bluuebunny/crossref_metadata_2025 huggingface dataset:
The Mixed Finite Element Reduced-Dimension Technique with Unchanged Basis Functions for Hydrodynamic Equation
Authors: Li Yuejie , Luo Zhendong , Liu Changan
Published: February 2023

The mixed finite element (MFE) method is one of the most valid numerical approaches to solve hydrodynamic equations because it can be very suited to solving problems with complex computing domains. Regrettably, the MFE method for the hydrodynamic equations would include lots of unknowns. Especially, when it is applied to settling the practical engineering problems, it could contain hundreds of thousands and even tens of millions of unknowns. Thus, it would bring about many difficulties for actual applications, such as consuming a long CPU running time and accumulating many round-off errors, so as to be very difficult to obtain the desired numerical solutions. Therefore, we herein take the two-dimensional (2D) unsteady Navier–Stokes equation in hydrodynamics as an example. Using the proper orthogonal decomposition to lower the dimension of unknown Crank–Nicolson MFE (CNMFE) solution coefficient vectors for the 2D unsteady Navier–Stokes equation about vorticity–stream functions, we construct a reduced-dimension recursive CNMFE (RDRCNMFE) method with unchanged basis functions. In the circumstances, the RDRCNMFE method can keep the basis functions unchanged in an MFE subspace and has the same precision as the classical CNMFE method. We employ the matrix method to analyse the existence and stability along with errors to the RDRCNMFE solutions, leading to a very simple theory analysis. We use the numerical simulations for the backwards-facing step flow to verify the effectiveness of the RDRCNMFE method. The RDRCNMFE method with unchanged basis functions only reduces the dimension of the solution coefficient vectors of the CNMFE, which is completely different from previous order reduction methods which greatly affects the accuracy by reducing the dimension of the MFE subspace.
Significance of Ternary Hybrid Nanoparticles on the Dynamics of Nanofluids over a Stretched Surface Subject to Gravity Modulation
Authors: Alanazi Meznah M. , Ahmed Hendi Awatif , Ahammad N. Ameer , Ali Bagh , Majeed Sonia , Shah Nehad Ali
Published: February 2023

Boosting the heat transfer rate in a base fluid is of interest to researchers; many traditional methods have been utilized to do this. One significant way is using nanofluid to boost thermal performance. This investigation sought to improve the transmission of a thermal above-stretching inclined surface over an upper surface to be influenced by the magnetic field B0 along the microgravity g*(τ)=g0(1+acos(πωt)). The G-jitter impacts were analyzed for three colloidal fluids flow; the mono micropolar nanofluid (alumina/water), micropolar hybrid nanofluid (alumina–titanium)/water, and micropolar trihybrid nanofluid (alumina–titanium–silicon)/water. Using suitable transformation, the governing formulation was changed into an ordinary differential equation. In a Matlab script, a computational code was composed to evaluate the impacts of the involved parameters on fluid dynamics. The fluid flow motion and thermal performance for the trihybrid case were greater than the mono and hybrid nanofluid cases subject to a microgravity environment. The fluid velocity and microrotation function decreased in opposition to the magnetic parameter’s increasing strength, but with an increasing trend in the fluid temperature function. Fluctuations in the velocity gradient and heat flow gradient increased as the modulation amplitude increased.
Double Risk Catastrophe Reinsurance Premium Based on Houses Damaged and Deaths
Authors: Surya Hilda Azkiyah , Napitupulu Herlina , Sukono
Published: February 2023

The peaks over threshold (POT) model for catastrophe (CAT) reinsurance pricing has been widely used, but has mainly focused on univariate CAT reinsurance pricing. We provide further justification and support for the model by considering the addition of more than one type of CAT risk in the context of extreme value theory. We further extend the applicability of the CAT reinsurance premium model by considering house damage and deaths as CAT risk. Using the proposed model, we present a simulation framework for pricing double risk CAT reinsurance, based on excess-of-loss reinsurance contract. Furthermore, we fit the POT model to the earthquake loss data in Indonesia. Finally, we provide the price of the double risk CAT reinsurance premium under the standard deviation premium principle. The framework results obtained show that the pricing formulas in this study are appropriate for the double risk claim and may be used as a basis for the pricing of double risk CAT excess-of-loss reinsurance contracts.
Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management
Authors: Lo Shih-Che , Chuang Ying-Lin
Published: February 2023

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test.
Optimal Design of Reverse Logistics Recycling Network for Express Packaging Considering Carbon Emissions
Authors: Mao Jia , Cheng Jinyuan , Li Xiangyu , Zhao Honggang , Lin Ciyun
Published: February 2023

With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme.
LNG Bunkering Station Deployment Problem—A Case Study of a Chinese Container Shipping Network
Authors: Qi Jingwen , Wang Shuaian
Published: February 2023

Liquefied natural gas (LNG) is a promising measure to reduce shipping emissions and alleviate air pollution problem, especially in coastal areas. Currently, the lack of a complete infrastructure system is preventing the extensive application of dual-fueled ships that are mainly LNG-powered. Given that groups of LNG bunkering stations are under establishment in various countries and areas, the construction plan becomes critical. In this paper, we focus on the LNG bunkering station deployment problem, which identifies the locations of the stations to be built. A large-scale case study of China’s container shipping network was conducted. The problem scale of this case paper exceeds those in previous academic studies. Thus, this study better validates the model and solution method proposed than numerical experiments that are randomly generated. Sensitive analyses on the LNG price, bunkering station construction costs, and total budget were carried out. The results yielded provide practical suggestions and managerial insights for the competent department. In addition to building a complete bunkering system, subsidies to ship operators for consuming LNG and higher production efficiency in bunkering station construction also help promote the application of LNG as marine fuel.
Local Correlation Integral Approach for Anomaly Detection Using Functional Data
Authors: Sosa Donoso Jorge , Flores Miguel , Naya Salvador , Tarrío-Saavedra Javier
Published: February 2023

The present work develops a methodology for the detection of outliers in functional data, taking into account both their shape and magnitude. Specifically, the multivariate method of anomaly detection called Local Correlation Integral (LOCI) has been extended and adapted to be applied to the particular case of functional data, using the calculation of distances in Hilbert spaces. This methodology has been validated with a simulation study and its application to real data. The simulation study has taken into account scenarios with functional data or curves with different degrees of dependence, as is usual in cases of continuously monitored data versus time. The results of the simulation study show that the functional approach of the LOCI method performs well in scenarios with inter-curve dependence, especially when the outliers are due to the magnitude of the curves. These results are supported by applying the present procedure to the meteorological database of the Alternative Energy and Environment Group in Ecuador, specifically to the humidity curves, presenting better performance than other competitive methods.
Frequency Domain Filtered Residual Network for Deepfake Detection
Authors: Wang Bo , Wu Xiaohan , Tang Yeling , Ma Yanyan , Shan Zihao , Wei Fei
Published: February 2023

As deepfake becomes more sophisticated, the demand for fake facial image detection is increasing. Although great progress has been made in deepfake detection, the performance of most existing deepfake detection methods degrade significantly when these methods are applied to detect low-quality images for the disappearance of key clues during the compression process. In this work, we mine frequency domain and RGB domain information to specifically improve the detection of low-quality compressed deepfake images. Our method consists of two modules: (1) a preprocessing module and (2) a classification module. In the preprocessing module, we utilize the Haar wavelet transform and residual calculation to obtain the mid-high frequency joint information and fuse the frequency map with the RGB input. In the classification module, the image obtained by concatenation is fed to the convolutional neural network for classification. Because of the combination of RGB and frequency domain, the robustness of the model has been greatly improved. Our extensive experimental results demonstrate that our approach can not only achieve excellent performance when detecting low-quality compressed deepfake images, but also maintain great performance with high-quality images.
Non-Asymptotic Bounds of AIPW Estimators for Means with Missingness at Random
Authors: Wang Fei , Deng Yuhao
Published: February 2023

The augmented inverse probability weighting is well known for its double robustness in missing data and causal inference. If either the propensity score model or the outcome regression model is correctly specified, the estimator is guaranteed to be consistent. Another important property of the augmented inverse probability weighting is that it can achieve first-order equivalence to the oracle estimator in which all nuisance parameters are known, even if the fitted models do not converge at the parametric root-n rate. We explore the non-asymptotic properties of the augmented inverse probability weighting estimator to infer the population mean with missingness at random. We also consider inferences of the mean outcomes on the observed group and on the unobserved group.
Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19
Authors: Štěpánek Lubomír , Habarta Filip , Malá Ivana , Štěpánek Ladislav , Nakládalová Marie , Boriková Alena , Marek Luboš
Published: February 2023

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into “time” and “event” components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset (n=663 individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models’ predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy 0.811), support vector machines (of accuracy 0.845), random forests (of accuracy 0.836), artificial neural networks (of accuracy 0.806) outperform the Cox proportional hazard model (of accuracy 0.796), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is 0.627 for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies.
A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions
Authors: Tharwat Alaa , Schenck Wolfram
Published: February 2023

Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution, whereby a few, high-quality data points are queried by searching for the most informative and representative points within the instance space. This strategy ensures high generalizability across the space and improves classification performance on data we have never seen before. In this paper, we provide a survey of recent studies on active learning in the context of classification. This survey starts with an introduction to the theoretical background of the AL technique, AL scenarios, AL components supported with visual explanations, and illustrative examples to explain how AL simply works and the benefits of using AL. In addition to an overview of the query strategies for the classification scenarios, this survey provides a high-level summary to explain various practical challenges with AL in real-world settings; it also explains how AL can be combined with various research areas. Finally, the most commonly used AL software packages and experimental evaluation metrics with AL are also discussed.
Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management
Authors: Arora Charu , Khetarpal Poras , Gupta Saket , Fatema Nuzhat , Malik Hasmat , Afthanorhan Asyraf
Published: February 2023

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted.
A Review of Optimization for System Reliability of Microgrid
Authors: Lei Bingyin , Ren Yue , Luan Huiyu , Dong Ruonan , Wang Xiuyuan , Liao Junli , Fang Shu , Gao Kaiye
Published: February 2023

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.
Extended Poststroke Rehabilitation Combined with Cerebrolysin Promotes Upper Limb Motor Recovery in Early Subacute Phase of Rehabilitation: A Randomized Clinical Study
Authors: Mitrović Sindi Z. , Konstantinović Ljubica M. , Miler Jerković Vera , Dedijer-Dujović Suzana , Djordjević Olivera C.
Published: February 2023

Background and Objectives: The recovery of stroke patients with severe impairment is usually poor and limited and, unfortunately, under-investigated in clinical studies. In order to support neuroplasticity and modulate motor recovery, Cerebrolysin combined with rehabilitation treatment has proven effective in the acute stroke phase in moderate to severe motor impairment. The aim of this study was to determine the efficacy of extended poststroke rehabilitation combined with Cerebrolysin on upper limb motor recovery in subacute stroke patients with severe upper limb motor impairment. Materials and Methods: A randomized, double-blind, placebo-controlled study was conducted. Sixty patients at the early stage of severe sub-acute stroke who fulfilled all eligibility criteria were randomly assigned to the Cerebrolysin group or placebo group (𝑛 = 30 each). Both groups, after conducting three weeks of conventional rehabilitation treatment five days per week, continued to perform conventional rehabilitation treatment three times per week until 90 days of rehabilitation treatment. The primary outcome measure was the Action Research Arm Test (ARAT), and the secondary outcomes were the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) motor score, Barthel index (BI), and the National Institutes of Health Stroke Scale (NIHSS). The outcome data were evaluated before, after three weeks of treatment, and on the 90th day of rehabilitation treatment, and compared within groups and between the two groups. There were no adverse events. Results: Both groups showed a significant improvement (p < 0.001) over time in BI, FMA-UE, ARAT, and NIHSS scores. Patients receiving Cerebrolysin showed more significant improvement in post-stroke upper limb motor impairment and functioning compared to the placebo group after only three weeks, and the trend was maintained after 90 days of follow up. Conclusion: Cerebrolysin delivered in the early subacute post-stroke phase added to extended conventional rehabilitation treatment is beneficial and improves motor functional recovery in patients with severe motor impairment, especially on the paretic upper extremity.
The Course and Surgical Treatment of Acute Appendicitis during the First and Second Wave of the COVID-19 Pandemic: A Retrospective Analysis in University Affiliated Hospital in Latvia
Authors: Lescinska Anna Marija , Sondore Elza , Ptasnuka Margarita , Mukans Maksims , Plaudis Haralds
Published: February 2023

Background and Objectives: Acute appendicitis is the most common abdominal emergency requiring surgery and it has an estimated lifetime risk of 6.7 to 8.6%. The COVID-19 pandemic has transformed medical care worldwide, influencing diagnostic tactics, treatment modalities and outcomes. Our study aims to compare and analyze management of acute appendicitis before and during the first and second waves of the pandemic. Materials and Methods: Patients suffering acute appendicitis were enrolled retrospectively in a single-center study for a 10-month period before the pandemic (pre-COVID-19 period: 1 March to 31 December 2019) and during the pandemic (COVID-19 period: 1 March to 31 December 2020). The total number of patients, disease severity, diagnostic methods, complications, length of hospitalization and outcomes were analyzed. Results: A total number of 863 patients were included, 454 patients in the pre-COVID-19 period and 409 patients in the COVID-19 period. Compared to the pre-COVID-19 period, the number of complicated appendicitis increased in the COVID-19 period (24.4% to 37.2%; p < 0.001). The proportion of laparoscopic appendectomies increased during the COVID-19 period but did not show statistically significant differences between periods. In both time periods, we found that open technique was the chosen surgical approach more frequently in elderly patients (p < 0.001). Generalized peritonitis was significantly more common during the COVID-19 period (3.5% vs. 6.1%, p < 0.001). The postoperative course of patients was similar in the pre-COVID-19 period and during the COVID-19 period, with no significant differences in ICU admissions, overall hospital stay or morbidity. Conclusions: The COVID-19 pandemic has led to a significant increase in complicated forms of acute appendicitis; however, no significant impact was observed in terms of diagnostic or treatment approach.
Factors Affecting Survival and Local Control in Patients with Bone Metastases Treated with Radiotherapy
Authors: Makita Kenji , Hamamoto Yasushi , Kanzaki Hiromitsu , Nagasaki Kei , Takata Noriko , Tsuruoka Shintaro , Uwatsu Kotaro , Kido Teruhito
Published: February 2023

The aim of this study was to evaluate the expected prognosis and factors affecting local control (LC) of the bone metastatic sites treated with palliative external beam radiotherapy (RT). Between December 2010 and April 2019, 420 cases (male/female = 240/180; median age [range]: 66 [12–90] years) with predominantly osteolytic bone metastases received RT and were evaluated. LC was evaluated by follow-up computed tomography (CT) image. Median RT doses (BED10) were 39.0 Gy (range, 14.4–71.7 Gy). The 0.5-year overall survival and LC of RT sites were 71% and 84%, respectively. Local recurrence on CT images was observed in 19% (n = 80) of the RT sites, and the median recurrence time was 3.5 months (range, 1–106 months). In univariate analysis, abnormal laboratory data before RT (platelet count, serum albumin, total bilirubin, lactate dehydrogenase, or serum calcium level), high-risk primary tumor sites (colorectal, esophageal, hepatobiliary/pancreatic, renal/ureter, and non-epithelial cancers), no antineoplastic agents (ATs) administration after RT, and no bone modifying agents (BMAs) administration after RT were significantly unfavorable factors for both survival and LC of RT sites. Sex (male), performance status (≥3), and RT dose (BED10) (<39.0 Gy) were significantly unfavorable factors for only survival, and age (≥70 years) and bone cortex destruction were significantly unfavorable factors for only LC of RT sites. In multivariate analysis, only abnormal laboratory data before RT influenced both unfavorable survival and LC of RT sites. Performance status (≥3), no ATs administration after RT, RT dose (BED10) (<39.0 Gy), and sex (male) were significantly unfavorable factors for survival, and primary tumor sites and BMAs administration after RT were significantly unfavorable factors for LC of RT sites. In conclusion, laboratory data before RT was important factor both prognosis and LC of bone metastases treated with palliative RT. At least in patients with abnormal laboratory data before RT, palliative RT seemed to be focused on the only pain relief.
Janus Distillation Membrane via Mussel-Inspired Inkjet Printing Modification for Anti-Oil Fouling Membrane Distillation
Authors: Afsari Morteza , Park Myoung Jun , Kaleekkal Noel Jacob , Motsa Mxolisi M. , Shon Ho Kyong , Tijing Leonard
Published: February 2023

In this work, inkjet printing technology was used to print a thin layer of a hydrophilic solution containing polydopamine as a binder and polyethyleneimine as a strong hydrophilic agent on a commercial hydrophobic membrane to produce a Janus membrane for membrane distillation. The pristine and modified membranes were tested in a direct-contact membrane distillation system with mineral oil-containing feedwater. The results revealed that an integrated and homogenous hydrophilic layer was printed on the membrane with small intrusions in the pores. The membrane, which contained three layers of inkjet-printed hydrophilic layers, showed a high underwater oil contact angle and a low in-air water contact angle. One-layer inkjet printing was not robust enough, but the triple-layer coated modified membrane maintained its anti-oil fouling performance even for a feed solution containing 70 g/L NaCl and 0.01 v/v% mineral oil concentration with a flux of around 20 L/m2h. This study implies the high potential of the inkjet printing technique as a facile surface modification strategy to improve membrane performance.
Ultrasonic Joining of Additively Manufactured Metal-Composite Hybrid Joints: A Comparison between Vertical and Horizontal Vibration Modes
Authors: de Carvalho Willian S. , Colvin Nathaniel F. , Benatar Avraham , Amancio-Filho Sergio T.
Published: February 2023

Ultrasonic Joining (U-Joining) is a novel friction-based joining technique that produces through-the-thickness reinforced hybrid joints between surface-structured metals and thermoplastics. The process feasibility has been successfully demonstrated to join metals and unreinforced or fiber-reinforced polymer parts by applying horizontal vibration. However, intense tool wear was observed for the explored combinations of materials, which could diminish the mechanical performance of the produced joints and hinder the process application. These investigations left an unexplored field regarding the application of different vibration modes, which could represent good solutions to minimize the intense tool wear reported. Therefore, the present study aims to explore the application of vertical vibration and to identify possible advantages and disadvantages of this variation. The case-study combination of additively manufactured 316L stainless steel and 20%-short-carbon-fiber reinforced poly-ether-ether-ketone was selected for this purpose. Initially, a set of optimized joining parameters was obtained for the vertical variation following a one-factor-at-a-time approach. In a previous study, the joining parameters were already optimized for the horizontal mode, and the results were used for comparison purposes. Single-lap shear joints were produced using both optimized modes, and the process monitoring indicated that joints produced using vertical vibration reached a lower joining energy input for a given joining time. The produced joints were tested, and joints produced with the horizontal variation achieved higher ultimate lap shear forces than the ones achieved by the vertical ones: 3.6 ± 0.3 kN and 1.6 ± 0.3 kN, respectively. Microstructural investigations at the fractured surfaces showed that this difference is due to insufficient frictional heat generation at the metal-composite interface when vertical vibration is applied. Therefore, the temperatures reached during the joining cycle are not enough to melt the polymer completely at the interface, preventing a complete surface wetting of the metal and reducing the micromechanical interlocking and adhesion bond between the parts, thereby diminishing the mechanical performance of the produced joints.
Synthesis, Analysis, and Characterization of Aluminum Nanoparticles Coated with 2,2,4-Trimethylpentane
Authors: Wang Guanyi , Wang Huixin , Cui Qingzhong , Li Xiaoping , Wu Xingyu , Liao Hongzhe , Zhang Zhe
Published: February 2023

In this study, to solve the problem of low activity of aluminum nanoparticles in combustion, aluminum nanoparticles were coated with 2,2,4-trimethylpentane (C8H18-Al), enabling the deactivation of aluminum nanoparticles to be effectively inhibited. The morphological characteristics, particle size distribution, chemical state, and thermal properties of C8H18-Al were characterized via SEM, TEM, DLS, XPS, and TG-DSC. The stability and energy performance of C8H18-Al were studied based on the national standard test method. The results showed that C8H18-Al had a typical shell–core structure with a smooth surface and good sphericity. The particle size was normally distributed, and the content of active aluminum nanoparticles was high (85.45%), with good thermal stability and a fast energy release rate (about four times that of ordinary nano aluminum particles). The results demonstrated that an in situ C8H18 coating is beneficial for the preparation of structurally stable aluminum nanoparticle composites with good performance.
Diterpenoids from Euphorbia gedrosiaca as Potential Anti-Proliferative Agents against Breast Cancer Cells
Authors: Yazdiniapour Zeinab , Sohrabi Mohammad Hossein , Motinia Newsha , Zolfaghari Behzad , Mehdifar Pegah , Ghanadian Mustafa , Lanzotti Virginia
Published: February 2023

Isolated diterpenes from various species of Euphorbia are important compounds for drug discovery with a broad spectrum of structures and biological effects. In this study, Euphorbia gedrosiaca, one of the endemic species of Iran, was analyzed in terms of the presence and structural determination of diterpenoid compounds. They were extracted with dichloromethane/acetone (2:1) from aerial parts of this plant and purified by chromatographic methods such as MPLC and HPLC. Four premyrsinane compounds and one myrsinane diterpene were isolated from Euphorbia gedrosiaca. They were characterized by extensive 1D and 2D NMR and HRMS analyses. Additionally, their activities were evaluated against two breast cancer cell lines, MDA-MB-231 and MCF-7, by MTT proliferation assay. They exhibited cytotoxic effects in a dose-dependent manner with promising results, which can help to find possible therapeutic application of diterpenoids in breast cancer treatment.
A Metabolomic Signature of Obesity and Risk of Colorectal Cancer: Two Nested Case–Control Studies
Authors: Yang Mingjia , Zhu Chen , Du Lingbin , Huang Jianv , Lu Jiayi , Yang Jing , Tong Ye , Zhu Meng , Song Ci , Shen Chong , Dai Juncheng , Lu Xiangfeng , Xu Zekuan , Li Ni , Ma Hongxia , Hu Zhibin , Gu Dongfeng , Jin Guangfu , Hang Dong , Shen Hongbing
Published: February 2023

Obesity is a leading contributor to colorectal cancer (CRC) risk, but the metabolic mechanisms linking obesity to CRC are not fully understood. We leveraged untargeted metabolomics data from two 1:1 matched, nested case–control studies for CRC, including 223 pairs from the US Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial and 190 pairs from a prospective Chinese cohort. We explored serum metabolites related to body mass index (BMI), constructed a metabolomic signature of obesity, and examined the association between the signature and CRC risk. In total, 72 of 278 named metabolites were correlated with BMI after multiple testing corrections (p FDR < 0.05). The metabolomic signature was calculated by including 39 metabolites that were independently associated with BMI. There was a linear positive association between the signature and CRC risk in both cohorts (p for linear < 0.05). Per 1-SD increment of the signature was associated with 38% (95% CI: 9–75%) and 28% (95% CI: 2–62%) higher risks of CRC in the US and Chinese cohorts, respectively. In conclusion, we identified a metabolomic signature for obesity and demonstrated the association between the signature and CRC risk. The findings offer new insights into the underlying mechanisms of CRC, which is critical for improved CRC prevention.
A Control Method Based on a Simple Dynamic Optimizer: An Application to Micromachines with Friction
Authors: Acho Leonardo
Published: February 2023

In Micromachines, like any mechanical system, friction compensation is an important topic for control design application. In real applications, a nonlinear control scheme has proven to be an efficient method to mitigate the effects of friction. Therefore, a new regulation control method based on a simple dynamic optimizer is proposed. The used optimizer has a finite-time convergence to the optimal value of a given performance index. This dynamic process is then modified to produce a new control scheme to resolve the regulation control statement. A stability test is also provided along with numerical simulations to support our approach. We used the Lyapunov theory to confirm the stability, in finite-time, of the obtained closed-loop system. Furthermore, we tested this controller in a scenario where the reference signal was a time-varying function applied to a micromachine with friction. Numerical experiments showed acceptable performance in mitigating the effects of friction in the mechanism. In the simulations, the well-known LuGre friction model was invoked.
Facile Fabrication of Silk Fibroin/Off-Stoichiometry Thiol-Ene (OSTE) Microneedle Array Patches
Authors: Yang Yuqian , Xiao Zhiqing , Sun Lexin , Feng Zitao , Chen Zejingqiu , Guo Weijin
Published: February 2023

Microneedles have been used in various applications in biomedical engineering, including drug delivery, biosensing, and vaccine delivery. In this study, we develop a novel protocol to fabricate silk fibroin/off-stoichiometry thiol-ene (OSTE) hybrid microneedle array patches. Silk fibroin, as a natural biomaterial, has been proven to be suitable as a drug carrier. Firstly, drug (we use insulin in this experiment) dissolved in silk fibroin solution is deposited on a microneedle mold and dried thoroughly. After that, silk fibroin needle tips are formed on the OSTE base by replica molding. We investigated the influence of the silk fibroin concentration on the length of silk needle tips and found that the silk concentration had a small influence on the tip length. We also tested the mechanical strength of the microneedles by inserting them into gelatin gel for dummy drug delivery tests. Such composite structures have the potential to increase the delivery efficiency by delivering the whole silk tip into the dermis.
High Step-Up Ratio DC-AC Converter Using Fourth-Order LCLC Resonant Circuit for Ultrasonic Fingerprint-Sensor Drivers
Authors: Liu Wei , Shi Yunlai , Sun Zhijun , Zhang Li , Zhang Qian
Published: February 2023

Ultrasonic fingerprint sensors are becoming more widely used in thick or flexible displays. In order to better identify fingerprint information, ultrasonic sensors need to generate more ultrasonic energy, which can be transmitted to the display surface through media with higher acoustic impedance. In this paper, a DC-AC converter with a high lift ratio was proposed to enhance the transmission energy of the ultrasonic fingerprint sensor, thus helping to improve the identification. The converter comprises a full-bridge inverter and two LC resonant circuits. The introduction of an additional LC resonant circuit into the traditional Class-D LC resonant converter effectively increases the boost ratio of the proposed converter from 5 to 22. When used as a part of the ultrasonic fingerprint sensing system, the proposed converter can amplify the 20-V low-voltage DC required to drive the piezoelectric organic film to 376 V high-voltage AC. The voltage of the wave received from this new driver is equal to 970 mV, which greatly exceeds the 376 mV achieved by using the Class-D converter alone. In this paper, the topology proposed by the ultrasonic fingerprint sensor converter driver was experimentally verified, which greatly improved the boost ratio and can be considered suitable for wider applications.
The Effect of Acetylene Carbon Black (ACB) Loaded on Polyacrylonitrile (PAN) Nanofiber Membrane Electrolyte for DSSC Applications
Authors: Pujiarti Herlin , Pangestu Zahrotul Ayu , Sholeha Nabella , Nasikhudin Nasikhudin , Diantoro Markus , Utomo Joko , Aziz Muhammad Safwan Abd
Published: February 2023

Nanofiber membranes are starting to be used as an electrolyte storage medium because of their high porosity, which causes ionic conductivity, producing high energy. The ability of nanofiber membranes to absorb electrolytes proves their stability when used for a long time. In this study, the loading of acetylene carbon black (ACB) on polyacrylonitrile (PAN) is made by the electrospun method, which in turn is applied as an electrolyte medium in DSSC. Materials characterization was carried out through FTIR to determine the functional groups formed and SEM to observe morphology and diameter distribution. Furthermore, for DSSC performance, efficiency and EIS tests were carried out. The optimum nanofiber membrane was shown by esPACB1, with the highest efficiency reaching 1.92% with a porosity of 73.43%, nanofiber diameter of 172.9 ± 2.2 nm, an absorbance of 1850, and an electron lifetime of 0.003 ms.
Influences of Material Selection, Infill Ratio, and Layer Height in the 3D Printing Cavity Process on the Surface Roughness of Printed Patterns and Casted Products in Investment Casting
Authors: Nguyen Thanh Tan , Tran Van Tron , Pham Thi Hong Nga , Nguyen Van-Thuc , Thanh Nguyen Chi , Thi Hong Minh Nguyen , Duy Nguyen Vu Anh , Thanh Duy Nguyen , Nguyen Van Thanh Tien
Published: February 2023

As 3D-printed (3DP) patterns are solid and durable, they can be used to create thin wall castings, which is complicated with wax patterns because of the wax’s fragility and high shrinkage ratio. According to this study’s experiment results, polylactic acid (PLA), polyvinyl butyral (PVB), and castable wax (CW) are suitable materials for preparing investment casting (IC) cavities. The results indicate that the casting product with the highest-quality surface is obtained using a cavity prepared using a CW-printed pattern. PLA- and PVB-printed patterns provide a good surface finish for casted products. In addition, the roughness of both the printed and casted surfaces increases as the printing layer height increases. The roughness of the casted surface varies from 2.25 μm to 29.17 μm. This investigation also considers the correlation between the infill ratio and mechanical properties of PLA-printed patterns. An increase in the infill ratios from 0% to 100% leads to a significant increase in the tensile properties of the PLA-printed pattern. The obtained results can be practically used.
Staphylococcus aureus Host Spectrum Correlates with Methicillin Resistance in a Multi-Species Ecosystem
Authors: Ngoubangoye Barthelémy , Fouchet David , Boundenga Larson Amédée , Cassan Cécile , Arnathau Céline , Meugnier Helene , Tsoumbou Thierry-Audrey , Dibakou Serge Ely , Otsaghe Ekore Désiré , Nguema Yasmine Okomo , Moukodoum Nancy Diamella , Mabicka Arsene , Ferry Tristan , Rasigade Jean Phillipe , Prugnolle Franck , Bañuls Anne-Laure , Renaud François , Pontier Dominique
Published: February 2023

Although antibiotic resistance is a major issue for both human and animal health, very few studies have investigated the role of the bacterial host spectrum in its dissemination within natural ecosystems. Here, we assessed the prevalence of methicillin resistance among Staphylococcus aureus (MRSA) isolates from humans, non-human primates (NHPs), micromammals and bats in a primatology center located in southeast Gabon, and evaluated the plausibility of four main predictions regarding the acquisition of antibiotic resistance in this ecosystem. MRSA strain prevalence was much higher in exposed species (i.e., humans and NHPs which receive antibiotic treatment) than in unexposed species (micromammals and bats), and in NHP species living in enclosures than those in captivity—supporting the assumption that antibiotic pressure is a risk factor in the acquisition of MRSA that is reinforced by the irregularity of drug treatment. In the two unexposed groups of species, resistance prevalence was high in the generalist strains that infect humans or NHPs, supporting the hypothesis that MRSA strains diffuse to wild species through interspecific transmission of a generalist strain. Strikingly, the generalist strains that were not found in humans showed a higher proportion of MRSA strains than specialist strains, suggesting that generalist strains present a greater potential for the acquisition of antibiotic resistance than specialist strains. The host spectrum is thus a major component of the issue of antibiotic resistance in ecosystems where humans apply strong antibiotic pressure.
Targeted Therapy of Severe Infections Caused by Staphylococcus aureus in Critically Ill Adult Patients: A Multidisciplinary Proposal of Therapeutic Algorithms Based on Real-World Evidence
Authors: Gatti Milo , Viaggi Bruno , Rossolini Gian Maria , Pea Federico , Viale Pierluigi
Published: February 2023

(1) Introduction: To develop evidence-based algorithms for targeted antibiotic therapy of infections caused by Staphylococcus aureus in critically ill adult patients. (2) Methods: A multidisciplinary team of four experts had several rounds of assessment for developing algorithms concerning targeted antimicrobial therapy of severe infections caused by Staphylococcus aureus in critically ill patients. The literature search was performed by a researcher on PubMed-MEDLINE (until August 2022) to provide evidence for supporting therapeutic choices. Quality and strength of evidence was established according to a hierarchical scale of the study design. Two different algorithms were created, one for methicillin-susceptible Staphylococcus aureus (MSSA) and the other for methicillin-resistant Staphylococcus aureus (MRSA). The therapeutic options were categorized for each different site of infection and were selected also on the basis of pharmacokinetic/pharmacodynamic features. (3) Results: Cefazolin or oxacillin were the agents proposed for all of the different types of severe MSSA infections. The proposed targeted therapies for severe MRSA infections were different according to the infection site: daptomycin plus fosfomycin or ceftaroline or ceftobiprole for bloodstream infections, infective endocarditis, and/or infections associated with intracardiac/intravascular devices; ceftaroline or ceftobiprole for community-acquired pneumonia; linezolid alone or plus fosfomycin for infection-related ventilator-associated complications or for central nervous system infections; daptomycin alone or plus clindamycin for necrotizing skin and soft tissue infections. (4) Conclusions: We are confident that targeted therapies based on scientific evidence and optimization of the pharmacokinetic/pharmacodynamic features of antibiotic monotherapy or combo therapy may represent valuable strategies for treating MSSA and MRSA infections.
Antimicrobial Potential, Antioxidant Activity, and Phenolic Content of Grape Seed Extracts from Four Grape Varieties
Authors: Krasteva Dimitrina , Ivanov Yavor , Chengolova Zlatina , Godjevargova Tzonka
Published: February 2023

The aim of this study was to evaluate the total phenolic content, composition, and antioxidant and antibacterial activities of four grape seed extracts (Cabernet Sauvignon, Marselan, Pinot Noir, and Tamyanka). The total phenolic content (TPC) and flavonoid, anthocyanin, procyanidin, ascorbic acid, DPPH, and ABTS antioxidant capacities of the grape seed extracts (GSEs) were determined. The extracts showed high TPC values (79.06–111.22 mg GAE/g). The individual components in the GSEs were determined using HPLC. High contents of catechin, epicatechin, and procyanidin B1 were found in the extracts. The antimicrobial activity of the obtained GSEs against Staphylococcus aureus, Bacillus cereus, and Escherichia coli was evaluated using the agar diffusion test and a test to determine the minimum inhibitory concentration (MIC). According to the effect on the growth of pathogens, the extracts were ranked in the following order: Pinot Noir > Marselan > Cabernet Sauvignon > Tamyanka. The tested bacteria showed high sensitivity to the extracts (MIC = 0.12–0.50 mg/mL). According to the MIC values, the bacteria were in the following order: S. aureus > B. cereus > E. coli. A correlation was found between the phenolic content of the GSEs and their antibacterial potential. The obtained results show that the studied GSEs have good potential as antioxidant and antimicrobial agents.