bluuebunny/crossref_metadata_2025 huggingface dataset:
Nanomaterials Reinforced Polymer Filament for Fused Deposition Modeling: A State-of-the-Art Review
Authors: Luo Xinchun , Cheng Hailong , Wu Xin
Published: July 2023

For the past years, fused deposition modeling (FDM) technology has received increased attention in the applications of industrial manufacturing fields, particularly for rapid prototyping, small batch production and highly customized products, owing to the merits of low-cost, user-friendliness and high design freedom. To further expand the application potential and promote the performance of the as-manufactured products, many efforts have been spent on the development of suitable materials for FDM applications. In recent years, the involvement of nanomaterials in the FDM-based polymer matrix, which has been demonstrated with great opportunities to enhance the performance and versatility of FDM printed objects, has attracted more and more research interest and the trend is expected to be more pronounced in the next few years. This paper attempts to provide a timely review regarding the current research advances in the use of nanomaterials to reinforce polymer filaments for the FDM technique. Polymer composite filaments based on nanomaterials such as carbon nanotubes, nanoclay, carbon fibers, graphene, metal nanoparticles and oxides are discussed in detail regarding their properties and applications. We also summarized the current research challenges and outlooked the future research trends in this field. This paper aims at providing a useful reference and guidance for skilled researchers and also beginners in related fields. Hopefully, more research advances can be stimulated in the coming years.
The Influence of Starch Sweeteners on Functional Properties of Cellulose Fat Mimetics: Rheological and Textural Aspects
Authors: Nikolić Ivana , Petrović Jovana , Pajin Biljana , Lončarević Ivana , Šubarić Drago , Ačkar Đurđica , Miličević Borislav , Šereš Zita , Dokić Ljubica , Šoronja-Simović Dragana , Jozinović Antun
Published: July 2023

Starch sweeteners are commonly used in many confectionery food products. Usually, considering the trend of producing low-energy and low-fat products, these products include fat mimetics. The aim of this study was to investigate the influence of fructose sweeteners on the development of functional properties of MCG fat mimetic, such as rheological and textural behavior. Fat mimetics made from Microcrystalline cellulose gel (MCG) consist of colloidal microcrystalline cellulose (MCC) and sodium carboxymethyl cellulose (NaCMC) and were observed in five different concentrations (1, 3, 5, 7, and 10%). The amount of starch sweetener in the mixture with the fat mimetics was 20%. The effect of pure crystalline fructose and a mixture of crystalline-fructose and high-fructose corn syrup in a ratio of 1:1 was analyzed. Rheological parameters significantly decreased with the application of starch sweeteners. By adding a mixture of starch sweeteners, this decrease was further increased by 10%. At higher gel concentrations of 5, 7, and 10%, the dominance of the elastic modulus G′ was preserved. Texture parameters such as firmness, consistency, cohesiveness, and viscosity index were reduced accordingly. The presence of starch sweeteners significantly disrupted the networking of the three-dimensional structure of the MCG gel and the proper hydration process during the formation of fat mimetics.
UV LED Curable Perfluoropolyether (PFPE)-Urethane Methacrylate Transparent Coatings for Photonic Applications: Synthesis and Characterization
Authors: Dreyer Christian , Motoc Dana Luca , Koehler Mathias , Goldenberg Leonid
Published: July 2023

The contribution aims to bring forth a novel synthesis route in developing transparent UV LED-curable coatings accounting for various exposure options. A selection of perfluoropolyether (PFPE)-urethane methacrylate and acrylate resins, free-radical photo-initiator Omnirad 2100, and two distinct silane-based crosslinking agents were blended under a weight ratio of 75:20:5 (without crosslinker) and 70:15:5:10, respectively. The coatings were cured under a UV LED 4 × 3 matrix light emitting source, in a chamber under a controlled atmosphere, by means of an in-house developed conveyor belt type platform, at different conveyor belt speeds (5, 50, 150, 250, and 500 mm/s). The morphologies of fabricated coatings were characterized by FTIR revealing high conversion rates (e.g., from 98 to 100%) for increased exposure time as a result of the 5 or 50 mm/s values, on all combinations. Dynamic–mechanical and optical properties of UV LED-cured transparent coatings were also investigated. A negative shift of the glass transition temperature values with a decrease in exposure time, in all combinations, from about 60 °C to 30 °C, along with storage moduli lowering in the glassy plateau further favors higher exposure times for curing. The refractive indices of poly-mers were from 1.38 to 1.40, whereas the thermo-optic coefficients are showing minor changes around the value of 2.55∙10−4 K−1.
Effect of the Filler Modification on the Thermal and Mechanical Properties of Composite Polypropylene/Wollastonite Drawn Fibers
Authors: Leontiadis Konstantinos , Achilias Dimitris S. , Tsivintzelis Ioannis
Published: July 2023

Polypropylene (PP) is one of the most commercially used thermoplastics, while a significant amount of PP is used in the form of fibers. In this study, the effects of modification of the filler on the thermal and mechanical properties of composite polypropylene/wollastonite drawn fibers were investigated. In this direction, the surface modification of wollastonite with various organic acids, such as myristic, maleic, malonic glutaric, pimelic, and suberic acid, and the use of two solvents were studied. The surface-modified wollastonite particles were used to produce composite polypropylene drawn fibers. The modification efficiency was found to be slightly better when a non-polar solvent (carbon tetrachloride) was used instead of a polar one (ethanol). FTIR experiments showed that myristic, maleic, malonic, and pimelic acid can strongly interact with wollastonite’s surface. However, the mechanical strength of the composite fibers was not increased compared to that of the neat PP fibers, suggesting inadequate interactions between PP and wollastonite particles. Furthermore, it was observed that the drawing process increased around 10% the crystallinity of all samples. Wollastonite modified with malonic acid acted as a nucleating agent for β-crystals. The onset decomposition temperature increased by 5–10 °C for all samples containing 2% wollastonite, either modified or not. The suggested modifications of wollastonite might be more suitable for less hydrophobic polymers.
Effects of Processing Conditions on the Properties of Monoammonium Phosphate Microcapsules with Melamine-Formaldehyde Resin Shell
Authors: Han Shenjie , Li Jingpeng , Ding Qingyun , Zang Jian , Lu Yulian , Zhang Longfei , Hu La
Published: July 2023

To develop monoammonium phosphate (MAP) as a novel acid source for durable intumescent fire retardants (IFR), MAP microcapsules (MCMAPs) containing MAP as the internal core and melamine-formaldehyde (MF) as the external shell were prepared by in situ polymerization in this study. The influences of synthesis conditions (including reaction temperature, polymerization time, and reaction pH value) on the properties of obtained MCMAPs (MAP content, yield, morphologies, and thermal properties) were then investigated systematically. The morphologies, chemical structures, and thermal properties were characterized by optical microscopy, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Fourier transform infrared (FTIR) spectroscopy, and thermogravimetry analyzer (TGA). The results show that MAP was well encapsulated by MF resin. No microcapsules are obtained at <55 °C or with polymerization times <1 h. Optimal preparation conditions of reaction temperature, polymerization time, and reaction pH value are 75 °C, 3 h, and 5.5, respectively. Those results provide process reference and theoretical basis for preparing MCMAPs and could promote the application of MAP microcapsules in wood flame-retardant materials.
A CFD-Based Methodology for Impact Assessment of Industrial Emissions and Evaluation of Mitigation Measures for Regulatory Purposes
Authors: Antuña-Yudego Elena , Fernández-Pacheco Víctor Manuel , Álvarez-Álvarez Eduardo , Carús-Candás Juan Luis , Suárez-López María José
Published: July 2023

In a context where air quality has become a global concern, modelling techniques are becoming very popular for analysing pollutant dispersion conditions. While models based on empirical formulations are most widely used for industrial scenarios, singular cases involving complex terrain or large obstacles in the vicinity of emission sources require a more robust approach to evaluate the dispersion conditions. In this research, a computational fluid dynamics (CFD) model is developed to analyse the dispersion of pollutants from an industrial facility whose location and characteristics suggest the occurrence of complex flow features that affect the dispersion patterns. In addition, the variation of the gas exit temperatures of waste heat recovery systems is proposed as a measure to mitigate the impact of the plume. The simulation results show unexpected deviations in the plume path affecting vulnerable areas in a nearby mountainside, but increasing the gas exit temperature is useful to prevent this event. Therefore, the proposed methodology can be considered a decision support tool to find a compromise between the environmental impact and the fuel consumption of the plant.
Energy Dissipation and Fracture Mechanism of Layered Sandstones under Coupled Hydro-Mechanical Unloading
Authors: Song Zhixiang , Zhang Junwen , Wu Shaokang
Published: July 2023

Rock burst is easy to occur in the water-rich roadway of coal mines, which is closely related to the energy dissipation and fracture mechanism of rocks under coupled hydro-mechanical (H-M) unloading. Therefore, in combination with the triaxial loading and unloading process and H-M coupling effect, the mechanical test of layered sandstones under coupled hydro-mechanical unloading (TLUTP) was conducted. The energy dissipation and fracture mechanism were revealed. The results show that: (1) The influence of layered angles on the peak volumetric strain is more sensitive than that of confining pressure under conventional triaxial loading with H-M coupling (CTLTP). On the contrary, the influence of confining pressure on the peak volumetric strain is more sensitive than that of layered angles under TLUTP. (2) With increasing layered angles, the peak elastic energy density under CTLTP shows the “W” shaped evolution characteristic, while that of under TLUTP shows the “N” shaped evolution characteristic. (3) The “Energy Flow” chain is proposed. Meanwhile, combined with the domino effect and the structural evolution theory, the energy dissipation and fracture mechanism of layered sandstones under coupled hydro-mechanical unloading are both revealed. The conclusions obtained can provide certain fundamental theoretical references for the effective prevention of rock burst in a layered water-rich roadway.
Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification
Authors: Liu Ruhao , Zhang Lei , Wang Xinrui , Zhang Xuejuan , Liu Xingzhou , He Xin , Zhao Xiaoming , Xiao Dianshi , Cao Zheng
Published: July 2023

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.
Dried Droplets of Diluted Blood to Detect a High Concentration of Lipids
Authors: Ancheyta-Palacios Monserrat , Velasco-Terán Iris G. , Carreón Yojana J. P. , González-Gutiérrez Jorge
Published: July 2023

Hyperlipidemia is the elevated concentration of lipids in the blood, and it increases the probability of arterial obstruction, infarctions, and other complications of the circulatory system. While there are indications that qualitative analysis of blood stains could potentially identify patients with this pathology, the efficacy of this method remains uncertain. In this paper, we report an experimental study that investigates the formation of patterns in dried blood droplets with varying concentrations of ultrapure water. Two blood samples, one healthy and one with moderate hyperlipidemia, were examined to determine the ideal water and blood mixtures for detecting high lipid concentrations. Numerous intricate patterns were observed throughout the central region and periphery of the dried droplet. These patterns encompass various forms, such as plaques, bump-like patterns, and a range of cracks including random, radial, and ortho-radial configurations. By calculating the entropy of the Gray Level Co-occurrence Matrix (GLCM) and analyzing ROC curves, we determined that solutions with 4% and 12% hematocrit (indicating a high percentage of ultrapure water) exhibit over 95% accuracy in differentiating high lipid concentrations. These findings provide a promising outlook for the development of diagnostic methods based on the study of diluted blood coatings.
The Historical Context of Boat Processions in Adriatic Maritime Pilgrimages
Authors: Katić Mario , Vedriš Trpimir
Published: July 2023

In this article, we argue that the different ritual structures of maritime pilgrimages result from the different historical backgrounds of each site. We have focused on two maritime pilgrimage sites in the Adriatic Sea: Nin, in contemporary Croatia (Northern Dalmatia), and Perast, in contemporary Montenegro (Kotor Bay). We compared these two locations and maritime pilgrimage processions because they have similar historical backgrounds (both were under Venice’s significant influence), and comparable boat processions with similar structural elements. We concluded that multilayered customs, consisting of diverse popular traditions, were fused in these pilgrimages through ecclesiastical (para)liturgical processions. Based on material presented in this article, we concluded that the Nin and Perast elites drew on local traditions and developed maritime pilgrimage boat processions in order to draw out their political, religious, social, and economic potentials.
Credit Scoring for Peer-to-Peer Lending
Authors: Ahelegbey Daniel Felix , Giudici Paolo
Published: July 2023

This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the notion that the more familiar/similar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. We then cluster the factors using the standard k-mean algorithm. This enables us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance.
A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas
Authors: Truong Tran Xuan , Nhu Viet-Ha , Phuong Doan Thi Nam , Nghi Le Thanh , Hung Nguyen Nhu , Hoa Pham Viet , Bui Dieu Tien
Published: July 2023

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models’ predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.
A New Upwelling Index for the Moroccan Atlantic Coast for the Period between 1982–2021
Authors: Belmajdoub Hanae , Minaoui Khalid , El Aouni Anass , Hilmi Karim , Saadane Rachid , Chehri Abdellah
Published: July 2023

Being a component of the Eastern Boundary Upwelling (EBU) ecosystem, Morocco’s Atlantic coast presents high biological production throughout the year, with seasonal variations in upwelling dynamics. This characterization reflects the inherent nature of EBU’s ecosystems. In this work, we develop a novel methodology to compute a new upwelling index based on the analysis of sea surface temperature (SST) images. Our new upwelling index is not only simple to calculate but also efficient. Indeed, it is limited only to the upwelling region, which has allowed the improvement of the quantification and analysis of the seasonal and interannual variability of the upwelling dynamics. The new proposed upwelling index is based on the application of a recent segmentation method that allows for the monitoring of upwelling dynamics using satellite observations. The proposed upwelling index is applied to a 40-year database of weekly SST images covering the period from 1982 to 2021, and the results are used to analyze seasonal and interannual variations of the upwelling in the region.
Periodic-Filtering Method for Low-SNR Vibration Radar Signal
Authors: Lin Yun , Zhang Linghan , Han Hongwei , Li Yang , Shen Wenjie , Wang Yanping
Published: July 2023

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR.
An Investigation of lp-Norm Minimization for the Artifact-Free Inversion of Gravity Data
Authors: Li Zelin , Yao Changli
Published: July 2023

The l2-norm minimization is a common means for the 3D inversion of gravity data. The unconstrained l2-norm inversion will produce a smooth solution, which contains redundant structures and artifacts. Positivity-constrained l2-norm inversion can eliminate redundant structures and artifacts, resulting in a more reliable solution. However, the positivity constraint restricts the applications of gravity inversion to some extent because the measured gravity data are likely to be caused by both positive and negative sources. To address this issue, we propose a strategy that combines the lp-norm regularization and fine adjustment of the depth weighting function to refine the unconstrained gravity inversion results. Synthetic tests show that the proposed strategy yields an improved smooth solution compared with the unconstrained l2-norm inversion method. The proposed strategy is also applied to the inversion of gravity data collected over a Layikeleke iron–copper skarn deposit, Xinjiang, China.
Reflectance Measurements from Aerial and Proximal Sensors Provide Similar Precision in Predicting the Rice Yield Response to Mid-Season N Applications
Authors: Rehman Telha H. , Lundy Mark E. , Reis Andre Froes de Borja , Akbar Nadeem , Linquist Bruce A.
Published: July 2023

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha−1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency.
Identification and Classification of Human Body Exercises on Smart Textile Bands by Combining Decision Tree and Convolutional Neural Networks
Authors: Koo Bonhak , Nguyen Ngoc Tram , Kim Jooyong
Published: July 2023

In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness exercises, utilizing a decision tree as the first stage and a one-dimension convolutional neural network as the second stage. The data acquisition was carried out by five participants performing exercises while wearing an inertial measurement unit sensor attached to a wristband on their wrists. However, only data acquired along the z-axis of the IMU accelerator was used as input to train and test the proposed model, to simplify the model and optimize the training time while still achieving good performance. To examine the efficiency of the proposed method, the authors compared the performance of the cascaded model and the conventional 1D-CNN model. The obtained results showed an overall improvement in the accuracy of exercise classification by the proposed model, which was approximately 92%, compared to 82.4% for the 1D-CNN model. In addition, the authors suggested and evaluated two methods to optimize the clustering outcome of the first stage in the cascaded model. This research demonstrates that the proposed model, with advantages in terms of training time and computational cost, is able to classify fitness workouts with high performance. Therefore, with further development, it can be applied in various real-time HAR applications.
A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
Authors: Devan P. Arun Mozhi , Ibrahim Rosdiazli , Omar Madiah , Bingi Kishore , Abdulrab Hakim
Published: July 2023

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.
Predictive Maintenance of Pins in the ECD Equipment for Cu Deposition in the Semiconductor Industry
Authors: Amato Umberto , Antoniadis Anestis , De Feis Italia , Fazio Domenico , Genua Caterina , Gijbels Irène , Granata Donatella , La Magna Antonino , Pagano Daniele , Tochino Gabriele , Vasquez Patrizia
Published: July 2023

Nowadays, Predictive Maintenance is a mandatory tool to reduce the cost of production in the semiconductor industry. This paper considers as a case study a critical part of the electrochemical deposition system, namely, the four Pins that hold a wafer inside a chamber. The aim of the study is to replace the schedule of replacement of Pins presently based on fixed timing (Preventive Maintenance) with a Hardware/Software system that monitors the conditions of the Pins and signals possible conditions of failure (Predictive Maintenance). The system is composed of optical sensors endowed with an image processing methodology. The prototype built for this study includes one optical camera that simultaneously takes images of the four Pins on a roughly daily basis. Image processing includes a pre-processing phase where images taken by the camera at different times are coregistered and equalized to reduce variations in time due to movements of the system and to different lighting conditions. Then, some indicators are introduced based on statistical arguments that detect outlier conditions of each Pin. Such indicators are pixel-wise to identify small artifacts. Finally, criteria are indicated to distinguish artifacts due to normal operations in the chamber from issues prone to a failure of the Pin. An application (PINapp) with a user friendly interface has been developed that guides industry experts in monitoring the system and alerting in case of potential issues. The system has been validated on a plant at STMicroelctronics in Catania (Italy). The study allowed for understanding the mechanism that gives rise to the rupture of the Pins and to increase the time of replacement of the Pins by a factor at least 2, thus reducing downtime.
Microfabrication Process Development for a Polymer-Based Lab-on-Chip Concept Applied in Attenuated Total Reflection Fourier Transform Infrared Spectroelectrochemistry
Authors: Atkinson Noah , Morhart Tyler A. , Wells Garth , Flaman Grace T. , Petro Eric , Read Stuart , Rosendahl Scott M. , Burgess Ian J. , Achenbach Sven
Published: July 2023

Micro electro-mechanical systems (MEMS) combining sensing and microfluidics functionalities, as are common in Lab-on-Chip (LoC) devices, are increasingly based on polymers. Benefits of polymers include tunable material properties, the possibility of surface functionalization, compatibility with many micro and nano patterning techniques, and optical transparency. Often, additional materials, such as metals, ceramics, or silicon, are needed for functional or auxiliary purposes, e.g., as electrodes. Hybrid patterning and integration of material composites require an increasing range of fabrication approaches, which must often be newly developed or at least adapted and optimized. Here, a microfabrication process concept is developed that allows one to implement attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) and electrochemistry on an LoC device. It is designed to spatially resolve chemical sensitivity and selectivity, which are instrumental for the detection of chemical distributions, e.g., during on-flow chemical and biological reaction chemistry. The processing sequence involves (i) direct-write and soft-contact UV lithography in SUEX dry resist and replication in polydimethylsiloxane (PDMS) elastomers as the fluidic structure; (ii) surface functionalization of PDMS with oxygen plasma, 3-aminopropyl-triethoxysilane (APTES), and a UV-curable glue (NOA 73) for bonding the fluidic structure to the substrate; (iii) double-sided patterning of silicon nitride-coated silicon wafers serving as the ATR-FTIR-active internal reflection element (IRE) on one side and the electrode-covered substrate for microfluidics on the back side with lift-off and sputter-based patterning of gold electrodes; and (iv) a custom-designed active vacuum positioning and alignment setup. Fluidic channels of 100 μm height and 600 μm width in 5 mm thick PDMS were fabricated on 2” and 4” demonstrators. Electrochemistry on-chip functionality was demonstrated by cyclic voltammetry (CV) of redox reactions involving iron cyanides in different oxidation states. Further, ATR-FTIR measurements of laminar co-flows of H2O and D2O demonstrated the chemical mapping capabilities of the modular fabrication concept of the LoC devices.
3D TDOA Emitter Localization Using Conic Approximation
Authors: Dogancay Kutluyil , Hmam Hatem
Published: July 2023

This paper develops a new time difference of arrival (TDOA) emitter localization algorithm in the 3D space, employing conic approximations of hyperboloids associated with TDOA measurements. TDOA measurements are first converted to 1D angle of arrival (1D-AOA) measurements that define TDOA cones centred about axes connecting the corresponding TDOA sensor pairs. Then, the emitter location is calculated from the triangulation of 1D-AOAs, which is formulated as a system of nonlinear equations and solved by a low-complexity two-stage estimation algorithm composed of an iterative weighted least squares (IWLS) estimator and a Taylor series estimator aimed at refining the IWLS estimate. Important conclusions are reached about the optimality of sensor–emitter and sensor array geometries. The approximate efficiency of the IWLS estimator is also established under mild conditions. The new two-stage estimator is shown to be capable of outperforming the maximum likelihood estimator while performing very close to the Cramer Rao lower bound in poor sensor–emitter geometries and large noise by way of numerical simulations.
Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based African Vulture Optimization Algorithm for Data Fusion in Wireless Sensor Networks
Authors: Qaffas Alaa A.
Published: July 2023

A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.
In Vitro Cytotoxicity and Antioxidant Studies of Dovyallis caffra-Mediated Cassiterite (SnO2) Nanoparticles
Authors: Adeyemi Jerry O.
Published: July 2023

Many medicinal plants found in Africa, such as Dovyallis caffra, have been reported to contain various bioactive compounds, which have been found to reduce metal salts into their corresponding metal-based nanoparticles. In this paper, the evaluation of synthesis, characterization, and biological properties of Dovyallis caffra-mediated cassiterite (SnO2) nanoparticles was carried out. The physicochemical properties of the synthesized material were investigated using X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) techniques. The characterization studies revealed that the material possessed a single tetragonal cassiterite SnO2 phase, having a cluster-like foam appearance and an irregular spherical morphology with diameters ranging from 6.57 to 34.03 nm. The biological screening revealed that the prepared cassiterite (SnO2) nanoparticles exhibited cytotoxicity against the MCF-7 breast cancer cells, with an IC50 value of 62.33 µg mL−1, better than the standard drug 5-fluorouracil, with an IC50 value of 71.21 µg mL−1. The radical scavenging potential of the nanoparticles, using the DPPH assay, showed that it possessed a slightly better activity than ascorbic acid, a common antioxidant. These results suggest that the Dovyallis caffra-mediated cassiterite (SnO2) nanoparticles possess the potential to simultaneously generate and scavenge excess ROS, which in turn results in the exhibition of good cytotoxicity and antioxidant properties.
Anticorrosive Effects of Essential Oils Obtained from White Wormwood and Arâr Plants
Authors: Beniaich Ghada , Beniken Mustapha , Salim Rajae , Arrousse Nadia , Ech-chihbi Elhachmia , Rais Zakia , Sadiq Asmae , Nafidi Hiba-Allah , Bin Jardan Yousef A. , Bourhia Mohammed , Taleb Mustapha
Published: July 2023

This article is part of the contribution to the development of two medicinal plants widely used by the Moroccan population: white wormwood (Artemisia herba-alba) andArâr (Juniperus phoenicea), species belonging to the Asteraceae and Cupressaceae families, respectively. The present work was conducted to investigate the chemical composition and anticorrosive properties of essential oils (EOs) extracted from these plants. The chemical analysis of the essential oils (EOs) was carried out by GC-MS/MS. Potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and quantum chemical calculations by density-functional theory at B3lYP were used to study the anticorrosive effect of the researched oils on mild steel in 1 M hydrochloric acid solution. Moreover, SEM-EDX analysis was used to identify the surface morphology of mild steel surface. GC-MSMS results showed the presence of 32 potentially active compounds in the EOs of Artemisia herba-alba. The average yield of the EOs was about 1.39 ± 0.17 mL/100 g dry matter. Beta thujone (30.07%) and alpha thujone (13.32%) are the main components, while for the EOs of Juniperus phoenicea, the study showed the presence of 30 constituents, with alpha-pinene (43.61%) and manoyl oxide (11.5%) as the main components. The average yield of HE was 1.10 ± 0.03 mL/100 g dry matter. The findings demonstrated an important anticorrosive action of EOs from Artemisia herba-alba and Juniperus phoenicea. Notably, the experimental results showed good efficiency of the studied essential oils and correlated well with the density-functional theory (DFT) calculations. The results of potentiodynamic polarization measurements showed that hydrazone acted as a mixed-type inhibitor. The EIS results showed an increase in charge transfer resistance accompanied by a noticeable decrease in Cdl values, revealing that both studied oils were effective as reliable inhibitors for the protection of mild steel in 1 M HCl solution. Also, the efficiency decreased with decreasing inhibitor concentrations. Surface studies ensure the effectiveness of both investigated oils and the reduction of the surface roughness of mild steel. Furthermore, DFT results of the major constituents of Artemisia herba-alba and Juniperus phoenicea EOs revealed insights into the chemical reactivity of the tested oils while supporting the experimental conclusions and showed outstanding adsorption ability of both investigated EOs on the steel surface.
Repositioning Ethnicity and Transnationalism: Community Resilience Strategies among the Non-Migratory Segment of Turkish Jewry
Authors: Moreno Aviad , Karkason Tamir
Published: July 2023

The methods that communities exploit to cope with national hegemonies that dispossess and exclude them have attracted the interest of migration scholars who emphasize the development of transnational strategies as community-building vehicles. Some scholars focus on migrant communities, whereas other studies analyze the “stayers”—those who remain in the countries of origin—in their analyses of the impacts of transnational trends on these groups. Yet how such transnational dynamics influence the “stayers” among ethnonational communities whose members rapidly “repatriate” en masse to their perceived nation-state, such as the migration of Middle Eastern Jews to Israel in the era of regional decolonization and nationalization, remain understudied. This article focuses on the community of “stayers” among Turkish Jews, whose leaders sought methods to cope with the effects of rising nationalism on their community structure and the intensity of an emigration crisis that engulfed them due to the vacuum they faced after losing 40 percent of their members in 1948–1949 to Israel. We analyze Şalom, the most important newspaper that Turkish Jewry continued to publish well after 1948. To escape marginalization and to re-establish their base in Turkey, one of Şalom’s main strategies, we find, is conveying to its readership in Turkey the advantage of connecting and twinning the two national centers that had become the focal points of most of the community by 1950—the Turkish Republic and the State of Israel.
Keeping in Motion or Staying Put: Internal Migration in the United States and China
Authors: Dietz Joshua , Li Bulin , Castañeda Ernesto
Published: July 2023

The rate of people moving within the United States is at one of its lowest points in U.S. history, while China has experienced unprecedented levels of domestic migration. While both are world-leading economies, these countries demonstrate stark contrasts in geographic mobility, urbanization, and economic growth. Despite these differences, social factors affect migration capabilities in both places, with some people more mobile than others. This study assesses internal migration and highlights the structural and social determinants of mobility in both countries. This article’s analysis reveals an accentuated downturn and the longest annual decline recorded in the U.S. Evidence shows declining internal migration in the U.S. primarily occurring at the local level and amongst renters. Large and expensive American cities have had significant losses of residents in recent years. By contrast, China’s “floating population” has increased as migrants move to cities. This study examines trends from 2010 to 2020 to compare the two countries regarding internal migration, urbanization, housing, social mobility, and economic growth. However, rather than simply comparing them, the paper argues that the internal migration dynamics in both countries are, to a degree, connected.
A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network
Authors: Han Rui , Yang Shiqi
Published: July 2023

A more scientific, objective, and reasonable renewal orientation is gradually becoming a research hotspot in the field of industrial heritage conservation and renewal. This study selected five samples to carry out field investigation and face-to-face interviews. POI data were collected and analyzed, which revealed the relationship between environmental resources and the five samples based on the kernel density estimation method. Sequentially, we unprecedentedly created a complete BN-POI-AHP hybrid Bayesian network model that was used to implement simulation analysis of the industrial heritage of the Former Site Museum of Changchun Film Studio. A renewal orientation and a strategy for the community comprehensive sports ground were determined based on the results of the simulation data through the previous model. We eventually achieved a sustainable renewal strategy and innovative research method for industrial heritage, from objective data collection and simulation model creation to generation of a final reasonable plan.
The Effect of FDI on Environmental Degradation in Romania: Testing the Pollution Haven Hypothesis
Authors: Chiriluș Alexandru , Costea Adrian
Published: July 2023

The study examines the relationship between CO2 emissions, trade openness, GDP growth and foreign direct investment (FDI) in Romania. The research aims to provide empirical evidence for either the pollution haven hypothesis (PHH) or the pollution halo effect (PHE). The pollution haven hypothesis suggests that countries with weaker environmental regulations and lower environmental quality are more attractive to FDI, while the pollution halo effect posits that countries with high levels of environmental protection and quality can generate positive spillover effects for FDI. The findings suggest a significant relationship between CO2 emissions, GDP growth and FDI inflows, with GDP growth having a greater effect on FDI than CO2 emissions. GDP growth has a causal effect on CO2 emissions, while CO2 emissions have a causal effect on FDI. These findings have important policy implications, as they highlight the interplay between economic growth, environmental degradation, and foreign investment. Policies aimed at reducing emissions must be comprehensive and coordinated in order to achieve significant emissions reductions and strike a balance between economic growth and environmental protection.
Are the Barriers to Private Solar/Wind Investment in Vietnam Mainly Those That Limit Network Capacity Expansion?
Authors: Urakami Akiko
Published: July 2023

This study addresses whether the main barrier to private solar/wind investment is the network side instead of the generation side, as a hypothesis, and how the network-related barrier could be reduced to encourage a more extensive range of private investment. It mainly employs a review of the literature and semi-structured interviews with relevant stakeholders. The result showed weak grid capacity is a critical barrier in solar power projects’ congested areas. Another critical barrier is policy uncertainty in that the government has not issued any alternative mechanisms for developers who failed to meet the Commercial Operation Date for approximately two years after the FITs ended. This is likely due to the fact that the Power Development Plan 8 (PDP8) for 2021-30 had been slow to be approved by the Prime Minister. In the absence of policies, the government committed to net-zero emission toward 2050 at COP26 and concluded the Just Energy Transition Partnership agreement in December 2022. These might lead the government to set its ambitious RE targets in the power mix of PDP8 approved in May 2023. In addition, amendments of the Law on Electricity which allows private firms to invest in the grid may contribute to improving quality and capacity of the grid.