Information For Decision Research Articles

Path planning method for Camellia oleifera forest trenching operation based on human-robot collaboration

In order to realize the independent and safe operation of trenching robot in Camellia oleifera forest, operation path planning is the key link. Aiming at the problem of trenching operation path planning in complex dynamic Camellia oleifera forest environment, a trenching operation path planning method of Camellia oleifera forest based on human-robot collaboration was proposed. In order to meet the requirements of trenching operation in Camellia oleifera forest, the ring trenching operation path was planned around Camellia oleifera tree; Aiming at the unknown obstacles in the dynamic Camellia oleifera forest environment, the digital twin technology was used to map the dynamic Camellia oleifera forest environment into the twin space in real time, assist the operator to recognize the dynamic Camellia oleifera forest environment information and issue decision instructions. According to the man-machine collaborative decision-making instructions and dynamic environmental information, the twin environment model of Camellia oleifera forest was updated in real-time, and the collision detection method combining virtual and real was adopted to avoid collision and re-plan the trenching operation path. The experimental results show that the repetition rate of the trenching operation path of the Camellia oleifera forest planned by this method is reduced by 1.64 percentage points, the slope standard deviation is reduced by 40.365 %, and the operation path is more stable; In addition, the re-planning of the operation path of the trenching robot based on digital twin under human-robot cooperation prevents collision with the dynamic obstacles of the Camellia oleifera forest, which provides a new idea for the intelligentization of the trenching operation of the Camellia oleifera forest.

Male Pagurus minutus hermit crabs use multiple types of information in decisions to give up male–male contests

Organisms use information to make adaptive decisions in various contexts, including aggression. Potentially weaker, but better-informed, contestants should give up earlier to reduce fighting costs by using information related to their own lower success such as their size relative to their opponent and past contest outcomes to make this choice. Here, we examined whether intruders of the hermit crab Pagurus minutus could use information about their (1) smaller size, (2) past contest defeats, (3) opponent’s past wins, or (4) relationship in the dominance hierarchy to their opponent when making a decision to give up during male–male contests for a female. In all trials, we randomly matched a smaller intruder with a larger opponent that was guarding a female. Our analyses suggest that P. minutus intruders can use all four types of information to decide whether to give up a contest without escalation or decrease its duration after escalation; it is the first species of Pagurus reported to do so, and the second reported to be able to distinguish familiar opponents from others in the context of male–male contests. These findings demonstrate the importance of cognitive abilities in minimizing costs when competing for vital resources.

The evolutionary characteristics of temperature change in China during 1961–2020

AbstractThe changes of annual average, minimum and maximum temperature (Tavg, Tmin and Tmax) in China over 1961–2020 were examined based on the Chinese gridded observation dataset (CN05.1), and the relative contributions of Tmin and Tmax to the tendency of Tavg were addressed. The results show that the nationwide mean warming rate reached 0.29°C·decade−1 for Tavg, 0.39°C·decade−1 for Tmin and 0.24°C·decade−1 for Tmax, respectively, from 1961 to 2020, which depended on temporal windows. The relative contributions of Tmin and Tmax to the trends of Tavg over China during 1961–2020 are 56% and 44%, respectively. On the spatial scale, the majority of China exhibits a greater relative contribution of Tmin than Tmax, except for some areas along the Hu Huanyong Line. As in the case of the warming rate, the temporal patterns of the magnitude of the relative contribution of Tmin and Tmax are also identified. For the first 30 years (1961–1990), Tmin is the primary contributor to changes in the trend of Tavg over a broad area excluding the Qinling Mountain‐Huai River to the Nanling Mountain. The national average magnitudes of the relative contribution of Tmax is slightly larger than Tmin in the latter 30 years (1991–2020). The areas with larger contributions of Tmax are mainly scattered along Hu Huanyong Line, the southern part of the Qinghai‐Tibet Plateau and the patchy parts of Northwest China. Changes in downward longwave radiation, solar radiation, cloud cover, land use, soil moisture, atmospheric circulation and urbanization likely accounted for much of the trend difference of Tmin and Tmax, thereby leading to various relative contributions across different regions during different periods. The warming mechanisms in China have been subjected to novel changes. The study could provide information for decision making on scientific response to warming.

Cognitive ability and voting behaviour in the 2016 UK referendum on European Union membership.

On June 23rd 2016 the UK voted to leave the European Union. The period leading up to the referendum was characterized by a significant volume of misinformation and disinformation. Existing literature has established the importance of cognitive ability in processing and discounting (mis/dis) information in decision making. We use a dataset of couples within households from a nationally representative UK survey to investigate the relationship between cognitive ability and the propensity to vote Leave / Remain in the 2016 UK referendum on European Union membership. We find that a one standard deviation increase in cognitive ability, all else being equal, increases the likelihood of a Remain vote by 9.7%. Similarly, we find that an increase in partner's cognitive ability further increases the respondent's likelihood of a Remain vote (7.6%). In a final test, restricting our analysis to couples who voted in a conflicting manner, we find that having a cognitive ability advantage over one's partner increases the likelihood of voting Remain (10.9%). An important question then becomes how to improve individual and household decision making in the face of increasing amounts of (mis/dis) information.

Validation of Decision-Making Experimental Tasks in a Social Commerce Environment

This study aimed to validate two purchase decision experimental tasks in a social commerce environment. This refers to online interactions and their contributions to users on websites in order to assist in their acquisition of goods and services. For that purpose, a quasi-experiment was developed to simulate the decision invoved in making a hotel reservation and purchasing a TV, using the eye tracking technique to analyze the processing of information. Task validation comprised the operationalization of the eye tracker, validation with specialists, and a pilot test. The fixation metrics and heat maps identified the areas of interest that received the most visual attention from the participants. It should be noted that the products’ recommendations were the most considered criterion. This study contributes to understanding the search for information and purchase decisions in social commerce.

A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment

Abstract We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a healthcare technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the healthcare system. The framework takes into account the cost of decision errors in the payoff function, allowing the decision maker to compute the cost of taking a decision when evidence is far from the optimal decision triggers. We show, using a real-world cost-effectiveness study based on clinical trial evidence, how rules derived from a sequential adaptive design approach can lead to quicker decisions when compared to the value of information decision framework. Our application shows that a sequential approach has the potential to lead to quicker decisions, higher payoffs, and better health outcomes.

An extensible complex spherical fuzzy decision making model based selection framework for the food waste treatment method

Food waste has been recognized as one of the most severe environmental problems globally. Food waste management is an effective procedure for achieving global food security, which requires rational and efficacious waste treatment and disposal techniques. Thus, selecting proper and suitable food waste treatment methods is one of the most significant food waste management issues. However, existing selection frameworks based on decision-making models for food waste treatment methods can seldom handle this issue with complicated uncertainty and periodic information. Accordingly, this paper aims to generate a complex spherical fuzzy information-based selection framework for evaluating the food waste treatment method with mutual support and periodicity decision information. First, the complex spherical fuzzy sets express subjective and uncertain selection information. Then, the power weighted average (PWA) operator for complex spherical fuzzy numbers is introduced to fuse the decision information of mutual support from experts that can reflect the impact of information with supportive relationships. Next, an extensible ARAS (Additive Ratio Assessment) approach-based selection framework is constructed to determine the most sustainable food waste treatment method in which the distance-measure-based weighting method is utilized to calculate the importance degrees of criteria. Finally, a case study of evaluating food waste treatment methods is expounded to display the application of the proposed framework. The result indicates that the alternativea2(Anaerobic digestion) is the most sustainable method for food waste treatment with the largest utility degree (0.920). After that, the comparison studies are also performed to illustrate further the reasonability and effectiveness of the proposed selection framework. The result shows that stakeholders can adopt the proposed methodology to improve the performance of the evaluation of food waste treatment.

Faculty perspectives and values toward mathematics and science content information used in teacher preparation admissions processes

AbstractThis qualitative study examines the information collected about applicants to mathematics or science teacher preparation programs (MSTPPs) and how university faculty perceive and value this information in admissions decisions. Based on document review and interviews with MSTPP faculty and admissions directors, we found that broad measures of mathematics and science content background (e.g., achievement test scores, past mathematics and science courses taken) were used more frequently than information on applicants‐specific mathematics and science content knowledge and dispositions. In many cases, application components (such as interviews and personal essay statements) were perceived by faculty to be conducive to surfacing applicants‐content knowledge and dispositions; however, they were not constructed or employed in a way that afforded the obtainment of this information. We highlight salient examples of MSTPPs‐collection and use of information related to mathematics and science and discuss implications for TPP admissions processes.

Outlier detection for partially labeled categorical data based on conditional information entropy

Labeling a large amount of data is exceptionally costly and practically infeasible, and thus available data may have missing labels. In this article, we investigate outlier detection for partially labeled categorical data based on conditional information entropy. Firstly, the equivalence class in a partially labeled categorical decision information system (p-CDIS) is introduced, so that the missing labels can be predicted by use of conditional probability. Then, conditional information entropy in a p-CDIS is calculated, which provides a more comprehensive measure of uncertainty. Additionally, the relative information entropy and relative cardinality in a p-CDIS are proposed. Next, the degree of outlierness and the weight function are presented to find outlier factors. Finally, an outlier detection method in a p-CDIS based on conditional information entropy is proposed, and a corresponding conditional information entropy algorithm (CEOF) is designed. To evaluate the stability of the CEOF algorithm, experiments are performed on ten UCI Machine Learning Repository datasets. Compared with five other algorithms, the proposed method is shown to have good effectiveness and adaptability for categorical data.

Cluster-based fusion detection of soft and hard decisions for underwater non-cooperative targets

Underwater target detection performs the essential contribution in marine exploration. Unfortunately, the little energy available in the underwater environment makes detection difficult. To address the aforementioned obstacles, this paper proposed a detection scheme based on soft and hard decision fusion. The detection scheme consists of two stages: network topology control and the fusion of local and global detection. The first stage, an improved low energy adaptive clustering hierarchy (LEACH) algorithm is introduced into the underwater sensor network to choose the most suitable cluster heads and to introduce relay nodes to establish multi-hop routes between clusters, effectively balancing node energy consumption and extending network lifetime. With the network topology, the generalized likelihood ratio rule (GLRT) is employed by each sensor to detect the underwater non-cooperative target and make the corresponding decision during the second phase. Specifically, the member nodes make the multi-bit local soft decision, and then the cluster head node fuses the decision information from each member nodes to make the one-bit hard decision and transmits it to the fusion center, and finally the fusion center makes the global decision, such that the tradeoff between detection accuracy and energy consumption may be balanced by generating the soft and hard decision fusion technique. Simulations show that the strategy maintains detection performance extremely comparable to that of centralized and conventional multi-bit soft decisions while eliminating communication overhead.

Optimization of Emergency Alternatives for Hydrogen Leakage and Explosion Accidents Based on Improved VIKOR

Hydrogen leakage and explosion accidents have obvious dangers, ambiguity of accident information, and urgency of decision-making time. These characteristics bring challenges to the optimization of emergency alternatives for such accidents. Effective emergency decision making is crucial to mitigating the consequences of accidents and minimizing losses and can provide a vital reference for emergency management in the field of hydrogen energy. An improved VIKOR emergency alternatives optimization method is proposed based on the combination of hesitant triangular fuzzy set (HTFS) and the cumulative prospect theory (CPT), termed the HTFS-CPT-VIKOR method. This method adopts the hesitant triangular fuzzy number to represent the decision information on the alternatives under the influence of multi-attributes, constructs alternatives evaluation indicators, and solves the indicator weights by using the deviation method. Based on CPT, positive and negative ideal points were used as reference points to construct the prospect matrix, which then utilized the VIKOR method to optimize the emergency alternatives for hydrogen leakage and explosion accidents. Taking an accident at a hydrogen refueling station as an example, the effectiveness and rationality of the HTFS-CPT-VIKOR method were verified by comparing with the existing three methods and conducting parameter sensitivity analysis. Research results show that the HTFS-CPT-VIKOR method effectively captures the limited psychological behavior characteristics of decision makers and enhances their ability to identify, filter, and judge ambiguous information, making the decision-making alternatives more in line with the actual environment, which provided strong support for the optimization of emergency alternatives for hydrogen leakage and explosion accidents.

Visual attention and memory in professional traders

Professional traders need to process a large amount of visual information in their daily activity to judge how risky it is to trade specific investment products. Despite some studies investigating the effects of display clutter on traders, visual attention and memory were never investigated in controlled experimental tasks in this population. Following a preliminary study with 30 participants, visual selective attention and visual working memory were measured and compared between two groups of 15 traders and 15 non-traders (salespeople, acting as a control group) from a large-scale banking group in three experimental tasks measuring selective attention in complex visual contexts, simulating display clutter situations (Visual search), cognitive interference (Stroop task), and a delayed recall visual working memory task. In the Visual search task, traders displayed faster response times (RTs) than non-traders for small display sets, while their performance overlapped for large sets. In the Stroop task, traders showed faster RTs than non-traders but were nevertheless affected by cognitive interference. The memory task highlighted no significant differences between the groups. Therefore, this study found an advantage in traders’ attention when processing visual information in small sets with no retention. This result could influence trading activity—determining an immediate use of relevant visual information in decision making—and traders’ display layout organization.

Distributed algorithm for social cost minimization with second-order nonlinear dynamics over weight-unbalanced digraphs

In this paper, the emerging social cost minimization framework of second-order nonlinear systems over weight-unbalanced digraphs is studied. It provides a general framework containing several important classes of problems. Therein each local cost function depends on a global decision variable consisting of all the agents’ decisions not limited to the form of aggregative terms. Under the partial decision and cost function information setting, distributed algorithms are designed based on the state feedback and left eigenvector estimation mechanism to overcome the challenges posed by second-order nonlinear systems and weight-unbalanced digraphs. The asymptotic convergence of the algorithm is demonstrated via the Lyapunov stability theory. Finally, numerical simulations of the vehicle monitoring problem are provided to support the algorithm design.

Molecular Eye: A System for Precise Diagnosis and Treatment of Major Clinical Diseases Based on Molecular Probe Technology.

With the flourishing development of precision medicine, theranostics, generally recognized as the integration of diagnosis and treatment, has emerged as a prominent trend in clinical research. However, theranostics primarily emphasizes the end result of integration, without providing sufficient details on how precise diagnosis and synergetic individualized treatment could be achieved and what clinical challenges could be effectively addressed in clinical practice. Molecular probe technology provides a robust method to bridge the gap between theory and practice. Through meticulous design of the chemical structure, imaging labels or drugs were conjugated to tumor-targeting peptides, antibodies, or inducers to form molecular probes, which allow a seamless switch between targeted intervention and targeted imaging with consistency in time, space, and biodistribution. Thus, this review proposes a concept called "molecular eye", which refers to a comprehensive system for precise diagnosis and treatment of major clinical diseases based on molecular probe technology. This medical system emphasizes the chemical basis of probe development and optimization, which can provide precise actionable information for clinical decision making, allow molecular-targeted therapy, expand the indications of old therapy, and accelerate the regulatory approval of molecular drugs. "Molecular eye" resembles the piercing eye of the Monkey King, which can detect previously "invisible" diseases and facilitate disease diagnosis, treatment, real-time evaluation, and pathology research, guiding drug development. The emergence of the "molecular eyes" will provide opportunities and challenges in the fields of clinical practice and medical research and propel the progression of contemporary medicine toward precision medicine.

Target-path planning and manufacturability check for robotic CLT machining operations from BIM information

Mass timber is one of the trending construction styles in the last years thanks to its sustainability and offsite manufacturing properties, where cross laminated timber (CLT) is the most common material used. In an effort to link design and manufacturing information in a single location, this study proposes a generative process planning algorithm for CLT machining in robotic environments. The algorithm focuses on automatic feature-based interpretation of the geometry of CLT panels to obtain the targets required to guide the robots for its machining. The method developed detects primitives geometries of the CLT panels, determines the appropriate operations (either sawing, drilling, or milling), select the robot based on manufacturing capabilities (reach and tool availability), and generates the target-path planning for its machining process. The proposed method is tested in a robotic machining station for CLT panels simulated in RobotStudio® as a case study. The results showcase the capabilities of the proposed algorithm to provide manufacturing results out of the process planning process from geometric information available at the design stage. These results include process duration, path planning, resource allocation and utilization. This study provides a framework to include manufacturing information in design decisions to facilitate planning or cost estimations and anticipate issues downstream generated during the design phase.

Rescuing Drugs That are Discontinued in Adult Oncology Development for the Benefit of Children and Adolescents With Cancer - AnACCELERATE Multistakeholder Consensus.

Better therapies for childhood cancer remain an unmet need to improve the dismal prognosis of certain malignancies and to reduce the burden of toxicity. Rescuing discontinued or shelved drugs for children, adolescents, and young adults is a strategy to identify new uses for approved or investigational medicines outside the scope of their original medical indication. Our proposed multistakeholder consensus focuses on the development of innovative, patent-protected targeted agents, sourced from previously shelved or discontinued programs that have the potential to provide significant benefit to underserved patient populations, with unmet medical needs. There are several challenges to continuing/rescuing drugs for pediatric oncology development, which include the lack of information for decision making, corporate strategy considerations underlying the decision to invest in pediatric development, and the contracting and technology transfer complexities required to enable divestment and subsequent development. The multistakeholder approach for drug development has the advantage of conveying a consensus among academia, patient advocates, and importantly industry itself. We propose three areas of action to facilitate rescuing potentially beneficial drugs for children and adolescents with cancer: (i) initiatives to provide information to companies considering developing these drugs and a standards framework; (ii) incentives both in Europe and in the United States to encourage companies to develop pediatric-only drugs, with the reform of the EU Pharmaceutical Legislation posing an important opportunity; and (iii) communication of the issues to all stakeholders. Ultimately, this will benefit children and adolescents with cancer.

Water quality assessment of Kuwano River, Basti (U.P.) India, with reference to statistical analysis

The present study analyses water quality parameters in the Kuwano River, Basti district, using correlation and regression analysis to establish relationships between variables and provide a comprehensive understanding of the factors influencing water quality. Kuwano is the main river that flows through Basti city. The water samples were collected at three locations. The values of different physicochemical parameters of the river water sample were found to be dependent on the hydrology of the area. The pH was strongly associated with TDS (r = 0.885), DO (r = 0.744), COD (r = 0.969), TH (r = 0.806), and Mg (r = 0.944). The biological oxygen demand (BOD) (-0.345), nitrate (-0.235), and calcium (-0.128) exhibited an inverse correlation with total dissolved solids (TDS), whereas nitrate and calcium had a positive correlation with all other physicochemical parameters. The mean TDS value of the river water sample (81.2) was within the permissible limit for drinking water. The total coliform counts established a negative correlation with most of the parameters studied, e.g., dissolved oxygen (-0.628), BOD (-0.983), chemical oxygen demand (-0.194), total hardness (-0.549), nitrate (-0.955), Ca (-0.918) and Mg (-0.279). The study's findings may provide practical information for decision making in river pollution management.

Does information disclosure alleviate overcrowding? An empirical study based on large-scale COVID-19 nucleic acid test

Nucleic acid testing is a critical means of combating COVID-19. However, the overcrowding caused by large-scale nucleic acid testing increases the risk of virus infection and challenges the existing health supply chain. Aiming at alleviating overcrowding, this paper studies the determinants of people’s choice of testing sites. The results show that physical distance and decision information exert positive influences, whereas the impact of urgency is not significant. The results of a specially designed computer simulation program verify the empirical study. It was also found that information disclosure does not affect the overall completion rate of the nucleic acid testing and the average time taken to complete the testing. However, it aggravates overcrowding in queues. The findings overturn the previous view that information disclosure improves social efficiency. This paper acts as a reference for governments dealing with overcrowding during large-scale social mobilization.

Q-Multi Cubic Pythagorean Fuzzy Sets and Their Correlation Coefficients for Multi-Criteria Group Decision Making

Q-multi cubic Pythagorean fuzzy sets (Q-mCPFSs) are influential, effective and symmetrical for representing uncertain and imprecise information in decision making processes. Q-mCPFSs extend the concept of Q-multi fuzzy sets by introducing the notion of cubic Pythagorean membership functions, which provide a more flexible and accurate representation of uncertainty. First, we will introduce the concepts of Q-mPFSs and Q-mIVPFSs. With the combination of Q-mPFSs and Q-mIVPFSs, we will present the concept of Q-mCPFSs. Then, we propose two correlation coefficients for Q-mCPFSs. Furthermore, multi-criteria GDM methods using Q-mCPFSs will be discussed, highlighting their advantages in handling uncertain and imprecise information. Finally, we will provide an illustrative example, to demonstrate the effectiveness of Q-mCPFSs in decision making processes.The main contributions of the Q-mCPFS information expression, correlation coefficients and GDM methods in the Q-mCPFS setting of both uncertainty and certainty are thus highlighted in this study. These contributions provide valuable insights into the application of Q-mCPFSs in decision making processes, allowing decision makers to make more informed and effective choices. Additionally, the illustrative example serves as a practical demonstration of how these methods can be applied in real-world scenarios, further emphasizing their effectiveness and relevance.

Special Issue on Algorithms in Decision Support Systems Vol.2

Currently, decision support systems (DSSs) are essential tools that provide information and support for decision making on possible problems that, due to their level of complexity, cannot be easily solved by humans [...]