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In cold rolling of silicon steel strip, edge drop directly affects the side cutting and yield of silicon steel. To improve the edge drop of silicon steel in the cold rolling process, a multi-stand coordinated control strategy and model based on multi-objective optimization are proposed. The first step is to analyze the influence of each control stand work roll shifting (WRS) on edge drop through the finite element model (FEM), then determine the edge drop control effectiveness of each work roll shifting actuator. Meanwhile, a fitting model is proposed to solve the limitation caused by the discrete change of efficiency factors in solving the optimal adjustments of each WRS. On this basis, the target model for silicon steel edge drop control is formulated, and the optimal adjustment model of WRS of each stand is established. To realize the optimal coordinated control among the rolling mill stands, a multi-objective optimal model of edge drop control has been established by the overall modeling method. The improved penalty function algorithm is developed to calculate the optimal adjustment of WRS for each stand. The experiments and application show that the proposed multi-objective optimal control strategy and model can effectively improve the accuracy of edge drop control in silicon steel continuous cold rolling.

Objective: This review assessed evidence on the impact of prior coronary artery bypass grafting (CABG) on outcomes of percutaneous coronary intervention (PCI) for chronic total occlusions (CTO). Methods: PubMed, CENTRAL, Embase, ScienceDirect, and Google Scholar databases were searched from 1st January 1980 up to 10th January 2022 for studies assessing outcomes of CTO-PCI in patients with and without prior-CABG. Results: Eight studies were included. The meta-analysis demonstrated significantly reduced odds of procedural success in patients with prior history of CABG (OR: 0.51 95% CI: 0.41, 0.64 I2=84% p

In this work, we designed an effective method to suppress the migration of lattice oxygen from the lattice structure via incorporating the boron (B) element into Cu2O, which strengthens the hybridization of Cu–O and stabilizes the active Cu+ species.

VS2−x (V2+, V4+) and VS2 (V4+) are prepared to investigate the effect of valence states of vanadium on the catalytic activity. The adsorption of N2 molecules is exothermic on the V4+ site, but endothermic on the V2+ site. The spontaneous formation of *N2 at V4+ substantially boosts the ENRR.

We propose a new measure of variable importance in high-dimensional regression based on the change in the LASSO solution path when one covariate is left out. The proposed procedure provides a novel way to calculate variable importance and conduct variable screening. In addition, our procedure allows for the construction of p-values for testing whether each coefficient is equal to zero as well as for testing hypotheses involving multiple regression coefficients simultaneously; bootstrap techniques are used to construct the null distribution. For low-dimensional linear models, our method can achieve higher power than the t-test. Extensive simulations are provided to show the effectiveness of our method. In the high-dimensional setting, our proposed solution path based test achieves greater power than some other recently developed high-dimensional inference methods. We extend our method to logistic regression and demonstrate in simulation that our leave-one-covariate-out solution path tests can provide accurate p-values.

Background A locking compression plate (LCP) of the distal femur is used as an external fixator for lower tibial fractures. However, in clinical practice, the technique lacks a standardized approach and a strong biomechanical basis for its stability. Methods In this paper, internal tibial LCP fixator (Group IT-44), external tibial LCP fixator (Group ET-44), external distal femoral LCP fixator (Group EF-44, group EF-33, group EF-22), and conventional external fixator (Group CEF-22) frames were used to fix unstable fracture models of the lower tibial segment, and anatomical studies were performed to standardize the operation as well as to assess the biomechanical stability and adjustability of the distal femoral LCP external fixator by biomechanical experiments. Results It was found that the torsional and flexural stiffnesses of group EF-44 and group EF-33 were higher than those of group IT-44 and group ET-44 (p p > 0.05); and the compressive stiffness of all three EF groups was higher than that of group ET-44 (p p p > 0.05). Group CEF-22 showed the highest stiffnesses, while group ET-44 had the lowest stiffnesses (P Conclusions The study shows that the distal femoral LCP has good biomechanical stability and adjustability and is superior to the tibial LCP as an external fixator for distal tibial fractures, as long as the technique is used in a standardized manner according to the anatomical studies in this article.

Computer-aided process engineering and conceptual design in energy and chemical engineering has played a critical role for decades. Conventional computer-aided process and systems design generally starts with process flowsheets that have been developed through experience, which often relies heavily on subject matter expertise. These widely applied approaches require significant human effort, either providing the initially drafted flowsheet, alternative connections, or a set of well-defined heuristics. These requirements make the system design highly reliant on the engineer’s experiences and expertise. In this study, a novel reinforcement learning (RL) based automated system for conceptual design is introduced and demonstrated. The RL approach provides a generic tool for identifying process configurations and significantly decreases the dependence on human intelligence for energy and chemical systems conceptual design. An artificial intelligence agent performs the conceptual design by automatically deciding which process-units are necessary for the desired system, picking the process-units from the candidate process-units pool, connecting them together, and optimizing the operation of the system for the user-defined system performance targets. The AI agent automatically interacts with a physics-based system-level modeling and simulation toolset, the Institute for the Design of Advanced Energy System Integrated Platform, to guarantee the system design is physically consistent.

This study is focusing on leveraging the system design tools set for next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides user-friendly graphic user interface built within Jupyter Notebooks, and the whole tools set is open-source public available.

BACKGROUND The main clinical manifestation of Alzheimer’s disease (AD) is memory loss, which can be accompanied by neuropsychiatric symptoms at different stages of the disease. Amygdala is closely related to emotion and memory. AIM To evaluate the diagnostic value of amygdala on structural magnetic resonance imaging (sMRI) for AD. METHODS In this study, 22 patients with AD and 26 controls were enrolled. Their amygdala volumes were measured by sMRI and analyzed using an automatic analysis software. RESULTS The bilateral amygdala volumes of AD patients were significantly lower than those of the controls and were positively correlated with the hippocampal volumes. Receiver operating characteristic curve analyses showed that the sensitivity of the left and right amygdala volumes in diagnosing AD was 80.8% and 88.5%, respectively. Subgroup analyses showed that amygdala atrophy was more serious in AD patients with neuropsychiatric symptoms, which mainly included irritability (22.73%), sleep difficulties (22.73%), apathy (18.18%), and hallucination (13.64%). CONCLUSION Amygdala volumes measured by sMRI can be used to diagnose AD, and amygdala atrophy is more serious in patients with neuropsychiatric symptoms.

In recent years, with the rapid development of industrial technology, industrial materials have also begun to develop in more and more refined directions. Nanotechnology is one of the most developed core technologies in industrial technology, and these characteristics of nanomaterials make them shine in many fields, especially in the field of construction. Nowadays, conventional materials still have the problems of heavy weight and high thermal conductivity as thermal insulation materials for external walls. At the same time, it has outstanding performance that conventional materials do not have. Nanomaterials are widely used in various fields with rapid growth due to their many excellent properties and have produced huge economic benefits and social significance. This article describes the basic characteristics of nanomaterials, including surface effects, quantum size effects, small size effects, and macroscopic quantum tunneling effects. As nanomaterials and nanotechnology become more and more mature, the research on architectural coatings in our country has been slightly strengthened. Nanomaterials have a huge impact on future economic development and have become the most promising objects in the current material field. The experimental results prove that the foaming ability of nanomaterials is much better than that of ordinary materials, and the fault tolerance rate of the cement paste is also the highest. This can also positively show that the relationship between nanotechnology and construction engineering is getting closer and closer, and the combination of nanomaterials and construction materials shows people the broad prospects of construction materials for the development of ecological construction materials that improve the living environment and improve the quality of life.

Introduction Spinocerebellar ataxias (SCAs) are a heterozygous group of neurodegenerative disorders. Spinocerebellar ataxia type 5 (SCA5) is a rare autosomal-dominant ataxia with pure cerebellum involvement. The clinical characteristics are limb and gait ataxia, trunk ataxia, sensory deficits, abnormal eye movement, dysarthria, and hyperactive tendon reflexes. Spectrin beta nonerythrocytic 2 gene (SPTBN2), coding β-III spectrin protein, was identified to be associated with SCA5. To date, more than 19 variants of SPTBN2 have been reported. Methods A family and an apparently sporadic patient with ataxia and cerebellar atrophy were recruited from Shandong Province (China). To discover the disease-causing variants, capillary electrophoresis and targeted next-generation sequencing were performed in the proband of the family and the sporadic patient. The candidate variants were verified by Sanger sequencing and analyzed by bioinformatics software. Results In our study, we verified two novel heterozygous variants in SPTBN2 in a SCA pedigree and a sporadic patient. The proband of the pedigree and her mother presented with walking instability and progressively getting worse. The sporadic patient suffered from slurred speech, walking instability, and drinking water choking cough. MRI examination of the proband and sporadic patient both displayed moderate cerebellar atrophy. The variants identified were traditionally conserved and predicted probably damaging and disease-causing by bioinformatics analysis. Conclusion We identified two novel heterozygous variants of SPTBN2 resulting in severe ataxia which further delineated the correlation between the genotype and phenotype of SCA5, and pathogenesis of variants in SPTBN2 should be further researched.

Donor–acceptor (D–A)-type photocatalysts containing the polycyclic aromatic donor (BTT-CPP) and nonpolycyclic aromatic donor (TTB-CPP) are designed and synthesized. Their physicochemical properties are thoroughly investigated. The photocatalytic hydrogen generation experiments show that both polymers exhibit distinct photocatalytic performance. BTT-CPP displays an attractive hydrogen evolution rate (HER) of 37,866 μmol g–¹ h–¹ without further addition of the metal co-catalyst, one of the highest values reported for organic photocatalysts. The optimal HER of TTB-CPP is 8466 μmol g–¹ h–¹. These results highlight the important role of the donor unit on the performance of D–A-type photocatalysts and demonstrate that thiophene-contained polycyclic aromatic unit BTT is an excellent donor unit for high HER photocatalysts.

The oxidation/weathering of molybdenite (MoS2) is too slow to be monitored, even under pure oxygen and high temperatures, while it proceeds rapidly through humid air. The adsorption of water molecules on molybdenite is necessary for the wet oxidation/weathering of molybdenite. Therefore, we employ kinetic Monte Carlo modeling to clarify the adsorption isotherm, site preferences and kinetics of water on different surfaces of molybdenite. Our results indicate that (1) the adsorption capacity and adsorption rate coefficient of H2O on the (110) surface are significantly larger than those on the (001) surface at a temperature of 0~100 °C and a relative humidity of 0~100%, suggesting that the (110) surface is the predominant surface controlling the reactivity and solubility of molybdenite in its interaction with water; (2) the kinetic Monte Carlo modeling considering the adsorption/desorption rate of H2O, dissociation/formation rate of H2O and adsorption/desorption of dissociated H indicates that the adsorption and dissociation of H2O on the (110) surface can be completed in one microsecond (ms) at 298 K and in wet conditions; (3) the adsorption and dissociation of H2O on molybdenite are not the rate-limiting steps in the wet oxidation/weathering of molybdenite; and (4) kinetic Monte Carlo modeling explains the experimental SIMS observation that H2O and OH (rather than H+/H− or H2O) occupy the surface of MoS2 in a short time. This study provides new molecular-scale insights to aid in our understanding of the oxidation/weathering mechanism of molybdenite as the predominant mineral containing molybdenum in the Earth’s crust.

Mn3O4 is a typical electrode material for supercapacitors with high theoretical energy density. However, the electrochemical performance of Mn3O4 is hindered by the sluggish charge transfer kinetics and poor cycling lifetimes. Herein, Mn3O4 with c-axis stretched lattice distortion (oxygen vacancy, Ov-Mn3O4) was prepared through a simple sulfurization/desulfurization treatment. The resultant Ov-Mn3O4 displays a high capacity of 331.1 F g−1 at 1 A g−1, a significant rate capability of 258.2 F g−1 at 20 A g−1, and a promising cycling stability with 83% capacity retention after 15 000 cycles. An asymmetric supercapacitor with Ov-Mn3O4 as the cathode delivers an energy density of 52.5 W h kg−1 at a power density of 1000 W kg−1. In situ Raman results demonstrate that Ov-Mn3O4 can effectively suppress and accommodate the cooperative Jahn–Teller distortion, contributing to a prolonged cycling life. DFT results suggest that the c-axis stretched lattice distortion induces electron delocalization, thus facilitating the electron transfer. Moreover, Na+ exhibits accelerated transfer kinetics in c-axis stretched lattice distorted Ov-Mn3O4 with broadened ion transfer channels. This work highlights the advantage of c-axis stretched lattice distortion for Na ion storage in Mn3O4, which can be expanded to optimize the electronic configuration of other metal oxides.

Exploiting various nanocarbons for supercapacitors with excellent energy retention under ultrahigh power output in high-voltage ionic liquid electrolytes possesses enormous potential for next-generation supercapacitor applications. To address the poor rate capability of the carbon electrodes in ionic liquid electrolytes, we develop an efficient strategy toward porous nanocarbons with controllable microstructure that fully solves this bottleneck to gain an outstanding capacitive performance. We found the introduction of laser-induced graphene and Mg as the additives can serve multiple roles not only as the morphology modifier agents but also as the template for the developing rich mesopores. In particular, a curved nanosheet network-like porous carbons constructed by nanocages with ultrahigh specific surface area and considerable amount of mesopores can be rationally obtained. When served as an electrode for ionic liquid-based supercapacitor, it can display an impressive specific capacitance of 201.1 F/g at 1 A/g, superb rate performance (71% capacitance retained at 100 A/g) and encouraging energy density up to 60.76 Wh/kg at a recording power density of 87.5 kW/kg, which is comparable with the best results reported for carbon-based supercapacitors in ionic liquid electrolyte reported so far.

Copper oxide-based materials effectively electrocatalyze carbon dioxide reduction (CO

When choosing a third-party cloud storage platform, the confidentiality of data should be the primary concern. To address the issue of one-to-many access control during data sharing, it is important to encrypt data with an access policy that enables fine-grained access. The attribute-based encryption scheme can be used for this purpose. Additionally, attribute-based proxy re-encryption (ABPRE) can generate a secret key using the delegatee’s secret key and access policy to re-encrypt the ciphertext, allowing for one-to-many data sharing. However, this scheme still has some flaws, such as low efficiency, inability to update access rules, and private data leakage. To address these issues, we proposed a scheme that combines attribute-based encryption (ABE) and identity-based encryption (IBE) to achieve efficient data sharing and data correctness verification. We also integrated this scheme with blockchain technology to ensure tamper-proof and regulated data storage, addressing issues such as data tampering and lack of supervision on third-party servers. Finally, to demonstrate the security of our scheme, we evaluated the communication overhead and computation overhead. Our results showed that our scheme is more efficient than other schemes and is secure against chosen plaintext attacks with verifiable properties.

To determine the heavy metal(loid)s (HMs) contamination of agricultural soil in hotbed chives hometown of Tangchang, 788 topsoil samples were collected and analyzed for their heavy metal(loid)s concentration. The index of geo-accumulation (Igeo), pollution index (PI) and potential ecological risk index (EIi) were used to assess the degree of pollution. Correlation analysis and principal component analysis (PCA) were used to determine the sources of soil HMs. Human health risks estimated with hazard index (HI) and carcinogenic risk (CR) indices based on ingestion, inhalation and dermal exposure pathways for adults and children. The mean values of Cd, Hg, As, Pb, Cr, Cu, Ni and Zn were 0.221, 0.155, 9.76, 32.2, 91.9, 35.2, 37.1 and 108.8 mg kg−1, respectively, which did not exceed the threshold values of the risk screening value for soil contamination. The potential ecological risk of soil heavy metal(loid)s was low level and there was no significant human health risk. Based on PCA, Pb and Hg may originate from transportation and atmospheric deposition, Zn, Cr and Ni may originate from natural sources and industrial activities, and Cu and Cd may originate from agricultural activities. Overall, from the perspective of HMs content, the soil quality in this study area was at a clean level. This study provides a reference and a basis for formulating effective measures to prevent and control HMs enrichment in agricultural soils.

This article develops a two-sample nonparametric goodness-of-fit (GOF) test for uniform stochastic ordering (USO) when observations are taken in pairs. We propose a data-driven critical value that controls the type I error and yields a consistent test. A simulation study illustrates the finite-sample performance of our test. All the proofs are included in the supplemental file.

Ultrathin graphene-like porous carbon nanosheets (GPCNSs) have been proved to be promising candidates for electrochemical energy storage application in view of their extremely large accessible surf...

Group testing, as a cost-effective strategy, has been widely used to perform large-scale screening for rare infections. Recently, the use of multiplex assays has transformed the goal of group testing from detecting a single disease to diagnosing multiple infections simultaneously. Existing research on multiple-infection group testing data either exclude individual covariate information or ignore possible retests on suspicious individuals. To incorporate both, we propose a new regression model. This new model allows us to perform a regression analysis for each infection using multiple-infection group testing data. Furthermore, we introduce an efficient variable selection method to reveal truly relevant risk factors for each disease. Our methodology also allows for the estimation of the assay sensitivity and specificity when they are unknown. We examine the finite sample performance of our method through extensive simulation studies and apply it to a chlamydia and gonorrhea screening data set to illustrate its practical usefulness.

Two-dimensional (2D) nanocarbons have attracted considerable researching interest in electrochemical energy storage owing to their large surface area and shortened ion-diffusion distance. Herein, we propose a versatile co-activation strategy for preparing porous carbon nanosheets by employing easily available petroleum asphalt as the precursor. Besides KOH, introducing of NaOH as the co-activating reagent not only realized the morphological change from microparticles to nanosheets, but also altered the textural properties of the products, which is totally different from the traditional template route, offers a new possibility towards 2D nanocarbons. The large specific surface area and highly-developed micropores combined with its unique structural advantages, make it a very promising electrode material for high-rate supercapacitor in ionic liquid electrolyte. For instance, the porous carbon nanosheets deliver a large specific capacitance (169 F/g 1 A/g), superior rate capability (72.8% capacitance retention at 50 A/g), and good cycling stability after 5000 cycles. Moreover, the energy density can reach to 52.3 Wh/kg even at an ultrahigh power density of 43.75 kW/kg, which have shown great potential for applications in electrochemical energy storage devices such as supercapacitors.

Controllable synthesis of nanocarbons with tunable morphologies from sustainable biomass through facile one-step route for ionic liquid based supercapacitors with high energy density and high power characteristics is of great interest but remains an ongoing challenge. Herein, we report the design and synthesis of nanocarbons with tunable morphologies (i.e., honeycomb-like porous carbons, carbon nanosheets and irregular-shaped carbon microparticles) from sustainable pine barks through reliable one-step chemical activation process. We found that the honeycomb-like porous carbons can be produced via direct KOH activation while the uniform carbon nanosheets were obtained by employing magnesium powder as the reactive self-sacrificing template. These three samples have different morphologies and textural properties render them totally disparate electrochemical performance in ionic liquid electrolyte. Benefiting from its distinct structural advantages, the as-obtained porous carbon nanosheets has excellent electrochemical capacitive performance including large specific capacitance (193 F/g at 1 A/g), superior rate capability (superior specific capacitance retention of 69.4% at 40 A/g), good cycling stability (good capacitance retention after 8000 cycles) and integrated high energy density& high power characteristics (excellent energy density of 55.8 Wh/kg even at an ultrahigh power density of 39.375 kW/kg). We believed that this work reported here not only provides a versatile strategy towards nanocarbons with tunable morphologies from sustainable biomass but also sheds some new light to efficiently regulate the morphologies of nanocarbons for electrochemical energy storage applications.

Emerging evidence addresses the link between the aberrant epigenetic regulation of gene expression and numerous diseases including neurological disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD). LncRNAs, a class of ncRNAs, have length of 200 nt or more, some of which crucially regulate a variety of biological processes such as epigenetic-mediated chromatin remodeling, mRNA stability, X-chromosome inactivation and imprinting. Aberrant regulation of the lncRNAs contributes to pathogenesis of many diseases, such as the neurological disorders at the transcriptional and post-transcriptional levels. In this review, we highlight the latest research progress on the contributions of some lncRNAs to the pathogenesis of neurodegenerative diseases via varied mechanisms, such as autophagy regulation, Aβ deposition, neuroinflammation, Tau phosphorylation and α-synuclein aggregation. Meanwhile, we also address the potential challenges on the lncRNAs-mediated epigenetic study to further understand the molecular mechanism of the neurodegenerative diseases.

This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power dissipation. A key observation is that the input signal to always-on hardware is typically sparse in time. This is a great opportunity that a SNN classifier can leverage because the switching activity and the power consumption of SNN hardware can scale with spike rate. To leverage this scalability, the proposed SNN classifier architecture employs event-driven architecture, especially fine-grained clock generation and gating and fine-grained power gating, to obtain very low static power dissipation. The prototype is fabricated in 65 nm CMOS and occupies an area of 1.99 mm2. At 0.52 V supply voltage, it consumes 75 nW at no input activity and less than 300 nW at 100% input activity. It still maintains competitive inference accuracy for KWS and other always-on classification workloads. The prototype achieved a power consumption reduction of over three orders of magnitude compared to the state-of-the-art for SNN hardware and of about 2.3X compared to the state-of-the-art KWS hardware.

Human biomonitoring involves measuring the accumulation of contaminants in biological specimens (such as blood or urine) to assess individuals exposure to environmental contamination. Due to the expensive cost of a single assay, the method of pooling has become increasingly common in environmental studies. The implementation of pooling starts by physically mixing specimens into pools, and then measures pooled specimens for the concentration of contaminants. An important task is to reconstruct individual-level statistical characteristics based on pooled measurements. In this article, we propose to use the varying-coefficient regression model for individual-level biomonitoring and provide methods to estimate the varying-coefficients based on different types of pooled data. Asymptotic properties of the estimators are presented. We illustrate our methodology via simulation and with application to pooled biomonitoring of a brominated flame retardant provided by the National Health and Nutrition Examination Survey (NHANES). This article is protected by copyright. All rights reserved.