Assist Decision Makers Research Articles

Intelligent Tracking Technology of Light Source Based on Feedforward Inverse Compensation Control

In the construction of a photovoltaic power station, the effect of ray tracing directly affects the efficiency of power generation. In order to effectively control the tracking photovoltaic bracket and present the actual situation of the tracking bracket truly, intuitively and conveniently, a roamable photovoltaic tracking bracket control system is designed in this study. The system provides assistant decision-making for operators, and can remotely control the tracking support in real-time. It can also work independently according to the set parameters to realize the functions of the sun position calculation, tracking angle calculation, and tracking command output, and can communicate with the monitoring terminal. During the experimental test, the computer resource occupation is low, the operation is stable and reliable, and the monitoring terminal can run with a relatively smooth frame number. According to the system architecture mentioned in this paper, the verification platform is built, and the effectiveness of the photovoltaic tracking bracket control system is confirmed. The control system of the photovoltaic tracking bracket designed in this paper can effectively solve the problem of solar tracking accuracy of the photovoltaic power station, and play an important role in improving the power generation efficiency of the photovoltaic power station. At the same time, it can provide a reference for the same industry in light source tracking technology.

19. International Union for the Conservation of Nature (IUCN)

The IUCN is an international organization uniquely composed of both governments and civil society organizations. It provides public, private, and non-governmental organizations and actors with the knowledge and tools to combine efforts to conserve nature and accelerate the transition to sustainable development. Created in 1948, the IUCN has evolved into the world’s largest and most diverse environmental network. It harnesses the experience, resources, and reach of its more than 1,300 members and the input of over ten thousand experts. The IUCN is the global authority on the status of the natural world and the measures needed to safeguard it. The IUCN’s experts volunteer in six commissions dedicated to species survival, environmental law, protected areas, social and economic policy, ecosystem management, and education and communication. The IUCN Environmental Law Programme (ELP) is an integrated program of activities that assists decision makers with information, analysis, advisory services, legislative drafting, mentoring, and capacity development at the national, regional, and global levels. It includes the Environmental Law Centre (ELC) and collaborates with and supports the World Commission on Environmental Law (WCEL). The ELP, WCEL, and ELC collaborate in their endeavours with the IUCN Academy of Environmental Law.

AHP and GIS for assessment of groundwater suitability for irrigation purpose in coastal-arid zone: Gabes region, southeastern Tunisia.

In southern Tunisia especially in Gabes region, irrigated agriculture is the major economic sector. The intensive exploitation of groundwater have been gradually rising, leading the water quality to deteriorate. To ensure sustainable development and preserve water, soil, and land cover resources, a careful estimation of the irrigation water quality, which influence on the regional agriculture activities. The objective of this study is to investigate the irrigation water suitability based on hydrogeological, hydrochemical, and analytic hierarchy approach (AHP) and water quality index (WQI). Results highlighted that TDS of the groundwater ranged from 0.4 to 6 g/L. The most abundant chemical species in the water were Ca2+, Cl-, SO42-, and Na+, with mean concentration of 460 mg/L, 1320 mg/L, 1450 mg/L, and 1200 mg/L, respectively. The largest amounts were discovered along the shoreline and in deep aquifers embedded in Cretaceous sediments. AHP has used as multicriteria decision analysis, to weighting groundwater quality criteria, and groundwater suitability for irrigation has been mapped. Based on the Quality index "WQI", the water samples are categorized into three types: excellent, acceptable, and worthless. A 45% of sampled water is not suitable for irrigation use. However, groundwater quality is increasingly poor towards the coastal areas and north of the study area. This study concluded that the method adopted in assessing the groundwater quality in arid coastal areas led to important results and could be helpful scholar to improve their research especially in similar areas. It can also assist decision makers in taking proactive solutions to protect and preserve groundwater resources from pollution risks.

Evaluation of hydrological connectivity in a river floodplain system and its influence on the vegetation coverage

The river floodplain system is the basis of material and energy exchange between the river and floodplain in a region, and its connectivity is an important basis for water health evaluation and ecological protection. To clarify the effects of hydrological connectivity changes on vegetation, the graph theory method was improved based on ArcGIS and a quantitative evaluation model of river floodplain system hydrological connectivity was established. Taking the Jiahetan river floodplain system in the lower Yellow River basin as an example, the variation trend of hydrological connectivity in this region in the last 36 years and the influence of the Xiaolangdi Dam on hydrological connectivity was analyzed, and the response law of vegetation in the floodplain to the change of hydrological connectivity under different water surface ratios was obtained. The results show that because of Xiaolangdi Dam operation, the hydrological connectivity and vegetation coverage of Jiahetan increased by 28.09 % and 17.73 % on average, respectively, in the flood season. In the non-flood season, hydrological connectivity decreased by 22.91 % and vegetation coverage increased by 164.82 % on average. It is beneficial for vegetation growth to keep the hydrological connectivity value around 0.4 in the flood season and 0.3 in the non-flood season. These results provide a quantitative analysis technique for the evaluation of river floodplain hydrological connectivity, with the characteristics of a simple evaluation process and good applicability, and have important scientific significance and wide application value in providing guidance and decision-making assistance for the protection and restoration of floodplain water ecology, river system connectivity planning, and water and sediment regulation.

Intermodal Green p-Hub Median Problem with Incomplete Hub-Network

In the literature, hub-networks have often been modeled such as only one mode is considered for all transportation. Also, the consolidated demand flows are assumed to be transferred directly between each origin-destination hub pairs. The previous assumptions impose restrictions on the practical applications of such hub-networks. In fact, various transport modes are usually retained for freight transport, and intermodal terminals (e.g., rail terminals) may not realistically be fully connected. Thus, to assist decision makers, we investigate if the appropriate use of more eco-friendly transportation modes in incomplete networks may contribute to the accomplishment of the significant global reduction goals in carbon emissions. In this paper, we study the intermodal green p-hub median problem with incomplete hub-network. For each p located hub nodes, the hub-network is connected by at most q hub-links. The objective is to minimize the total transportation-based CO2 emission costs incurred through the road- and rail-transportation of each o-d demand flows. We present a MILP formulation for the studied problem and propose a novel genetic algorithm to solve it. A penalty cost is considered on solutions where train capacity is exceeded. Additionally, we present a best-path construction heuristic to generate the initial population. Furthermore, we develop a demand flows routing heuristic to efficiently determine the partition of demand flows in the incomplete road-rail network. And we implement novel crossover and mutation operators to produce new off-springs. Extensive computational experiments show that the proposed solution approach outperforms the exact solver CPLEX. Also, we perform a comparison between the unimodal and intermodal cases, and offer a discussion on the tuning of freight trains.

Technology Opportunity Recognition Algorithm and Decision Assistance for Non-Drug Antidepressant Field in China

The aim of this study is to propose a framework for non-pharmaceutical antidepressant technology opportunity identification in China, based on patent analysis and assisted decision making. Semantic mining technology was used to divide technology life cycle, classify technology, identify key technology associated opportunities, and identify core technology functions of 532 non-drug antidepressant patent data in China from 1995 to 2021. Combined with co-occurrence and multi-dimensional scale analysis, strategic coordinate maps and technology decision assistance maps were drawn. The development of non-drug antidepressant technology in China was divided into embryonic stage, formative stage, and development stage. Six technical categories, 30 high-frequency technical associated opportunities, and low-frequency and high-frequency core technical opportunities for non-drug antidepressants were summarized. Based on four technical fields and four functional clusters of strategic coordinate graphs, the technical opportunity decision auxiliary atlas was constructed. By comparison, this map is more intuitive than the technology-efficacy matrix, when presenting technology efficiency mapping. This study provides a set of systematic algorithms for opportunity identification and decision assistance based on patent data, in addition to reference for strategic decision making of non-drug antidepressant technology subjects in China.

Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk.

Type 2 diabetes (T2D) with increasing prevalence is a significant global public health challenge. Obesity, unhealthy diet, and low physical activity are one of the major determinants of the rise in T2D prevalence. In addition, family history and genetic risk of diabetes also play a role in the process of developing T2D. Therefore, solutions for the early identification of individuals at high risk for T2D for early targeted detection of T2D, prevention, and intervention are highly preferred. Recently, novel genomic-based polygenic risk scores (PRSs) have been suggested to improve the accuracy of risk prediction supporting the targeting of preventive interventions to those at highest risk for T2D. Therefore, the aim of the present study was to assess the cost-utility of an additional PRS testing information (as a part of overall risk assessment) followed by a lifestyle intervention and an additional medical therapy when estimated 10-year overall risk for T2D exceeded 20% among Finnish individuals screened as at the high-risk category (i.e., 10%–20% 10-year overall risk of T2D) based on traditional risk factors only. For a cost-utility analysis, an individual-level state-transition model with probabilistic sensitivity analysis was constructed. A 1-year cycle length and a lifetime time horizon were applied in the base-case. A 3% discount rate was used for costs and QALYs. Cost-effectiveness acceptability curve (CEAC) and estimates for the expected value of perfect information (EVPI) were calculated to assist decision makers. The use of the targeted PRS strategy reclassified 12.4 percentage points of individuals to be very high-risk individuals who would have been originally classified as high risk using the usual strategy only. Over a lifetime horizon, the targeted PRS was a dominant strategy (i.e., less costly, more effective). One-way and scenario sensitivity analyses showed that results remained dominant in almost all simulations. However, there is uncertainty, since the probability (EVPI) of cost-effectiveness at a WTP of 0€/QALY was 63.0% (243€) indicating the probability that the PRS strategy is a dominant option. In conclusion, the results demonstrated that the PRS provides moderate additional value in Finnish population in risk screening leading to potential cost savings and better quality of life when compared with the current screening methods for T2D risk.

Value Creation Measurement and Management in Times of Radical Social and Technological Change

This article tries to deepen our knowledge on the nature of value creation and management in times of turbulent environments both for business management and academic research. The current uncertainties in our economy—coming from exogenous shocks (e.g., financial crises, political standoffs [US–China], pandemic and Ukrainian war) or from demand for more sustainable activities—affect existing companies and provide opportunities for new ventures. Both type of firms need to think of innovative and out-of-the-box ideas to keep generating value for all stakeholders, and particularly to their customers. The nuanced understanding of the factors describing the current economic situation and their effect on value creation and management affects management by, for example, efficient use of resources, and assists in strategy formulation and decision-making. Customer value creation is a dual concept: creating value for customers (value-to-customer [V2C]) as well as extracting some of that customer value at the same time to create value for the firm (value-to-firm [V2F]). While this might be known, it is rarely implemented. In order to deliver V2C, firms should look at the customer journey from a customers’ point of view rather than a firms’ point of view. Only then, the real revenue potential can be realized when thinking about monetizing the customer interface and, hence, developing the customer relationship options beyond the current business. Even if it exists for a long time already, customer lifetime value (CLV) und customer equity (CE) are the right techniques to measure and manage value in such times. Besides, in today’s environment, the gap between CLV and CE and shareholder value needs to be closed. Existing customer-based corporate valuation studies do exactly so and, more importantly, will become even more important in the future due to new business models.

A novel nomogram for decision-making assistance on exemption of axillary lymph node dissection in T1-2 breast cancer with only one sentinel lymph node metastasis.

BackgroundT1–2 breast cancer patients with only one sentinel lymph node (SLN) metastasis have an extremely low non-SLN (NSLN) metastatic rate and are favorable for axillary lymph node dissection (ALND) exemption. This study aimed to construct a nomogram-based preoperative prediction model of NSLN metastasis for such patients, thereby assisting in preoperatively selecting proper surgical procedures.MethodsA total of 729 T1–2 breast cancer patients with only one SLN metastasis undergoing sentinel lymph node biopsy and ALND were retrospectively selected from Harbin Medical University Cancer Hospital between January 2013 and December 2020, followed by random assignment into training (n=467) and validation cohorts (n=262). A nomogram-based prediction model for NSLN metastasis risk was constructed by incorporating the independent predictors of NSLN metastasis identified from multivariate logistic regression analysis in the training cohort. The performance of the nomogram was evaluated by the calibration curve and the receiver operating characteristic (ROC) curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.ResultsOverall, 160 (21.9%) patients had NSLN metastases. Multivariate analysis in the training cohort revealed that the number of negative SLNs (OR: 0.98), location of primary tumor (OR: 2.34), tumor size (OR: 3.15), and lymph-vascular invasion (OR: 1.61) were independent predictors of NSLN metastasis. The incorporation of four independent predictors into a nomogram-based preoperative estimation of NSLN metastasis demonstrated a satisfactory discriminative capacity, with a C-index and area under the ROC curve of 0.740 and 0.689 in the training and validation cohorts, respectively. The calibration curve showed good agreement between actual and predicted NSLN metastasis risks. Finally, DCA revealed the clinical utility of the nomogram.ConclusionThe nomogram showed a satisfactory discriminative capacity of NSLN metastasis risk in T1–2 breast cancer patients with only one SLN metastasis, and it could be used to preoperatively estimate NSLN metastasis risk, thereby facilitating in precise clinical decision-making on the selective exemption of ALND in such patients.

Exploring the Role of Family-School Partnerships in School Choice

Parental access to useful information about schools continues to be a structural barrier that limits the equity potential of school choice programs. While “information interventions” or simplified and readable knowledge resources show promise for counteracting information disparities between families, this line of research has provided limited insight on the particular preferences and needs of parents or the value of person-to-person interpretive assistance in school decisions. This exploratory study uses qualitative methods to investigate parent-school collaboration for the purpose of school selection in Washington, D.C. Drawing on a social capital framework, this study analyzes information transactions from parents’ perspectives to clarify ways in which school personnel currently function as liaisons of choice. Findings from this study indicate future opportunities for districts to improve the outcome of school choice through better training of school and district personnel.

A Deep Machine Learning-Based Assistive Decision System for Intelligent Load Allocation under Unknown Credit Status.

Nowadays, the banks are facing increasing business pressure in loan allocations, because more and more enterprises are applying for it and financial risk is becoming vaguer. To this end, it is expected to investigate effective autonomous loan allocation decision schemes that can provide guidance for banks. However, in many real-world scenarios, the credit status information of enterprises is unknown and needs to be inferred from business status. To handle such an issue, this paper proposes a two-stage loan allocation decision framework for enterprises with unknown credit status. And the proposal is named as TLAD-UC for short. For the first stage, the idea of deep machine learning is introduced to train a prediction model that can generate credit status prediction results for enterprises with unknown credit status. For the second stage, a dynamic planning model with both optimization objective and constraint conditions is established. Through such model, both the profit and risk of banks can be well described. Solving such a dynamic planning model via computer simulation programs, the optimal allocation schemes can be suggested.

Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study.

BackgroundKnowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM).ObjectiveThis paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM.MethodsA new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task.ResultsExperiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models.ConclusionsExperiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.

Spatial Analysis of Public Transportation Accessibility for Pedestrian in Central Jakarta

Public transportation in Jakarta is one of the leading facilities which currently being improved by the local government. Transportation facilities in Jakarta are now being strived to be integrated. However, not all areas in Jakarta have access to public transportation. This study aim to show how public transportation, especially in Central Jakarta, can be reached by pedestrians. This research used a geographic information system-based approach for assessing pedestrian accessibility in metropolitan areas, focusing on access to public transportation stops and stations. This methodology attempts to provide integrated methods to urban planning and mobility planning and decision-making assistance, with the dual goals: (1) supporting sustainable and non-motorized mobility, and (2) also boosting the appeal of public transportation. There are two main steps in method. First, identify the pedestrian walking zone of urban spaces around public transportation nodes; and second, mapping the access times to create a detailed pedestrian isochrones, and calculate how residents (or how many opportunities and facilities) are well served by the public transportation system to measure accessibility levels. This study indicates that some of the main areas already have integrated public transportation facilities, but the stopping distance is quite burdensome for pedestrians to access.

Development of a Methodological Framework for Measuring Energy Performance of Urban Patterns in New Egyptian Communities (Toward Achieving Sustainable Urban Regeneration)

The concept of Sustainable Urban Regeneration/Retrofitting (SUR) is considered a promising approach for addressing most of the current environmental issues. Developed countries have shown a growing interest in achieving SUR in consistence with raising the efficiency of urban performance. Since the energy sector greatly impacts urban development, it is necessary to adapt new approaches & frameworks (FW) to activate Energy Efficiency (EE) in planning a new, not just Egyptian settlement. Enhancing Energy Performance of Urban Patterns (EPUP) is achieved by: (i) controlling energy consumption of urban patterns; & (ii) pursuing energy self-sufficiency. Egypt still confronts major challenges in how to manage growing consumption rates in a way that is not commensurate with the rates of growth of local production. Practical case studies confirmed the failure in applying the principles of EE in most urban pattern of new cities. Therefore, the research problem is formulated as: the current urban planning methodology in Egypt fails to consider EPUP. Consequently, this research aimed to develop a comprehensive Methodological Framework for Energy Performance Measures (MFEPM) of spatial urban patterns of new Egyptian communities. The MFEPM was developed based on the analysis of relevant Literature & comparative analysis of SUR case studies & tools, resulting in a set of indicators (checklist) to measure & minimize EE of the main urban systems forming the diverse urban patterns. Next, the suitability of the checklist for the Egyptian context was improved based on national strategies & guidelines, and through questionnaires with local stakeholders. Then, the MFEPM was tested on urban pattern samples from new Egyptian cities through an extensive qualitative analysis with GIS tools. Finally, the MFEPM will be integrated into the strategic urban planning methodology adopted by the researcher. The MFEPM assists decision makers to apply the concept of contingency planning through developing more efficient interventions to cope with the dynamic urban context.

Land Use Land Cover (LULC) Change Detection Mapping by using Remote Sensing Techniques In Case Kurabas kebele, Takussa woreda , centeral Gondar Zone, Ethiopia

Land use land cover (LULC) change analysis emerged as one of the most significant factors which assist decision makers to ensure sustainable development and to understand the dynamics of our changing environment. An integrated approach of remote sensing and GIS has been used to study the land use land cover dynamics of Kurabas Kebele, Takussa Woreda, Centeral Gondar zone. Landsat satellite imageries of three different time periods, i.e., Landsat data of 2001, 2011 and 2021 were acquired and used to quantify the land use land cover changes in the study area from 2001 to 2021 over a period of thirty years. ERDAS Imagine 14 software has been used to carry out the supervised classification using a maximum likelihood technique. The purpose of this study is therefore concerned with identifying the change in land use and land cover detection of Kurabas Kebele, Takussa Woreda, Centeral Gondar zone. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within three dates of 2001, 2011 and 2021 using Landsat TM 30 m resolution images. The classification had done using four land cover (cultivated, shrub land, grassland, forestland, and residential land) class. Preprocessing and classification of the images had analyzed and accuracy assessment was tested separately using the kappa coefficient. The results showed that overall accuracy in the area is 82.14% and kappa coefficient of 0.72% for the years of 2021. This study indicated that in the last 20 years period, cultivated land and Settlement areas had significantly increased by 34.34% and 77.3% respectively in the area. Therefore, proper land management practices, and active participation of the local community should be advance to protect undesirable LULC change in the study area. Objective Therefore, the aim of this study is to assess the trend of land use and land cover change in the Kurabas Kebele, Takussa Woreda, Centeral Gondar zone, Ethiopia. Therefore, I expect that the finding of this study could contribute information for decision-makers, land managers and planners for sustainable development and management of the natural resource. DOI: 10.7176/JESD/13-17-02 Publication date: September 30 th 2022

Risk prediction model for food safety based on improved random forest integrating virtual sample

Food safety has a severe impact on the world economy and global health, and improving the prediction accuracy and prevention ability of food safety risks and hazards protection is of great significance to the society sustainable development. However, the sample size of most food data is very small, resulting in insufficient training sample data for data-driven risk prediction models in low prediction accuracy and inability to provide effective prevention and control measures. Therefore, this paper proposes a food safety risk prediction model based on an improved random forest prediction method integrating Monte Carlo algorithm to protect and reduce the food safety risk. The Monte Carlo algorithm can expand the small sample data for obtaining the generated virtual sample. Then the extended input of a random forest method to construct the novel risk prediction model of food safety for predicting food risk levels and ensuring personnel safety. Finally, the accuracy and effectiveness of the proposed method is verified by the sterilized milk data. The experimental results show that the novel risk prediction model out-performs state-of-the art, which has stronger generalization ability and higher prediction accuracy for realizing effective risk early warning. Moreover, the proposed model can provide decision assistance and technical support for relevant departments to prevent and control in advance, and reduce the occurrence of food risk events.

Cuproptosis scoring model predicts overall survival and assists in immunotherapeutic decision making in pancreatic carcinoma.

Background: Cuproptosis is a newly identified form of non-apoptotic cell death that is associated with the progression and treatment responses in pancreatic adenocarcinoma (PAAD). However, its impact on oncology and tumor microenvironment (TME) remains unclear. Methods: Hub genes were identified using least absolute shrinkage and selection operator (LASSO) Cox regression for 25 newly reported cuproptosis-related regulators and subjected to stepwise regression to obtain cuproptosis-related score (CuRS). Additionally, the clinical significance, functional status, role on TME, and genomic variation of CuRS were further examined systematically. Results: A CuRS model incorporating TRAF2, TRADD, USP21, FAS, MLKL, TNFRSF10B, MAPK8, TRAF5, and RIPK3 was developed. The stability and accuracy of this risk model as an independent prognostic factor for PAAD were confirmed in the training and external validation cohorts. Patients in the high-CuRS group had “cold” tumors with active tumor proliferation and immunosuppression, whereas those in the low-CuRS group comprised “hot” tumors with active immune function and cell killing capacity. Additionally, patients in the high-CuRS group carried fewer genomic copy number variations (CNVs) and greater somatic mutations. Furthermore, patients in the low- and high-CuRS groups exhibited increased sensitivity to immunotherapy and chemotherapy, respectively. Conclusion: We developed and validated a robust CuRS model based on cuproptosis to assess patients’ prognoses and guide clinical decision-making. Overall, the findings of this study are expected to contribute to the comprehensive understanding of cuproptosis and facilitate precise treatment of PAAD.

Dashboards for Decision Making in Higher Education


 
 
 Dashboards are used as a graphical user interface that provides at-a-glance views into key measures and trends to assist decision makers in the Singapore University of Social Sciences (SUSS) to enhance learning support for students.
 A student’s first contact with SUSS often starts at the point of application (i.e., applying for places in the University). Hence, the process of application, selection and offer has been identified as the business problem in this project. An effective dashboard will empower users to make more informed decisions with a visibility to data and ease in generating insights, ultimately streamlining the selection and offer process. The users in this project are the stakeholders who deploy the dashboard in the process of and decision making related to selection and offer.
 Quality of information on the dashboard is highly dependent on the state of data, including their completeness and accuracy. Quality of a dashboard can be measured by how much impact it can make, which is directly related to actions taken by users with information extracted from the dashboard. With the right knowledge and skills in developing an effective dashboard, business objectives can be met and user requirements can be catered for. If every decision maker is able to customise his/her own dashboards that meet business objectives and user requirements, the University will be empowered at a functional level, with various departments supporting students through their respective roles and functions. Challenges in data and people issues will be discussed in this paper alongside the future directions in overcoming these issues.
 
 

Design of an Assistant Decision Support System for Sports Training Based on Association Rules

In order to quantitatively evaluate the effect of sports training, it is necessary to track the dynamic characteristics of sports by using sports training aided decision support system. When the existing sports training assistant decision support system extracts the features of decision association rules, some redundant features will appear when establishing the global association rules, which increases the amount and difficulty of data calculation and affects the effect of assistant decision support. On this basis, the data fusion of assistant decision support information is carried out, and the optimal assistant decision support scheme is obtained according to the fusion results. The experimental results show that the design system is superior to the existing auxiliary decision-making system in motion recognition rate, motion result accuracy rate, and decision-making accuracy rate, which can provide users with auxiliary decision-making support for sports training and has good practical application effect.

Project Investment Plan Valuation Using Discounted Cash Flow Analysis (Case Study of LLP Compression Project Investment Plan at Tango Field, Mehacca Block)

This article performs a valuation analysis of LLP Compression in project investment plan at Tango Field in Mehacca Block. The LLP Compression investment project plan is currently under consideration for a Final Investment Decision (FID). The analysis is conducted by using quantitative methodology approach to evaluate 2 project scenario based on its cash flow. The outcome of financial valuation could assist decision maker in the company, in determining whether the project is economically profitable, whether it should be executed, what scenario can generate maximum economic profit for the company, and also what financial factor affecting LLP Compression Project cash flow that should be managed to avoid negative financial results and to forecast project likelihood of success. For DCF analysis, with company discount rate at 10.2%, the Net Present Value (NPV) result is 1.12 Million USD for Scenario 1 and 0.91 Million USD for Scenario 2. Internal Rate of Return (IRR) for both scenarios are higher than company’s discount rate. Payback Period for both scenarios are also the same in year 2026, or 2 years after production. Profitability Index (PI) for Scenario 1 is slightly higher than Scenario 1 amounting at 1.07, while PI for scenario 2 is 1.03. All of the parameters from DCF gives positive result more than expected return, with scenario 1 that provide better value than scenario 2. Monte Carlo simulation is also used to provide a likelihood of having a negative NPV. The result is that both scenarios have probability negative NPV amounting 28% for scenario 1 and 30% for scenario 2. Based on Monte Carlo Simulation result, scenario 1 has slightly lower probability of generating negative NPV than Scenario 2. Sensitivity analysis is also being used to look how large NPV project varies if the parameter inputs are changed. Four parameters tested with assumption change at ± 25% from original condition. Based on sensitivity analysis, it is observed that Gas Production, Gas Price are the most sensitive parameter for both scenarios of LLP Compression project. In summary, the LLP Compression Project investment plan is feasible to be executed for both scenarios, with scenario 1 provides higher economic profit than scenario 2, and it also generates slightly lower probability negative NPV than scenario 2. Since LLP Compression Project has probability of gaining negative NPV based on Monte Carlo Simulation Result, hence several strategies to minimize project risk exposure are required to be determined before project execution.