Research on Cavitation Characteristics and Influencing Factors of Herringbone Gear Pump

Cavitation is a common issue in pumps,causing a decrease in pump head,a fall in volumetric efficiency,and an intensification of outlet flow pulsation.It is one of the main hazards that affect the regular operation of the pump.Research on pump cavitation mainly focuses on mixed flow pumps,jet pumps,external spur gear pumps,etc.However,there are few cavitation studies on external herringbone gear pumps.In addition,pumps with different working principles significantly differ in the flow and complexity of the internal flow field.Therefore,it is urgent to study the cavitation characteristics of external herringbone gear pumps.Compared with experimentalmethods,visual research and cavitation area identification are achieved through computation fluid dynamic(CFD),and changing the boundary conditions and shape of the gear rotor is easier.The simulation yields a head error of only 0.003%under different grid numbers,and the deviation between experimental and simulation results is less than 5%.The study revealed that cavitation causes flow pulsation at the outlet,and the cavitation serious area is mainly distributed in the meshing gap and meshing area.Cavitation can be inhibited by reducing the speed,increasing the inlet pressure,and changing the helix angle can be achieved.For example,when the inlet pressure is 5 bar,the maximumgas volume fraction in themeshing area is less than 50%.These results provide a reference for optimizing the design and finding the optimal design parameters to reduce or eliminate cavitation.

Disparities in the impact of economic well-being on self-esteem in adulthood:Race and ethnicity

BACKGROUND Most studies have defined economic well-being as socioeconomic status,with little attention given to whether other indicators influence self-esteem.Little is known about racial/ethnic disparities in the relationship between economic wellbeing and self-esteem during adulthood.AIM To explore the impact of economic well-being on self-esteem in adulthood and differences in the association across race/ethnicity.METHODS The current study used data from the National Longitudinal Survey of Youth 1979.The final sample consisted of 2267 African Americans,1425 Hispanics,and 3678 non-Hispanic Whites.Ordinary linear regression analyses and logistic regression analyses were conducted.RESULTS African Americans and Hispanics were more likely to be in poverty in comparison with non-Hispanic Whites.More African Americans were unemployed than Whites.Those who received fringe benefits,were more satisfied with jobs,and were employed were more likely to have higher levels of self-esteem.Poverty was negatively associated with self-esteem.Interaction effects were found between African Americans and job satisfaction predicting self-esteem.CONCLUSION The role of employers is important in cultivating employees’self-esteem.Satisfactory outcomes or feelings of happiness from the workplace may be more important to non-Hispanic Whites compared to African Americans and Hispanics.

DPT‐tracker:Dual pooling transformer for efficient visual tracking

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input images.To alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time dimensions.MCPT aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking prediction.DPT tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking methods.Extensive experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

A Review of Rechargeable Zinc-Air Batteries:Recent Progress and Future Perspectives

Zinc-air batteries(ZABs)are gaining attention as an ideal option for various applications requiring high-capacity batteries,such as portable electronics,electric vehicles,and renewable energy storage.ZABs offer advantages such as low environmental impact,enhanced safety compared to Li-ion batteries,and cost-effectiveness due to the abundance of zinc.However,early research faced challenges due to parasitic reactions at the zinc anode and slow oxygen redox kinetics.Recent advancements in restructuring the anode,utilizing alternative electrolytes,and developing bifunctional oxygen catalysts have significantly improved ZABs.Scientists have achieved battery reversibility over thousands of cycles,introduced new electrolytes,and achieved energy efficiency records surpassing 70%.Despite these achievements,there are challenges related to lower power density,shorter lifespan,and air electrode corrosion leading to performance degradation.This review paper discusses different battery configurations,and reaction mechanisms for electrically and mechanically rechargeable ZABs,and proposes remedies to enhance overall battery performance.The paper also explores recent advancements,applications,and the future prospects of electrically/mechanically rechargeable ZABs.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.

Value of glucose transport protein 1 expression in detecting lymph node metastasis in patients with colorectal cancer

BACKGROUND There are limited data on the use of glucose transport protein 1(GLUT-1)expre-ssion as a biomarker for predicting lymph node metastasis in patients with colorectal cancer.GLUT-1 and GLUT-3,hexokinase(HK)-II,and hypoxia-induced factor(HIF)-1 expressions may be useful biomarkers for detecting primary tumors and lymph node metastasis when combined with fluorodeoxyglucose(FDG)uptake on positron emission tomography/computed tomography(PET/CT).AIM To evaluate GLUT-1,GLUT-3,HK-II,and HIF-1 expressions as biomarkers for detecting primary tumors and lymph node metastasis with 18F-FDG-PET/CT.METHODS This retrospective study included 169 patients with colorectal cancer who underwent colectomy and preoperative 18F-FDG-PET/CT at Chungbuk National University Hospital between January 2009 and May 2012.Two tissue cores from the central and peripheral areas of the tumors were obtained and were examined by a dedicated pathologist,and the expressions of GLUT-1,GLUT-3,HK-II,and HIF-1 were determined using immunohisto-chemical staining.We analyzed the correlations among their expressions,various clinicopathological factors,and the maximum standardized uptake value(SUVmax)of PET/CT.RESULTS GLUT-1 was found at the center or periphery of the tumors in 109(64.5%)of the 169 patients.GLUT-1 positivity was significantly correlated with the SUVmax of the primary tumor and lymph nodes,regardless of the biopsy site(tumor center,P<0.001 and P=0.012;tumor periphery,P=0.030 and P=0.010,respectively).GLUT-1 positivity and negativity were associated with higher and lower sensitivities of PET/CT,respectively,for the detection of lymph node metastasis,regardless of the biopsy site.GLUT3,HK-II,and HIF-1 expressions were not significantly correlated with the SUVmax of the primary tumor and lymph nodes.CONCLUSION GLUT-1 expression was significantly correlated with the SUVmax of 18F-FDG-PET/CT for primary tumors and lymph nodes.Clinicians should consider GLUT-1 expression in preoperative endoscopic biopsy in interpreting PET/CT findings.

A Review on Interface Engineering of MXenes for Perovskite Solar Cells

With an excellent power conversion efficiency of 25.7%,closer to the Shockley–Queisser limit,perovskite solar cells(PSCs)have become a strong candidate for a next-generation energy harvester.However,the lack of stability and reliability in PSCs remained challenging for commercialization.Strategies,such as interfacial and structural engineering,have a more critical influence on enhanced performance.MXenes,two-dimensional materials,have emerged as promising materials in solar cell applications due to their metallic electrical conductivity,high carrier mobility,excellent optical transparency,wide tunable work function,and superior mechanical properties.Owing to different choices of transition elements and surface-terminating functional groups,MXenes possess the feature of tuning the work function,which is an essential metric for band energy alignment between the absorber layer and the charge transport layers for charge carrier extraction and collection in PSCs.Furthermore,adopting MXenes to their respective components helps reduce the interfacial recombination resistance and provides smooth charge transfer paths,leading to enhanced conductivity and operational stability of PSCs.This review paper aims to provide an overview of the applications of MXenes as components,classified according to their roles as additives(into the perovskite absorber layer,charge transport layers,and electrodes)and themselves alone or as interfacial layers,and their significant importance in PSCs in terms of device performance and stability.Lastly,we discuss the present research status and future directions toward its use in PSCs.

Overview of emerging catalytic materials for electrochemical green ammonia synthesis and process

The concept of“green-ammonia-zero-carbon emission”is an emerging research topic in the global community and many countries driving toward decarbonizing a diversity of applications dependent on fossil fuels.In light of this,electrochemical nitrogen reduction reaction(ENRR)received great attention at ambient conditions.The low efficiency(%)and ammonia(NH_(3))production rates are two major challenges in making a sustainable future.Besides,hydrogen evolution reaction is another crucial factor for realizing this NH_(3)synthesis to meet the large-scale commercial demand.Herein,the(i)importance of NH_(3)as an energy carrier for the next future,(ii)discussion with ENRR theory and the fundamental mechanism,(iii)device configuration and types of electrolytic systems for NH_(3)synthesis including key metrics,(iv)then moving into rising electrocatalysts for ENRR such as single-atom catalysts(SACs),MXenes,and metal–organic frameworks that were scientifically summarized,and(v)finally,the current technical contests and future perceptions are discussed.Hence,this review aims to give insightful direction and a fresh motivation toward ENRR and the development of advanced electrocatalysts in terms of cost,efficiency,and technologically large scale for the synthesis of green NH_(3).

Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent years.Existing DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis.In this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral adherence.This review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

Self‐training maximum classifier discrepancy for EEG emotion recognition

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming labelling.Recent study proposed to solve the limited label problem via domain adaptation methods.However,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this study.The proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'outputs.Besides,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is proposed.Finally,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is constructed.Extensive experiments on SEED and SEED‐IV are conducted.The experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

CEER:Cooperative Energy-Efficient Routing Mechanism for Underwater Wireless Sensor Networks Using Clusters

Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.

Boosting electrocatalytic activity with the formation of abundant heterointerfaces and N, S dual-doped carbon nanotube for rechargeable Zn-air battery

Herein,a facile synthetic strategy is proposed to fabricate high-performance electrocatalysts for rechargeable Zn-air batteries(ZABs).Heterostructured NiCo/NiCo_(2)S_(4) nanoparticles encapsulated in N-,S-co-doped CNT(NiCo/NiCo_(2)S_(4)@NSCNT) are synthesized via co-precipitation,thermal carbonization,and partial sulfidation processes.The strongly coupled NiCo/NiCo_(2)S_(4) heterostructure can improve the redox property and charge transfer ability.Also,the CNTs with abundant foreign dopants provide high electrical conductivity and abundant defect sites for both the oxygen evolution reaction(OER) and oxygen reduction reaction(ORR).The prepared NiCo/NiCo_(2)S_(4)@NSCNT electrocatalyst exhibits a low overpotential of 349 mV at a current density of 10 mA cm-2 and a half-wave potential of 0.865 V for the OER and ORR,respectively.Moreover,the ZAB assembled using as-prepared NiCo/NiCo_(2)S_(4)@NSCNT can provide superior specific capacity(756.16 mA h g_(Zn)^(-1)],peak power density(155.82 mW cm^(-2)),and long-term cyclability compared to those of the precious metal-based electrocatalyst(Pt/C+RuO_(2)).

Development of mathematically motivated hybrid soft computing models for improved predictions of ultimate bearing capacity of shallow foundations

Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.

Sulfur-doped hard carbon hybrid anodes with dual lithium-ion/metal storage bifunctionality for high-energy-density lithium-ion batteries

Bifunctional hybrid anodes(BHAs),which are both a high-performance active host material for lithium-ion storage as well as a guiding agent for homogeneous lithium metal nucleation and growth,exhibit significant potential as anodes for next-generation high-energy-density lithium-ion batteries(LIBs).In this study,sulfur-doped hard carbon nanosphere assemblies(S-HCNAs)were prepared through a hydrothermal treatment of a liquid organic precursor,followed by high-temperature thermal annealing with elemental sulfur for application as BHAs for LIBs.In a carbonate-based electrolyte containing fluoroethylene carbonate additive,the S-HCNAs showed high lithium-ion storage capacities in sloping as well as plateau voltage sections,good rate capabilities,and stable cyclabilities.In addition,high average Coulombic efficiencies(CEs)of~96.9%were achieved for dual lithium-ion and lithium metal storage cycles.In the LIB full-cell tests with typical NCM811 cathodes,the S-HCNA-based BHAs containing~400 mA h g^(−1) of excess lithium led to high energy and power densities of~500Wh kg^(−1) and~1695Wkg^(−1),respectively,and a stable cycling performance with~100%CEs was achieved.

Graphene Bridge Heterostructure Devices for Negative Differential Transconductance Circuit Applications

Two-dimensional van der Waals(2D vdW)material-based heterostructure devices have been widely studied for high-end electronic applications owing to their heterojunction properties.In this study,we demonstrate graphene(Gr)-bridge heterostructure devices consisting of laterally series-connected ambipolar semiconductor/Gr-bridge/n-type molybdenum disulfide as a channel material for field-effect transistors(FET).Unlike conventional FET operation,our Gr-bridge devices exhibit nonclassical transfer characteristics(humped transfer curve),thus possessing a negative differential transconductance.These phenomena are interpreted as the operating behavior in two series-connected FETs,and they result from the gate-tunable contact capacity of the Gr-bridge layer.Multi-value logic inverters and frequency tripler circuits are successfully demonstrated using ambipolar semiconductors with narrow-and wide-bandgap materials as more advanced circuit applications based on non-classical transfer characteristics.Thus,we believe that our innovative and straightforward device structure engineering will be a promising technique for future multi-functional circuit applications of 2D nanoelectronics.

Coalitional game based resource allocation in D2D-enabled V2V communication

The joint resource block(RB)allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle(V2V)links in the device-to-device(D2D)-enabled V2V communication system,where one feasible cellular user(FCU)can share its RB with multiple V2V pairs.The problem is first formulated as a nonconvex mixed-integer nonlinear programming(MINLP)problem with constraint of the maximum interference power in the FCU links.Using the game theory,two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection,where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation.The successive convex approximation(SCA)is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links.Finally,numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

Exercise therapy for sarcopenia and diabetes

Aging is characterized by the gradual deterioration of function at the molecular,cellular,tissue,and organism levels in humans.The typical diseases caused by changes in body composition,as well as functional decline in the human body’s organs due to aging include sarcopenia and metabolic disorders.The accumulation of dysfunctional agingβcells with age can cause decreased glucose tolerance and diabetes.Muscle decline has a multifactorial origin,involving lifestyle habits,disease triggers,and age-dependent biological changes.The reduced function ofβcells in elderly people lowers insulin sensitivity,which affects protein synthesis and interferes with muscle synthesis.The functional decrease and aggravation of disease in elderly people with less regular exercise or physical activity causes imbalances in food intake and a continuous,vicious cycle.In contrast,resistance exercise increases the function ofβcells and protein synthesis in elderly people.In this review,we discuss regular physical activities or exercises to prevent and improve health,which is sarcopenia as decreased muscle mass and metabolic disorders as diabetes in the elderly.

Magmatic Evolution of the Western Branch of the East African Rift System Melts: Evidence by Silicate Melt Inclusions, Rock Petrography and Geochemistry of the Nyiragongo 1977 and 2002 Lavas in DRC

Nyiragongo volcanic eruptions of 1977 and 2002 emitted silica-undersaturated lavas named melilite-nephelinites with microlithic to sub-porphyritic textures, and consisted of olivine, clinopyroxene (augite), phlogopite, melilite, magnetite, and rare plagioclases. This melilite-nephelinite as an evolved rock, shows low SiO2 (38.40 - 39.52 wt%) and MgO (3.10 - 4.01 wt%), and relatively high FeOt (13.76 - 14.10 wt%), Al2O3 (15.01 - 16.48 wt%), CaO (11.00 - 12.29 wt%) and Na2O + K2O (10.34 - 11.85 wt%). Unlike LA-ICP-MS on silicate melt inclusions (SMIs) hosted in augite show a pristine melt of picrobasaltic (low Ti-picrite) rock poor in SiO2 (31.14 - 32.26 wt%), FeOt (2.19 - 2.79 wt%), Al2O3 (8.01 - 9.57 wt%), and Na2O + K2O (2.34 - 3.05 wt%), while enriched in MgO (20.27 - 28.63 wt%), and CaO (24.95 - 33.17 wt%). The sums (∑REEs) for lavas and SMIs are ranging 712 - 799 and 43 - 119 ppm respectively. REE contracted multi-element patterns showed a W-feature for most lavas except for SMIs. High Rb/Sr, and low Ba/Rb, Zr/Nb, and Sm/Hf ratios of lavas suggest a phlogopite-rich source of materials. .

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.