Judgment Method Research Articles

Structural performance and durability assessment of ultrafine powdered concrete in modern buildings

Abstract This paper investigates the impact of various ultrafine powders on the flow characteristics and mechanical properties of concrete, with the aim of enhancing its overall performance. Examine the compressive and flexural strengths of cement, establish distinct matching ratios, and clarify the actual mixing quantity of silica fume and ultrafine powder. Analyze the durability of concrete structures and propose causes for carbonation and formation of concrete. Obtain the durability evaluation indexes, propose the variable weight fuzzy comprehensive judgment method, and establish the durability evaluation system for ultrafine powder concrete buildings. When the on-site test results for reinforcement corrosion, concrete strength, carbonation depth, cracks, and damage degree are added together, the values of each assessment vector of the fuzzy comprehensive evaluation are found. This lets us figure out the bridge’s durability grade. The highest compressive degree was achieved by both ultrafine silica-aluminum material and ultrafine mineral fishery powder at a 20% dosage. The submicron silica material achieved the highest compressive strength with a 40% dosage. In the test and analysis of the ultrafine powder concrete bridge building structure, the fuzzy comprehensive judgment grade is III. That is, there is a certain degree of damage state, and measures must be taken, which is consistent with the field test results.

Research and judgement method of social network hot news public opinion based on knowledge graph

Research and judgement method of social network hot news public opinion based on knowledge graph

Application of industrial robot based on infrared sensor technology in monitoring of electrical equipment

Abstract In this paper, infrared sensor technology is applied to the monitoring of electrical equipment, and a mathematical model of infrared image processing is built. In addition, the infrared image recognition model of electrical equipment is combined with the improved CenterNet, and ResNeXt50, which has stronger feature extraction capability, is used as the backbone network of CenterNet to improve detection accuracy. An infrared dataset of electrical equipment is used to verify the effectiveness of the improved CenterNet model and the other two models. Finally, the faults in substation equipment are analyzed and studied for real cases applied in practical work as an example. The results show that the algorithm in this paper has an mAP value of 93.7% and a frame rate of 40/S, which can balance the detection accuracy and detection speed in the electrical equipment infrared scene. Compared to traditional electrical equipment detection methods, the detection method of infrared sensor imaging is more reliable, with a 90% accuracy rate, and the highest has reached 100%. The article analyzes the characteristics and relationship between the local heating and temperature increase of the fault point of the equipment concerned, summarizes the fault judgment method, and verifies the effectiveness of infrared sensor imaging technology.

Experimental simulation study on gas flow field in combined goaf during the transition period of coal pillar-free working face relocation

Regarding the gas disaster caused by the complex ventilation system and the changeable flow field in the combined goaf during the transition period of the coal pillar-free working face, the self-designed physical simulation test bench for the combined goaf of coal pillar-free mining was used to compare and analyze the distribution law of wind pressure, gas, and oxygen concentration field in the combined goaf during the moving period of the working face and the normal mining period. The results show that during the relocation period, the gas concentration in the stope will always be slightly higher than that in the normal mining period, and the existing air leakage channel will lead to complex air flow in the combined goaf, which is easy to cause gas explosion accidents. The formation conditions, judgment principles, and judgment methods of the dangerous area of gas explosion coupling disaster in goaf are analyzed. By changing the air distribution ratio of the main and auxiliary air intake roadways, it is concluded that the explosion area will form an “L”-shaped explosion zone in the goaf, and the area of gas explosion disaster area during the relocation period will be larger than that the normal mining period. According to the size of the gas explosion area, the optimal air distribution ratio of the main and auxiliary air intake roadway during the transition period of the working face relocation and the normal mining period is 2:1. Under this air distribution ratio, the gas explosion area is the smallest, which is 132 284 and 113 534 mm2, respectively. The research results are of great significance to reveal the mechanism of gas disaster induced by goaf during the transition period of coal pillar-free relocation.

Personal Income Tax Compliance Behaviour in Kathmandu Valley

This article aimed to explore whether factors such as tax awareness and knowledge, the perceived fairness of the tax system, its complexity, tax rates, and income levels influence the compliance behavior of registered personal income taxpayers in the Kathmandu Valley. A theoretical framework was employed to test hypotheses based on responses gathered from a survey of these taxpayers. Data collection was carried out using a purposive judgmental sampling method, and questionnaires were distributed as part of a descriptive and analytical research design. The collected data were analyzed using least squares regression and SPSS 26.0. The results indicated that personal income tax compliance, the dependent variable, was significantly and positively influenced by five key factors: tax awareness and knowledge, fairness of the tax system, complexity of the tax system, tax rates, and income level. The findings also revealed that taxpayers often lack sufficient technical knowledge and perceive the tax system as overly complex. These factors—along with tax awareness, fairness, complexity, tax rates, and income level—are thought to contribute to taxpayer non-compliance. To enhance tax compliance, the study recommends that the government should focus on improving public services, increasing awareness about the benefits of paying taxes, and educating the public on compliance measures. Additionally, conducting regular audits can act as a deterrent to non-compliance. Future research could investigate the impact of social and referral groups on compliance behavior, as social norms and ethical values may offer further incentives for tax compliance.

Traffic flow simulation study under large gathering activities based on VISSIM

Based on the above background, this paper starts from the theory of traffic flow and the macro-basic diagram, and simulates and researches the traffic congestion problems generated in the period of large-scale gathering activities during festivals. First of all, this paper summarizes the time correlation and spatial correlation of traffic flow, analyses the judgement method and rating index of traffic congestion, and completes the macroscopic basic map; after defining the simulation parameters of each road segment and signal intersection in the road network, it is implemented by using the VISSIM simulation software; finally, it carries out the simulation analysis with the example of the New Year's Eve firework display at the Chaotianmen Gate in Chongqing, and calculates and analyses the traffic flow state of the road network with output data, and then compares the output data with that of the road network. Finally, we use the output data to calculate and analyses the traffic flow status of the road network, compare it with the traffic flow data under the smooth period, judge the congestion characteristics, draw the macroscopic basic map of the road network under each period, simulate the changes of the macroscopic basic map, analyses the impact of traffic congestion on the traffic flow of the road network, calculate the degree of improvement of the traffic status of the network, and put forward the reasonable optimization scheme to provide a decision-making basis for the evacuation of the traffic under the festival and large-scale gathering activities.

An improved binary particle swarm optimization algorithm for clinical cancer biomarker identification in microarray data

Background and ObjectiveThe limited number of samples and high-dimensional features in microarray data make selecting a small number of features for disease diagnosis a challenging problem. Traditional feature selection methods based on evolutionary algorithms are difficult to search for the optimal set of features in a limited time when dealing with the high-dimensional feature selection problem. New solutions are proposed to solve the above problems. MethodsIn this paper, we propose a hybrid feature selection method (C-IFBPFE) for biomarker identification in microarray data, which combines clustering and improved binary particle swarm optimization while incorporating an embedded feature elimination strategy. Firstly, an adaptive redundant feature judgment method based on correlation clustering is proposed for feature screening to reduce the search space in the subsequent stage. Secondly, we propose an improved flipping probability-based binary particle swarm optimization (IFBPSO), better applicable to the binary particle swarm optimization problem. Finally, we also design a new feature elimination (FE) strategy embedded in the binary particle swarm optimization algorithm. This strategy gradually removes poorer features during iterations to reduce the number of features and improve accuracy. ResultsWe compared C-IFBPFE with other published hybrid feature selection methods on eight public datasets and analyzed the impact of each improvement. The proposed method outperforms other current state-of-the-art feature selection methods in terms of accuracy, number of features, sensitivity, and specificity. The ablation study of this method validates the efficacy of each component, especially the proposed feature elimination strategy significantly improves the performance of the algorithm. ConclusionsThe hybrid feature selection method proposed in this paper helps address the issue of high-dimensional microarray data with few samples. It can select a small subset of features and achieve high classification accuracy on microarray datasets. Additionally, independent validation of the selected features shows that those chosen by C-IFBPFE have strong correlations with disease phenotypes and can identify important biomarkers from data related to biomedical problems.

The Future Study of Government Funding in the Healthcare Supply Chain: A Scenario Analysis Approach

Background: Health financing is very important and means providing money, budget or necessary resources for it. Considering the vital role of the government in financing health, in this research, the drivers of financing were identified by the government and possible futures were investigated.
 Method: The present qualitative study was developed in 4 phases. Judgmental and snowball sampling methods were applied in all stages. In the first stage, based on theoretical saturation, 15 people and in other stages, 12 experts participated. In the first phase, financing drivers were identified through interviews and thematic analysis. In the second phase, using the researcher-made questionnaire and Micmac 6.1.2 software, the main drivers were extracted. In the third phase, the uncertainty of each of the key drivers was determined by an interactive management approach, and finally, in the fourth phase, compatible scenarios were determined using a researcher-made questionnaire and its analysis with the Scenariowizard 4.3.1 software.
 Results: In this research, 14 factors influencing the government funding of the healthcare sector were identified. Moreover, 5 compatible scenarios for financing were obtained. In the "wealthy government" scenario, the financing conditions were favorable by the government, but in the scenarios of "calm inefficient", "inefficient without a plan", "inefficient unstable" and "bankrupt government", the government had many problems and the health sector should focus on other financing methods.
 Conclusion: Due to the lack of favorability of most scenarios, it is necessary for healthcare managers to reduce dependence on the government budget and promote financing from other methods. Methods such as financing through the provision of services, charitable donations and insurances, and the stock market should be given serious consideration.

Knowledge Management Maturity Level of Steel Fabricator Companies in Indonesia

Improving the maturity of knowledge management implementation is considered a promising approach that can help companies to gain a competitive advantage and improve company competitiveness. This study was conducted to evaluate the achievement of the maturity level of knowledge management implementation in steel fabricator companies in Indonesia using the methodology published by the Asian Productivity Organization (APO) in 2020. The evaluation was carried out on 7 steel fabricator companies in Indonesia with a non-random judgmental quota sampling method by distributing questionnaires, each of the question of indicators can be rate from 1 (doing poorly) to 5 (doing very well). Based on the results of the analysis, found that from 7 categories, there are some categories need to be improved, they are "Learning and Innovation", "Knowledge Processes" and "People”. It is found that the maturity level of the application of knowledge management for steel fabricators in Indonesia is at the expansion level, where knowledge management has been implemented but has not been carried out thoroughly in the organization.

Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System.

The fuel pump serves as the central component of the aircraft fuel system, necessitating real-time data acquisition for monitoring purposes. As the number of sensors increases, there is a substantial rise in data volume, leading to a simultaneous increase in computational processing for traditional Prognostics and Health Management methods while computational efficiency decreases. In response to this challenge, a novel health monitoring approach for aircraft fuel pumps is proposed based on the collaborative utilization of cloud-edge resources. This approach enables efficient cooperation among the sensor side, edge side, and cloud side to achieve timely fault warnings and accurate fault classification for fuel pumps. Within this method, anomaly judgment tasks are allocated to the edge side, and an anomaly judgment method that integrates the 3σ threshold and "3/5 strategy" is devised. Additionally, a fault diagnosis algorithm, founded on a convolutional auto-encoder, is formulated in the cloud to discern various fault types and severities. Comparative results demonstrate that, in contrast to long short-term memory networks, convolutional neural networks, extreme learning machines, and support vector machines, the proposed method yields improvements in accuracy of 4.35%, 6.40%, 17.65%, and 19.35%, respectively. Consequently, it is evident that the proposed method exhibits notable efficacy in the condition monitoring of aircraft fuel pumps.

Autonomous Wireless Power Transfer System with Constant Output Voltage in a Wide Load Range

In this paper, an autonomous wireless power transfer (WPT) system with constant voltage output in a wide load range is presented. Here, combining self-oscillating control and phase-shift control, a new implementation of the autonomous WPT system is proposed. The proposed autonomous WPT system operates using a self-oscillating control method in the strong coupling region, which can automatically maintain the constant output voltage. In the weak coupling region, a phase-shift control method with a fixed frequency and a variable duty cycle is implemented, and a control strategy based on output voltage estimation is proposed to obtain the constant output voltage. In addition, according to the operating frequency characteristic of the proposed autonomous WPT system, a corresponding coupling region judgment method is presented to guarantee the realization of switching between the two control methods. An experimental prototype with a 24 V output voltage is constructed to validate the practicability of the proposed method. The experimental results show the proposed autonomous WPT system can obtain constant output voltage in a wide load range.

Security Evaluation and Improvement of the Extended Protocol EIBsec for KNX/EIB

The European Installation Bus(EIB) protocol, also known as KNX/EIB, is widely used in building and home automation. An extension of the KNX/EIB protocol, EIBsec, is primarily designed to meet the requirements for data transmission security in distributed building automation systems. However, this protocol has some security issues in the request, key distribution, and identity authentication processes. This paper employs a formal analysis method that combines Colored Petri Net (CPN) theory with the Dolev-Yao attack model to evaluate and enhance the EIBsec protocol. It utilizes the CPN Tools to conduct CPN modeling analysis on the protocol and introduces a security assessment model to carry out intrusion detection and security assessment. Through this analysis, vulnerabilities in the protocol, such as tampering and replay attacks, are identified. To address these security concerns, we introduce hash verification and timestamp judgment methods into the original protocol to enhance its security. Subsequently, based on the improved protocol, we conduct CPN modeling and verify the security of the new scheme. Finally, through a comparison and analysis of the performance and security between the original protocol and the improved scheme, it is found that the improved scheme has higher security.

Financial Development Strategies: Defining Objectives and Priorities

In recent years, scientific and practical interest in the strategic management of countries' financial development has grown significantly. However, an analysis of publications on this topic indicates the absence of a methodology to make strategic decisions on the formation of goals and priorities for financial development. The purpose of this study is to develop a methodology for the strategic management of the financial development of countries. The study uses methods of systemic, comparative, GEP analysis, and a rule-based judgment method. The result of the study is the development of a concept that allows defining strategy, quantified strategic goals and priorities for financial development, and its testing in OECD countries. The novelty of the research lies in the systematic approach to the formation of strategic goals and priorities based on the System of National Accounts, the multi-level system of key indicators of financial development, criteria formalizing alternative strategies, and rules for making strategic decisions. This is the first time such a concept has been proposed. The advantages of the developed concept are high representativeness, objectivity, and a wide range of applications. Its use will improve the quality of strategic management of countries' financial development and ensure transparency in government decisions. Doi: 10.28991/ESJ-2023-07-06-09 Full Text: PDF

What is Behind the Shine? The Dark Side of Research Evaluation: A Conceptual Framework

Research evaluation (RE) is presently a widely discussed and prominent subject within the academic community. This article provides a conceptual framework that categorizes the dark side of RE and provides strategies to diminish it. At its core, this framework includes a generic model summarizing RE dark effects and anti-dark effects remedies and elucidating their relations. Four research questions were raised regarding the research interest in the dark side of RE, the nature of the dark effects of RE and remedies for them, their literature prominence, and whether it is possible to create a well-founded model for mitigating the dark side of RE. Theoretically, this framework is based on concepts of the philosophy of science. Methodologically, a structured literature review, methods of expert judgment, analysis and synthesis, and a sampler of 35 articles underpin it. Stakeholder and logical approaches were applied for classification. The suggested framework is the first in-depth attempt to elucidate the dark side of RE and how to mitigate it. In practical terms, the framework can be incorporated into university/government RE systems to “enlighten”/control their dark side, thereby improving the academic governance toolkit. It can also be used to develop subject-area/country-specific models for the dark side of RE.

171. Gram-Positive Blood Culture Contamination Rates: A Comparative Assessment Between Verigene® and Conventional Method

Abstract Background The estimated cost of treatment for septicemia among hospitalized patients is $15.4 billion. Approximately 20%-50% of all positive blood cultures were likely false positives. The rates of the reported contaminations in the literature range from 0.6%-12.5%. The false positive report of bacteremia has significantly impacted overall healthcare by increasing mortality rates, prolonging hospital stays, and expanding the cost of care. The use of Verigene® offers polymerase chain reaction (PCR) amplification in < 5 minutes. In our study, we examine the Verigene®, compared to the standard blood culture by the Vitek-2®, to assess for detecting Gram-positive blood contamination (GPBC). Methods Blood cultures were collected from patients with sepsis in four hospitals in El Paso, Texas (1/1/2018-5/30/2022). Samples were run through Verigene®. Simultaneously, all specimens were cultured using the conventional method and then incubated for five days. Gram stain used for positive samples. Speciation and antimicrobial susceptibility using Vitek-2®. GPBC in Vitek-2® was identified when one set of two grows microorganisms, universally known as skin flora, without clinical sepsis within 48hr. GPBC in Verigene® was identified when it recognized any skin flora without clinical sepsis within 24hr. The analysis was made using McNemar’s Chi-square test. An observation was statistically significant if the p-value was ≤0.05. Results A total of 816 Gram-positive results were obtained. The prevalence of GPBC based on the traditional technique was 98.65% and 64.25% based on the Verigene®. Staphylococcus epidermidis was the most detected at 52%, followed by Staphylococcus aureus at 26%. The sensitivity of the Verigene® for GPBC detection was 64.22%, and the specificity was 33.33%.Figure 1.Frequency of Gram-positive Microorganisms DetectionFigure 2.Sensitivity and Specificity of Verigene® in Identification of Contamination in Gram-positive Blood CultureTable 1.Sensitivity and Specificity of Verigene® in Identification of Contamination in Gram-positive Blood Culture Conclusion The use of Verigene® alone in identifying GPBC is insufficient. Despite the higher sensitivity in detecting microorganisms compared to Vitek-2®, this should be paired with clinical judgment and conventional methods. The ability to recognize GPBC by Verigene® was affected by the lack of clinical evidence to exclude sepsis in the first 24hr. This study is the first to evaluate Verigene® for identifying GPBC. Further interventions should be introduced, i.e., reporting titer by PCR on any detection. Disclosures All Authors: No reported disclosures

Estimating design positions of suspension bridge tower saddles in the completed bridge state: An analytical approach

Saddle position is an essential parameter in the form-finding of main cables and for the construction control of suspension bridges. In actual suspension bridges, the center of the arc-shaped tower saddle in the completed bridge state may sometimes deviate toward the river and the river bank. However, the influencing factors and the judgment method for the deviation have yet to be thoroughly studied. This study proposes an analytical approach for estimating saddles positions in the completed bridge state of suspension bridges under two definitions of the intersection point (IP) of the main cable in the tower top: (i) IP between the two extended catenary segments of the main cable and (ii) IP between the two extended straight lines of the main cable. For catenary-based IP, the calculation of the saddle position is separable from the form-finding of the main cable. Once the shape of the main cable is derived, equations can be established based on the geometric compatibility conditions for the saddle and main cable, their solving required for deriving saddles positions. For straight line-based IP, the calculation of the saddle position is inseparable from the form-finding of the main cable, and the two processes are conducted simultaneously. Equations can be established based on mechanical equilibrium and the geometric compatibility conditions for the saddle and the main cable and then solved. The relationship between the position of the center of the arc-shaped tower saddle and the centerline of the tower is analyzed, proving that the saddle deviates toward the side with a smaller catenary parameter. The eccentric compression of the tower is analyzed, implying that the eccentric moment acting on the tower in the completed bridge state is primarily produced by the self-weight of the saddle and the main cable segment in the saddle. Finally, the feasibility of the proposed method and the correctness of the above findings are verified through a suspension bridge with a main span of 730 m.

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

A Proposal for Lodging Judgment of Rice Based on Binocular Camera

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

The Sensitivity Level of the Coastal Areas in Bulukumba Regency to Waste Pollution

The presence of waste in coastal environments can lead to increased coastal damage and burden. Most of the population's activities in Bulukumba Regency are concentrated in coastal areas, thus making this region susceptible to significant pressure from waste pollution. This research aims to determine the level of coastal area sensitivity in Bulukumba towards waste pollution. The study was conducted from October to December 2022. The research location is the coastal area of Bulukumba Regency, which includes seven subdistricts: Gantarang, Ujung Bulu, Ujung Loe, Bonto Bahari, Bontotiro, Herlang, and Kajang. Primary data were obtained through interviews and direct observations at the research locations, while secondary data were collected through literature studies and relevant institutions in Bulukumba. The results of parameter weighting using the expert judgment method indicate that five important parameters are used to assess the sensitivity of the coastal environment to waste pollution. These parameters consist of current velocity (20.27%), distance of the ecosystem from the harbor (18.92%), distance of the ecosystem from settlements (18.92%), distance of the ecosystem from rivers (17.57%), and the presence of waste on the coast (17.57%). The distribution of coastal environmental sensitivity levels to waste pollution shows that the eastern coastal areas are more sensitive to waste pollution than the southern coastal areas. The current velocity is the most significant parameter influencing the coastal environment's sensitivity to waste pollution and holds the highest weight and score across all research areas.

New Research in Forecasting

All institutions in Georgia are changing. Decision-makers in Government agencies and private organizations need the best available data and analyses to meet the challenges of changing times. Evidence-based forecasting can help officials to predict the likely outcomes of their decisions. This paper reports on work being done by THE GEORGIA FORECASTTM and The University of Georgia Forecasting Center to identify and apply modern methods to Georgia’s most pressing issues. These methods include statistical techniques, such as time series and regression, and judgmental adjustments, such as Delphi, surveys of consumers and producers, and scenario scripting. One recent application studied in this research project, forecasting tourism in Georgia, is an example of how modern methods may improve decision-making by those who make public policy, allocate resources, and make business decisions.Statistical forecasting techniques assume that the near future will look substantially like the recent past. We can, therefore, use data from recent history to make predictions about the near future. The longer the horizon, the weaker the assumption. Two general classes of statistical forecasts are (1) Causal and (2) Non-Causal. The most common causal model is regression: the variable to be determined (Y) is related to one or more predictor variables (Xi). For example, the quantity demanded of a good (Qd) may be estimated from the price of that good (P) and other factors, such as income, relative prices of substitute and complementary goods, weather, etc. Time series models are non-causal. We may not know why a variable of interest rises or falls, but we can detect patterns from the recent past, such as trend, seasonality, and cycles. Judgmental methods assume some degree of knowledge that may not bereflected in the statistics. These may include, among others, a Delphi panel of experts, survey of consumers and producers, game theory, and scenario scripting. Because of the high degree of uncertainty in the Georgian economic, political, and social environment, the use of one model is unlikely to yield reliable forecasts. Therefore, THE GEORGIA FORECASTTM and The University of GeorgiaForecasting Center use a combination of methods and continually evaluate the performance of their forecasts. This paper uses the case of forecasting tourism to demonstrate these techniques.