TRG-DAtt: The Target Relational Graph and Double Attention Network Based Sentiment Analysis and Prediction for Supporting Decision Making

The management of public opinion and the use of big data monitoring to accurately judge and verify all kinds of information are valuable aspects in the enterprise management decision-making process. The sentiment analysis of reviews is a key decision-making tool for e-commerce development. Most existing review sentiment analysis methods involve sequential modeling but do not focus on the semantic relationships. However, Chinese semantics are different from English semantics in terms of the sentence structure. Irrelevant contextual words may be incorrectly identified as cues for sentiment prediction. The influence of the target words in reviews must be considered. Thus, this paper proposes the TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information to support decision making. First, dependency tree-based TRG is introduced to independently and fully mine the semantic relationships. We redefine and constrain the dependency and use it as the edges to connect the target and context words. Second, we design dependency graph attention network (DGAT) and interactive attention network (IAT) to form the DAtt and obtain the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information. Next, the target emotional enhancement features obtained by the DGAT are input to the IAT. The influence of each target word on the review can be obtained through the interaction. Finally, the target emotional enhancement features are weighted by the impact factor to generate the review's emotional features. In this study, extensive experiments were conducted on the car and Meituan review data sets, which contain consumer reviews on cars and stores, respectively. The results demonstrate that the proposed model outperforms the existing models.

Priority selection of agro-meteorological parameters for integrated plant diseases management through analytical hierarchy process

To understand the influence of agro-meteorological parameters to take decisions related to various factors in an integrated plant disease management, it becomes vital to carry out scientific studies on the factors affecting it. The different agro-meteorological parameters namely temperature, humidity, moisture, rain, phenological week, cropping season, soil type, location, precipitation, heat index, and cloud coverage have been considered for this study. Each parameter has been allocated the ranking by using a technique called analytical hierarchical process (AHP). The parameter priorities are determined by calculating the Eigenvalues. This helps to make decisions related to integrated plant disease management where the prediction of plant disease occurrence, yield prediction, irrigation requirements, and fertilization recommendations can be taken. To take these decisions which parameters are good indicators can be identified using this method. The parameters majorly contribute to plant diseases and pest management decision making while delivers minor contribution in irrigation and fertilizer management related decision making. The manual results are compared with software generated results which indicates that both the results correlate with each other. Therefore, AHP technique can be successfully implemented for prioritizing agro-meteorological parameters for integrated plant diseases management as the results for both levels are consistent (consistency ratio < 0.1).

Incidence of histopathological variants of oral squamous cell carcinoma: An institutional study

Squamous cell carcinoma is the most important and the most common malignant mucosal neoplasm of the head and neck accounting for over 90% of all malignancies. Conventional oral Squamous cell carcinoma is frequently present in general cancerous conditions. It is bundled up with six different variants. Histomorphologically every variant shows a unique appearance. This raises an opportunity for the different diagnostic consideration with the precise management decision.All cases of OSCC reported at our institution Dentopath Pathologies Amravati in past two months were scrutinized. Representative sections containing the full thickness of the tumor were used for histopathological grading. The structure and identification of carcinomas were done microscopically by two expert dentopathologist.In the present study, we screened 100 slides of a conventional epithelial cell carcinoma. Amonst which 30 Slides showed the verrucous carcinoma. On 5 slides adenoid squamous cell carcinoma were observed. Incidence of Papillary squamous cell carcinoma and basaloid squamous cell carcinoma was only 1 out of 100 slides each. Whereas, the spindle cell/sarcomatoid carcinoma was observed on 2 slides. Adenosquamous carcinoma is the rarest variant and hence no incidence of this carcinoma were observed in our study. The behavior of the OSCC varies amongst due to the presence of different morphological type of tumor. A few studies on OSCC malignancy grading with different clinical parameters were made. In the present study different types of variants are seen according to their histopathological appearances.Histopathological knowledge is very important for the precise diagnosis. Squamous cell carcinoma is the most common neoplasm of oral cavity. However, variants of the same show very less frequency. Hence, it became challenge for the appropriate diagnosis as many times a misdiagnosis affects the course of treatment of the patient

AN ONLINE REVIEWS INFORMATION FUSION METHOD AND ITS APPLICATION TO PUBLIC PROPERTY SERVICE QUALITY EVALUATION

With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

Numerical Studies of Statistical Management Decisions in Conditions of Stochastic Chaos

The research presented in this article is dedicated to analyzing the acceptability of traditional techniques of statistical management decision-making in conditions of stochastic chaos. A corresponding example would be asset management at electronic capital markets. This formulation of the problem is typical for a large number of applications in which the managed object interacts with an unstable immersion environment. In particular, this issue arises in problems of managing gas-dynamic and hydrodynamic turbulent flows. We highlight the features of observation series of the managed object’s state immersed in an unstable interaction environment. The fundamental difference between observation series of chaotic processes and probabilistic descriptions of traditional models is demonstrated. We also present an additive observation model with a chaotic system component and non-stationary noise which provides the most adequate characterization of the original observation series. Furthermore, we suggest a method for numerically analyzing the efficiency of conventional statistical solutions in the conditions of stochastic chaos. Based on numerical experiments, we establish that techniques of optimal statistical synthesis do not allow for making effective management decisions in the conditions of stochastic chaos. Finally, we propose several versions of compositional algorithms focused on the adaptation of statistical techniques to the non-deterministic conditions caused by the specifics of chaotic processes.

Titer and Distribution of Candidatus Phytoplasma pruni in Prunus avium

‘Candidatus Phytoplasma pruni’ infection in cherries causes small, misshapen fruit with poor color and taste, rendering the fruit unmarketable. However, this is a disease with a long development cycle and a scattered, non-uniform symptom distribution in the early stages. To better understand the biology as well as the relationship between pathogen titer and disease expression, we carried out seasonal, spatial, and temporal examinations of ‘Ca. P. pruni’ titer and distribution in infected orchard-grown trees. Sequential sampling of heavily infected trees revealed marked seasonal patterns, with differential accumulation in woody stem and leaf tissues, and most notably within fruit in the early stages of development from bloom to pit hardening. Furthermore, mapping phytoplasma distribution and titer in trees at different stages of infection indicated that infection proceeds through a series of stages. Initially, infection spreads basipetally and accumulates in the roots before populating aerial parts of the trees from the trunk upwards, with infection of specific tissues and limbs followed by an increasing phytoplasma titer. Finally, we observed a correlation between phytoplasma titer and symptom severity, with severe symptom onset associated with 3-4 orders of magnitude more phytoplasma than mild symptoms. Cumulatively, these data aid in accurate sampling and management decision making and furthers our understanding of disease development.

Aligned but not integrated: UK academic library support to mental health and well-being during COVID-19

PurposeTo discover how UK academic libraries sought to support student mental health and well-being during the COVID-19 pandemic.Design/methodology/approachThe data was from a 24-question survey of UK universities distributed in May 2021 which received 56 responses from 47 different Higher Education Institution libraries. Descriptive statistics are combined with thematic analysis of open text comments.FindingsLibraries were undertaking a wide range of activities, targeted chiefly at students and broadcast via Twitter, other social media and library web sites. The problem being addressed was the stresses of studying in the context of the pivot online and isolation caused by social distancing. Digital well-being seemed also to be an increased concern. COVID-19 had proved the value of digital support but created a number of challenges such as loss of physical space, communication barriers and lack of extra resource. The role had a somewhat informal place in the organisation. Overall library activities were aligned but not strongly integrated into institutional efforts.Research limitations/implicationsThis was a study in one specific national context with a relatively limited number of total responses. There could be a non-response bias where respondents were doing more than was typical in the sector.Originality/valueThe paper is one of the first papers to gather sector wide data and move beyond case studies of what individual libraries due to support to mental health and well-being. It also offers a case study of the impacts of COVID-19 on management pointing to its catalysing the digital shift, creating constraints on resources and communication and prompting the emergence of staff well-being as a consideration in management decision making.

Vulnerable, but still poorly known, marine ecosystems: how to make distribution models more relevant and impactful for conservation and management of VMEs?

Human activity puts our oceans under multiple stresses, whose impacts are already significantly affecting biodiversity and physicochemical properties. Consequently, there is an increased international focus on the conservation and sustainable use of oceans, including the protection of fragile benthic biodiversity hotspots in the deep sea, identified as vulnerable marine ecosystems (VMEs). International VME risk assessment and conservation efforts are hampered because we largely do not know where VMEs are located. VME distribution modelling has increasingly been recommended to extend our knowledge beyond sparse observations. Nevertheless, the adoption of VME distribution models in spatial management planning and conservation remains limited. This work critically reviews VME distribution modelling studies, and recommends promising avenues to make VME models more relevant and impactful for policy and management decision making. First, there is an important interplay between the type of VME data used to build models and how the generated maps can be used in making management decisions, which is often ignored by model-builders. We encourage scientists towards founding their models on: (i) specific and quantitative definitions of what constitute a VME, (ii) site conservation value assessment in relation to VME multi-taxon spatial predictions, and (iii) explicitly mapping vulnerability. Along with the recent increase in both deep-sea biological and environmental data quality and quantity, these modelling recommendations can lead towards more cohesive summaries of VME’s spatial distributions and their relative vulnerability, which should facilitate a more effective protection of these ecosystems, as has been mandated by numerous international agreements.

Smart Financial Management System Based on Data Ming and Man-Machine Management

To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.