Search results for: testing benchmark

Authors: Peyman Sindareh Esfahani, Jeffery Kurt Pieper

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In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l₂-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.

Keywords: linear fractional transformation, linear matrix inequality, robust model predictive control, state feedback control

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Authors: Xinyi Xu

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This study conducts a detailed examination of Asian stock markets over the period from 2008 to 2023, with a focus on the dynamics of cointegration and their relevance for long-term investment strategies. Specifically, we assess the co-movement and potential for pairs trading—a strategy where investors take opposing positions on two stocks, indices, or financial instruments that historically move together. For example, we explore the relationship between the Nikkei 225 (N225), Japan’s benchmark stock index, and the Straits Times Index (STI) of Singapore, as well as the relationship between the Korea Composite Stock Price Index (KS11) and the STI. The methodology includes tests for normality, stationarity, cointegration, and the application of Vector Error Correction Modeling (VECM). Our findings reveal significant long-term relationships between these pairs, indicating opportunities for pairs trading strategies. Furthermore, the research underscores the challenges posed by model instability and the influence of major global incidents, which are identified as structural breaks. These findings pave the way for further exploration into the intricacies of financial market dynamics.

Keywords: normality tests, stationarity, cointegration, VECM, pairs trading

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Authors: Aung Phyo Kyaw, Kuo Chieh Chao

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Expansive soils are more complicated than normal soils, although the soil itself is not very complicated. When evaluating foundation performance on expansive soil, it is important to consider soil expansion. The primary focus of this study is on hydraulic cement and expansive soil mixtures, and the research aims to identify key parameters for controlling the swell of the expansive soil-hydraulic cement mixture. Treatment depths can be determined using hydraulic cement ratios of 4%, 8%, 12%, and 15% for treating expansive soil. To understand the effect of hydraulic cement percentages on the swelling of expansive soil-hydraulic admixture, performing the consolidation-swell test σ''ᶜˢ is crucial. This investigation primarily focuses on consolidation-swell tests σ''ᶜˢ, although the heave index Cₕ is also needed to determine total heave. The heave index can be measured using the percent swell in the specific inundation stress in both the consolidation-swell test and the constant-volume test swelling pressure. Obtaining the relationship between swelling pressure and σ''ᶜⱽ determined from the "constant volume test" is useful in predicting heave from a single oedometer test. The relationship between σ''ᶜˢ and σ''ᶜⱽ is based on experimental results of expansive soil behavior and facilitates heave prediction for each soil. In this method, the soil property "m" is used as a parameter, and common soil property tests include compaction, particle size distribution, and the Atterberg limit. The Electricity Generating Authority of Thailand (EGAT) provided the soil sample for this study, and all laboratory testing is performed according to American Society for Testing and Materials (ASTM) standards.

Keywords: expansive soil, swelling pressure, total heave, treatment depth

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Authors: Siobhan O’Shea, Sangeetha Vijaysri Nair, Hee Cheol Kim, Charles Thomas Nugent, Cheuk Yan William Tong, Sam Douthwaite, Andrew Worlock

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The Aptima® HIV-1 Quant Dx Assay is a fully automated assay on the Panther system. It is based on Transcription-Mediated Amplification and real time detection technologies. This assay is intended for monitoring HIV-1 viral load in plasma specimens and for the detection of HIV-1 in plasma and serum specimens. Nine-hundred and seventy nine specimens selected at random from routine testing at St Thomas’ Hospital, London were anonymised and used to compare the performance of the Aptima HIV-1 Quant Dx assay and Roche COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 Test, v2.0. Two-hundred and thirty four specimens gave quantitative HIV-1 viral load results in both assays. The quantitative results reported by the Aptima Assay were comparable those reported by the Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0 with a linear regression slope of 1.04 and an intercept on -0.097. The Aptima assay detected HIV-1 in more samples than the Roche assay. This was not due to lack of specificity of the Aptima assay because this assay gave 99.83% specificity on testing plasma specimens from 600 HIV-1 negative individuals. To understand the reason for this higher detection rate a side-by-side comparison of low level panels made from the HIV-1 3rd international standard (NIBSC10/152) and clinical samples of various subtypes were tested in both assays. The Aptima assay was more sensitive than the Roche assay. The good sensitivity, specificity and agreement with other commercial assays make the HIV-1 Quant Dx Assay appropriate for both viral load monitoring and detection of HIV-1 infections.

Keywords: HIV viral load, Aptima, Roche, Panther system

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Authors: Ghazwan Al-Haji

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Traffic safety has become a major concern in Southeast Asia due to the increasing number of road accidents resulting in fatalities and injuries. Southeast Asia has one of the highest road traffic fatality rates in the world, in terms of both population and number of cars, nearly six times higher than the EU region. One of the reasons for this concerning trend is the increasing share of motorcycles as a form of transportation throughout Southeast Asia. The purpose of this study is to benchmark traffic safety situations and statistics in six countries in Asia and the EU, which Indonesia, Malaysia, Vietnam, Italy, Portugal and Sweden. The research will assess the priorities and causes of road accidents in the target nations. Further, the study will analyze the existing practices and promote best practices that can be implemented toward safer roads in Asian target countries. In order to achieve this goal, the study categorizes various factors contributing to traffic accidents and best practices into 4 pillars (Safer Behavior, Safer Roads, Safer Vehicles and Road Safety Management). The result of the study consists of a list of recommendations that can be applied by policymakers to promote safer roads in Asia towards 2030. The study is co-financed by the EU project ASIASAFE.

Keywords: traffic safety, ASIASAFE, Southeast Asia, EU project

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Authors: Raffaella Sesana, Nazanin Sheibanian, Luca Corsaro, Sedat Özbilen, Rocco Lupoi, Francesco Artusio

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High Entropy Alloy (HEA) coatings of Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi on Mg substrates were prepared from mechanically alloyed HEA powder feedstocks and at three different Cold Spray (CS) process gas (N2) temperatures (650, 750 and 850°C). Mechanically alloyed and cold-sprayed HEA coatings were characterized by macro photography, OM, SEM+EDS study, micro-hardness testing, roughness, and porosity measurements. As a result of mechanical alloying (MA), harder particles are deformed and fractured. The particles in the Cu-rich region were coarser and more globular than those in the A1 phase, which is relatively soft and ductile. In addition to the A1 particles, there were some separate Cu-rich regions. Due to the brittle nature of the powder and the acicular shape, Mn-HEA powder exhibited a different trend with smaller particle sizes. It is observed that MA results in a loose structure characterized by many gaps, cracks, signs of plastic deformation, and small particles attached to the surface of the particle. Considering the experimental results obtained, it is not possible to conclude that the chemical composition of the high entropy alloy influences the roughness of the coating. It has been observed that the deposited volume increases with temperature only in the case of Al0.1 and Mg-based HEA, while for the rest of the Al-based HEA, there are no noticeable changes. There is a direct correlation between micro-hardness and the chemical composition of a coating: the micro-hardness of a coating increases as the percentage of aluminum increases in the sample. Compared to the substrate, the coating has a much higher hardness, and the hardness measured at the interface is intermediate.

Keywords: characterisation, cold spraying, HEA coatings, SEM+EDS

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Ghada Moussa, Mamoud Owais

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Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Tafadzwa Shumba, Rose C. Nyatondo, Regret Sunge

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This research analyses the impact of budget deficits on the economic performance of Zimbabwe. The study employs the autoregressive distributed lag (ARDL) confines testing method to co-integration and long-run estimation using time series data from 1980-2018. The Augmented Dick Fuller (ADF) and the Granger approach were used to testing for stationarity and causality among the factors. Co-integration test results affirm a long term association between GDP development rate and descriptive factors. Causality test results show a unidirectional connection between budget shortfall to GDP development and bi-directional causality amid debt and budget deficit. This study also found unidirectional causality from debt to GDP growth rate. ARDL estimates indicate a significantly positive long term and significantly negative short term impact of budget shortfall on GDP. This suggests that budget deficits have a short-run growth retarding effect and a long-run growth-inducing effect. The long-run results follow the Keynesian theory that posits that fiscal deficits result in an increase in GDP growth. Short-run outcomes follow the neoclassical theory. In light of these findings, the government is recommended to minimize financing of recurrent expenditure using a budget deficit. To achieve sustainable growth and development, the government needs to spend an absorbable budget deficit focusing on capital projects such as the development of human capital and infrastructure.

Keywords: ARDL, budget deficit, economic performance, long run

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Authors: Wanutchaya Duanginta, Napaporn Apiratmateekul, Tippawan Sangkaew, Sunaree Wekinhirun, Kunchit Kongros, Wanvisa Treebuphachatsakul

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Thailand has a tropical climate with an average outdoor ambient air temperature of over 30°C, which can exceed manufacturer recommendations for the storage of glucose test strips. This study monitored temperature and humidity at actual sites of five sub-district health promoting hospitals (HPH) in Phitsanulok Province for the storage of glucose test strips in refrigerated conditions. Five calibrated data loggers were placed at the actual sites for glucose test strip storage at five HPHs for 8 weeks between April and June. For the stress test, two lot numbers of glucose test strips, each with two glucose meters, were kept in a plastic box with desiccants and placed in a refrigerator with the temperature calibrated to 4°C and at room temperature (RT). Temperature and humidity in the refrigerator and at RT were measured every hour for 30 days. The mean temperature for storing test strips at the five HPHs ranged from 29°C to 33°C, and three of the five HPHs (60%) had a mean temperature above 30°C. The refrigerator temperatures were 3.8 ± 2.0°C (2.0°C to 6.5°C), and relative humidity was 51 ± 2% (42 to 54%). The maximum of blood glucose testing by glucose meters when the test strips were stored in a refrigerator were not significantly different (p > 0.05) from unstressed test strips for both glucose meters using amperometry-GDH-PQQ and amperometry-GDH-FAD principles. Opening the test strip vial daily resulted in higher variation than when refrigerated after a single-use. However, the variations were still within an acceptable range. This study concludes that glucose tested strips can be stored in plastic boxes in a refrigerator if it is well-controlled for temperature and humidity. Storage of glucose-tested strips in the refrigerator during hot and humid weather may be useful for HPHs with limited air conditioners.

Keywords: environmental stressed test, thermal stressed test, quality control, point-of-care testing

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Authors: Hadi Mousavi, Majid Davoudi Nasr

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Executives can maximize profits by recognizing the factors that affect investment and using them to obtain the optimal level of investment. Inefficient markets have shortcomings that can impact the optimal level of investment, leading to the process of over-investment or under-investment. In the present study, the relationship between the overconfidence of senior managers and abnormal cash fluctuations with respect to financial flexibility in companies listed in the Tehran stock exchange from 2009 to 2013 were evaluated. In this study, the sample consists of 84 companies selected by a systematic elimination method and 420 year-companies in total. In this research, EVIEWS software was used to test the research hypotheses by linear regression and correlation coefficient and after designing and testing the research hypothesis. After designing and testing research hypotheses that have been used to each hypothesis, it was concluded that there was a significant relationship between the overconfidence of senior managers and abnormal cash fluctuations, and this relationship was not significant at any level of financial flexibility. Moreover, the findings of the research showed that there was a significant relationship between senior manager’s overconfidence and positive abnormal cash flow fluctuations in firms, and this relationship is significant only at the level of companies with high financial flexibility. Finally, the results indicate that there is no significant relationship between senior managers 'overconfidence and negative cash flow abnormalities, and the relationship between senior managers' overconfidence and negative cash flow fluctuations at the level of companies with high financial flexibility was confirmed.

Keywords: abnormal cash fluctuations, overconfidence of senior managers, financial flexibility, accounting

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Authors: Yasaswi Palagummi, Sareh Rowlands

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Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

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Authors: Farhad Arian Far, Anjanette Q. Eleazar, Francis Aldrine A. Uy, Mary Joyce Anne V. Uy

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The Unified Philippine Accident Reporting and Database System (UPARDS), is a newly developed system by Dr. Francis Aldrine Uy of the Mapua Institute of Technology. The main purpose is to provide an advanced road accident investigation tool, record keeping and analysis system for stakeholders such as Philippine National Police (PNP), Metro Manila Development Authority (MMDA), Department of Public Works and Highways (DPWH), Department of Health (DOH), and insurance companies. The system is composed of 2 components, the mobile application for road accident investigators that takes advantage of available technology to advance data gathering and the web application that integrates all accident data for the use of all stakeholders. The researchers with the cooperation of PNP’s Vehicle Traffic Investigation Sector of the City of Manila, conducted the field-testing of the application in fifteen (15) accident cases. Simultaneously, the researchers also distributed surveys to PNP, Manila Doctors Hospital, and Charter Ping An Insurance Company to gather their insights regarding the web application. The survey was designed on information systems theory called Technology Acceptance Model. The results of the surveys revealed that the respondents were greatly satisfied with the visualization and functions of the applications as it proved to be effective and far more efficient in comparison with the conventional pen-and-paper method. In conclusion, the pilot study was able to address the need for improvement of the current system.

Keywords: accident, database, investigation, mobile application, pilot testing

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Authors: Rylle Evan Gabriel Zamora, Micah Dennise Malia, Abygail Deniese Villabona

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When examining studies on aggression, the studies about individual aggression vastly outnumbers those studies on group aggression. Given the nature of aggression to be violent and cyclical, and the amount violent events that have occurred in the near present, the study of group aggression is relevant now more than ever. This discrepancy is parallel with the number of valid and reliable psychological tests that measure group aggression. Throughout history, one of the biggest causes of group based violence and aggression is religion. This is particularly true within the context of the Philippines as there are a large number of religious groups. Thus, this study aimed to develop a standardized test that measures an individual’s tendency to be aggressive to those who are in conflict with his or her religious beliefs. This study employs a test development design that employs a qualitative phase to ensure the validity of the scale. Thus, the study was divided into three phases. First is a pilot test wherein an instrument was designed from existing literature which was then administered to 173 respondents from the four largest religious groups in the Philippines. After extensive factor analysis and reliability testing, new items were then formed from the qualitative data collected from eight participants, consisting of two individuals per religious group. The final testing integrates all statistically significant items from the first phase, and the newly formed items from the second phase, which was then administered to 200 respondents. The results were then tested further for reliability using Cronbach’s alpha and validity through factor analysis. The items that were proven to be significant were then combined to create a final instrument that may be used by future studies.

Keywords: religious aggression, group aggression, test development, psychological assessment, social psychology

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Authors: Siva Sankara Arudra, Kamaruzaman Baharuddin, Ir. Ahmed Fadzil Mustafa Kamal, Ir. Abdul Latif Mohamed

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The advancement of electrical and electronics technologies has rewarded the oil and gas industry with great opportunities to embed more environmentally solutions into design. Most offshore oil and gas producers have their engineering and production asset goals to promote greater use of environmentally friendly compression system technologies to eliminate hazardous emissions from conventional gas compressor drivers. Therefore, this paper comprehensively elaborates the parametric study conducted in integrating the latest electrical and electronics drives technology into the existing compression system. This study was conducted in aspects of layout, reliability & availability, maintainability, emission, and cost. An existing offshore facility that utilized gas turbines as the driver for gas compression was set as Conventional Case for this study. The Electrification Case will utilize electric motor drives as the driver for the compression system. Findings from this study indicate more advantages in driver electrification compared to conventional compression systems. The findings of this paper can be set as a benchmark for future offshore driver selection for gas compression systems of similar operating parameters and power range.

Keywords: turbomachinery, electrification, emission, compression system

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Authors: Rutebuka Evariste, Zhang Lixiao

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Mobile phone sales and stocks showed an exponential growth in the past years globally and the number of mobile phones produced each year was surpassing one billion in 2007, this soaring growth of related e-waste deserves sufficient attentions paid to it regionally and globally as long as 40% of its total weight is made from metallic which 12 elements are identified to be highly hazardous and 12 are less harmful. Different research and methods have been used to estimate the obsolete mobile phones but none has developed a dynamic model and handle the discrepancy resulting from improper approach and error in the input data. The study aim was to develop a comprehensive dynamic system model for simulating the dynamism of e-waste production from mobile phone regardless the country or region and prevail over the previous errors. The logistic model method combined with STELLA program has been used to carry out this study. Then the simulation for Rwanda has been conducted and compared with others countries’ results as model testing and validation. Rwanda is about 1.5 million obsoletes mobile phone with 125 tons of waste in 2014 with e-waste production peak in 2017. It is expected to be 4.17 million obsoletes with 351.97 tons by 2020 along with environmental impact intensity of 21times to 2005. Thus, it is concluded through the model testing and validation that the present dynamic model is competent and able deal with mobile phone e-waste production the fact that it has responded to the previous studies questions from Czech Republic, Iran, and China.

Keywords: carrying capacity, dematerialization, logistic model, mobile phone, obsolescence, similarity, Stella, system dynamics

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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: P. Parthiban, Sonu Rajak, N. Kannan, R. Dhanalakshmi

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This paper deals with the Vehicle Routing Problem (VRP) with environmental considerations which is called Pollution Routing Problem (PRP). The objective is to minimize the operational and environmental costs. It consists of routing a number of vehicles to serve a set of customers, and determining fuel consumption, driver wages and their speed on each route segment, while respecting the capacity constraints and time windows. In this context, we presented an Ant Colony Optimization (ACO) approach, combined with a Speed Optimization Algorithm (SOA) to solve the PRP. The proposed solution method consists of two stages. Stage one is to solve a Vehicle Routing Problem with Time Window (VRPTW) using ACO and in the second stage a SOA is run on the resulting VRPTW solutions. Given a vehicle route, the SOA consists of finding the optimal speed on each arc of the route in order to minimize an objective function comprising fuel consumption costs and driver wages. The proposed algorithm tested on benchmark problem, the preliminary results show that the proposed algorithm is able to provide good solutions.

Keywords: ant colony optimization, CO2 emissions, combinatorial optimization, speed optimization, vehicle routing

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: Yanhua Zhao, Chenli Rao, Dongdong Li, Chuanmin Tao

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The Chinese Centers for Disease Control and Prevention (CCDC) issued a new laboratory-based algorithm for HIV diagnosis on April 2016, which initially screens with a combination HIV-1/HIV-2 antigen/antibody fourth-generation immunoassay (IA) followed, when reactive, an HIV-1/HIV-2 undifferentiated antibody IA in duplicate. Reactive specimens with concordant results undergo supplemental tests with western blots, or HIV-1 nucleic acid tests (NATs) and non-reactive specimens with discordant results receive HIV-1 NATs or p24 antigen tests or 2-4 weeks follow-up tests. However, little data evaluating the application of the new algorithm have been reported to date. The study was to evaluate the performance of new laboratory-based HIV diagnostic algorithm in an inpatient population of Southwest China over the initial 6 months by compared with the old algorithm. Plasma specimens collected from inpatients from May 1, 2016, to October 31, 2016, are submitted to the laboratory for screening HIV infection performed by both the new HIV testing algorithm and the old version. The sensitivity and specificity of the algorithms and the difference of the categorized numbers of plasmas were calculated. Under the new algorithm for HIV diagnosis, 170 of the total 52 749 plasma specimens were confirmed as positively HIV-infected (0.32%). The sensitivity and specificity of the new algorithm were 100% (170/170) and 100% (52 579/52 579), respectively; while 167 HIV-1 positive specimens were identified by the old algorithm with sensitivity 98.24% (167/170) and 100% (52 579/52 579), respectively. Three acute HIV-1 infections (AHIs) and two early HIV-1 infections (EHIs) were identified by the new algorithm; the former was missed by old procedure. Compared with the old version, the new algorithm produced fewer WB-indeterminate results (2 vs. 16, p = 0.001), which led to fewer follow-up tests. Therefore, the new HIV testing algorithm is more sensitive for detecting acute HIV-1 infections with maintaining the ability to verify the established HIV-1 infections and can dramatically decrease the greater number of WB-indeterminate specimens.

Keywords: algorithm, diagnosis, HIV, laboratory

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Authors: Ruilian Zhao, Shukai Zhang, Yan Wang, Weiwei Wang

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Repairing broken test cases is an expensive and challenging task in evolving software systems. Although an automated repair technique with intent preservation has been proposed, but it does not take into account the association between test repairs and assertions, leading to a large number of irrelevant candidates and decreasing the repair capability. This paper proposes an assertion-driven test repair approach. Furthermore, an intent-oriented priority criterion is raised to guide the repair candidate generation, making the repairs closer to the intent of the test. In more detail, repair targets are determined through post-dominance relations between assertions and the methods that directly cause compilation errors. Then, test repairs are generated from the target in a bottom-up way, guided by the intent-oriented priority criteria. Finally, the generated repair candidates are prioritized to match the original test intent. The approach is implemented and evaluated on the benchmark of 4 open-source programs and 91 broken test cases. The result shows that the approach can fix 89% (81/91) of broken test cases, which is more effective than the existing intentpreserved test repair approach, and our intent-oriented priority criteria work well.

Keywords: test repair, test intent, software test, test case evolution

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Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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Authors: Ibrahim Umar, Ashiru Rajab, Sumayya Chindo, Emmanuel Olashore

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INTRODUCTION: Kano is the most populous state in Nigeria and one of the two states with the highest TB burden in the country. The state notifies an average of 8,000+ TB cases quarterly and has the highest yearly notification of all the states in Nigeria from 2020 to 2022. The contribution of the state TB program to the National TB notification varies from 9% to 10% quarterly between the first quarter of 2022 and second quarter of 2023. The Kano State TB Situation Room is an innovative platform for timely data collection, collation and analysis for informed decision in health system. During the 2023 second National TB Testing week (NTBTW) Kano TB program aimed at early TB detection, prevention and treatment. The state TB Situation room provided avenue to the state for coordination and surveillance through real time data reporting, review, analysis and use during the NTBTW. OBJECTIVES: To assess the role of innovative information and communication technology platform for real-time TB reporting during second National TB Testing week in Nigeria 2023. To showcase the NTBTW data cascade analysis using TSR as innovative ICT platform. METHODOLOGY: The State TB deployed a real-time virtual dashboard for NTBTW reporting, analysis and feedback. A data room team was set up who received realtime data using google link. Data received was analyzed using power BI analytic tool with statistical alpha level of significance of <0.05. RESULTS: At the end of the week-long activity and using the real-time dashboard with onsite mentorship of the field workers, the state TB program was able to screen a total of 52,054 people were screened for TB from 72,112 individuals eligible for screening (72% screening rate). A total of 9,910 presumptive TB clients were identified and evaluated for TB leading to diagnosis of 445 TB patients with TB (5% yield from presumptives) and placement of 435 TB patients on treatment (98% percentage enrolment). CONCLUSION: The TB Situation Room (TBSR) has been a great asset to Kano State TB Control Program in meeting up with the growing demand for timely data reporting in TB and other global health responses. The use of real time surveillance data during the 2023 NTBTW has in no small measure improved the TB response and feedback in Kano State. Scaling up this intervention to other disease areas, states and nations is a positive step in the right direction towards global TB eradication.

Keywords: tuberculosis (tb), national tb testing week (ntbtw), tb situation rom (tsr), information communication technology (ict)

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Authors: A. Harras, A. Tsuji, K. Terada

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Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: automatic detection, tracking, pedestrians, counting

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Authors: Vedansh More, Lalatendu Bal, Ronak Panchal, Atharva Kulkarni

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The project aims at developing a cost-effective test bench capable of testing and validating the complete powertrain package of an electric vehicle. Emrax 228 high voltage synchronous motor was selected as the prime mover for study. A tandem type dynamometer comprising of two loading methods; inertial, using standard inertia rollers and absorptive, using a separately excited DC generator with resistive coils was developed. The absorptive loading of the prime mover was achieved by implementing a converter circuit through which duty of the input field voltage level was controlled. This control was efficacious in changing the magnetic flux and hence the generated voltage which was ultimately dropped across resistive coils assembled in a load bank with all parallel configuration. The prime mover and loading elements were connected via a chain drive with a 2:1 reduction ratio which allows flexibility in placement of components and a relaxed rating of the DC generator. The development will aid in determination of essential characteristics like torque-RPM, power-RPM, torque factor, RPM factor, heat loads of devices and battery pack state of charge efficiency but also provides a significant financial advantage over existing versions of dynamometers with its cost-effective solution.

Keywords: absorptive load, chain drive, chordal action, DC generator, dynamometer, electric vehicle, inertia rollers, load bank, powertrain, pulse width modulation, reduction ratio, road load, testbench

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Authors: T. Qasim, B. El Masoud, D. Ailabouni

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This in vitro study focused on investigating the fatigue resistance of veneered zirconia molar crowns with different veneering ceramic thicknesses, simulating the relative wear depths under simulated cyclic loading. A mandibular first molar was prepared and then scanned using computer-aided design/computer-aided manufacturing (CAD/CAM) technology to fabricate 32 zirconia copings of uniform 0.5 mm thickness. The manufactured copings then veneered with 1.5 mm, 1.0 mm, 0.5 mm, and 0.0 mm representing 0%, 33%, 66%, and 100% relative wear of a normal ceramic thickness of 1.5 mm. All samples were thermally aged to 6000 thermo-cycles for 2 minutes with distilled water between 5 ˚C and 55 ˚C. The samples subjected to cyclic fatigue and fracture testing using SD Mechatronik chewing simulator. These samples are loaded up to 1.25x10⁶ cycles or until they fail. During fatigue, testing, extensive cracks were observed in samples with 0.5 mm veneering layer thickness. Veneering layer thickness 1.5-mm group and 1.0-mm group were not different in terms of resisting loads necessary to cause an initial crack or final failure. All ceramic zirconia-based crown restorations with varying occlusal veneering layer thicknesses appeared to be fatigue resistant. Fracture load measurement for all tested groups before and after fatigue loading exceeded the clinical chewing forces in the posterior region. In general, the fracture loads increased after fatigue loading and with the increase in the thickness of the occlusal layering ceramic.

Keywords: all ceramic, cyclic loading, chewing simulator, dental crowns, relative wear, thermally ageing

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Turin Datta, Kisor K. Sahu

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Structural materials used in today’s oil and gas exploration and drilling of both onshore and offshore oil and gas wells must possess superior tensile properties, excellent resistance to corrosive degradation that includes general, localized (pitting and crevice) and environment assisted cracking such as stress corrosion cracking and hydrogen embrittlement. The High Pressure and High Temperature (HPHT) wells are typically operated at temperature and pressure that can exceed 300-3500F and 10,000psi (69MPa) respectively which necessitates the use of exotic materials in these exotic sources of natural resources. This research investigation is focussed on the evaluation of tensile properties and corrosion behavior of AISI 4140 High-Strength Low Alloy Steel (HSLA) possessing tempered martensitic microstructure and Duplex 2205 Stainless Steel (DSS) having austenitic and ferritic phase. The selection of this two alloys are primarily based on economic considerations as 4140 HSLA is cheaper when compared to DSS 2205. Due to the harsh aggressive chemical species encountered in deep oil and gas wells like chloride ions (Cl-), carbon dioxide (CO2), hydrogen sulphide (H2S) along with other mineral organic acids, DSS 2205, having a dual-phase microstructure can mitigate the degradation resulting from the presence of both chloride ions (Cl-) and hydrogen simultaneously. Tensile properties evaluation indicates a ductile failure of DSS 2205 whereas 4140 HSLA exhibit quasi-cleavage fracture due to the phenomenon of ‘tempered martensitic embrittlement’. From the potentiodynamic polarization testing, it is observed that DSS 2205 has higher corrosion resistance than 4140 HSLA; the former exhibits passivity signifying resistance to localized corrosion while the latter exhibits active dissolution in all the environmental parameters space that was tested. From the Scanning Electron Microscopy (SEM) evaluation, it is understood that stable pits appear in DSS 2205 only when the temperature exceeds the critical pitting temperature (CPT). SEM observation of the corroded 4140 HSLA specimen tested in aqueous 3.5 wt.% NaCl solution reveals intergranular cracking which appears due to the adsorption and diffusion of hydrogen during polarization, thus, causing hydrogen-induced cracking/hydrogen embrittlement. General corrosion testing of DSS 2205 in acidic brine (pH~3.0) solution at ambient temperature using coupons indicate no weight loss even after three months whereas the corrosion rate of AISI 4140 HSLA is significantly higher after one month of testing.

Keywords: DSS 2205, polarization, pitting, SEM

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Authors: Mohammad Khalid, Mariya Munir, Jacelyn Rice Boyaue

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Highways are considered a major source of pollution to storm-water, and its runoff can introduce various contaminants, including nutrients, Indicator bacteria, heavy metals, chloride, and phosphorus compounds, which can have negative impacts on receiving waters. This study assessed the ability of biochar for contaminants removal and to improve the water holding capacity of soil biochar mixture. For this, ten commercially available biochar has been strategically selected. Lab scale batch testing was done at 3% and 6% by the weight of the soil to find the preliminary estimate of contaminants removal along with hydraulic conductivity and water retention capacity. Furthermore, from the above-conducted studies, six best performing candidate and an application rate of 6% has been selected for the column studies. Soil biochar mixture was filled in 7.62 cm assembled columns up to a fixed height of 76.2 cm based on hydraulic conductivity. A total of eight column experiments have been conducted for nutrient, heavy metal, and indicator bacteria analysis over a period of one year, which includes a drying as well as a deicing period. The saturated hydraulic conductivity was greatly improved, which is attributed to the high porosity of the biochar soil mixture. Initial data from the column testing shows that biochar may have the ability to significantly remove nutrients, indicator bacteria, and heavy metals. The overall study demonstrates that biochar could be efficiently applied with clay soil to improve the soil's hydraulic characteristics as well as remove the pollutants from the stormwater runoff.

Keywords: biochar, nutrients, indicator bacteria, storm-water treatment, sustainability

Procedia PDF Downloads 107