined SVM model, incorporating clinical features and signature of 3D ResNet-18 model, demonstrated satisfactory prediction in the test set with an AUC value of 0.628 (95% confidence interval [CI]: 0.365, 0.891) and accuracy of 0.727 (95% CI: 0.498, 0.893).ConclusionA DL method based on 68Ga-PSMA-11 PET may have a role in predicting pathological upgrading from biopsy to RP in patients with PCa.

Abstract Thermal homeostasis is vital for mammals and is controlled by brain neurocircuits. Yet, the neural pathways responsible for cold defense regulation are still unclear. Here, we found that a pathway from the lateral parabrachial nucleus (LPB) to the dorsomedial hypothalamus (DMH), which runs parallel to the canonical LPB to preoptic area (POA) pathway, is also crucial for cold defense. Together, these pathways make an equivalent and cumulative contribution, forming a parallel circuit. Specifically, activation of the LPB → DMH pathway induced strong cold-defense responses, including increases in thermogenesis of brown adipose tissue (BAT), muscle shivering, heart rate, and locomotion. Further, we identified somatostatin neurons in the LPB that target DMH to promote BAT thermogenesis. Therefore, we reveal a parallel circuit governing cold defense in mice, which enables resilience to hypothermia and provides a scalable and robust network in heat production, reshaping our understanding of neural circuit regulation of homeostatic behaviors.

High-quality demand-side management requires an abundance of load profiles to support decision-making processes. However, customer energy consumption data often contains sensitive personal information, and service providers face significant challenges in accessing a substantial amount of energy consumption data. To generate a large volume of customer data without compromising privacy, this study introduces a data-driven approach integrating Information Maximizing Generative Adversarial Networks (InfoGAN) with Multivariate Kernel Density Estimation (MKDE) for the generation of load profiles. InfoGAN is firstly trained based on existing customer load profiles, with the Q network disentangling the load into feature variables and the generator producing realistic profiles. Subsequently, MKDE is utilized to assess the distribution of these features, enabling the generation of new profiles by sampling new feature variables. The proposed method circumvents the need for intricate sampling or modeling processes and generates realistic data that represents the inherent uncertainties and fluctuations characterizing customers’ electricity consumption. The generated data could be used as the substitution for real electricity consumption data, thereby facilitating further applications without compromising privacy concerns.

【Objective】By investigating the ecological niche characteristics of the dominant species of the forest communities in the coastal zone of Dapeng Peninsula, Shenzhen, the ability and similarity of the species to use environmental resources are understood, providing a scientific basis for forest management in this area.【Method】On the basis of the field survey, by using the calculation methods of Levins niche breadth, Shannon-Wiener niche breadth, Pianka niche overlap and niche similarity, dominant species from tree layer and shrub layer in the Egong Village sample site and Luowutian Reservoir sample site in the coastal zone were selected for niche quantitative research.【Result】In the communities of the Egong Village sample site and the Luowutian Reservoir sample site, (1) the species with the larger niche breadth in the tree layer were Litsea rotundifolia var. oblongifolia and Aporosa dioica, with Levins indices of 16.40 and 22.70, and Shannon-Wiener indices of 2.89 and 3.16, respectively; the species with the larger niche breadth in the shrub layer were Diplospora dubia and Syzygium rehderianum, with Levins indices of 19.10 and 23.97 and Shannon indices of 3.07 and 3.20, respectively. (2) The overall mean values of niche overlap in the tree layer were 0.434 and 0.641, respectively, and the species with the larger niche overlap were Ilex pubescens and Rhodomyrtus tomentosa (0.997), Psydrax dicocca and Garcinia oblongifolia (0.923); the overall mean values of niche overlap in the shrub layer were 0.441 and 0.468, respectively, and species with the larger niche overlap were Pavetta hongkongensis and Sterculia lanceolata (0.910), Aporosa dioica and Psychotria asiatica (0.959). (3) The tree species with larger niche similarity were Pavetta hongkongensis and Mallotus paniculatus (0.820), Psydrax dicocca and Garcinia oblongifolia (0.817), Rhaphiolepis indica and Diplospora dubia (0.751), Syzygium rehderianum and Psychotria asiatica (0.878).【Conclusion】In coastal zone forest communities, Litsea rotundifolia var. Oblongifolia, Diplospora duia, Syzygium rehderianum have larger ecological niche breadth. The community structure and species composition in the Egong Village sample site are in a relatively stable state, and most species in the Egong Village sample site have widely varying needs for environmental resources. Interspecific competition is more intense in the tree layer than that in the shrub layer in the Luowutian Reservoir sample site, the forest communities in this site are still in the successional stage as a whole, and the ecological niche differentiation has not yet been stabilized.

Abstract Objective To evaluate the ability of 68Ga‐PSMA PET/CT maximum standard uptake value (SUVmax) to distinguish prostate cancer (PCa) International Society of Urological Pathology ISUP grade group (GG) 2 and GG3. Methods The PET/CT images and data of 147 patients were analyzed retrospectively, and the SUVmax of the index lesions were measured. The receiver operating characteristic curve was used to analyze the diagnostic value of PET/CT for PCa. The correlation between SUVmax and ISUP GG was analyzed. A logistic regression model was established with SUVmax and was validated to predict its value of diagnosing intermediate‐ and high‐risk PCa (ihPCa). Results Of the 147 patients, 112 cases were PCa (76.2%), and 35 cases were benign lesions (23.8%). There was a significant difference between the benign and the malignant groups (p 0.05). The specificity and the positive predictive value of 68Ga‐PSMA PET/CT in the diagnosis of PCa were 97% and 99% with cut‐off SUVmax of 6.94, while the specificity and the positive predictive value of ihPCa were 95% and 96% with cut‐off SUVmax of 10.12. Conclusion 68Ga‐PSMA PET/CT can reliably distinguish GG2 from GG3 PCa.

Background: Depression and anxiety are the most common psychiatric comorbidities in patients with epilepsy (PWE). However, they are often unrecognized and consequently untreated. Objective: The study was conducted to evaluate the prevalence and risk factors of anxiety and depression among Chinese adult PWE. Design: Cross-sectional study. Methods: Adult PWE were recruited from 13 tertiary epilepsy centers from February to September 2022. Generalized Anxiety Disorder-7 and Neurological Disorders Depression Inventory for Epilepsy were applied to evaluate anxiety and depression, respectively. Both univariate and multivariate logistic regression analyses models were performed to explore the risk factors of anxiety and depression. Results: A total of 1326 PWE were enrolled in this study. The prevalence of anxiety and depression was 31.45% and 27.30%, respectively. Being female [odds ratio (OR) = 1.467, 95% CI: 1.134–1.899; p = 0.004], focal and focal to bilateral tonic-clonic seizures (TCSZ) (OR = 1.409, 95% CI: 1.021–1.939; p = 0.036), and seizure occurrence in the last 3 months (OR = 1.445, 95% CI: 1.026–2.044; p = 0.036) were the risk factors for anxiety. Focal and focal to bilateral TCSZ (OR = 1.531, 95% CI: 1.094–2.138; p = 0.013) and seizure occurrence in the last 3 months (OR = 1.644, 95% CI: 1.130–2.411; p = 0.010) were the risk factors for depression. In addition, for every 1-year increment of age, the odds of developing depression were decreased by 3.8% ( p = 4.12e −5 ). Nevertheless, up to 70% of PWE did not receive any treatment for comorbidity. Conclusion: There were approximately 30% of PWE screened positive for anxiety or depression. Both focal and focal to bilateral TCSZ and seizure occurrence in the last 3 months were estimated as risk factors for anxiety and depression. However, the current status of treatment was not optimal.

With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

Abstract Background This study aimed to develop a novel analytic approach based on a radiomics model derived from 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT for predicting intraprostatic lesions in patients with prostate cancer (PCa). Methods This retrospective study included consecutive patients with or without PCa who underwent surgery or biopsy after 68Ga-PSMA-11 PET/CT. A total of 944 radiomics features were extracted from the images. A radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm with tenfold cross-validation in the training set. PET/CT images for the test set were reviewed by experienced nuclear medicine radiologists. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for the model and radiologists’ results. The AUCs were compared. Results The total of 125 patients (86 PCa, 39 benign prostate disease [BPD]) included 87 (61 PCa, 26 BPD) in the training set and 38 (61 PCa, 26 BPD) in the test set. Nine features were selected to construct the radiomics model. The model score differed between PCa and BPD in the training and test sets (both P

Investigating the economic and carbon emission performance of green production in marketing cooperatives is important in clarifying China’s green transition path and promoting green development. In this paper, we estimated the economic and carbon emission performance of green production in marketing cooperatives using survey data from 340 samples in Shandong Province, China. The results show that green farming improved the performance of marketing cooperatives, and the larger the scale, the better the performance. It is still valid after weakening the endogenous problem through endogenous transformation regression. Particularly, green farming has a more significant effect on the performance of low-performing marketing cooperatives. In addition, the carbon emission per unit area of green produces are significantly lower than that of conventional produces, and the carbon emission per unit yield of most green produces lower than that of conventional produces. Therefore, strengthening the standardized development of marketing cooperatives, promoting green technology research and development, and standardizing the market supervision of green produces are important ways to achieve the economic and carbon emission performance of green produces in China, as well as to promote China's green transformation.

District heating systems have been widely used in large and medium-sized cities. Typical district heating systems consist of the primary heating system (PHS) and the secondary heating system (SHS) operating in isolation. However, the isolated dispatch of the PHS and the SHS has poor adjustability and large losses, resulting in unnecessary operation costs. To address these issues, a coordinated economic dispatching model (CEDM) for the primary and secondary heating systems considering the boiler’s supplemental heating is proposed in this study, which characterized the physical properties of the PHS and the SHS in detail. Considering that the PHS and the SHS are controlled separately without central operators in practice, it is difficult to dispatch them in a centralized method. Thus, the master-slave splitting algorithm is innovatively introduced to solve the CEDM in a decentralized way. Finally, a P6S12 system is utilized to analyze and verify the effectiveness and optimality of the proposed algorithm.

Constraints on each node and line in power systems generally have upper and lower bounds, denoted as two-sided constraints. Most existing power system optimization methods with the distributionally robust (DR) chance-constrained program treat the two-sided DR chance constraint separately, which is an inexact approximation. This letter derives an equivalent reformulation for the generic two-sided DR chance constraint under the interval moment based ambiguity set, which does not require the exact moment information. The derived reformulation is a second-order cone program (SOCP) formulation and is then applied to the optimal power flow (OPF) problem under uncertainty. Numerical results on several IEEE systems demonstrate the effectiveness of the proposed SOCP formulation and show the differences with other DR chance-constrained OPF approaches.

This paper investigates the power sharing and voltage regulation issues of islanded single-/three-phase microgrids (S/T-MGs) where both sources and loads are unbalanced and the presence of adversarial cyber-attacks against sensors of distributed generator (DG) units is considered. Firstly, each DG unit is modeled as a heterogeneous linear dynamic agent with disturbances caused by sources and loads, then the problem is formulated as a distributed containment control problem. After that, to guarantee satisfactory power sharing and voltage control performance asymptotically achieved for the S/T-MGs, an attack-resilient distributed secondary control approach is developed by designing a distributed adaptive observer. With this approach, the effect of the cyber-attacks can be neutralized to ensure system stability and preserve bounded voltage synchronization. Simulation results are presented to demonstrate the effectiveness of the proposed control approach.

The selective hydrogenation of nitriles to prepare corresponding amines is highly desired in chemistry industry. Here, the authors selectively obtained secondary amines and primary amines over two types of atomically dispersed Pd catalysts supported on the nanodiamond-graphene hybrid support.

Combined heat and electricity operation with variable mass flow rates promotes flexibility, economy, and sustainability through synergies between electric power systems (EPSs) and district heating systems (DHSs). Such combined operation presents a highly nonlinear and nonconvex optimization problem, mainly due to the bilinear terms in the heat flow model—that is, the product of the mass flow rate and the nodal temperature. Existing methods, such as nonlinear optimization, generalized Benders decomposition, and convex relaxation, still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency. To resolve this problem, we herein first reformulate the district heating network model through an equivalent transformation and variable substitution. The reformulated model has only one set of nonconvex constraints with reduced bilinear terms, and the remaining constraints are linear. Such a reformulation not only ensures optimality, but also accelerates the solving process. To relax the remaining bilinear constraints, we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model. To improve the quality of the McCormick relaxation, we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables. We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution. Case studies show that, compared with the interior point method and the method implemented in a global bilinear solver, the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.

As an important research direction of the energy system, the integrated energy system of a park plays an important role in the field of energy optimization and sustainable economic operation. In this study, a low-carbon optimal operation model of the integrated energy system of an industrial park is proposed, considering a controllable flexible load response. First, the typical structure of the integrated energy system of the park and the model of each subsystem are introduced; then, under the premise that flexible electrical and thermal loads can be used for adjustment of energy utilization, a complete dispatch scheme is constructed according to the energy consumption and system operation characteristics of the integrated energy system. Finally, an optimal scheduling model for the combined supply and demand of the integrated energy system is established with the aim of minimizing the total operating cost. For experimental results, the YALMIP toolbox and the CPLEX solver are used to verify the results of the study; simulation results show that the optimal scheduling of controllable loads can effectively reduce the comprehensive operating cost of a community integrated energy system.

Background: Although recent research suggests that alterations in gut microbiota and metabolites play a critical role in the pathophysiology of immunoglobulin A nephropathy (IgAN), the causal relationship between specific intestinal flora and metabolites and the risk of IgAN remains unclear. Method: This study employed Mendelian randomization (MR) to investigate the causal association between gut microbiota and IgAN. To explore potential associations between gut microbiota and various outcomes, four MR methods were applied: inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode. If the results of the four methods are inconclusive, we prefer the IVW as the primary outcome. Additionally, MR-Egger, MR-PRESSO-Global, and Cochrane’s Q tests were used to detect heterogeneity and pleiotropy. The stability of MR findings was assessed using the leave-one-out approach, and the strength of the causal relationship between exposure and outcome was tested using Bonferroni correction. Additional clinical samples were utilized to validate the results of Mendelian randomization, and the outcomes were visualized through an ROC curve, confusion matrix, and correlation analysis. Result: This study examined a total of 15 metabolites and 211 microorganisms. Among them, eight bacteria and one metabolite were found to be associated with the risk of IgAN (p < 0.05). The Bonferroni-corrected test reveals that only Class. Actinobacteria (OR: 1.20, 95% CI: 1.07–1.36, p = 0.0029) have a significant causal relationship with IgAN. According to Cochrane’s Q test, there is no substantial heterogeneity across different single-nucleotide polymorphisms (p > 0.05). Furthermore, MR-Egger and MR-PRESSO-Global tests (p > 0.05) showed no evidence of pleiotropy. No reverse causal association was found between the risk of IgAN and microbiota or metabolites (p > 0.05). Clinical specimens demonstrated the effectiveness and accuracy of Actinobacteria in distinguishing IgAN patients from those with other glomerular diseases (AUC = 0.9, 95% CI: 0.78–1.00). Additionally, our correlation analysis revealed a potential association between Actinobacteria abundance and increased albuminuria (r = 0.85) and poorer prognosis in IgAN patients (p = 0.01). Conclusion: Through MR analysis, we established a causal link between Actinobacteria and the incidence of IgAN. Moreover, clinical validation using fecal samples indicated that Actinobacteria might be associated with the onset and poorer prognosis of IgAN. This finding could provide valuable biomarkers for early, noninvasive detection of the disease and potential therapeutic targets in IgAN.

Abstract Background Epilepsy is a severe chronic neurologic disease with a prevalence of 0.7% worldwide; anti-seizure medications (ASMs) are the mainstay of epilepsy treatment. The effects of sociodemographic factors on the characteristics of initial treatment in patients with newly diagnosed focal epilepsy in Western China are unknown. This study was conducted to explore sociodemographic factors associated with initial treatment characteristics. Methods Patients with focal epilepsy on continuous ASM treatment who visited to our epilepsy center at Sichuan Provincial People’s Hospital between January 2018 and December 2019 were recruited. Data on initial treatment status and sociodemographic variables were obtained from the patients with a questionnaire designed by our researchers. We examined whether sociodemographic factors were associated with epileptic patients’ access to neurologists and prescriptions of individual ASMs. Results A total of 569 patients completed this study. We found that patients with a higher education level, aged

Immunomagnetic separation based on Fe3O4 magnetic nanoparticles (MNPs) has been widely performed in sample pretreatment. The oriented conjugation strategy can achieve a better capture effect than the N-(3-dimethylamlnopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) /N-hydroxysuccinimide (NHS) method. However, immunoglobulin yolk (IgY) cannot be oriented through an SPA strategy like immunoglobulin G (IgG). In this article, an oriented conjugation nanoprobe was prepared for the enrichment of bacteria based on pH adjusting. The main factors affecting the enrichment efficiency were studied, such as the pH of the buffer system, the concentration of IgY, the concentration of nanoprobe, and the enrichment time. Under the optimal conditions, the enrichment efficiency toward target bacteria could reach 92.8%. Combined with PCR, the limit of detection (LOD) was found to be 103 CFU/ml, which was lower than the PCR only. In conclusion, we provided a new protocol for the oriented conjugation of IgY and high sensitivity detection with simple pretreatment.

The stimulator of interferon genes (STING) is a critical protein in the activation of the immune system in response to DNA. It can participate the inflammatory response process by modulating the inflammation-preferred translation program through the STING-PKR-like endoplasmic reticulum kinase (PERK)-eIF2α pathway or by inducing the secretion of type I interferons (IFNs) and a variety of proinflammatory factors through the recruitment of TANK-binding kinase 1 (TBK1) and interferon regulatory factor 3 (IRF3) or the regulation of the nuclear factor kappa-B (NF-κB) pathway. Based on the structure, location, function, genotype, and regulatory mechanism of STING, this review summarizes the potential value of STING inhibitors in the prevention and treatment of infectious diseases, psoriasis, systemic lupus erythematosus, non-alcoholic fatty liver disease, and other inflammatory and autoimmune diseases.

Spatial load forecasting (SLF) is important for regional power infrastructure construction planning and power grid management. However, for rapidly developing urban regions, SLF is generally inaccurate due to insufficient historical data. Hence, it is important to introduce the spatial load density (SLD) from similar regions to improve the accuracy of SLF. To select similar regions appropriately and acquire SLDs with limited available auxiliary data, this study proposes a spatial electric load forecasting method based on the high-level encoding of high-resolution remote sensing images called SELF-HE. In particular, SELF-HE introduces high-level ground object features as a key index to describe the characteristics of electric loads in a region and can establish connections between the remote sensing image features and SLD similarity. Based on this functionality, SELF-HE achieves more accurate SLF in regions with insufficient historical data. In the experiments, SELF-HE was compared with four traditional methods, and the results revealed that SELF-HE achieved improved SLF accuracy. Given that the high-resolution remote sensing images fully covered urban areas and were readily obtained, the proposed method can improve the accuracy of SLF with extremely low data collection costs and is applicable to rapidly developing urban regions.

Mitochondrial apoptosis is controlled by BCL2 family proteins, and the BH3-only proteins often act as sensors that transmit apoptotic signals. Here the authors show how the BH3-only proteins BMF and HRK can directly activate the BCL2 protein BAK and interact with BAK through an alternative binding groove.

Presently, Coronary Acute Syndrome (CAS) is a widespread and extreme heart disease which is considered as one of the major concerns for death in the world with long-term disability. For early intervention and care, the prediction of CAS clinical risk is more critical during analysis. A minimum number of manually selected dimensions of risk are used for current CAS risk assessment models and statistical variables are often dichotomized to optimize storage in the Internet of medical things platform(IoMT) on the Encoder layer during data analysis. This research develops a Convolution Denoising Regularized Auto Encoder Stacked Method (CDRAESM) to normalize CAS patient's medical risks from high volumes of patient records and the characteristics have been analyzed during prediction. In this research, a true medical dataset of 3,464 CAS samples is used for experimental analysis and numerical reliability has been analyzed in Area Characteristics Curve (ACC) with an Accuracy range of 96.77%. The results show that the current health risk prediction using CDRAESM achieves competitive concert than conventional models which prevails in practice. Further, the reconstructive learning strategy approach can extract informational risk from the CAS and the risk factors has been identified with existing knowledge of the clinical domain and include theories that might be confirmed by further medical research.

In the day-ahead economic dispatch of combined heat and power systems, the electric power system is adjusted in minutes because of the short dynamic time and the requirement of real-time power balance, while the heating system is dispatched in hours due to its large inertia and long dynamic process. However, the different dispatch time scales of electricity and heat are ignored by existing synchronous dispatch methods, which limits the improvement of system efficiency. To address this challenge, in this article, the asynchronous dispatch method is proposed and instantiated by two different dispatch models featuring a short electric dispatch time scale and a long heat dispatch time scale. Case studies demonstrate the asynchronous dispatch method can overcome the efficiency and reliability problems caused by the existing synchronous dispatch methods.

Distributed integrated multi-energy systems (DIMSs) can be regarded as virtual power plants to provide additional flexibility to the power system. This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region. The aggregation model includes the power constraints, energy constraints, and ramping constraints to aggregate different types of resources in the DIMSs. The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system. A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem, which is more efficient than the KKT-based algorithms. Finally, a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model.

State estimation (SE) usually serves as the basic function of the energy management system (EMS). In this paper, the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established. Then, a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline. Meanwhile, to accommodate the measuring resolutions of the integrated network, a hybrid SE approach is developed based on the two-stage iterative algorithm. Results show that, in both steady and dynamic processes, the two-stage estimator has good accuracy and convergence. The hybrid estimator has good performance on tracking the variation of the states in the heating network, even when the available measurements are limited.

Abstract Head and neck squamous cell carcinomas (HNSCC) are one of the deadliest forms of cancer, and 90% of its origin is from squamous cells. NAD(P)H:quinone oxidoreductase 1 (NQO1), an enzyme overexpressed in squamous cell carcinoma, plays an important role in proliferation and chemoresistance. The main aims were to study the inhibitory effect of ß-lapachone (ARQ761 in clinical form) in HNSCC and to study the combinational effect of 5-FU and ß-lap in improving the therapeutic efficacy in HNSCC. Lipid bilayer–assembled mesoporous silica nanoparticles loaded with 5-FU/ß-lap were prepared and studied for its physicochemical and biological properties. ß-lap showed a concentration-dependent inhibition of NQO1 enzyme activity in Cal33 cells. Notably, significant inhibitory effect was observed at a dose of 20–50 μg/ml of ß-lap. Combination of 5-FU+ß-lap resulted in lower cell viability; most notably, 5-FU/ß-lap-loaded mesoporous silica nanoparticles (FNQ-MSN) exhibited significantly lower cell viability compared with that of any of the individual drug or physical combinations. ß-lap resulted in a decrease in the protein band of NQO1 compared with control; however, most notable decrease in the NQO1 level was observed in the FNQ-MSN-treated cell group. FNQ-MSN resulted in more than 60% of cell apoptosis (early and late apoptosis) and predominant nuclear fragmentation of cancer cells indicating the superior anticancer effect of a carrier-based combination regimen. Notable decrease in tumor volume was observed with the physical mixture of 5-FU+ß-lap; however, combined treatment of carrier-based 5-FU and ß-lap (FNQ-MSN) significantly delayed the tumor growth and prolonged the survival of tumor-bearing xenograft mice. These findings suggest the potential of NQO1 inhibitor in enhancing the chemotherapeutic potential of 5-FU in the treatment of HNSCC.

Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset.