Systematic Decision Research Articles

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

How Do Managerial Biases Affect Foreign Market Entry Decisions?

In this practitioner-focused study, we discuss how cognitive biases impair foreign market entry decisions. Focusing on three factors that are central to the evaluation of foreign markets: cost, control, and uncertainty, we explain how decision-making biases undermine their assessment and the ways in which the biases can be mitigated. Specifically, overconfidence bias and the sunk cost fallacy negatively affect the estimation of entry costs which can be mitigated by a strong governance structure and independent reviews; anchoring and confirmation biases undermine the assessment of an appropriate level of control requiring the use of systematic decision frameworks and decision extension tools; and representativeness and availability biases limit the comprehension of environmental uncertainty suggesting mitigation strategies such as the challenging of internal assumptions and devil’s advocacy. By explicating how managerial biases turn into flawed assessments of foreign markets, our study uncovers the mechanisms that explain and potentially remedy cognitive pitfalls during internationalization.

A Study on the Development of an Integrated Automatic Allocation Algorithms for Multilayer Defense System Intercept Missiles

This study aims to propose optimal missile interception allocation strategies for constructing a multi-layered missile defense system in situations where a large number of ballistic missiles are launched simultaneously. Specifically, we consider the threat level of ballistic missiles, the importance of defense zones, and the asset value of interception missiles, and develop a damage consumption index based on these factors. Through an algorithm that minimizes the sum of damage consumption indices for intercepting missiles defending upper and lower layers, we allocate them in optimal combinations to achieve maximum effectiveness with minimal cost. Additionally, we propose a decision-making system based on algorithmic integration rather than human decision-making, particularly in complex operational scenarios, to ensure systematic decision-making and maximize efficiency.

Why Do We Try to Dodge Difficult Decisions?

This article explores why leaders often try to avoid or delay making difficult decisions, and provides strategies for overcoming hesitancy. Common reasons for avoidance include the desire to maintain superficial harmony, fear of being wrong, and prioritizing personal comfort. However, the article argues that decision-making is a core leadership responsibility, and avoidance does more harm than good over the long run. A systematic decision process is recommended to replace fear and groupthink with diligent analysis. Key steps include clearly defining problems, gathering diverse input, establishing evaluation criteria, brainstorming options, objectively weighing pros and cons, piloting top options, following through on implementation, and reviewing outcomes. While uncomfortable in the short term, facing challenges directly can strengthen leadership and empower sound determinations that further organizational goals. With practice, leaders gain confidence in their ability to handle uncertainties through a disciplined decision-making framework.

Deciding how to decide: A conceptual model for consensually fostering urban infrastructure maintenance

Infrastructure owners face challenges in effective maintenance decision-making due to the process's multidisciplinary nature, spanning mathematics to cognitive science. This study delves into enhancing maintenance for complex infrastructure systems, specifically in scenarios where a single primary owner must consider the preferences and requisites of multiple stakeholders. In such unstructured problems characterised by diverse perspectives and potentially conflicting interests in uncertain environments, systematic decision analysis is paramount. In response, this paper proposes a generic conceptual model to outline the primary critical consideration of decision-making through a holistic scheme of the decision problem; it facilitates discovering shared views when defining the decision problem, eliciting objectives early in the decision-making process, and ensuring that selected agendas align with desired outcomes and core values. It provides valuable guidance for decision-modellers in infrastructure rehabilitation. These claims are reinforced with the application of the proposed model to case studies, illustrating its practicality in real-world settings.

Benchmarking produced water treatment strategies for non-toxic effluents: Integrating thermal distillation with granular activated carbon and zeolite post-treatment

The management of produced water (PW) generated during oil and gas operations requires effective treatment and comprehensive chemical and toxicological assessment to reduce the environmental risks associated with reuse or discharge. This study evaluated a treatment train that included a low-temperature thermal distillation pilot system followed by granular activated carbon (GAC) and zeolite post-treatment for processing hypersaline Permian Basin PW. Our study provides a unique and comprehensive assessment of the treatment efficiency considering a targeted chemical scheme together with whole effluent toxicity (WET) tests across four trophic levels regarding aquatic critical receptors of concern (ROC): Raphidocelis subcapitata, Vibrio fischeri, Ceriodaphnia dubia, and Danio rerio. The distillate from the thermal distillation process met various numeric discharge standards for salinity and major ions. However, it did not meet toxicity requirements established by the United States National Pollutant Discharge Elimination System program. Subsequent post-treatment using GAC and zeolite reduced the concentration of potential stressors, including volatile organics, NH3, Cd, Cr, Zn, and Mn in the final effluent to below detection limits. This resulted in a consistent toxicity reduction across all WET tests, with no observable adverse effects for R. subcapitata, C. dubia, and D. rerio (no observed effect concentration >100%), and V. fischeri effects reduced to 19%. This study realizes the feasibility of treating PW to non-toxic levels and meeting reuse and discharge requirements. It underscores the importance of implementing integrated treatment trains to remove the contaminants of concern and provides a systematic decision framework to predict and monitor environmental risks associated with PW reuse.

Decision Analysis and Practical Pathways for Innovative Teaching Methods in Information Technology Education

With the rapid advancement of information technology, its integration into modern education has become essential, with a focus on enhancing teaching outcomes and the student learning experiences through innovative teaching methods. However, existing studies predominantly suffer from a lack of systematic evaluation indicators and scientific decision models. The selection of evaluation indicators is often subjective, incomplete, and primarily qualitative, lacking quantitative data support. A systematic decision analysis method was proposed in this study to address these issues. This study comprises three components: the construction of an evaluation indicator system for innovative teaching methods in information technology education, a scale analysis based on gray relational degree, and the computational steps for the decision model concerning innovative teaching methods. This study provides scientific bases and practical pathways for the selection of innovative teaching methods in information technology education, aiming to enhance teaching effectiveness and educational quality.

Integrating socioeconomic and ecological data into restoration practice.

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

An integrated multi-attribute decision framework for sustainability assessment of renewable diesel fuel production pathways

Ascertaining the sustainability of a fuel production technology in the presence of conflicting factors out of different existing production pathways is a typical and time-consuming exercise. Thus, it is necessary to follow a resourceful and systematic methodology to decide the best fuel production method for any application. The present work proposes a systematic decision framework that utilizes various MCDM schemes for identifying suitable renewable diesel production technology to replace conventional diesel fuel. Sustainability assessment for a total of five production methods was conducted, based on fifteen criteria. The MOORA, VIKOR, and COPRAS techniques are utilized to compute ranking schemes for different alternatives according to integrated criteria weights based on AHP and CRITIC methods. Qualitative assessment in terms of rank reversal test and sensitivity analysis employed to compare and check the robustness of applied methodologies. The results demonstrated that FT diesel is the most suitable alternative followed by green diesel-I and feedstock price as the most persuasive criteria followed by PM2.5.The qualitative assessment revealed that the integrated weight-based MOORA technique is the most robust framework for the present decision problem, followed by VIKOR and COPRAS strategies respectively. Further, the proposed MCDM techniques can be suggested independently to encounter similar types of decision-making problems.

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

The scheduling of harbor tugboats is a crucial task in port operations, aiming to optimize resource allocation and reduce operational costs, including fuel consumption of tugboats and the time cost of vessels waiting for operations. Due to the complexity of the port environment, traditional scheduling methods, often based on experience and practice, lack scientific and systematic decision support, making it difficult to cope with real-time changes in vessel dynamics and environmental factors. This often leads to scheduling delays and resource waste. To address this issue, this study proposes a mathematical model based on fuzzy programming, accounting for the uncertainty of the arrival time of target vessels. Additionally, we introduce the NRPER-DDPG algorithm (DDPG Algorithm with Prioritized Experience Replay and Noise Reduction), which combines a prioritized replay mechanism with a decaying noise strategy based on the DDPG algorithm. This approach optimizes the time for tugboats to reach the task location as a continuous action space, aiming to minimize the total system cost and improve scheduling efficiency. To verify the effectiveness of the mathematical model and algorithm, this study conducted experimental validation. Firstly, the optimal algorithm hyperparameter combinations were adjusted through random examples to ensure the stability and reliability of the algorithm. Subsequently, large-scale examples and actual port cases were used to further verify the performance advantages of the algorithm in practical applications. Experimental results demonstrate that the proposed mathematical model and algorithm significantly reduce system costs and improve scheduling efficiency, providing new insights and methods for the sustainable development of port operations.

A systematic decision-making approach to optimizing microgrid energy sources in rural areas through diesel generator operation and techno-economic analysis: A case study of Baron Technopark in Indonesia

Microgrid systems are part of the most reliable energy supply technologies for rural communities that do not have access to electricity but the system is generally dominated by diesel generators (DG). The implementation of de-dieselization programs to ensure efficient diesel operations requires addressing several scenarios such as the replacement of diesel completely with 100% renewable energy sources at a significant cost. The design and selection of appropriate configuration, as well as operating patterns, need to be considered in adopting economical and reliable microgrid systems. Therefore, this study aimed to design an optimal configuration and operational pattern for microgrid systems for the frontier, outermost, and least developed (3T) regions using Baron Techno Park (BTP) in Indonesia as a case study. The optimization was conducted through HOMER software combined with benefit-cost analysis and the focus was on daily load variations, selection of control algorithms, reconfiguration of the power supply system, and setting of the diesel generator operating hours. The results showed that the optimum configuration was achieved using loads of resort, 24 kWp of PV, 288 kWh of BESS, load-following (LF) as dispatch controller, and 25 kVa of DG. Moreover, the proposed microgrid system produced 12% excess energy, 36% renewable fraction (RF), 13.25 tons reduction in CO2 emissions per year, $0.28 LCOE per kWh, $250,478 NPC, and a benefit-cost ratio (BCR) of 0.89. It also had a potential energy efficiency savings of 55.56% and a cost efficiency of 20.95% compared to existing system configurations. In conclusion, the study showed that the addition of DG to microgrid systems in 3T areas was more optimal than using only PV and batteries. An effective operating schedule for the DG was also necessary to improve RF and reduce expenses. Furthermore, other energy storage devices considered less expensive than batteries could be introduced to improve the economics of microgrid systems in the 3T region.

A Systematic Decision-Making Approach for Quality Function Deployment Based on Hesitant Fuzzy Linguistic Term Sets

As a powerful tool for improving customer satisfaction, quality function deployment (QFD) can convert customer requirements (CRs) into engineering characteristics (ECs) during product development and design. Aiming to address the deficiencies of traditional QFD in expert evaluation, CRs’ weight determination and ECs’ importance ranking, this paper proposes an enhanced QFD model that integrates hesitant fuzzy binary semantic variables, the Best–Worst Method (BWM), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The objective is to determine the prioritization of product engineering characteristics. Indeed, hesitant fuzzy linguistic term sets (HFLTS) have found extensive application in decision-making problems. Compared to other fuzzy language methods, HFLTS offers greater convenience and flexibility in addressing decision-makers’ hesitations and uncertainties. Initially, the combination of hesitant fuzzy linguistic term sets with interval binary tuple language variables is employed to articulate the uncertainty in the assessment information provided by QFD team members. Subsequently, the improved BWM and TOPSIS methods based on HFLTS are used to improve the accuracy of the importance ranking of engineering characteristics by determining the weights of CRs and prioritizing ECs in two stages. Finally, the feasibility and effectiveness of the proposed method are validated through an illustrative example.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MOTOR BEKAS TERBAIK MENGGUNAKAN METODE TOPSIS DI SAMPIT


 Transportation is an isolation movement in the form of an object or person from one place to another. Choosing between several alternatives is a form of systematic decision making to determine a decision from several existing options. The desire for transportation is an absolute in modern life. Transportation facilities, especially non-public transportation capabilities, are needed by the community to transport various necessities of life every day. One of the means of land transportation that is most widely used by people in Indonesia is the motorcycle, because apart from being more economical compared to four-wheeled vehicles, motorbikes have the advantage of being able to reach their destination more quickly.
 
 Buying a new motorbike requires a lot of money, and for the lower class, they tend to turn their attention to used motorbikes. However, buying a used motorcycle also confronts them with the problem of what brand and type of motorbike to buy.

Solar neighborhoods: the impact of urban layout on a large-scale solar strategies application

The article addresses the challenges of evaluating energy performance in different neighborhood settings under various energy efficiency measures and proposes a methodology for selecting appropriate solar strategies on a neighborhood scale. The study selects five representative neighborhoods from various climatic zones with different building and street layouts. The proposed methodology involves a systematic three-step multi-domain workflow for implementing energy efficiency measures and solar strategies in the existing neighborhoods. The first step involves typical energy performance simulation, the second step involves energy simulation using high performance building envelope, and the third step involves the addition of solar strategies in combination with retrofitting materials to achieve net-zero status. The results of the study show that modifying the building envelope leads to a significant reduction in energy consumption, with up to 60% reduction observed. The study also finds that the optimal mix of solar strategies depends strongly on the type of neighborhood, its street layouts, and the type of buildings. The article highlights the importance of considering these factors when implementing solar strategies on a neighborhood scale to achieve energy efficiency and net-zero status. It provides urban planners with a systematic decision-making approach to evaluate and optimize neighborhoods to achieve net-zero energy status.

P-012 Time, risk & motion study of an integrated artificial intelligence robotic system for semen analysis

Abstract Study question Can integration of a robotic system for semen analysis reduce operational failure modes and eliminate risk in the IVF laboratory? Summary answer A robotic system for semen analysis reduces operational failure modes and eliminates severe risks including those associated with reduced patient experience and increased patient stress. What is known already Recently, the ever-evolving field of IVF has experienced a rise in innovations to boost laboratory performance. Considering the lack of robust evidence in some of them, decisions on implementation are often based on opinion and product cost. Currently, an approach to assess risk, time and effectiveness has not been fully described. IVF is not error-free (Sakkas et al., 2018), hence all new technology coming into the laboratory ought to reduce risk of errors happening. Study design, size, duration Failure mode effects analysis (FMEA) was carried out on the workflow integration into an IVF centre (>500 cycles per annum) to evaluate the possible procedural risks of manual semen analysis compared to a digitalised pathway. Possible sources of error were identified, and the Risk Priority Number (RPN), a product of likelihood, severity and detection of incidence was associated with each risk, as previously described by Rienzi et al., 2015. Participants/materials, setting, methods Using a systematic decision-making approach to quantify the value of incorporating the use of a robotic system (Mojo AISA) for semen analysis. Main results and the role of chance Five process phases were identified for conventional specimen analysis. There were eight associated process steps and 29 failure modes, among which 13 risks were given a moderate (RPN 15-50, i.e. data entry errors, incorrect count and sperm classification) and three were severe RPNs (RPN>50), resulting in reduction in efficacy of treatment. Protocol using the robotic system resulted in a reduction of process phases to four and failure modes to 16, with four moderate and none severe RPNs. Implementation of the robotic system improved workflow by optimising time in motion for the IVF specialist and further reducing active time by at least 30%. Limitations, reasons for caution FMEA is a proactive method to identify potential incidents and help to develop strategies to mitigate risks. Ultimately, this forms part of a scheme for responsible development. The likelihood of incidences and time analysis were estimated based on clinical experience. Risks inherent to sample handling remained present. Wider implications of the findings The robotic system has the potential to eliminate risks that exist when manually inputting data to electronic medical records, whilst offering support to IVF specialists. Trial registration number not applicable

Game Theory-Based Decision-Making and Iterative Predictive Lateral Control for Cooperative Obstacle Avoidance of Guided Vehicle Platoon

This paper presents a systematic decision-making and lateral control framework to realize cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in multiple scenarios. In the control framework, a centralized decision-making strategy is developed to determine optimal overtaking maneuver (i.e., OA) in the dynamic driving environment, and a distributed lateral controller with a safe driving corridor planner is designed for lateral reference trajectories tracking. The proposed decision-making strategy formulates the influence mechanism between the controlled platoon and potential surrounding vehicles based on an interactive behavior model. Moreover, a game theory-based algorithm is applied to calculate safe timing of overtaking and select efficient overtaking mode while avoiding the negative impact on the surrounding vehicles. For the distributed lateral control strategy, the control problem is solved by a linear time-varying model predictive control (LTV-MPC) algorithm so as to reduce the computational burden. Furthermore, to improve the accuracy of LTV-MPC algorithm in varying scenarios, a tailored iterative control logic is presented. The performance of the proposed framework is evaluated on the dSPACE hardware-in-loop platform. The results show that the proposed framework works well under various test scenarios involving multiple static and moving obstacles.

Assessment of Bleeding Risk in Patients Being Considered for VTE Pharmacoprophylaxis in a Large Nationwide Cohort

More than 100,000 patients are hospitalized in the United States each day. Half of all venous thromboembolism (VTE) events occur in hospitalized patients. Hospital-related pulmonary embolism was declared the leading preventable cause of in-hospital death by the US Surgeon General, Centers for Disease Control and Prevention, and Centers for Medicare and Medicaid Services. Risk-stratification and targeted-prophylaxis are keys to prevention. Pharmacoprophylaxis is highly effective, though with an increased risk of bleeding. Therefore, decisions for VTE prophylaxis must balance the predicted risk of VTE against the predicted risk of bleeding in individual patients. While there are several risk assessment models (RAMs) to predict the risk of VTE, there are only two RAMs proposed to predict bleeding from pharmacoprophylaxis and neither has been validated in a large, unselected cohort of general hospitalized patients. We analyzed all consecutive unique hospital admissions of surgical and nonsurgical patients at all 1298 VA facilities nationwide between January 2016 and December 2021. IMPROVE and Consensus RAM scores were generated using data from a repository of the unified national VA electronic medical record. We assessed the predictive ability of the RAMs for bleeding within 30, 60, and 90 days after admission and compared their performance in surgical and nonsurgical patients by computing the areas under their respective receiver operating-characteristic curves (AUC). We analyzed 1,228,448 patients; 26.5% (n = 324,959) surgical and 73.5% (n = 903,489) nonsurgical. 68,372 (5.6%) patients had major bleeding (as defined by the International Society on Thrombosis and Hemostasis) within 90 days postadmission (surgical 5.0%, nonsurgical 5.8%). IMPROVE scores ranged from 0 to 22 (median, 3.5; interquartile range, 2.5-5.0); Consensus scores ranged from −5.60 to −1.21 (medina, −4.93; interquartile range, −5.60 to −4.93). Higher scores were associated with higher bleeding rates, however, the ability of either RAM to predict 90-day bleeding was no better than a coin toss (AUCs: IMPROVE, 0.61: Consensus, 0.59), and was similarly low at 30 and 60 days postadmission (Table). Both RAMs performed better in surgical vs nonsurgical patients (AUCs: IMPROVE, 0.64 vs 0.61; P < .001; Consensus, 0.63 vs 0.58; P < .001) (Figure). In this first known validation study evaluating existing RAMs for bleeding from pharmacoprophylaxis in an unselected cohort, increasing scores correlated with bleeding rates; though both RAMs had low predictive ability for major bleeding postadmission. Both RAMs predicted bleeding better in surgical vs nonsurgical patients, but the differences were of limited clinical importance. Further studies are urgently required to improve existing bleeding RAMs to facilitate systematic decision-making for VTE prophylaxis in hospitalized patients.TableArea under receiver operating characteristic curves (AUC) for the IMPROVE and Consensus Bleeding risk assessment models (RAMs) at different follow-up intervalsTime from hospital admission, daysImprove bleeding RAMConsensus bleeding RAMAUC (95% confidence intervals)P valueAUC (95% confidence intervals)P value0-300.62 (0.62-0.62)<.0010.61 (0.60-0.61)<.0010-600.62 (0.61-0.62).0720.60 (0.60-0.60).0120-900.61 (0.61-0.62)1.00(ref)0.59 (0.59-0.60)1.00(ref) Open table in a new tab

Empowering tomorrow, controlling today: A multi-criteria assessment of distribution grid tariff designs

The energy transition is in full development with an ever-growing omnipresence of low voltage grid-connected distributed energy resources which cause congestion in the low voltage (LV) distribution grid. Two plausible solutions to network congestion issues are increased grid control (flexibility) or capacity power reinforcements. Electricity distribution tariffs are a promising intersection of these solutions as pricing structures that match consumer patterns can influence demand and grid investments can be recovered by cost-causal pricing.Designing electricity grid tariffs is complex due to its multi-disciplinary character. As previous research has addressed the singular aspects of distribution tariff structures, it is necessary to assess the performance of tariff designs from a multitude of perspectives. For the aforesaid reason, the goal of this paper is to investigate which distribution grid tariffs perform better by drawing upon a complete and coherent set of stakeholder-relevant criteria on all multi-disciplinary objectives of distribution tariff structures and assessing those structures with systematic decision theory. Results based on a systematic literature review, a TOPSIS multi-criteria decision assessment and statistical uncertainty analyses show that tariff performance significantly increases for capacity real-time pricing tariffs, capacity-based rates with Paris-Metro pricing and capacity peak load contribution charges.

Modelling cybersecurity regulations for automated vehicles

Technological advancements in Connected and Automated Vehicles (CAVs), particularly the integration of diverse stakeholder groups (communication service providers, road operators, automakers, repairers, CAV consumers, and the general public) and the pursuit of new economic opportunities, have resulted in the emergence of new technical, legal, and social challenges. The most pressing challenge is deterring criminal behaviour in both the physical and cyber realms through the adoption of CAV cybersecurity protocols and regulations. However, the literature lacks a systematic decision tool to analyze the impact of the potential cybersecurity regulations for dynamically interacting stakeholders, and to identify the leverage points to minimise the cyber-risks. To address this knowledge gap, this study uses systems theory to develop a dynamic modelling tool to analyze the indirect consequences of potential CAVs cybersecurity regulations in the medium to long term.It is hypothesized that CAVs Cybersecurity Regulatory Framework (CRF) is the property of the entire ITS stakeholders. The CRF is modelled using the System Dynamic based Stock-and-Flow-Model (SFM) technique. The SFM is founded on five critical pillars: the Cybersecurity Policy Stack, the Hacker's Capability, Logfiles, CAV Adopters, and intelligence-assisted traffic police. It is found that decision-makers should focus on three major leverage points: establishing a CRF grounded on automakers' innovation; sharing risks in eliminating negative externalities associated with underinvestment and knowledge asymmetries in cybersecurity; and capitalising on massive CAV-generated data in CAV operations. The formal integration of intelligence analysts and computer crime investigators to strengthen traffic police capabilities is pivotal. Recommendations for automakers include data-profiteering in CAV design, production, sales, marketing, safety enhancements and enabling consumer data transparency.Furthermore, CAVs-CRF necessitate a balanced approach to the trade-off between: i) data accessibility constraints on CAV automakers and ITS service providers; ii) regulator command and control thresholds; iii) automakers' business investment protection; and iv) consumers' data privacy guard.

Geotechnical Project Contract Management System

The contract management system is based on information technology and uses the advanced management ideas of modern enterprises to provide enterprises with all-round and systematic decision-making, planning, control and business performance evaluation of contract management platforms. With the advancement of science and technology, a perfect contract management system has become an important symbol of the healthy operation of the company. However, contract management can not be fully operated in most companies, and improving contract management requires the support of company resources, so how to adjust the contract management completely and scientifically is a major problem. Adhering to the tenet of "serving small and medium-sized enterprises", "Contract Management Expert CMExpert" has been successfully researched and developed, effectively solving the contradiction between simple and powerful and powerful, which provides nearly 100 contract templates, perfect combination of WORD, EXCEL, ACCESS SOFTWARE, CAN IMPORT THE PREVIOUS WORD FORMAT CONTRACT INTO THE SYSTEM, PROTECT THE USER'S INVESTMENT, SO THAT HALF AN HOUR CAN BE USED SMOOTHLY. Combined with the actual situation of various companies in China, the contract management system integrates financial management and project management into contract management, truly showing the purpose of optimizing the company's resources; The automatic contract settlement reminder effectively protects the company's capital operation security; The statistics function enables dragging and dropping fields to achieve different statistics, which really simplifies the tedious statistical process and is very efficient. This system combines the existing contract management, makes some of our adjustments, and can be well applied to some enterprise companies.