Optimal Results Research Articles

Quantum Technology in the Financial Sector: Econometric Analyses and Risk Management to Mitigate the Quantum Winter

In today's rapidly evolving financial landscape, methods as econometric modeling, predictive analysis, and risk analysis are becoming increasingly important. These methods offer critical tools for understanding and managing the potential of quantum technology in the financial sector. Quantum technology can achieve fast and precise results in data analysis, risk management, portfolio optimization, and market forecasting. However, to realize these possibilities, we need to overcome technical challenges as well as financial and strategic barriers. This paper discusses scientific awareness and proposed solutions to 'Quantum Winter' especially in the financial sector. Mitigating the risk of a 'Quantum Winter' is possible through steps such as building strategic partnerships, conducting risk analysis, developing specialized capabilities, securing government support, and establishing appropriate regulations. Econometric modeling, predictive analysis, and risk analysis are indispensable tools to understand the potential of quantum technology in the financial sector and to achieve sustainable success.

Alternative Plant-Based Gluten-Free Sourdough Pastry Snack Production by Using Beetroot and Legumes: Characterization of Physical and Sensorial Attributes.

Objective of this study was to design a formula of a sourdough pastry snack by adding starter inoculum into the formulation which was obtained by the fermentation process through beetroot (Beta vulgaris) puree with black-eyed pea (Vigna unguiculata) and fava bean (Vicia faba). With this development process, it was aimed to review the functional impact of legumes as gluten replacement and emphasize the importance regarding physical and sensory attributes in a pastry snack product. First, a starter inoculum was developed based on modification of the shalgam fermentation process with legumes. An experimental design suggested by the response surface methodology was used to optimize its microbial properties and level of antioxidants with the factors of amounts of beetroot puree, fava bean/black-eyed pea ratio, and fermentation time. In the second part, this starter inoculum was mixed with fava bean flour to obtain a sourdough pastry snack (FBS) with improved physical and sensory attributes and compared to the wheat control sourdough (WCS) pastry snack after the baking process. According to the optimization results to produce starter inoculum with the optimum results of lactic acid bacteria 9.55 log cfu/mL, the level of antioxidant activity 91.86 μM TE/mL, and total yeast level 6.96 log cfu/mL; 75 mL of beetroot puree, 100% for fava bean, and fermentation for 24 h were obtained. Compared to WCS, FBS has approximately 16% higher hardness values. Also, a significant difference was observed for stiffness and springiness among samples. The retention of moisture was higher in the first 4 days following the storage for 8 days; the moisture content continuously decreased with the final moisture content of 12.6%. When compared with the results of textural profile analysis in terms of hardness, stiffness, and springiness, sensory results were correlated. Comparing the overall acceptability of the FBS to WCS, FBS was from moderate to higher scores, which indicated that it could be a promising alternative to chemically developed snack products and a preferred product for people suffering from celiac disease and other gluten intolerances.

Optimizing methanol synthesis from CO2 using graphene-based heterogeneous photocatalyst under RSM and ANN-driven parametric optimization for achieving better suitability.

Assessment of the performance of linear and nonlinear regression-based methods for estimating in situ catalytic CO2 transformations employing TiO2/Cu coupled with hydrogen exfoliation graphene (HEG) has been investigated. The yield of methanol was thoroughly optimized and predicted using response surface methodology (RSM) and artificial neural network (ANN) model after rigorous experimentation and comparison. Amongst the different types of HEG loading from 10 to 40 wt%, the 30 wt% in the HEG-TiO2/Cu assisted photosynthetic catalyst was found to be successful in providing the highest conversion efficiency of methanol from CO2. The most influencing parameters, HEG dosing and inflow rate of CO2, were found to affect the conversion rate in the acidic reaction regime (at pH of 3). According to RSM and ANN, the optimum methanol yields were 36.3 mg g-1 of catalyst and 37.3 mg g-1 of catalyst, respectively. Through the comparison of performances using the least squared error analysis, the nonlinear regression-based ANN showed a better determination coefficient (overall R2 > 0.985) than the linear regression-based RSM model (overall R2 ∼ 0.97). Even though both models performed well, ANN, consisting of 9 neurons in the input and 1 hidden layer, could predict optimum results closer to RSM in terms of agreement with the experimental outcome.

Sustainable 3D printing with alkali-treated hemp fiber-reinforced polycarbonate composites

The study investigated the properties of alkali-treated hemp fiber-reinforced polycarbonate (PC) composites that can be formed by 3D printers for architectural applications. To determine the optimum alkali treatment to be applied to the fibers, the properties of the samples treated with 5% and 7% sodium hydroxide (NaOH) at both ambient temperature (AT) and 120 °C (HT) were determined by Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). It was determined that the alkali treatment that gave the optimum result was 5% HT. Composite specimens with fiber/matrix ratios of 10/90, 20/80, and 30/70 were prepared in filament form to be printed in a 3D printer as alkali-treated and untreated. These composites were characterized by conducting tensile strength, FTIR, differential scanning calorimetry (DSC), TGA, and scanning electron microscopy (SEM) analyses. Tensile strength results revealed the highest mechanical performance for 5% NaOH alkali-treated and 10 wt.% hemp fiber-reinforced PC composites. DSC results showed that slight changes occurred in the glass transition temperature values. Furthermore, SEM analysis showed that 5% NaOH-treated hemp fibers have better interfacial bonding with the PC matrix than untreated fibers. As a result, more natural and sustainable materials have been obtained for architectural applications without significantly decreasing in PC properties.

Optimization of Chlorophyll Extraction from Ulva Sp. with Ultrasound-Assisted Liquid Biphasic Systems Method

Ulva sp. is a type of green algae that is easily found in the shallow seas of Indonesia and contains various bioactive compounds. Chlorophyll is a bioactive compound that functions as a natural dye, free radical scavenger, and antioxidant in the body. Chlorophyll extraction with conventional methods requires a relatively long time and large amount of solvent. In this research, chlorophyll extraction from Ulva sp. with the Ultrasound-Assisted Liquid Biphasic System (UALBS) method. Ulva sp. as much as 2.5 grams mixed in acetone solvent, added K2HPO4 solution, and carried out the sonification process in a dark room. The extraction process performs at a parameter range of 5-15 minutes, ratio 0.05-0.1 g/mL, and 60-100 mesh particle size. The extracted filtrate was added with petroleum ether and distilled water to form a biphasic condition. The extracted chlorophyll was analyzed with chromatography and spectrophotometry method. Antioxidant activity was analyzed using the DPPH method. The optimum result of Ulva sp. chlorophyll extraction at an extraction time of 10 minutes, the ratio of solids to solvent was 0.1, the particle size of 60 mesh produced a yield of 1.88% with chlorophyll a 20.13664%, chlorophyll b 21.58672% and total chlorophyll 41.71012%, and the percentage inhibition 45.32%.

Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms

Rapid, onsite, and simple detection of honey integrity in real time provides consumers with the assurance that the honey purchased is free from adulteration such as sugar syrup. In this study, smartphone based camera technique was used to differentiate authentic honey from adulterated ones (using authentic honey samples from different locations and adulterated samples made by spiking authentic ones with sugar syrup in the laboratory) by employing two supervised machine learning algorithms. Experimental results showed that the prediction model based on Random Forest (RF) was superior to K-nearest neighbour (KNN). The optimum results were assessed based on the prediction rate, specificity, and sensitivity. The performance of the RF model was 100 % accuracy while the specificity of 100 %, and sensitivity of 100 %. These findings could be exploited for reliable and rapid classification of honey integrity in Ghana and West Africa in general. This will further improve consumers’ confidence in the honey trade due to the ease and availability of smartphone technology.

A NOVEL HYBRID DECISION-MAKING MODEL: FUZZY AHP-TOPSIS APPROACH FOR PRIORITISING COPPER SMELTING PROCESSES

The construction of a copper smelting facility and its undisturbed and profitable business undoubtedly contribute to the development of each country’s economy. These facilities employ many workers and produce a large amount of copper, reducing imports and dependence on this important raw material, thereby improving the economic situation in a given country. More than a hundred copper smelters operate worldwide, many of which use different types of copper extraction processes. Strict legislation relating to ecology and environmental protection as well as stakeholder involvement in selecting and constructing copper smelting facilities limit the maximisation of short-term economic objectives. The prioritisation of technological processes for the extraction of copper must consider the impacts of often mutually opposing economic, technical and environmental objectives. No research from the available literature analyses the economic, technical and environmental parameters systematically. Studies have mainly dealt with exploring individual influences of factors through the use of one selection method. This paper presents the development of a novel hybrid AHP-TOPSIS model in fuzzy environments that will provide both informative decisions and optimum results of decision making.

Sizing and Shape Optimization of Discrete Truss Employing a Target-Oriented Krill Herd Algorithm

Abstract The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the “best krill” and “suboptimal krill” to generate a robust “cross krill” for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the “center of food” and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum.

A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.

Optimization and PID Control of pH and Temperature in an Electrocoagulation Process

In this work, effects of temperature and pH in batch treatment of pulp and paper mill wastewater using electrocoagulation has been investigated. Conductivity, temperature, and pH are selected as controlled variables; supporting electrolyte, cooling water, acid and base flow rates are selected as manipulated variables, respectively. Real time experimental multi input-multi output (MIMO) control of conductivity, temperature, and pH under constant current conditions are achieved using MIMO Proportional Integral Derivative (PID) control algorithms coded in MATLAB™. A central composite design (CCD) has been applied to the system under controlled conditions and optimum pH and temperature values are obtained using response surface methodology (RSM). Both controlled and uncontrolled experiments are performed using optimum values and results are compared in terms of removal efficiencies of pollutants. Results show that 34.47% chemical oxygen demand (COD), 98.06% total suspended solids (TSS), 99.80% turbidity, 99.93% color, and 13.40% SO42- removal is achieved in 45 minutes of process operation under controlled conditions and COD, TSS, turbidity, color and SO42- removal are increased by 10.92, 2.97, 4.06, 2.89, 3.17 respectively in comparison with uncontrolled operation. The highest removal percentages are obtained under controlled operating conditions as 98.5% and 98.3% for turbidity and color, respectively, for 10 minutes operation. It is concluded that optimum process operating conditions for removal of turbidity and color of pulp and paper mill wastewater is obtained under constant 6.45 pH, 23.24 °C temperature, 1.78 mS/cm conductivity, and power consumption is reduced by 25.3% under controlled conditions.

Analisis pembuatan dan penggunaan koagulan poly aluminium chloride (PAC) pada proses penjernihan air

Background: Poly Aluminum Chloride (PAC) is a complex inorganic compound between hydroxyl (OH) ions and aluminum ions which undergo gradual chlorination. PAC is able to coagulate suspended substances or colloidal dispersions to produce floc that easily settles. Methods: The raw materials used in the manufacture of PAC consist of alumina hydrate Al (OH) 3, hydrochloric acid (HCl), sulfuric acid (H2SO4), and calcium carbonate (CaCO3). The experiments were carried out by reacting Al (OH) 3 as much as 100 grams, HCl as much as 180 ml, and H2SO4 as much as 127.1 grams at 90 ° C and 100 ° C temperature variations with time variations of 30s, 60s, and 90s. The characterization was tested in the form of a jar test and FTIR test. Finding: In the Jar Test, the optimum temperature and time are obtained at 90 ° C and 60s and at 100 ° C and 60s. At the optimum jar test results, the FTIR test was performed. Conclusion: From the test results, this study meets PAC manufacturing standards.

Comparing machine learning techniques for software requirements risk prediction

Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.

Fast Multilabel Feature Selection via Global Relevance and Redundancy Optimization.

Information theoretical-based methods have attracted a great attention in recent years and gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the existing methods consider a heuristic way to the grid search of important features, and they may also suffer from the issue of fully utilizing labeling information. Thus, they are probable to deliver a suboptimal result with heavy computational burden. In this article, we propose a general optimization framework global relevance and redundancy optimization (GRRO) to solve the learning problem. The main technical contribution in GRRO is a formulation for MLFS while feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, which can avoid repetitive entropy calculations to obtain a global optimal solution efficiently. To further improve the efficiency, we extend GRRO to filter out inessential labels and features, thus facilitating fast MLFS. We call the extension as GRROfast, in which the key insights are twofold: 1) promising labels and related relevant features are investigated to reduce ineffective calculations in terms of features, even labels and 2) the framework of GRRO is reconstructed to generate the optimal result with an ensemble. Moreover, our proposed algorithms have an excellent mechanism for exploiting the inherent properties of multilabel data; specifically, we provide a formulation to enhance the proposal with label-specific features. Extensive experiments clearly reveal the effectiveness and efficiency of our proposed algorithms.

Effect of Gibberellic Acid (GA3) Concentration and Seedling Media Composition on the Germination and Growth of Shallots (Allium ascalonicum L.) from True Shallot Seed

Shallots (Allium ascalonicum L.) is a vegetable commodity that contributes greatly to horticultural production and inflantion levels. The use of superior seeds is expected to produce maximum shallot production. The majority of Indonesian farmers use 30% of the harvested tubers as planting material, with production costs being quite expensive. Many shallot farmers also experience several problems with tuber seeds that are hollow and damaged. True Shallot Seeds can be an efficient and profitable altervative planting material. Implementing TSS shallot cultivation is considered difficult and requires longer time for sowing. Soaking seeds using Gibberelin can have a physiological effect on the germination process. Seedling media factor with a certain composition are needed to produce maximum seeds. The experiment was carried out in factorial manner using Random Design Complete with three replications. The GA3 concentration factor has four treatment levels: G0 = 0 ppm/control, G1 = 2 ppm, G2 = 4 ppm, and G3 = 6 ppm. The seedling media composition factors has four treatment levels: M0 = soil + compost/control (1:1), M1 = cocopeat + compost (1:1), M2 = husk charcoal + compost (1:1), and M3 = sand + compost (1:1). The data analysis used was Analysis of Variance, if there were significant differences between the treatments, a further tes was carried out using Duncan’s Multiple Range Test at the 5% level. The result showed that the interaction between GA3 concentration treatment and seedling media composition and the single factor GA3 concentration had no significant effect on all observed variables. The composition of the seedling media has a significant effect on the speed of seed growth, seed height, root length, and seed wet weight with optimum results for germination and vegetative growth of TSS seeds, namely the compositionof soil+compost/control (M0) seedling media on the variables of seed growth speed (%/etmal), seed height (cm), and seed wet weight (gram).

Combining Dissolved Air Flotation (DAF) and Modified Moving Bed Biofilm Reactors (MMBBR) for Synthetic Oily Wastewater Treatment

<p>Numerous industrial processes, including petroleum refining, food production, car washes, leather manufacturing, and slaughter houses, generate significant volumes of wastewater due to their substantial water consumption during processing. The presence of oily wastewater has detrimental effects on surface and ground water resources, Human health and aquatic ecosystems. This study aims to integrate dissolved air flotation (DAF) and moving bed biofilm reactors (MBBR) for the treatment of oily wastewater. For this reason, a synthetic oily wastewater was prepared by utilizing commercially available powdered starch as an organic source, along with diesel oil and a finely ground soil. In addition, the experiments were designed and analyzed using Response Surface Methodology (RSM). DAF reactor factors were flotation time and air flow rate. While the MBBR reactor variables were mixing time and hydraulic retention time (HRT). The responses were chemical oxygen demand (COD), oil and grease(O&G), ammonia (NH3-N) and total suspended solids (TSS). Optimum results obtained by RSM for the DAF were 10 min for the flotation time and air flow rate of 72 L/min. in addition, HRT for both MBBRs was 23.5 hr and mixing time of 13 min and 23 min for MBBR1 and MBBR2 respectively.</p>

Determination of Optimum Operating Parameters in a Non-Road Diesel Engine Fueled with 1-Heptanol/Biodiesel at Different Injection Pressures and Advances

It is important to reduce the negative environmental effects of non-road diesel engines, which are increasingly used in many facilities and machines, without loss of performance. Biodiesel is used as an alternative to fossil-based diesel fuels to eliminate these effects and ensure sustainability in energy. This study focused on the optimization of the operating parameters of a non-road diesel engine operating with a waste frying oil biodiesel mixture at 50% load. Pure biodiesel, 1-heptanol, different injection advances and pressures were determined as input parameters for optimization. The tests were designed according to Taguchi’s L16 orthogonal array. ANOVA analysis was performed to determine the importance of input parameters on engine performance and exhaust emissions. Optimization was made based on the highest brake thermal efficiency (BTE) in addition to the lowest values of brake-specific fuel consumption (BSFC), brake-specific hydrocarbon (BSHC), brake-specific nitrogen oxide (BSNOx) and smoke emissions. In the optimization carried out according to the response surface methodology (RSM), the optimum combinations to obtain the best engine characteristics were determined as 17.27% 1-heptanol, a 226-bar injection pressure, 27 CAD injection advance and B75. These optimization results were verified by engine experiments within the recommended error range.

Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods.
 OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage.
 METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms.
 RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively.
 CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS.

ResNet50 DeepFake Detector: Unmasking Reality

Objectives: The objective of this research is to detect video deepfakes with a higher accuracy and provide optimum results. The research aims to reduce time complexity for the media processing while simultaneously working on the model accuracy. Methods: This research has utilized CelebDF and FaceForensics++ Datasets for training and 32 epochs with the use of Single Nvidia Tesla T4 GPU. The above method of training and validating the model yielded error of <5% and is very capable. Using image scraping this model initially eliminates the unimportant areas of consideration. Thus, reducing the amount of scans that the model does in order to identify the face. This reduces that training to graph ratio and provides less error margin. Findings: This research’s findings reveal the model's robustness in detecting manipulated videos generated by deepfake techniques. Through extensive experimentation on diverse datasets, ResNet50 consistently demonstrated 97% accuracy, sensitivity, and specificity in distinguishing authentic content from deepfakes. The model exhibited exceptional generalization across various scenarios, including face-swapping and lip-syncing, showcasing its adaptability to evolving deepfake techniques. This research contributes to the already existing literature on ResNet50 deepfake detection tools. It contributes by adding the image scraping feature to the ResNet50 model and overcomes the gaps such as increasing error percentage of some of the models. The research of 1 has a 20% error percentage while this research has an error percentage of 5% with an accuracy of 97%. Novelty: The study employs ResNet50 to detect deepfake videos, utilizing novel image scraping techniques to minimize errors and enhance prediction accuracy. Keywords: Deepfakes, Deep Learning, GAN, ResNet50, FaceForensics++, CelebDF

Optimization Of Hybrid Composite Laminates With Various Materials Using GA/GPSA Hybrid Algorithm for Maximum Dimensional Stability

In this study, it is aimed to obtain the highest dimensional stability against temperature changes in fiber-reinforced hybrid composite laminates considering eight different composite materials: Aramid/Epoxy, AS4/Epoxy, Boron/Epoxy, E-Glass/Epoxy, IM6/Epoxy, GY70/Epoxy, Kevlar49/Epoxy, and Spectra/Epoxy. The study focuses on finding the optimum continuous and traditional fiber angle orientations for the hybrid composite plates that would provide the lowest coefficients of thermal expansion. For this purpose, two different laminate sequences were investigated, each including two materials. A hybrid algorithm combining genetic algorithm and generalized pattern search algorithm was used in the optimization. A great number of hybrid design problems were solved repeatedly, and their results were compared both within themselves and to the optimum non-hybrid laminate results. Furthermore, the thermal durability of the selected optimum hybrid designs was evaluated by Tsai-Wu failure and Hashin-Rotem criteria. The results reveal that substantial increase in dimensional stability can be achieved by stacking sequence optimization of hybrid composite laminates with multiple material selection, and this hybrid design approach can offer the desired laminated composite structures for aerospace applications required to withstand extreme temperature changes.

Strength prediction of modified self-compacting concrete using response surface method

AbstractThis study highlights the effectiveness of the response surface method in predicting the properties of hardened concrete containing spent foundry sand and manufactured-sand. In current research, different mix proportions are prepared with spent foundry sand and manufactured-sand as partial replacements for fine aggregate. The workability, strength, and durability studies are conducted on all concrete mixes. The test results confirm that the inclusion of 20% of spent foundry sand is optimum for enhancing the strength and durability. Also, the inclusion of 20% of spent foundry sand in manufactured-sand concrete also possesses optimum results in strength and durability. Further, the response surface method has been employed to develop a model for compressive strength prediction. The R-squared and residual sum of squares of error for the compressive strength model were 0.9863 and 2.345, which confirms the goodness of the fit.