Relevant Segments Research Articles

Conceptual framework of state support for innovative development of the national economy

Introduction. The paper represents a conceptual framework based on state support for innovative development of the national economy. The main directions of state institutional support and stimulation of innovations in the national macroe-conomic environment are determined. The main components of innovation in the national macroeconomic environment are identified. The purpose of the article is to form a conceptual basis for state support for innovative development of the national economy. Results. It is proved that the main factor of sustainable financial and economic development of the national economy in the conditions of transformation is scientific and technical progress. It was found out that the stimulation and provision of innovation and investment activity in the development of scientific and technological potential in the macroeconomic envi-ronment of the country is the main mission of state support for innovative development of the national economy. It is out-lined that the reproduction of knowledge is the basis of the innovation economy, which is impossible without an effective and efficient system of knowledge accumulation and their practical application, which, in turn, aims to preserve and develop the national environment for knowledge and competence generation. Elements of the system approach to the state support for innovative development of national economy are defined. Conclusions. It was outlined that the system approach requires the study of objects of state support that contribute to the innovative development of the national economy and the functioning processes of economic entities, taking into account the relationship of the relevant segments. It is substantiated that in the national innovation environment, measures of insti-tutional regulation of the innovation sphere of the country are determined by the nature of direct or indirect influence. The basic development principles of the national innovation infrastructure are determined. It is proved that ensuring the innova-tive development of the national economy in the context of the transformation of financial and economic processes is based on a combination of extensive and intensive indicators of the socio-economic system of the country. Key words: innovative development, national economy, financial and economic processes, state support.

Instrumental and affective communication with patients with limited health literacy in the palliative phase of cancer or COPD

BackgroundPatients have a ‘need to know’ (instrumental need) and a ‘need to feel known’ (affective need). During consultations with patients with limited health literacy (LHL) in the palliative phase of their disease, both the instrumental and the affective communication skills of healthcare providers are important. The study aims to explore instrumental and affective communication between care providers and LHL patients in the palliative phase of COPD or cancer.MethodsIn 2018, consultations between LHL patients in the palliative phase of cancer or COPD and their healthcare providers were video-recorded in four hospitals in the Netherlands. As there was no observation algorithm available for this setting, several items were created to parameterize healthcare providers’ instrumental communication (seven items: understanding, patient priorities, medical status, treatment options, treatment consequences, prognosis, and information about emotional distress) and affective communication (six items: hope, support, reassurance, empathy, appreciation, and emotional coping). The degree of each item was recorded for each consultation, with relevant segments of the observation selected and transcribed to support the items.ResultsConsultations between 17 care providers and 39 patients were video-recorded and analyzed. Care providers primarily used instrumental communication, most often by giving information about treatment options and assessing patients’ care priorities. Care providers assessed patients’ understanding of their disease less often. The patients’ prognosis was not mentioned in half the consultations. Within the affective domain, the care providers did provide support for their patients; providing hope, reassurance, empathy, and appreciation and discussing emotional coping were observed less often.ConclusionsCare providers used mostly instrumental communication, especially treatment information, in consultations with LHL patients in the palliative phase of cancer or COPD. Most care providers did not check if the patient understood the information, which is rather crucial, especially given patients’ limited level of health literacy. Healthcare providers did provide support for patients, but other expressions of affective communication by care providers were less common. To adapt the communication to LHL patients in palliative care, care providers could be less wordy and reduce the amount of information, use ‘teach-back’ techniques and pay more attention to affective communication.

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

UNSUPERVISED SEGMENTATION OF INDOOR 3D POINT CLOUD: APPLICATION TO OBJECT-BASED CLASSIFICATION

Abstract. Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds. Our contribution targets a low-level grouping beneficial to object-based classification. We argue that the use of relevant segments for object-based classification has the potential to perform better in terms of recognition accuracy, computing time and lowers the manual labelling time needed. However, fully unsupervised approaches are rare due to a lack of proper generalisation of user-defined parameters. We propose a self-learning heuristic process to define optimal parameters, and we validate our method on a large and richly annotated dataset (S3DIS) yielding 88.1% average F1-score for object-based classification. It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds.

Explainable Prediction of Medical Codes With Knowledge Graphs.

International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only translate the simple diagnosis descriptions into ICD codes. And it obscures the reasons and details behind specific diagnoses. Besides, the label (code) distribution is uneven. And there is a dependency between labels. Based on the above considerations, the knowledge graph and attention mechanism were expanded into medical code prediction to improve interpretability. In this study, a new method called G_Coder was presented, which mainly consists of Multi-CNN, graph presentation, attentional matching, and adversarial learning. The medical knowledge graph was constructed by extracting entities related to ICD-9 from freebase. Ontology contains 5 entity classes, which are disease, symptom, medicine, surgery, and examination. The result of G_Coder on the MIMIC-III dataset showed that the micro-F1 score is 69.2% surpassing the state of art. The following conclusions can be obtained through the experiment: G_Coder integrates information across medical records using Multi-CNN and embeds knowledge into ICD codes. Adversarial learning is used to generate the adversarial samples to reconcile the writing styles of doctor. With the knowledge graph and attention mechanism, most relevant segments of medical codes can be explained. This suggests that the knowledge graph significantly improves the precision of code prediction and reduces the working pressure of the human coders.

Operation Planning Tool Improves Quality, Efficiency of Offshore Risk Management

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195750, “Improving the Quality and Efficiency of Operational Planning and Risk Management With Machine Learning and Natural Language Processing,” by Claire Emma Birnie, SPE, Jennifer Sampson, and Eivind Sjaastad, Equinor, et al., prepared for the 209 SPE Offshore Europe Conference and Exhibition, Aberdeen, 3-6 September. The paper has not been peer reviewed. The complete paper discusses Equinor’s operation planning tool (OPT), developed to present planners with the technical conditions of a platform, identify potentially dangerous combinations of concurrent activities, and propose learnings from 8 years of incident recordings. The OPT provides a single interface detailing a plant’s technical conditions, all planned work orders, and relevant lessons from previous incidents. By reducing reliance on personal experience, the tool has improved risk identification and handling, achieved faster knowledge transfer to new employees, and focused cross-platform knowledge sharing. Introduction The operator’s requirement that all incidents or near-misses be reported has resulted in a database of over 108,000 incidents. Alongside lessons learned from previous incidents, planners must consider the suitability of concurrent jobs and any technical status that may affect the safety of executing a planned job at a specific time. Before 2018, planners had to manually consult eight data sources, each with a separate user interface (UI), to obtain an overview of the plant’s technical status, concurrent jobs planned, and relevant lessons learned from previous incidents. This work flow had two clear bottlenecks: Manual investigation of the separate data sources was time-consuming, often involving keyword searches or queries on structured data. No easy way existed to obtain an overview of full asset status, with discipline leaders focusing individually on their jobs. A high volume of planned jobs and the time-consuming nature of investigating multiple data sources dictated heavy reliance on personal experience of the production platform to accelerate searching of relevant data segments. However, this posed challenges to rapid onboarding of new employees and capture of cross-platform learnings. The OPT was developed to provide a single interface displaying data from all eight data sources. Using natural language processing (NLP) techniques, the tool leverages unstructured data to supplement structured data, creating additional information that previously did not exist in a machine-readable format. Because of the technical vocabulary used to describe offshore operations, language models had to be generated to capture descriptions of equipment used and tasks being performed. This information was used to help determine relevance of an incident to a planned activity.

Cognitive engagement during reading on digital tablet: Evidence from concurrent recordings of postural and eye movements.

The present study utilised a novel combination of eye movement and motion capture recordings to examine cognitive engagement during reading on a hand-held tablet computer. Participants read a multiple-page text with a specific task in mind and after reading recalled the main contents of text from memory. The results showed that head distance from screen was slightly shorter, and readers spent longer time reading task-relevant than irrelevant segments of text and had better memory for task-relevant than irrelevant text information, indicating that there are task-induced momentary changes in engagement during reading. Moreover, head motion and individual fixation durations decreased during the course of reading of relevant segments, and even though there was an overall increase in table motion during reading, the slope of this increase was steeper for irrelevant than relevant text segments. These results suggest that readers become more engaged with relevant and less engaged with irrelevant text segments across the text. The novel methodological combination of eye and postural movements seems to provide valuable information about cognitive engagement during reading in digital environments. The cumulation of evidence from this and previous studies suggests that reading on a tablet affords different interactions between the reader and the text than reading on a computer screen. Reading on a tablet might be more similar to reading on paper, and this may impact the attentional processes during reading.

Segmenting the Domestic Rural Tourists in India

Rural tourism in India is increasingly becoming competitive. Customers expect better services and unique experiences. It is therefore essential for the service providers to better understand the expectations of the visitors and carefully match their offers. Segmentation and targeting of the market are accepted principles of business and there has been a lot of research on the basis of segmentation. The primary objective of this article is to identify the profile of the different clusters (popularly known as segments) of motivations of tourists traveling within rural India. The intention is to decipher the causative clusters, which influences the tourists to visit the rural areas. These variables are used to identify the various rural domestic tourist market segments. A self-administered instrument was used to collect the data. Four factors have been identified through exploratory factor analysis: socialization, escape, rural experience, and self-indulgence. After identifying factors, K-means clustering was used to segment the market. The researchers have identified two relevant segments of domestic rural tourists. These are family retreaters and rural escapists. The study advances the understanding of the Indian domestic rural tourism market. It will help policymakers and practitioners to design products and marketing programs matching the expectations of these targeted markets.

Phenotypic and molecular characterization of macrolide resistance mechanisms among Streptococcus pneumoniae isolated in Tunisia.

Introduction. Streptococcus pneumoniae is responsible for many community infections, with the main ones being pneumonia and meningitis. Pneumococcus has developed increased resistance to multiple classes of antibiotics. The evolution of antibiotic resistance in pneumococcus was influenced by changes in serotype distribution under vaccine selection pressure.Aim. The aim of this study was to determine the genes involved in macrolide resistance, the antimicrobial susceptibility, the serotype distribution and the spread of international antibiotic-resistant clones among clinical isolates of S. pneumoniae.Methodology. We investigated 86 erythromycin-resistant S. pneumoniae strains isolated from respiratory (n=74) or non-respiratory (n=12) samples in Tunisia. Antimicrobial susceptibility was tested using the disk diffusion method. Macrolide-resistant strains were analysed by polymerase chain reaction (PCR) for ermA, ermB, mefA and msrD. We also investigated the macrolide resistance mechanisms in eight isolates (9.3%) by sequencing the L4 and L22 riboprotein-coding genes, plus relevant segments of the three 23S rRNA genes. Capsular serotypes were detected by multiplex PCR. Sequence types (STs) were explored using multilocus sequence typing (MLST).Results. Among the 86 studied strains, 70 (81.4 %) were resistant to penicillin G. The prevalent serotypes were 19F, 14, 19A and 23F. We observed that the cMLSB phenotype (66/86, 76.7%) was the most common in these pneumococci. In addition, ermB was the most frequent resistance gene. No mutation in ribosomal protein L22 or L4 or 23S rRNA was detected. Overall, 44 STs were identified in this study, including 16 that were described for the first time. Resistance to lincomycin, tetracycline and trimethoprim/sulfamethoxazole was observed in 55 (64 %), 34 (39.5 %) and 31 (36 %) isolates, respectively. Furthermore, an increase in fluoroquinolone use in particular may lead to the emergence of levofloxacin-resistant strains. Multidrug resistance was observed in 83 isolates (96.5%). Three global antibiotic-resistant clones were identified: Denmark14 ST230, Portugal19F ST177 and Spain9V ST156.Conclusion. This study shows that macrolide resistance among S. pneumoniae isolated in Tunisia is mainly related to target site modification. Our observations demonstrate a high degree of genetic diversity and capsular types among strains resistant to macrolides.

Developing a framework to support strategic supply chain segmentation decisions: a case study

There is a huge opportunity in mining operational data in the supply chain (SC) to support strategic segmentation decisions. This research has the objective of developing a framework to support strategic SC segmentation decisions. This research is exploratory in nature, with the methodology based on action research combined with a single empirical study in a large Portuguese multinational company. A data mining project, based on the CRISP-DM methodology, is adopted to develop the framework. The company had the strategic objective to move beyond a single make to order strategy towards a segmented SC strategy. By applying the framework, the most relevant criteria were identified (demand volume, demand variability, order corrections, delivery time window and delivery frequency). These were then used to identify four relevant segments each with a tailored SC strategy.

A Sparse Transformer-Based Approach for Image Captioning

Image Captioning is the task of providing a natural language description for an image. It has caught significant amounts of attention from both computer vision and natural language processing communities. Most image captioning models adopt deep encoder-decoder architectures to achieve state-of-the-art performances. However, it is difficult to model knowledge on relationships between input image region pairs in the encoder. Furthermore, the word in the decoder hardly knows the correlation to specific image regions. In this article, a novel deep encoder-decoder model is proposed for image captioning which is developed on sparse Transformer framework. The encoder adopts a multi-level representation of image features based on self-attention to exploit low-level and high-level features, naturally the correlations between image region pairs are adequately modeled as self-attention operation can be seen as a way of encoding pairwise relationships. The decoder improves the concentration of multi-head self-attention on the global context by explicitly selecting the most relevant segments at each row of the attention matrix. It can help the model focus on the more contributing image regions and generate more accurate words in the context. Experiments demonstrate that our model outperforms previous methods and achieves higher performance on MSCOCO and Flickr30k datasets. Our code is available at https://github.com/2014gaokao/ImageCaptioning .

Dual-Stage Attention Based Spatio-Temporal Sequence Learning for Multi-Step Traffic Prediction

Traffic prediction has great significance including but not limited to mitigating traffic congestion, reducing traffic accidents, and reducing waiting time. At the same time, traffic prediction, especially multi-step prediction, faces many difficulties including temporal correlations and spatial correlations. We propose a dual-stage attention based spatio-temporal sequence learning for multi-step traffic prediction which can not only express temporal correlation and spatial correlation, but also can adaptively learn the contribution weights of different related roads and historical moments. More specifically, for spatial dependencies, we first generate the input vector for each historical moment considering the information of relevant road segments by the method of spatial region of support and further add the first-stage attention termed spatial attention to automatically determine the weight of each relevant road segment for each historical moment. For temporal dependencies, we use LSTM based encoder-decoder networks to fully learn the temporal characteristic and make multi-step prediction considering temporal correlation between multi steps. We further add the second-stage attention termed temporal attention in the decoder part to automatically learn the contribution of different historical moments to each prediction moment. In addition, we consider external factors including weather and holidays and characterize their impacts using fully connected networks. Finally, the effectiveness of the proposed method is evaluated using traffic data in Hangzhou, China.

Methodological approaches to assessing the impact of agro-industrial enterprises on the development of territorial communities

The agro-industrial complex of Ukraine is one of the most relevant segments of the national economy, which provides an adequate level of food security of the regions and the state as a whole, as well as forms a significant amount of domestic expert material. However, institutional miscalculations of the state, underestimation of the peculiarities of the formation and functioning of domestic and foreign food markets, as well as ignoring the role of local governments and civil society institutions in rural development, led to agricultural enrichment, dominance of small-scale production, lack of motivation for integrated development formations, and depopulation of rural areas. The essence of modern EU agricultural policy in the third millennium and the need for our country to join it are studied. Methodological approaches to assessing the impact of agro-industrial enterprises on the integrated development of territorial communities in the context of decentralization of power are developed. The multifaceted nature of the process of functioning of agro-industrial production in the direction of ensuring the development of communities determines the influence of a significant number of factors. Generalizing scientific approaches in this area, all factors can be combined into several aggregate groups: production, consumption, social, institutional, and environmental orientation. The proposed methodological approaches to assessing such impact, in addition, provide an opportunity to develop areas for long-term mutually beneficial development of the entities in this system. The purpose of such promising parameters should be to develop mechanisms to ensure the production of high-quality food products, primarily for domestic consumption; to improve the level and quality of life of rural residents; to improve the system of management of territorial communities and formation of effective schemes of their interaction with business entities; to stimulate the activity of the population of the territories as a locomotive of these processes; to ensure the improvement of the ecological condition of settlements and environmental protection.

Conflictual relational demands of anaclitic and introjective depressive women

The depression is associated with typical ways of relating to and handling conflict with others. This study empirically examined the most frequent relational demands of female depressive patients, depending on their personality configuration and the characteristics of relevant segments within the session: change and stuck episodes. Ten psychotherapies (n = 230 sessions) were observed in order to identify, delimit and code relevant episodes within session (24 change episodes and 26 stuck episodes) using the CCRT-LU-S Category System (Albani et al., 2002), to determine patients’ relational demands. The results showed that introjective patients verbalized more relational demands on themselves as a relational object, and therefore more demands with a subject-subject direction. These patients expressed their needs and wishes centered on questioning themselves, to the detriment of generating significant interpersonal relationships. Furthermore, it was possible to predict a greater presence of relational demands referred to contents about “loving” during the change episodes, confirming that the most frequent themes in patients’ discourse were a strong wish to feel happy, despite their experiences of helplessness. These findings highlight the need for a more dimensional view of depression that takes into account patients’ personality configurations and their association with little and big outcomes.

Prefix Allomorphy and the Phonologisation of s‐Deletion in Latin

AbstractLatin s‐final prefixes show extensive allomorphy, which can be ascribed to different diachronic processes such as the drive to maintain contrast, analogical attraction and a sound change that deleted s before voiced consonants. We argue that the latter sound change led to three different phonologisation processes due to distributional imbalance in the relevant segments as well as a simultaneous sound change that also affected s. The evidence for the competing phonologisation processes is provided by the different alternations displayed by the s‐final prefixes.

Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson\u2019s Disease

Literature documents the impact of Parkinson’s Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identification of the disease. This study presents new approaches that are evaluated in three different corpora containing speakers suffering from PD with two main objectives: to investigate the influence of the different phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifiers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the differences in accuracy depending on the manner of articulation. Cross-validation results reach accuracies between 85% and 94% with AUC ranging from 0.91 to 0.98, while cross-corpora trials yield accuracies between 75% and 82% with AUC between 0.84 and 0.95, depending on the corpus. This is the first work analyzing the generalization properties of the proposed approaches employing cross-corpora trials and reaching high accuracies. Among the different phonemic groups, results suggest that plosives, vowels and fricatives are the most relevant acoustic segments for the detection of PD with the proposed schemes. In addition, the use of text-dependent utterances leads to more consistent and accurate models.

Using Social Media to Uncover Treatment Experiences and Decisions in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome Who Are Ineligible for Intensive Chemotherapy: Patient-Centric Qualitative Data Analysis.

BackgroundUntil recently, treatment options were limited for patients with acute myeloid leukemia and myelodysplastic syndrome (AML and MDS) who are ineligible for intensive chemotherapy. Owing to the condition’s rapid progression, it is difficult to identify what is most important to patients when making treatment decisions. Patients’ needs can be better addressed by gaining a deeper understanding of their perspectives, which is valuable in the decision-making process. The Food and Drug Administration recently encouraged the use of social media as a tool to gain insight on patients’ perspectives regarding symptoms experienced and the impacts of their disease.ObjectiveThis study aimed to use disease-specific social media posts by patients with AML or MDS who are ineligible for intensive chemotherapy and their caregivers to capture factors they feel are most important, and to provide current evidence to inform and characterize these perspectives.MethodsPosts by patients with AML or MDS and their caregivers were extracted from publicly available discussions on 3 large AML- or MDS–specific sites. These posts were manually reviewed to only include patients who are ineligible for intensive chemotherapy. A total of 1443 posts from 220 AML patients/caregivers and 2733 posts from 127 MDS patients/caregivers met the study inclusion criteria. A qualitative data analysis (QDA) of a sample of 85 patients’/caregivers’ posts was conducted to identify themes, and a targeted QDA of posts from 79 users focused on treatment decision discussions. Posts were manually reviewed, and relevant text segments were coded and grouped into categories and overall themes.ResultsEighty-six percent (73/85) of users in the overall QDA had relevant information about the key objectives. The most commonly discussed treatment experience theme was the humanistic burden of AML or MDS in terms of emotional/physical impact and impact on family (86%, 63/73 of users), followed by treatment decisions (56%, 41/73) and unmet needs (50%, 37/73). In the QDA of treatment decisions, 60 posts from 45 users contained relevant information. Patients commonly reported the desire to reach specific milestones, including birthdays and weddings. They wished for a better quality of life over quantity of life, did not want the risk of suffering from side effects, and expressed a clear preference to be at home rather than in a hospital or care home.ConclusionsThis study was a novel application of disease-specific social media. It highlighted experiences in the current treatment of AML and MDS, including information gaps, patient/caregiver uncertainty, and the importance of understanding patients’/caregivers’ goals and opinions. A clear finding from this research was the importance of reaching certain personal life goals and being at home with family and friends. The analysis showed that patients/caregivers face additional challenges, including humanistic impacts and a lack of information regarding treatment options.

Prosodic change and the (apparent) irregularities in the development of segments

AbstractAccording to the so-called Prosodic Change Hypothesis, sound change may be irregular if it is caused by a change in prosodic features of units to which the relevant segments belong. This theoretically not implausible hypothesis may provide an explanation for the well-known fact that some instances of sound change display what appears to be unexplainable exceptions. The present paper investigates the question of what kind of data can be used to verify the Prosodic Change Hypothesis. The paper establishes criteria which should be applied to potential evidence and demonstrates that, at present, the Prosodic Change Hypothesis has not yet been confirmed by reference to actual instances of sound change.

Judgment and optimization of video image recognition in obstacle detection in intelligent vehicle

• Binocular vision is a more suitable method to accurately collect road conditions. • Erosion image entropy algorithm can identify obstacles more accurately. • Through simulation, a more optimized scheme is proposed. The objective is to solve the problem of image recognition in intelligent vehicles and optimize the judgment of obstacles and the planning of subsequent routes of intelligent vehicles. Methods: The machine vision technology is used to collect images of relevant road segments, process these images with graying and binarization methods, and simulate the proposed method to observe its effect through data collection. Through the analysis of continuous direct-through driving after encountering the obstacles, it is found that the intelligent vehicles have small traveling errors once the routes are identified and planned. In addition, the error in the x-direction is no more than 0.006 m, while the error in the y-direction is no more than 0.003 m. The recognition effect of the vehicle has reached the expected result. Through the analysis of turning and rotary driving after encountering the obstacles, it is found that after the intelligent vehicles have identified the obstacles and planned the routes, a sudden change in the amplitude of the curve during the turn is caused. In addition, during the turning driving, the error in the x-direction is no more than 0.02 m, while the error in the y-direction is no more than 0.05 m. During the rotary driving, the error in the x-direction is no more than 0.03 m, while the error in the y-direction is not more than 0.04 m. The error variation range is also within the allowable error range. Through the research in this paper, it is found that the error of intelligent vehicle is within the allowable range and achieves the expected effect. Although there are some shortcomings in the experimental process, it can still provide an experimental basis for obstruction detection and route planning of intelligent vehicles in the later stage.

Who Is Interested in Purchasing Smart Irrigation Systems?

Water scarcity concerns have led to revolutionary new smart technologies for residential landscape irrigation, including evapotranspiration and soil-moisture sensor systems. The adoption of smart irrigation technologies into residential landscapes, however, has been slow. This 7-page publication written by Hayk Khachatryan, Alicia Rihn, Caroline R. Warwick, and Michael Dukes and published by the UF/IFAS Food and Resource Economics Department provides an overview of how different consumer groups perceive smart irrigation technology and the best promotions to encourage smart irrigation adoption in home landscapes. It is designed for landscapers, irrigation specialists, and marketing professionals who work with and are interested in promoting smart irrigation technologies to end consumers. Firms can use the results to tailor marketing strategies to target relevant customer segments and create promotions to encourage homeowners to adopt water-saving irrigation technologies. http://edis.ifas.ufl.edu/fe1069