Search results for: filter circuit

Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

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Authors: Nokubonga G. Zulu, Bongephiwe M. Thethwayo, Mapilane S. Madiba, Peter A. Olubambi

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In South Africa, PGMs’ metallurgy industry is now leaned on the Upper Group 2 (UG2) reef for the beneficiation of 4PGEs (Platinum, Palladium, Rhodium, and Ruthenium). The current effective beneficiation method is direct froth flotation which uses the hydrophobicity of liberated valuables minerals to carefully float them while hydrophilic gangue minerals report to the residue. PGMs are known to be associated with base metal sulphides which are hydrophobic; however, approximately 25% of PGMs from UG2 are associated with hydrophilic silicates, which results in high PGMs grade in the flotation residue. Further, the smallest size in which UG2 PGMs occur is approximately 9 microns which demands high grinding for liberation, imposing energy and cost implications. The use of Bacillus mucilaginosus to liberate PGMs using Bio-leaching of PGMs bearing Silicates is a promising cost-effective, energy-saving, and green solution to liberate PGMs locked in silicates. This is due to the ability of Bacillus mucilaginosus to generate extracellular polysaccharides (EPS) that are responsible for the leaching of silicate minerals. The bioleaching is done at a laboratory beaker using a cultivated Bacillus mucilaginosus as a lixiviant. The bioleaching residue is expected to have a reduced particle size due to silicate consumption, which reduces the need and cost associated with a secondary milling circuit. Moreover, the grade of the bioleaching product is increased since the silicates (gangue minerals) are consumed by Bacillus mucilaginosus; this serves as a pre-concentration step. This paper discusses an alternative liberation and pre-concentrating technique of PGMs that are associated with silicates using Bacillus mucilaginosus leaching to dissolve silicates.

Keywords: Bacillus mucilaginosus, bio-leaching of PGMs bearing silicates, liberation of PGMs, pre-concentration of PGMs

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Authors: Ann D. Christy, Beenish Saba

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The Microbial fuel Cell (MFC) is a successful integrated technology for power production and wastewater treatment. MFCs are recognized for their dual function, but research in this field is still ongoing to increase efficiency and power output. One such effort is successful integration of phototrophic and autotrophic microorganisms to create bacterio-algal MFCs for sustainable electricity production along with wastewater treatment and algal biomass production. An MFC is typically configured with an anaerobic anodic chamber containing exoelectrogenic microorganisms separated by a cation exchange membrane from an adjacent aerobic cathodic chamber. The two electrodes are connected by an external circuit. This conventional MFC can be converted into a phototrophic MFC by introducing photosynthetic microorganisms into the cathode chamber. This study examines adding a third desalination chamber to a two-chamber bacterio-algal MFC. Successful results have been observed from these three-chamber MFCs demonstrating wastewater treatment in the anodic chamber, phototrophic algal growth in the cathodic chamber, and desalination in the middle chamber. The present article will summarize successful results of the bacterio-algal fuel cells and offer insights about the mechanisms involved. Tables summarizing the input substrate along with optimized operational conditions and output performance in terms of power production and efficiencies of water and wastewater treatment will be presented. The negative impacts and challenges will be discussed, along with possible future research directions. Results suggest that the three chamber bacterio-algal desalination cell has potential as a feasible technology for power production, wastewater treatment and desalination, but it needs further investigation under optimized conditions.

Keywords: bacterio-algal MFC, three chamber, microbial fuel cell, wastewater treatment and desalination

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Authors: Marta Błażkiewicz-Mazurek, Adam Konefał

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The project can be divided into three main parts: i. modeling the Cf-252 neutron source and conducting an experiment to verify the correctness of the obtained results, ii. design of the BNCT system infrastructure, iii. analysis of the results from the logical detector. Modeling of the Cf-252 source included designing the shape and size of the source as well as the energy and spatial distribution of emitted neutrons. Two options were considered: a point source and a cylindrical spatial source. The energy distribution corresponded to various spectra taken from specialized literature. Directionally isotropic neutron emission was simulated. The simulation results were compared with experimental values determined using the activation detector method using indium foils and cadmium shields. The relative fluence rate of thermal and resonance neutrons was compared in the chosen places in the vicinity of the source. The second part of the project related to the modeling of the BNCT infrastructure consisted of developing a simulation program taking into account all the essential components of this system. Materials with moderating, absorbing, and backscattering properties of neutrons were adopted into the project. Additionally, a gamma radiation filter was introduced into the beam output system. The analysis of the simulation results obtained using a logical detector located at the beam exit from the BNCT infrastructure included neutron energy and their spatial distribution. Optimization of the system involved changing the size and materials of the system to obtain a suitable collimated beam of thermal neutrons.

Keywords: BNCT, Monte Carlo, neutrons, simulation, modeling

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Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: R. F. Vieira, S. A. Figueiredo, O. M. Freitas, V. F. Domingues, C. Delerue-Matos

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The presence of sulphur compounds like hydrogen sulphide and mercaptans is one of the reasons for waste-water treatment and waste management being associated with odour emissions. In this context having a quantifying method for these compounds helps in the optimization of treatment with the goal of their elimination, namely biofiltration processes. The aim of this study was the development of a method for quantification of odorous gases in waste treatment plants air samples. A method based on head space solid phase microextraction (HS-SPME) coupled with gas chromatography - flame photometric detector (GC-FPD) was used to analyse H2S and Metil Mercaptan (MM). The extraction was carried out with a 75-μm Carboxen-polydimethylsiloxane fiber coating at 22 ºC for 20 min, and analysed by a GC 2010 Plus A from Shimadzu with a sulphur filter detector: splitless mode (0.3 min), the column temperature program was from 60 ºC, increased by 15 ºC/min to 100 ºC (2 min). The injector temperature was held at 250 ºC, and the detector at 260 ºC. For calibration curve a gas diluter equipment (digital Hovagas G2 - Multi Component Gas Mixer) was used to do the standards. This unit had two input connections, one for a stream of the dilute gas and another for a stream of nitrogen and an output connected to a glass bulb. A 40 ppm H2S and a 50 ppm MM cylinders were used. The equipment was programmed to the selected concentration, and it automatically carried out the dilution to the glass bulb. The mixture was left flowing through the glass bulb for 5 min and then the extremities were closed. This method allowed the calibration between 1-20 ppm for H2S and 0.02-0.1 ppm and 1-3.5 ppm for MM. Several quantifications of air samples from inlet and outlet of a biofilter operating in a waste management facility in the north of Portugal allowed the evaluation the biofilters performance.

Keywords: biofiltration, hydrogen sulphide, mercaptans, quantification

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Authors: Hong Yu, Ion Matei

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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Hwasuk Jung, Seoi Lee, Dongchoon Ryou, Pyungjong Yoo, Seokmo Lee

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The Nakdong River being used as drinking water sources for Pusan metropolitan city has the vulnerability of water management due to the fact that industrial areas are located in the upper Nakdong River. Most citizens of Busan think that the water quality of Nakdong River is not good, so they boil or use home filter to drink tap water, which causes unnecessary individual costs to Busan citizens. We need to diversify water intake to reduce the cost and to change the weak water source. Under this background, this study was carried out for the environmental accounting of Namgang dam water treatment system compared to Nakdong River water treatment system by using emergy analysis method to help making reasonable decision. Emergy analysis method evaluates quantitatively both natural environment and human economic activities as an equal unit of measure. The emergy transformity of Namgang dam’s water was 1.16 times larger than that of Nakdong River’s water. Namgang Dam’s water shows larger emergy transformity than that of Nakdong River’s water due to its good water quality. The emergy used in making 1 m3 tap water from Namgang dam water treatment system was 1.26 times larger than that of Nakdong River water treatment system. Namgang dam water treatment system shows larger emergy input than that of Nakdong river water treatment system due to its construction cost of new pipeline for intaking Namgang daw water. If the Won used in making 1 m3 tap water from Nakdong river water treatment system is 1, Namgang dam water treatment system used 1.66. If the Em-won used in making 1 m3 tap water from Nakdong river water treatment system is 1, Namgang dam water treatment system used 1.26. The cost-benefit ratio of Em-won was smaller than that of Won. When we use emergy analysis, which considers the benefit of a natural environment such as good water quality of Namgang dam, Namgang dam water treatment system could be a good alternative for diversifying intake source.

Keywords: emergy, emergy transformity, Em-won, water treatment system

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Ayesha Aihetasham, Najma Arshad, Sobia Khan

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Damage causes by subterranean termites are of major concern today. Termites majorly treated with pesticides resulted in several problems related to health and environment. For this reason, plant-derived natural products specifically essential oils have been evaluated in order to control termites. The aim of the present study was to investigate the antitermitic potential of six essential oils on Heterotermes indicola subterranean termite. No-choice bioassay was used to assess the termiticidal action of essential oils. Further, gut from each set of treated termite group was extracted and analyzed for reduction in number of protozoa and bacteria by protozoal count method using haemocytometer and viable bacterial plate count (dilution method) respectively. In no-choice bioassay it was found that Foeniculum vulgare oil causes high degree of mortality 90 % average mortality at 10 mg oil concentration (10mg/0.42g weight of filter paper). Least mortality appeared to be due to Citrus sinensis oil (43.33 % average mortality at 10 mg/0.42g). The highest activity verified to be of Foeniculum vulgare followed by Eruca sativa, Trigonella foenum-graecum, Peganum harmala, Syzygium cumini and Citrus sinensis. The essential oil which caused maximum reduction in number of protozoa was P. harmala followed by T. foenum-graecum and E. sativa. In case of bacterial count E. sativa oil indicated maximum decrease in bacterial number (6.4×10⁹ CFU/ml). It is concluded that F. vulgare, E. sativa and P. harmala essential oils are highly effective against H. indicola termite and its gut microflora.

Keywords: bacterial count, essential oils, Heterotermes indicola, protozoal count

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Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

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Authors: Magarò Floriana, Zinno Raffaele

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Since 2009, with the coming into force of the January 14, 2008 Ministerial Decree "New technical standards for construction", and the explanatory ministerial circular N°.617 of February 2, 2009, the question of seismic hazard and the design of seismic-resistant structures in Italy has acquired increasing importance. One of the most discussed aspects in recent Italian and international scientific literature concerns the dynamic interaction between land and structure, and the effects which dynamic coupling may have on individual buildings. In effect, from systems dynamics, it is well known that resonance can have catastrophic effects on a stimulated system, leading to a response that is not compatible with the previsions in the design phase. The method used in this study to estimate the frequency of oscillation of the structure is as follows: the analysis of HVSR (Horizontal to Vertical Spectral Ratio) relations. This allows for evaluation of very simple oscillation frequencies for land and structures. The tool used for data acquisition is an experimental digital tromograph. This is an engineered development of the experimental Languamply RE 4500 tromograph, equipped with an engineered amplification circuit and improved electronically using extremely small electronic components (size of each individual amplifier 16 x 26 mm). This tromograph is a modular system, completely "free" and "open", designed to interface Windows, Linux, OSX and Android with the outside world. It an amplifier designed to carry out microtremor measurements, yet which will also be useful for seismological and seismic measurements in general. The development of single amplifiers of small dimension allows for a very clean signal since being able to position it a few centimetres from the geophone eliminates cable “antenna” phenomena, which is a necessary characteristic in seeking to have signals which are clean at the very low voltages to be measured.

Keywords: microtremor, HVSR, tromograph, structural engineering

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Authors: Jinan Fiaidhi, Hashmath Shaik

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Breaking the barrier for practicing evidence-based medicine relies on effective methods for rapidly identifying relevant evidence from the body of biomedical literature. An important challenge confronted by medical practitioners is the long time needed to browse, filter, summarize and compile information from different medical resources. Deep learning can help in solving this based on automatic question answering (Q&A) and transformers. However, Q&A and transformer technologies are not trained to answer clinical queries that can be used for evidence-based practice, nor can they respond to structured clinical questioning protocols like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that are based on transformer models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our transformer methods are reaching an acceptable state-of-the-art performance based on two staged bootstrapping processes involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question. Moreover, we are also reporting experimentations to empower our bootstrapping techniques with patch attention to the most important keywords in the clinical case and the PICO questions. Our bootstrapped patched with attention is showing relevancy of the evidence collected based on entropy metrics.

Keywords: automatic question answering, PICO questions, evidence-based medicine, generative models, LLM transformers

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Authors: Romeo Malik, Yashraj Tripathy, Anup Barai

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Transportation safety is a major concern for vehicle electrification on a large-scale. The failure cost of lithium-ion batteries is substantial and is significantly impacted by higher liability and replacement cost. With continuous advancement on the material front in terms of higher energy density, upgrading safety characteristics are becoming more crucial for broader integration of lithium-ion batteries. Understanding and impeding thermal runaway is the prime issue for battery safety researchers. In this study, a comprehensive comparison of thermal runaway mechanisms for two different cathode types, Li(Ni₀.₃Co₀.₃Mn₀.₃)O₂ and Li(Ni₀.₈Co₀.₁₅Al₀.₀₅)O₂ is explored. Both the chemistries were studied for different states of charge, and the various abuse scenarios that lead to thermal runaway is investigated. Abuse tests include mechanical abuse, electrical abuse, and thermal abuse. Batteries undergo thermal runaway due to a series of combustible reactions taking place internally; this is observed as multiple jets of flame reaching temperatures of the order of 1000ºC. The physicochemical characterisation was performed on cells, prior to and after abuse. Battery’s state of charge and chemistry have a significant effect on the flame temperature profiles which is otherwise quantified as heat released. Majority of the failures during transportation is due to these external short circuit. Finally, a mitigation approach is proposed to impede the thermal runaway hazard. Transporting lithium-ion batteries under low states of charge is proposed as a way forward. Batteries at low states of charge have demonstrated minimal heat release under thermal runaway reducing the risk of secondary hazards such as thermal runaway propagation.

Keywords: battery reliability, lithium-ion batteries, thermal runaway characterisation, tomography

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Authors: Bi-Huei Tsai

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This work explores the inter-region investment behaviors of integrated circuit (IC) design industry from Taiwan to China using the amount of foreign direct investment (FDI). According to the mutual dependence among different IC design industrial locations, Lotka-Volterra model is utilized to explore the FDI interactions between South and East China. Effects of inter-regional collaborations on FDI flows into China are considered. Evolutions of FDIs into South China for IC design industry significantly inspire the subsequent FDIs into East China, while FDIs into East China for Taiwan’s IC design industry significantly hinder the subsequent FDIs into South China. The supply chain along IC industry includes IC design, manufacturing, packing and testing enterprises. I C manufacturing, packaging and testing industries depend on IC design industry to gain advanced business benefits. The FDI amount from Taiwan’s IC design industry into East China is the greatest among the four regions: North, East, Mid-West and South China. The FDI amount from Taiwan’s IC design industry into South China is the second largest. If IC design houses buy more equipment and bring more capitals in South China, those in East China will have pressure to undertake more FDIs into East China to maintain the leading position advantages of the supply chain in East China. On the other hand, as the FDIs in East China rise, the FDIs in South China will successively decline since capitals have concentrated in East China. Prediction of Lotka-Volterra model in FDI trends is accurate because the industrial interactions between the two regions are included. Finally, this work confirms that the FDI flows cannot reach a stable equilibrium point, so the FDI inflows into East and South China will expand in the future.

Keywords: Lotka-Volterra model, foreign direct investment, competitive, Equilibrium analysis

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Authors: L. K. Nivedha, V. Maruthapandian, R. Kothandaraman

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Rechargeable zinc-air batteries (ZAB) are gaining significant research attention owing to their high energy density and copious zinc resources worldwide. However, the unsolved obstacles such as dendrites, passivation, depth of discharge and the lack of an efficient cathode catalyst restrict their practical application1. By and large, non-noble transition metal-based catalysts are well-reputed materials for catalysing oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) with greater stability in alkaline medium2. Herein, we report the synthesis and application of Mn₃O4-NiFeLDH/Carbon composite as a cathode catalyst for rechargeable ZAB. The synergetic effects of the mixed transition metals (Mn/Ni/Fe) have aided in catalysing ORR and OER in alkaline electrolyte with a shallow potential gap of 0.7 V. The composite, by its distinctive physicochemical characteristics, shows an excellent OER activity with a current density of 1.5 mA cm⁻² at a potential of 1.6 V and a superior ORR activity with an onset potential of 0.8 V when compared with their counterparts. Nevertheless, the catalyst prefers a two-electron pathway for the electrochemical reduction of oxygen which results in a limiting current density of 2.5 mA cm⁻². The bifunctional activity of the Mn₃O₄-NiFeLDH/Carbon composite was utilized in developing rechargeable ZAB. The fully fabricated ZAB delivers an open circuit voltage of 1.4 V, a peak power density of 70 mW cm⁻², and a specific capacity of 800 mAh g⁻¹ at a current density of 20 mA cm⁻² with an average discharge voltage of 1 V and the cell is operable upto 50 mA cm-2. Rechargeable ZAB demonstrated over 110 h at 10 mA cm⁻². Further, the cause for the diminished charge-discharge performance experienced beyond the 100th cycle was investigated, and carbon corrosion was testified using Infrared spectroscopy.

Keywords: rechargeable zinc-air battery, oxygen evolution reaction, bifunctional catalyst, alkaline medium

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Authors: Maryam Ghamsari, Mitchell Nye-Wood, Kelvin Wang, Angela Juhasz, Michelle Colgrave, Don Otter, Jun Lu, Nazimah Hamid, Thao T. Le

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Bee venom, a complex mixture of peptides, proteins, enzymes, and other bioactive compounds, has been widely studied for its therapeutic application. This study investigated the proteins present in New Zealand (NZ) honeybee venom (BV) using bottom-up proteomics. Two sample digestion techniques, in-solution digestion and filter-aided sample preparation (FASP), were employed to obtain the optimal method for protein digestion. Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH–MS) analysis was conducted to quantify the protein compositions of NZ BV and investigate variations in collection years. Our results revealed high protein content (158.12 µg/mL), with the FASP method yielding a larger number of identified proteins (125) than in-solution digestion (95). SWATH–MS indicated melittin and phospholipase A2 as the most abundant proteins. Significant variations in protein compositions across samples from different years (2018, 2019, 2021) were observed, with implications for venom's bioactivity. In vitro testing demonstrated immunomodulatory and antioxidant activities, with a viable range for cell growth established at 1.5-5 µg/mL. The study underscores the value of proteomic tools in characterizing bioactive compounds in bee venom, paving the way for deeper exploration into their therapeutic potentials. Further research is needed to fractionate the venom and elucidate the mechanisms of action for the identified bioactive components.

Keywords: honeybee venom, proteomics, bioactivity, fractionation, swath-ms, melittin, phospholipase a2, new zealand, immunomodulatory, antioxidant

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Authors: Yang Zhao, Feng Zhang, Xuanchu Duan

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Trans-differentiation of human Tenon fibroblasts (HTFs) to myo-fibroblasts and fibrosis of episcleral tissue are the most common reasons for the failure of glaucoma filtration surgery, with limited treatment options like antimetabolites which always have side-effects such as leakage of filter bulb, infection, hypotony, and endophthalmitis. Rosiglitazone, a specific thiazolidinedione is a synthetic high-affinity ligand for PPAR-r, which has been used in the treatment of type2 diabetes, and found to have pleiotropic functions against inflammatory response, cell proliferation and tissue fibrosis and to benefit to a variety of diseases in animal myocardium models, steatohepatitis models, etc. Here, in vitro we cultured primary HTFs and stimulated with TGF- β to induced myofibrogenic, then treated cells with Rosiglitazone to assess for fibrogenic response. In vivo, we used rabbit glaucoma model to establish the formation of post- trabeculectomy scarring. Then we administered subconjunctival injection with Rosiglitazone beside the filtering bleb, later protein, mRNA and immunofluorescence of fibrogenic markers are checked, and filtering bleb condition was measured. In vitro, we found Rosiglitazone could suppressed proliferation and migration of fibroblasts through macroautophagy via TGF- β /Smad signaling pathway. In vivo, on postoperative day 28, the mean number of fibroblasts in Rosiglitazone injection group was significantly the lowest and had the least collagen content and connective tissue growth factor. Rosiglitazone effectively controlled human and rabbit fibroblasts in vivo and in vitro. Its subconjunctiiva application may represent an effective, new avenue for the prevention of scarring after glaucoma surgery.

Keywords: fibrosis, glaucoma, macroautophagy, rosiglitazone

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Authors: Dante J. Dorantes-González, Aldrin Balsa-Yepes

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Existing learning techniques such as problem-based learning, project-based learning, or case study learning are learning techniques that focus mainly on technical details, but give no specific guidelines on learner’s experience and emotional learning aspects such as arousal salience and valence, being emotional states important factors affecting engagement and retention. Some approaches involving emotion in educational settings, such as social and emotional learning, lack neuroscientific rigorousness and use of specific neurobiological mechanisms. On the other hand, neurobiology approaches lack educational applicability. And educational approaches mainly focus on cognitive aspects and disregard conditioning learning. First, authors start explaining the reasons why it is hard to learn thoughtfully, then they use the method of neurobiological mapping to track the main limbic system functions, such as the reward circuit, and its relations with perception, memories, motivations, sympathetic and parasympathetic reactions, and sensations, as well as the brain cortex. The authors conclude explaining the major finding: The mechanisms of nonconscious learning and the triggers that guarantee long-term memory potentiation. Afterward, the educational framework for practical application and the instructors’ guidelines are established. An implementation example in engineering education is given, namely, the study of tuned-mass dampers for earthquake oscillations attenuation in skyscrapers. This work represents an original learning technique based on nonconscious learning mechanisms to enhance long-term memories that complement existing cognitive learning methods.

Keywords: emotion, emotion-enhanced memory, learning technique, STEM

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Authors: Iveta Bryjova

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Recording of viscoelastic strain-vs-time curves with the aid of the suction method and a follow-up analysis, resulting into evaluation of standard viscoelastic parameters, is a significant technique for non-invasive contact diagnostics of mechanical properties of skin and assessment of its conditions, particularly in acute burns, hypertrophic scarring (the most common complication of burn trauma) and reconstructive surgery. For elimination of the skin thickness contribution, usable viscoelastic parameters deduced from the strain-vs-time curves are restricted to the relative ones (i.e. those expressed as a ratio of two dimensional parameters), like grosselasticity, net-elasticity, biological elasticity or Qu’s area parameters, in literature and practice conventionally referred to as R2, R5, R6, R7, Q1, Q2, and Q3. With the exception of parameters R2 and Q1, the remaining ones substantially depend on the position of inflection point separating the elastic linear and viscoelastic segments of the strain-vs-time curve. The standard algorithm implemented in commercially available devices relies heavily on the experimental fact that the inflection time comes about 0.1 sec after the suction switch-on/off, which depreciates credibility of parameters thus obtained. Although the Qu’s US 7,556,605 patent suggests a method of improving the precision of the inflection determination, there is still room for nonnegligible improving. In this contribution, a novel method of inflection point determination utilizing the advantageous properties of the Savitzky–Golay filtering is presented. The method allows computation of derivatives of smoothed strain-vs-time curve, more exact location of inflection and consequently more reliable values of aforementioned viscoelastic parameters. An improved applicability of the five inflection-dependent relative viscoelastic parameters is demonstrated by recasting a former study under the new method, and by comparing its results with those provided by the methods that have been used so far.

Keywords: Savitzky–Golay filter, scarring, skin, viscoelasticity

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Authors: Charles Klein O. Gorit, Mark Tristan J. Quimque Jr., M. Cecilia V. Almeda, Concepcion M. Salvana

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Chitosan is a promising biopolymer commonly found in crustacean shells that has plausible effects in water purification and wastewater treatment. It is a primary derivative of chitin and considered second of the most abundant biopolymer prior to cellulose. Morphological analysis had been done using Scanning Electron Microscopy with Energy Dispersive Microscopy (SEM/EDS), and due to its porous nature, it showcases a certain degree of porosity, hence, larger adsorption site of heavy metal. The Energy Dispersive Spectroscopy of the chitosan and ‘lead-bound’ chitosan, shows a relative increase of percent abundance of lead cation from 1.44% to 2.08% hence, adsorption occurs. Chitosan, as a nitrogenous polysaccharide, subjected to Fourier transform infrared spectroscopy (FTIR) analysis shows amide bands ranging from 1635.36 cm⁻¹ for amide 1 band and 1558.40 cm-1 for amide 2 band with NH stretching. For ‘lead-bound’ chitosan, the FT-IR analysis shows a change in peaks upon adsorption of Pb(II) cation. The spectrum shows broadening of OH and NH stretching band. Such observation can be attributed to the probability that the attachment of Pb(II) ions is in these functional groups. A column filter was devised with lead-bound chitosan to determine the zero point charge (pHzpc) of the biopolymer. The results show that at pH 8.34, below than the zpc level of literatures cited for lead which ranges from pH 4 to 7, favors the adsorption site of chitosan and its capability to adsorb traces amount of aqueous lead.

Keywords: chitosan, biopolymer, FT-IR, SEM, zero-point charge, heavy metal, lead ions

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Authors: Chane-Yu Lai, Xiang-Yu Huang

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This study conducted an assessment of sampling result by using the new development rotation filtration device (RFD) filled with porous media filters integrating the method of cyclone centrifugal spins. The testing system established for the experiment used corn oil and potassium sodium tartrate tetrahydrate (PST) as challenge aerosols and were produced by using an Ultrasonic Atomizing Nozzle, a Syringe Pump, and a Collison nebulizer. The collection efficiency of RFD for oil aerosol was assessed by using an Aerodynamic Particle Sizer (APS) and a Fidas® Frog. The results of RFD for the liquid particles condition indicated the cutoff size was 1.65 µm and 1.02 µm for rotation of 0 rpm and 9000 rpm, respectively, under an 80 PPI (pores per inch）foam with a thickness of 80 mm, and sampling velocity of 13.5 cm/s. As the experiment increased the foam thickness of RFD, the cutoff size reduced from 1.62 µm to 1.02 µm. However, when increased the foam porosity of RFD, the cutoff size reduced from 1.26 µm to 0.96 µm. Moreover, as increased the sampling velocity of RFD, the cutoff size reduced from 1.02 µm to 0.76 µm. These discrepancies of above cutoff sizes of RFD all had statistical significance (P < 0.05). The cutoff size of RFD for three experimental conditions of generated liquid oil particles, solid PST particles or both liquid oil and solid PST particles was 1.03 µm, 1.02 µm, or 0.99 µm, respectively, under a 80 PPI foam with thickness of 80 mm, rotation of 9000 rpm, and sampling velocity of 13.5 cm/s. In addition, under the best condition of the experiment, two hours of sampling loading, the RFD had better collection efficiency for particle diameter greater than 0.45 µm, under a 94 PPI nickel mesh with a thickness of 68 mm, rotation of 9000 rpm, and sampling velocity of 108.3 cm/s. The experiment concluded that increased the thickness of porous media, face velocity, and porosity of porous media of RFD could increase the collection efficiency of porous media for sampling oil particles. Moreover, increased the rotation speed of RFD also increased the collection efficiency for sampling oil particles. Further investigation is required for those above operation parameters for RFD in this study in the future.

Keywords: oil aerosol, porous media filter, rotation, filtration

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Authors: Mansa Radhakrishnan, Anwar Ali, Muhammad Rizwan Mughal

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Many universities around the world are working on modular and low budget architecture of small spacecraft to reduce the development cost of the overall system. This paper focuses on the design of a modular solar power harvesting system for a hexagonal-shaped small satellite. The designed solar power harvesting systems are composed of solar panels and power converter subsystems. The solar panel is composed of solar cells mounted on the external face of the printed circuit board (PCB), while the electronic components of power conversion are mounted on the interior side of the same PCB. The solar panel with dimensions 16.5cm × 99cm is composed of 36 solar cells (each solar cell is 4cm × 7cm) divided into four parallel banks where each bank consists of 9 solar cells. The output voltage of a single solar cell is 2.14V, and the combined output voltage of 9 series connected solar cells is around 19.3V. The output voltage of the solar panel is boosted to the satellite power distribution bus voltage level (28V) by a boost converter working on a constant voltage maximum power point tracking (MPPT) technique. The solar panel module is an eight-layer PCB having embedded coil in 4 internal layers. This coil is used to control the attitude of the spacecraft, which consumes power to generate a magnetic field and rotate the spacecraft. As power converter and distribution subsystem components are mounted on the PCB internal layer, therefore it is mandatory to do thermal analysis in order to ensure that the overall module temperature is within thermal safety limits. The main focus of the overall design is on compactness, miniaturization, and efficiency enhancement.

Keywords: small satellites, power subsystem, efficiency, MPPT

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Ekki Kurniawan, Yusuf Nur Jayanto, Erna Sugesti, Efri Suhartono, Agus Ganda Permana, Jaspar Hasudungan, Jangkung Raharjo, Rintis Manfaati

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The purpose of this research is to study the current characteristic of the electrolysis of mineral water to produce hydrogen, alkaline water, and acid water. Alkaline and hydrogen water are believed to have health benefits. Alkaline water containing hydrogen can be an anti-oxidant that captures free radicals, which will increase the immune system. In Indonesia, there are two existing types of alkaline water producing equipment, but the installation is complicated, and the price is relatively expensive. The electrolysis process is slow (6-8 hours) since they are locally made using 311 VDC full bridge rectifier power supply. This paper intends to discuss how to make hydrogen and alkaline water by a simple portable mineral water ionizer. This is an electrolysis device that is easy to carry and able to separate ions of mineral water into acidic and alkaline water. With an electric field, positive ions will be attracted to the cathode, while negative ions will be attracted to the anode. The circuit equivalent can be depicted as RLC transient ciruit. The diode component ensures that the electrolytic current is direct current. Switch S divides the switching times t1, t2, and t3. In the first stage up to t1, the electrolytic current increases exponentially, as does the inductor charging current (L). The molecules in drinking water experience magnetic properties. The direction of the dipole ions, which are random in origin, will regularly flare with the direction of the electric field. In the second stage up to t2, the electrolytic current decreases exponentially, just like the charging current of a capacitor (C). In the 3rd stage, start t3 until it tends to be constant, as is the case with the current flowing through the resistor (R).

Keywords: current electrolysis, mineral water, ions, alkaline and acid waters, inductor, capacitor, resistor

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Authors: Sarah Ledoux

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This paper analyses the quantitative effects of political content consumption in social media platforms on self-reported ideological preference across the Latin American region. Initially praising the democratic potential of the internet and its social networking websites, digital politics scholars have transitioned their discourse to warning against the undemocratic side-effects it cultivates, such as hate speech, filter bubbles, and ideological polarisation. Holding technology solely responsible for political trends worldwide is an oversimplification of the factors influencing social change. Nonetheless, widespread use of social media in new democracies raises questions on the reproduction of recent trends that have been observed in the US and Western Europe. Through the analysis of ordered logistic regressions on data from the 2018 AmericasBarometer survey, this study examines the extent to which the relationship between the consumption of political content on social media is related to ideological polarisation in Latin America. The findings indicate that there is a close link between consumption of political information on social media, specifically on Facebook and WhatsApp, and ideological positioning on the extremes of the political left- and right-wings. This relation holds when controlling for individual-level demographic and attitudinal factors, as well as country-level effects. These results demonstrate with empirical evidence that viewing political content on social media has a significant positive effect on the likelihood that citizens position themselves on the extreme ends of the left-right ideological spectrum and implies that political polarisation is a phenomenon that accompanies politically driven social media use.

Keywords: Latin America, polarisation, political consumption, political ideology, social media, survey

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Authors: Usarat Thawornchaisit, Kamalasiri Juthaisong, Kasama Parsongjeen, Phonsiri Phoengchan

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In this study, sludge samples that were collected from the wastewater treatment plant of a printed circuit board manufacturing industry in Thailand were subjected to acid extraction using sulfuric acid as the chemical extracting agent. The effects of sulfuric acid concentration (A), the ratio of a volume of acid to a quantity of sludge (B) and extraction time (C) on the efficiency of copper extraction were investigated with the aim of finding the optimal conditions for maximum removal of copper from the wastewater treatment sludge. Factorial experimental design was employed to model the copper extraction process. The results were analyzed statistically using analysis of variance to identify the process variables that were significantly affected the copper extraction efficiency. Results showed that all linear terms and an interaction term between volume of acid to quantity of sludge ratio and extraction time (BC), had statistically significant influence on the efficiency of copper extraction under tested conditions in which the most significant effect was ascribed to volume of acid to quantity of sludge ratio (B), followed by sulfuric acid concentration (A), extraction time (C) and interaction term of BC, respectively. The remaining two-way interaction terms, (AB, AC) and the three-way interaction term (ABC) is not statistically significant at the significance level of 0.05. The model equation was derived for the copper extraction process and the optimization of the process was performed using a multiple response method called desirability (D) function to optimize the extraction parameters by targeting maximum removal. The optimum extraction conditions of 99% of copper were found to be sulfuric acid concentration: 0.9 M, ratio of the volume of acid (mL) to the quantity of sludge (g) at 100:1 with an extraction time of 80 min. Experiments under the optimized conditions have been carried out to validate the accuracy of the Model.

Keywords: acid treatment, chemical extraction, sludge, waste management

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Authors: Slim Naifar, Sonia Bradai, Christian Viehweger, Olfa Kanoun

Abstract:

Vibration provides an interesting source of energy since it is available in many indoor and outdoor applications. Nevertheless, in order to have an efficient design of the harvesting system, vibration converters have to satisfy some criterion in terms of robustness, compactness and energy outcome. In this work, an electromagnetic converter based on mechanical spring principle is proposed. The designed harvester is formed by a coil oscillating around ten ring magnets using a mechanical spring. The proposed design overcomes one of the main limitation of the moving coil by avoiding the contact between the coil wires with the mechanical spring which leads to a better robustness for the converter. In addition, the whole system can be implemented in a cavity of a screw. Different parameters in the harvester were investigated by finite element method including the magnet size, the coil winding number and diameter and the excitation frequency and amplitude. A prototype was realized and tested. Experiments were performed for 0.5 g to 1 g acceleration. The used experimental setup consists of an electrodynamic shaker as an external artificial vibration source controlled by a laser sensor to measure the applied displacement and frequency excitation. Together with the laser sensor, a controller unit, and an amplifier, the shaker is operated in a closed loop which allows controlling the vibration amplitude. The resonance frequency of the proposed designs is in the range of 24 Hz. Results indicate that the harvester can generate 612 mV and 1150 mV maximum open circuit peak to peak voltage at resonance for 0.5 g and 1 g acceleration respectively which correspond to 4.75 mW and 1.34 mW output power. Tuning the frequency to other values is also possible due to the possibility to add mass to the moving part of the or by changing the mechanical spring stiffness.

Keywords: energy harvesting, electromagnetic principle, vibration converter, moving coil

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