Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

Characterization and Selection of Microcrystalline Cellulose from Oil Palm Empty Fruit Bunches for Strengthening Hydrogel Films

Microcrystalline cellulose(MCC)is one of the cellulose derivatives produced as a result of the depolymerization of a part of cellulose to achieve high crystallinity.When implemented in other polymers,high crystallinity correlates with greater strength and stiffnes,but it can reduce the water-holding capacity.The acid concentration and hydrolysis time will affect the acquisition of crystallinity and water absorption capacity,both of which have significance as properties of hydrogel filler.The study aimed to evaluate the properties and select the MCC generated from varying the proportion of hydrochloric acid(HCl)and the appropriate hydrolysis time as a filler for film hydrogel.MCC was produced by hydrolyzing cellulose of oil palm empty fruit bunches(OPEFB)with the HCl solution at varied concentrations and periods.The results show that the longer hydrolysis times and higher HCl concentrations increase crystallinity and density while lowering yield and water absorption.The extensive acid hydrolysis reduces the amorphous area significantly,allowing the depolymerization to occur and extend the crystalline area.The morphological properties of the MCC,which are smaller but compact,indicate the presence of disintegrating and diminishing structures.A 2.5 N HCl concentration and a 45-min hydrolysis time succeed in sufficient crystallinity as well as maintaining good water absorption capacity.The treatment produced MCC with absorption capacity of 4.03±0.26 g/g,swelling capacity of 5.03±0.26 g/g,loss on drying of 1.44%±0.36,bulk and tapped density of 0.27±0.031 g/cm^(3) and 0.3±0.006 g/cm^(3),respectively,with a crystallinity index of 88.89%±4.76 and a crystallite size of 4.23±0.70 nm.The MCC generated could potentially be utilized as a hydrogel film filler,since a given proportion will be able to maintain the strength of the hydrogel,not readily dissolve but absorb water significantly.

Determination of Material Parameters of EVA Foam under Uniaxial Compressive Testing Using Hyperelastic Models

The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Robot calligraphy visually reflects the motion capability of robotic manipulators.While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works.The key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data hierarchically.In this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module.The robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with GANs.Then the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is evaluated.Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

Growth and inhibition of zinc anode dendrites in Zn-air batteries:Model and experiment

Zinc(Zn)-air batteries are widely used in secondary battery research owing to their high theoretical energy density,good electrochemical reversibility,stable discharge performance,and low cost of the anode active material Zn.However,the Zn anode also leads to many challenges,including dendrite growth,deformation,and hydrogen precipitation self-corrosion.In this context,Zn dendrite growth has a greater impact on the cycle lives.In this dissertation,a dendrite growth model for a Zn-air battery was established based on electrochemical phase field theory,and the effects of the charging time,anisotropy strength,and electrolyte temperature on the morphology and growth height of Zn dendrites were studied.A series of experiments was designed with different gradient influencing factors in subsequent experiments to verify the theoretical simulations,including elevated electrolyte temperatures,flowing electrolytes,and pulsed charging.The simulation results show that the growth of Zn dendrites is controlled mainly by diffusion and mass transfer processes,whereas the electrolyte temperature,flow rate,and interfacial energy anisotropy intensity are the main factors.The experimental results show that an optimal electrolyte temperature of 343.15 K,an optimal electrolyte flow rate of 40 ml·min^(-1),and an effective pulse charging mode.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

Color and Gloss Changes of a Lignin-Based Polyurethane Coating under Accelerated Weathering

The purpose of this research study was to investigate the properties of polyurethane coatings based on lignin nano-particles.For this purpose,the prepared coatings were applied to pine wood surfaces and weathered artificially.Subsequently,color and gloss of the coatings were measured before and after the weathering test.Field emission scanning electron microscopy(FE-SEM)micrographs prepared from the coatings showed that the average size of nano-particles in the polyurethane substrate was approximately 500 nm.Nuclear magnetic resonance(13C-NMR)spectroscopy showed that strong urethane bonds were formed in the nano-lignin-based polyurethane.Differential calorimetric analysis(DSC)test revealed that the glass-transition temperature(Tg)of lignin nanoparticles modified with diethylenetriamine(DETA)was 112.8℃ and Tg of lignin nano-particles modified with ethylenediamine(EDA)was 102.5℃,which is lower than the Tg of un-modified lignin(114.6℃)and lignin modified with DETA(126.8℃)and lignin modified with EDA(131.3℃).The coatings modified with lignin nano-particles had a greater change in gloss.The lignin nano-particles in the modified coating are trapping hydroxyl radicals which reduces photoactivity and yellowing of the polyurethane by about 3 times compared to unmodified polyurethane coatings.After weathering test,the nano-lignin-based coating had a rougher surface with a lower contact angle(0.78°)compared to the unmodified polyurethane coating(0.85°).

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Integration of Environmental and Social Values in Cultural Spaces:Sustainable and Inclusive Wayfinding Materials for Museums

In the context of use of large museum centers,numerous national and international methodological experiments show that the wayfinding project must consider the needs of both habitual users(user-centered design)and local communities(design for communities)and the importance of environmental protection(eco-design)as a priority interest of the community.This“double target”,“user-centered”and“environment-centered”can be applied during the selection process of materials to be used in the project.With respect to these possibilities,this contribution intends to present the results of research focused on material characterization of the reception and distribution spaces of large museum centers.This characterization is based on use of sensory materials and aims to evaluate their impact on the usability and sustainability of wayfinding systems.The paper directed towards a proposal for organization of integrated information on new generation so-called smart materials;within the design of a wayfinding system,these can balance the aesthetic-perceptual and performance and environmental impact,in order to allow designers to make informed decisions oriented towards inclusion and sustainability.The study was addressed by conducting two phases of systematic literature and library review of materials.The investigations conducted led to achievement of a first research result which consists in the identification of a“standard sheet”for the mapping and cataloging of the materials used for wayfinding.The“standard sheet”allows organizing the information on smart,sensorial,and eco-friendly materials,balancing the aesthetic-perceptive component with the performance on the environmental impact along the entire life cycle in a circular perspective.This tool could guide designers towards an environmental communication project oriented towards sustainability and is effective for usability and wayfinding.

Basic Characteristics of an Appropriate Waste Fillers for Solvent Free and Water-Borne Industrial Polymer Floors and Their Utilization

Recently the manufacture of epoxy coating and flooring materials begun to be under strong pressure to use more environmentally friendly raw materials in its composition.First tendency to reduce of solvents and diluents contained in the materials appeared at the end of 90´s.This situation was supported by the Council of Europe in 2004 to reduce VOC emissions to zero till 2020.Solvent materials were thus largely replaced by solvent free materials from which the volatile substances are not released into the air.But pressure continued to increase,and over the past decade began to take centre stage water-based epoxy.On the Czech market solvent based material is still occasionally used,but predominant are solvent free materials.There are no commonly used materials containing wastes as fillers in new water-borne and solvent-free epoxy materials.Characteristics identification of the waste material as a potential filler is a set of properties that determine the limits of secondary raw materials or waste as a filler.This paper describes the basic characteristics which must be selected to meet the requirements,to affect negatively the workability,sedimentation,properties and behavior of the final floor system.Some materials must comply with special requirements,such as resistance to chemicals,etc.Next part of paper talks about utilization of polymer floors and their mechanical properties.

Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

Effects of Fresh Cupressus sempervirens Leaves Infusion on Growth Performance, Intestinal Microbiota and Haemato-Biochemical Parameters in Broilers

The accumulation of growth-promoting antibiotic residues in animal products and the resistance developed by bacteria in poultry farms has led to a search for natural compounds derived from plants. This study was designed to promote the production performance of broiler chickens using fresh Cupressus sempervirens leaves infusion. Fresh Cupressus sempervirens leaves were harvested, washed, chopped and ground to a paste using a blender and fermented for three days in a closed container at a rate of 500 g/L of water. The solution obtained was filtered and added at the rate of 2, 4, 6, 8 and 10 ml/L of drinking water. The chickens fed on the graded level of the solution were compared to a control ration without an additive and positive control group supplemented with 1 g antibiotic/kg feed. At the finisher phase and throughout the study period, water intake increased significantly (P < 0.05) with increasing levels of infusion. Feed intake decreased significantly (P < 0.05) with 2 and 4 ml of infusion/L drinking water. Live weight and weight gain were significantly (P < 0.05) higher with 6 ml/L, while feed conversion significantly (P < 0.05) decreased with the same treatment compared with the control treatment without additives (T0). Carcass characteristics were not significantly (P > 0.05) affected by the inclusion of Cupressus sempervirens infusion. Haematological parameters significantly (P < 0.05) increase independently of the rate of incorporation of the infusion into the drinking water, with the exception of RBCs, MCHT and PCT. Serum content in total protein, globulins, LDL cholesterol and triglycerides were significantly (P < 0.05) high with 8 and 10 ml Cupressus sempervirens infusion/litre drinking water as compared to all other treatments. AST, ALT, urea, creatine, albumin, total cholesterol and HDL-cholesterol were not significantly affected. The lactic acid bacteria load increased significantly (P E. coli and salmonella counts decreased significantly (P < 0.05) with infusion compared to the control without additive. In conclusion, 6 ml of Cupressus sempervirens infusion can be used as an alternative to antibiotic feed additives to promote growth performance in broilers.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Ethanol steam reforming over Ni/ZSM-5 nanosheet for hydrogen production

Compared to reforming reactions using hydrocarbons,ethanol steam reforming(ESR)is a sustainable alternative for hydrogen(H_(2))production since ethanol can be produced sustainably using biomass.This work explores the catalyst design strategies for preparing the Ni supported on ZSM-5 zeolite catalysts to promote ESR.Specifically,two-dimensional ZSM-5 nanosheet and conventional ZSM-5 crystal were used as the catalyst carriers and two synthesis strategies,i.e.,in situ encapsulation and wet impregnation method,were employed to prepare the catalysts.Based on the comparative characterization of the catalysts and comparative catalytic assessments,it was found that the combination of the in situ encapsulation synthesis and the ZSM-5 nanosheet carrier was the effective strategy to develop catalysts for promoting H_(2) production via ESR due to the improved mass transfer(through the 2-D structure of ZSM-5 nanosheet)and formation of confined small Ni nanoparticles(resulted via the in situ encapsulation synthesis).In addition,the resulting ZSM-5 nanosheet supported Ni catalyst also showed high Ni dispersion and high accessibility to Ni sites by the reactants,being able to improve the activity and stability of catalysts and suppress metal sintering and coking during ESR at high reaction temperatures.Thus,the Ni supported on ZSM-5 nanosheet catalyst prepared by encapsulation showed the stable performance with~88% ethanol conversion and~65% H_(2) yield achieved during a 48-h longevity test at 550-C.

Mechanisms underlying the role of endoplasmic reticulum stress in the placental injury and fetal growth restriction in an ovine gestation model

Background Exposure to bisphenol A(BPA),an environmental pollutant known for its endocrine-disrupting properties,during gestation has been reported to increase the risk of fetal growth restriction(FGR)in an ovine model of pregnancy.We hypothesized that the FGR results from the BPA-induced insufficiency and barrier dysfunction of the placenta,oxidative stress,inflammatory responses,autophagy and endoplasmic reticulum stress(ERS).However,precise mechanisms underlying the BPA-induced placental dysfunction,and subsequently,FGR,as well as the potential involvement of placental ERS in these complications,remain to be investigated.Methods In vivo experiment,16 twin-pregnant(from d 40 to 130 of gestation)Hu ewes were randomly distributed into two groups(8 ewes each).One group served as a control and received corn oil once a day,whereas the other group received BPA(5 mg/kg/d as a subcutaneous injection).In vitro study,ovine trophoblast cells(OTCs)were exposed to 4 treatments,6 replicates each.The OTCs were treated with 400μmol/L BPA,400μmol/L BPA+0.5μg/m L tunicamycin(Tm;ERS activator),400μmol/L BPA+1μmol/L 4-phenyl butyric acid(4-PBA;ERS antagonist)and DMEM/F12 complete medium(control),for 24 h.Results In vivo experiments,pregnant Hu ewes receiving the BPA from 40 to 130 days of pregnancy experienced a decrease in placental efficiency,progesterone(P4)level and fetal weight,and an increase in placental estrogen(E2)level,together with barrier dysfunctions,OS,inflammatory responses,autophagy and ERS in type A cotyledons.In vitro experiment,the OTCs exposed to BPA for 24 h showed an increase in the E2 level and related protein and gene expressions of autophagy,ERS,pro-apoptosis and inflammatory response,and a decrease in the P4 level and the related protein and gene expressions of antioxidant,anti-apoptosis and barrier function.Moreover,treating the OTCs with Tm aggravated BPA-induced dysfunction of barrier and endocrine(the increased E2 level and decreased P4 level),OS,inflammatory responses,autophagy,and ERS.However,treating the OTCs with 4-PBA reversed the counteracted effects of Tm mentioned above.Conclusions In general,the results reveal that BPA exposure can cause ERS in the ovine placenta and OTCs,and ERS induction might aggravate BPA-induced dysfunction of the placental barrier and endocrine,OS,inflammatory responses,and autophagy.These data offer novel mechanistic insights into whether ERS is involved in BPA-mediated placental dysfunction and fetal development.