Exhaustive Technique Research Articles

Strategic Decision-Making Enhancement Framework (SDE-Framework): Leveraging Neutrosophic Logic and Fuzzy Mathematics for Optimized Outcomes in IT Management and Computational Systems

The created SDE-Framework combines neutronosophic logic and fuzzy mathematics in a novel method, aiming at facilitating more informed decision outcomes in computational systems and information technology management. This method hopes to aid in determining strategic solutions by controlling the expected sophistication and ambiguity in these two technologically dynamic industries. Neutronosophic logic divides data into three components: truth, indeterminacy, and falsity, build an exhaustive technique for addressing contradiction and indeterminacy. This significantly increases the method by enabling a more complete exploration of potential options with ambiguous and inadequate data. Second, the fuzzy mathematics gives a valuable contribution. It offers a refined method for managing the levels of probability and certainty through membership features, resulting in more exact and flexible evaluations. By the usage of such compared sophisticated mathematics concepts, SDE-Framework addresses potential decision-making scenarios by letting the computer formulates do the judgements for the determinable and in determinable explicit data. The subsequent crucial parameters are adopted to tolerance values: validity and responsibility, falseness foreach, indeterminacy magnitude to each, and truth value. This guarantees its combination of complexity supportive rand reading of actual surroundings.

Phytochemical Screening, Antioxidative, Antiobesity, Antidiabetic and Antimicrobial Investigations of Artemisia scoparia Grown in Palestine

Validating ethnobotanical data from underexplored traditional plant remedies provides an infinite source of new pharmaceutical chemicals. The purpose of this study was to determine the phytochemical composition and several biological activities (antioxidant, anti-lipase, anti-α-amylase, anti-α-glucosidase, and antimicrobial) of aqueous, ethanol, hexane, and acetone Artemisia scoparia leaf extracts. An exhaustive technique was employed to extract A. scoparia four extracts. At the same time, standard analytical and biochemical assays were utilized to determine preliminary phytochemical screening, anti-DPPH, anti-lipase, anti-α-glucosidase, and anti-α-amylase activities. Furthermore, the antimicrobial effects against seven microbial strains were evaluated using a broth micro-dilution assay. Acetone A. scoparia extract exhibited the highest DPPH scavenging and anti-α-glucosidase activities (IC50 = 21.87 ± 0.71, and 149.75 ± 1.33 µg/mL, respectively), as well as the ethanol extract, exhibited the highest anti-α-amylase activity (IC50 = 251 ± 1.34 µg/mL) while the aqueous extract had the best anti-lipase activity (IC50 = 102 ± 0.27 µg/mL) among other extracts. Moreover, A. scoparia hexane extract has more powerful activity against Methicillin-Resistant Staphylococcus aureus (MRSA), Staphylococcus aureus, Proteus vulgaris, Pseudomonas aeruginosa than Ciprofloxacin and Ampicillin antibiotics with MICs of 0.78 ± 0.01, 0.39 ± 0.01, 0.78 ± 0.01, and 1.56 ± 0.22 µg/mL, respectively. In addition, hexane and acetone extracts of A. scoparia have the same antifungal power as Fluconazole (1.56 ± 0.22 µg/mL). The outcomes of the current study indicated that the A. scoparia acetone, ethanol, and aqueous extracts had promising antioxidant, anti-lipase, and anti-α-amylase effects, while hexane and acetone extracts had interesting antimicrobial potential. A. scoparia four extracts of potentially bioactive compounds can be selected for further isolation and purification. Moreover, clinical investigations and in vivo approaches should be implemented to confirm the pharmaceutical benefits of these extracts against diabetes, obesity, oxidative stress, and microbial infections.

The tunnel numbers of all 11- and 12-crossing alternating knots

Using exhaustive techniques and results from Lackenby and many others, we compute the tunnel number of all 1655 alternating 11 and 12 crossing knots and of 881 non-alternating 11 and 12 crossing knots. We also find all 5525 Montesinos knots with 14 crossings or fewer.

Hydrosols from Rosmarinus officinalis, Salvia officinalis, and Cupressus sempervirens: Phytochemical Analysis and Bioactivity Evaluation.

The present work evaluates the aromatic waters of rosemary (Salvia rosmarinus Spenn. syn. Rosmarinus officinalis L.), sage (Salvia officinalis L.), and cypress (Cupressus sempervirens L.) obtained as innovative commercial products of a hydrodistillation process. All extracts were exhaustively analysed by GC-MS, 1H-NMR, and LC-MS in order to evaluate potential metabolite fingerprint differences. GC-MS appears to be the most exhaustive technique for the qualitative identification of the single constituents, although in this case, the use of 1H-NMR and LC-MS techniques allowed some useful considerations in semi-quantitative terms. Antimycotic effects were studied against Tricophyton, Candida, and Arthroderma species, resulting in weak activity. The toxicological impact was partly evaluated in vitro by means of allelopathy and brine shrimp lethality. Cytotoxicity was investigated in human colon cancer cells (HCT116) and in hypothalamic cells (Hypo-E22) challenged with hydrogen peroxide. Sage and rosemary hydrosols were the most effective antimycotics, whereas all hydrosols displayed antiradical effects. Cytotoxic effects against HCT116 cells (at 500 µL/mL) were related in silico to the endovanilloid TRPM8 and TRPV1 receptors. At lower concentrations (5–50 µL/mL), the hydrosols protected hypothalamic neurons Hypo-E22 cells from hydrogen peroxide-induced toxicity. The overall experience indicates that hydrolates are an important source of relevant phytochemicals with significant pharmacological potential.

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Identification of the metabolites regulated in soybean-Rhizobia symbiosis through solid phase microextraction coupled with LC-MS

Legumes provide one of the uniquely nutrient-rich food sources to the population and are one of the primary field crops that play significant roles in agricultural sustainability. Inoculation with Bradyrhizobium japonicum is necessary for the high yield of leguminous crops, i.e. soybean. Nodulation of soybean by Bradyrhizobium japonicum is a complex process that is essential for cultivation of these legumes and external stress factors, such as draught and soil acidity, that influence the nodulation and crop yield. Alterations in the nodule metabolites are known to identify the type of stress that mitigates nodulation and lowers crop yield. Current techniques aimed at understanding the metabolic activities in the symbiont, such as in the case of metabolic regulations in varying nodule growth phases, rely on exhaustive techniques based on the removal of nodules or other plant tissue. Aiming to capture a more in-depth, accurate profile of this system without quenching the metabolic activity in the nodules, or removing the nodules, a workflow was prepared for the metabolite sampling through in vivo solid phase microextraction in thin film format (TF-SPME). This technique was followed by LC-QTOF-MS instrumental analysis with subsequent metabolite annotation and reference standard validation. Our approach is unique in terms of eliminating the effects that arise due to analyte partition coefficients. We show that the symbiont undergoes metabolic regulations throughout the cultivation period, displaying the efficacy of TF-SPME as a non-exhaustive sampling method that can be used as a tool to investigate the metabolic alterations in nodules. These alterations would potentially fingerprint the environmental effects on soybean yield.

Optimal novel approach for estimating the pavement transition probabilities used in Markovian prediction models

ABSTRACT An optimal novel approach is proposed to estimate the transition probabilities associated with both homogeneous and non-homogeneous Markov chains. The approach applies an exhaustive optimisation technique to search for the optimal transition probabilities associated with minimal sum of squared errors (SSE), wherein the error defined as the difference between predicted and observed pavement condition ratings. Three state transitions are allowed in constructing the relevant transition probability matrix (TPM). In the homogenous chain, the approach yields one optimal TPM applicable to an analysis period of (n) years. Whereas, one distinct optimal TPM can be derived for each year if non-homogenous chain is deployed. A sequential iterative optimisation approach is proposed wherein the optimal TPM for a given year becomes the input for the subsequent year. Sample results are presented for two projects (A & B) with superior and inferior performances, respectively. The sample results indicate that the non-homogenous chain provided significant reduction in the SSE compared to the homogeneous one. However, the use of 10 condition states instead of 5 resulted in moderate reduction in the SSE considering both homogeneous and non-homogeneous Markov chains. Also, the results indicate that the use of three state transitions made significant impact when deploying 10 condition states instead of 5 especially in the case of inferior performance.

A Bounded Exhaustive Search Technique for Optimal Phasor Measurement Unit Placement in Power Grids

This paper proposes a technique to determine the possible optimal placement of the phasor measurement unit (PMU) in power grids for normal operating conditions. All possible combinations of PMU placement, including infeasible combinations, are typically considered in finding the optimal solution, which could be a massive search space. An integer search algorithm called the bounded search technique is introduced to reduce the search space in solving a minimum number of PMU allocations whilst maintaining full system observability. The proposed technique is based on connectivity and symmetry constraints that can be derived from the observability matrix. As the technique is coupled with the exhaustive technique, the technique is called the bounded exhaustive search (BES) technique. Several IEEE test systems, namely, IEEE 9-bus, IEEE 14-bus, IEEE 24-bus and IEEE 30-bus, are considered to showcase the performance of the proposed technique. An initial Monte Carlo simulation was carried out to evaluate the capability of the bounded search technique in providing a smaller feasible search space. The effectiveness of the BES technique in terms of computational time is compared with the existing exhaustive technique. Results demonstrate that the search space can be reduced tremendously, and the computational burden can be eased, when finding the optimal PMU placement in power grids.

Troubleshooting Complex Vascular Cases in the Kidney Graft: Multiple Vessels, Aneurysms, and Injuries During Harvesting Procedures.

To update the most relevant literature regarding complex vascular cases in kidney transplant setting involving the graft, especially during the harvesting procedure and back-table preparation from the subsequent implant. Challenging situations affecting the kidney graft such as multiple vessels, renal artery aneurysms, kidney anatomical anomalies, or major injuries do not contraindicate the transplant, but require an exhaustive graft viability assessment and several bench surgery techniques. Graft vessel conditioning in the back-table might include simple anastomosis between them, enlarging with venous patch or reconstruction with donor or synthetic grafts. Compared with conventional transplant, literature reports longer warm ischemia time (40 vs 32min) and slightly increased rates of delayed graft function (10.3% vs 8.2%) and vascular complications (10.8% vs 8.1%), but similar graft and patient survival. Kidney graft vascular complex cases require exhaustive assessment, meticulous harvesting, good surgical technique in the bench table, and proper surgery in the recipient. Despite its complexity, vascular complex kidney transplant offers comparable outcomes in the long term to conventional population when technically well performed, with slightly increased rates of vascular complications and delayed graft function.

PCTL model checking based on Giraph

At present, ensuring system security and verifying whether the system meets the given requirements have been the focus of computer engineers. Model checking is an automatic and exhaustive technique to prove whether there has absence of such errors in a system which has been widely applied. It has a regular requirement for a system model under consideration and a desired property. Then it systematically checks whether or not the given model satisfies this property. However, the traditional algorithm on Hadoop for checking Computation Tree Logic (CTL) formula does not scale well because of the need of conducting a huge number of states with each passing day. To deal with the state space explosion problem, we propose a scalable algorithm on the platform of Giraph, which is a new rising iterative graph processing platform with high scalability and employs the Bulk Synchronous Parallel (BSP) model. We also address this problem by selecting Probabilistic Computation Tree Logic (PCTL) as the property specification, which is strictly more expressive and makes a great advancement on using probability to describe the probability of an event occurring. This paper comes up with a novel method based on Giraph to verify one PCTL formula, which is used to represent a maximum probability of reaching a certain state, being regarded as a quantitative description of a property in system detection. Owing much to the Giraph platform, our experiment is of great interest as it displays much more efficiently than current methods which are based on Hadoop and so on. Ultimately, we discover that the processing time of Giraph platform applying message model is much shorter than that of Hadoop platform applying iterative model through experimental comparison.

Risk Factors of Obesity among Pregnant Women in Biringkanaya Sub District at Makassar City from 2014 to 2015

The aim of this study is to determine the risk factors of obesity among pregnant women in Biringkaya sub district at Makassar city. The study type was case control study design with pregnant women who had medical record in public health centres (Puskesmas) Sudiang, Sudiang Raya and Paccerakkang in year 2014–2015 as subject. The body mass index (BMI) tool is used in order to measure the index of weight for height as to classify underweight, overweight and obese adults. Total samples were 152 pregnant women with 38 cases and 114 controls. The samples were obtained by using exhaustive sampling technique for case group and purposive sampling techniques for control group. The result found Odds Ratio (OR) of obese pregnant women is influenced by age (OR= 4.632; CI 95% = 2.119–10.126), parity (OR=3.107; 95% CI = 1.262–7.649), family background (OR=3.4; 95% CI= 1.578–7.527), socioeconomic status (OR=3.612; 95% CI= 1.189–10.973) and attitude (OR= 0.838; 95% CI = 0.401–1.753). In conclusions, pregnant women's age ≥ 35 years old, parity ≥ 35, family history, high socioeconomic status are risk factors contributing towards obesity among pregnant women.

The Factors Related to Work Fatigue of Block Paving Workers at CV. SumberGalian in 2017

Block paving workers play an important role in CV. SumberGalian with various tasks that require moderate to high work activities and they have to make as many as 600 blocks per day. This can cause workers to experience fatigue easily. Thus, this observational analytic with cross sectional approach research, aimed to determine the relationship of work stress, energy intake, work period and haemoglobin level with work fatigue in block paving workers in CV. SumberGalian, Makassar City in 2017. The total number of samples was 40 people, taken by exhaustive technique sampling. Data were taken from respondents using the Reaction Timer questionnaire to measure the work fatigue and Nutri Survey and food recall to obtain energy intake data. The data are then analysed using Chi-square test and logistic regression. The results showed that there was a relationship between work stress p = 0.002, energy intake p = 0.005, work period p= 0.028 with work fatigue among block paving workers at CV. SumberGalian. The most influential variable in this study was work stress.

Noniterative Content-Adaptive Distributed Encoding Through ML Techniques

Distributed encoding is desirable for content preparation cloud workflows to reduce turnaround times. Content-adaptive bit allocation strategies have been proposed to achieve efficiencies in storage and delivery. Many of these methods tend to be iterative in nature and consume significant additional compute resources. There is a need to limit this increase in computational complexity. In this paper, we propose a noniterative codec-agnostic approach that employs machine learning techniques to achieve average bitrate savings and a target consistent quality by selecting a content-adaptive bitrate and resolution for each adaptive bitrate (ABR) segment within each ABR representation in a manner that makes it equally suitable for live and on-demand workflows. Test results are presented over a wide range of content types. Initial results indicate that the proposed approach can recover ~85% of the bit savings possible with more exhaustive techniques while its computational complexity is only 15%–20% of two-pass variable bitrate (VBR) encoding.

Determinant factors analysis of PCC (Paracetamol, Caffein, Carisoprodol) drug abuse in Kendari City

The aim of study was to determine factor of drug abuse of PCC (paracetamol, caffein, carisoprodol) in Kendari City, 2017. This study was an analytical research with cross sectional study approach. The population in this study were all drug users recorded in the Health Agency of Southeast Sulawesi in 2017 which were 90 people, while the sample in this study was the entire population which was using exhaustive sampling technique. The results of this study with 95% confidence level showed RP = 1.481 with CI range = 1.037-2, 115. The value of RP> 1, means that respondents with introverted personality were 1.5 times more likely to abuse PCC drugs than those with extroverted personality. RP value = 3.500 with range CI = 1, 529-8, 012. RP value> 1, meaning high curiosity respondents were 3.5 times more likely to abuse PCC drugs than those with low curiosity. RP value = 0.103 with range CI = 0, 041-0, 260. The value of RP 1, means that respondents whose family environment was at risk 1.3 times more likely to abuse PCC drugs than the poor family environment. Advice To the government authorities to increase supervision and enforcement of the law against the circulation of drugs so the affordability of these drugs cannot be easily obtained by everyone.

Efficient Enhancement for Spatial Scalable Video Coding Transmission

Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

Fast coding unit partitioning method based on edge detection for HEVC intra-coding

High efficiency video coding (HEVC) standard is the latest video coding standard generation. It employs powerful coding tools to obtain improved compression efficiency. To better exploit the redundancies, HEVC adopts a very flexible quad-tree coding structure, allowing the encoder to use a block partition that matches the image features. This exhaustive technique may achieve a higher coding efficiency; however, it induces a significant computational complexity in the encoding engine. This paper proposes a new texture parameter for classifying digital videos as a first contribution and then introduces an efficient coding unit (CU) partitioning algorithm based on the early defined texture parameter in order to speed up the encoding process. In fact, the proposed technique is based on edge detection by performing SOBEL filtering in order to decide the appropriate CU size. Compared to the original HEVC, the average execution time-saving is about 31 % while maintaining almost the same output video quality.

Enhanced heuristic for finance-based scheduling of construction projects

Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.

Performance Analysis and Coding Strategy of ECOC SVMs

The theoretical upper bound of generalization error for ECOC SVMs is derived based on Fat-Shattering dimensionality and covering number. The factors affecting the generalization performance of ECOC SVMs are analyzed. From the analysis, it is believed that in real classification tasks, the performance of ECOC depends on the performance of the classifiers corresponding to its coding columns, which is irrelevant to the mathematical characteristics of the ECOC itself. The essence of ECOC SVMs is how to construct an optimal voting machine consisting of a number of SVMs, how to choose Sub-SVMs which have better generalization ability, and how to determine the number of Sub-SVMs taking part in voting, that is the most important issue. Data sets including “Segment” are selected for test. All the ECOC code columns are constructed using an exhaustive technique. A Sub-SVM is trained for each code column, and the generalization ability of each Sub-SVM is evaluated by classification intervals and error rates estimated by cross validation. Then, all the ECOC code columns are sorted by the generalization performance of Sub-SVMs. Three categories of ECOC SVMs, including superior, inferior and ordinary categories, are constructed from the sorted ECOC code columns, by using forward, backward and original sequences. Experimental results show that the performance of ECOC SVMs which consist of Sub-SVMs with better generalization ability is better and vice versa, which validates our view and points out the direction for improving ECOC SVMs.

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.