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1
De Michele, C., N. T. Kottegoda, and R. Rosso. "IDAF (intensity-duration-area frequency) curves of extreme storm rainfall: a scaling approach." Water Science and Technology 45, no. 2 (January 1, 2002): 83–90. http://dx.doi.org/10.2166/wst.2002.0031.
Abstract:
Intensity-duration-area frequency curves, IDAF, are determined for the evaluation of design storms using a scaling approach. The variability of maximum annual rainfall intensity in area and duration is represented through the scaling properties in time and space. Thus the scaling relationships of mean rainfall intensity with area and duration are derived using the concepts of dynamic scaling and statistical self-affinity. For a lognormal distribution of rainfall intensity a multiscaling lognormal model is obtained. This gives the IDAF curves of extreme storm rainfall. An application is made to the metropolitan area of Milano.
2
Roncallo Dow, Sergio, and Germán Antonio Arango-Forero. "Introducing three dimensions of audience fragmentation." Signo y Pensamiento 36, no. 70 (July 6, 2017): 74. http://dx.doi.org/10.11144/javeriana.syp36-70.idaf.
Abstract:
Audience fragmentation has become a recurrent theoretical framework in the early 21 Century, used mainly to depict the new complex and dynamic relationships established between media and consumers. However, some academic studies have been published which expand on the meanings and implications of the so called fragmentation from the audience perspective. This paper is based on empirical research undertaken in Colombia, among young people (17-24 year-olds) who live in the ten most important urban areas of this country located at the north-west corner of South America. A mixed methodology was used, combining quantitative and qualitative methods with a statistical sample. Conclusions support a theoretical proposal based on what the authors call the three dimensions of audiences’ fragmentation: intramedia, intermedia and transmedia fragmentation as a way to understand the new relationships established between media content producers and active and participative consumers.
3
Hughes, Will. "Lessons from the Integrated Domestic Abuse Programme, for the implementation of Building Better Relationships." Probation Journal 64, no. 2 (March 29, 2017): 129–45. http://dx.doi.org/10.1177/0264550517701199.
Abstract:
This paper offers reflections on the Integrated Domestic Abuse Programme (IDAP), and its implications for the Building Better Relationships programme (BBR), which has now replaced IDAP as the main criminal justice intervention for male domestic violence perpetrators in England and Wales. While the BBR programme should be regarded with optimism, many of the principles underpinning IDAP are of ongoing relevance for practice with abusive men. There has been a tendency to distort IDAP and the broader Duluth model in discussions of interventions for perpetrators of domestic abuse. Although the BBR programme constitutes some changes of direction, its successful implementation requires continuity in the application of facilitator judgement, knowledge of group dynamics, non-judgemental dialogue, willingness to ‘challenge’, and responsiveness to individual service users.
4
Bergman, Ann-Sofie, Ulrika Järkestig Berggren, and Kerstin Arnesson. "”Andra ögon idag?”." Socialvetenskaplig tidskrift 30, no. 3 (January 2, 2024): 739–58. http://dx.doi.org/10.3384/svt.2023.30.3.4580.
Abstract:
“Other eyes today?” ̶ Social services’ handling of child protection investigations in cases of violence Social work involving child protection investigations is extensive within social services. The largest proportion of investigations are initiated due to reports of concern. Between the years 2010 and 2021, national statistics indicates that reports of concern increased by 200 percent. This article analyses the social services’ handling of child protection investigations initiated due to concerns about violence. The article is part of a larger study of privatisation in child protection investigations in six municipalities, with comparisons between investigations carried out by municipally employed social workers and private consultants. A high proportion of the investigations examined were initiated due to concerns about violence. The article analyses child protection investigations regarding 70 children, where the reports of concern contain information about violence, either concern about violence against the child or concern that the child experienced violence in close relationships. The article is based on qualitative and quantitative content analyses of the investigations and interviews with professionals. The results show that violence could tend to disappear in the documentation during the investigation process. The investigator could reformulate the questions that guided the investigation and reformulate the statements about violence from one person (father) towards another person (mother) into parental “conflicts”. Instead of a focus on violence, the focus of the investigation could be directed towards the parents’ own problems or the child’s behaviour. One explanation could be that, according to the reports of concern, children who were exposed to violence did not always have the opportunity to express themselves in safe and secure ways during the investigation process. The study shows differences between investigations carried out by private consultants and employed social workers in terms of children’s participation and the extent to which investigations led to any intervention.
5
Yardeni, MD, Israel Z., Benzion Beilin, MD, Eduard Mayburd, MD, Yifat Alcalay, MSc, and Hanna Bessler, PhD. "Relationship between fentanyl dosage and immune function in the postoperative period." Journal of Opioid Management 4, no. 1 (January 30, 2018): 27. http://dx.doi.org/10.5055/jom.2008.0005.
Abstract:
Background: Anesthesia and surgery are associated with impairment of the immune system expressed as an excessive proinflammatory immune response and suppression of cell mediated immunity. Opioids, an integral part of anesthetic technique, possess an inhibitory effect on both humoral and cellular immune responses. It was the aim of the present study to examine the effect of various doses of fentanyl on cytokine production during the perioperative period.Intervention: The effect of large (LDFA, 70-100 μg/kg), intermediate (IDFA, 23-30 μg/kg) and small (SDFA, 2-4 μg/kg) doses of fentanyl on the immune function in the postoperative period was investigated.Participants: Sixty patients, randomly assigned to one of the three groups according to the dose of fentanyl were included in the study.Methods: The ex vivo secretion of IL-1β, IL-2, IL-6, and IL-10 and NK cell cytotoxicity (NKCC) of peripheral blood mononuclear cells (PBMC) was tested before, and at 24, 48, and 72 hours following surgery.Results: The pattern of postoperative secretion of the proinflammatory cytokines IL-1β and IL-6 and that of the anti-inflammatory cytokine IL-10 differed significantly between patients receiving SDFA and those receiving IDFA and LDFA, but was similar between the last two groups. A similar suppression of NKCC and IL-2 secretion was observed in the three groups.Conclusions: The diminished proinflammatory cytokine response observed in patients treated by LDFA and IDFA suggests that although more stable immune function can be achieved by those methods in comparison with SDFA, it is recommendable to apply IDFA to avoid the side effects that might be observed using LDFA method.
6
Zhu, Yinglin, Wenbin Zheng, and Hong Tang. "Interactive Dual Attention Network for Text Sentiment Classification." Computational Intelligence and Neuroscience 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8858717.
Abstract:
Text sentiment classification is an essential research field of natural language processing. Recently, numerous deep learning-based methods for sentiment classification have been proposed and achieved better performances compared with conventional machine learning methods. However, most of the proposed methods ignore the interactive relationship between contextual semantics and sentimental tendency while modeling their text representation. In this paper, we propose a novel Interactive Dual Attention Network (IDAN) model that aims to interactively learn the representation between contextual semantics and sentimental tendency information. Firstly, we design an algorithm that utilizes linguistic resources to obtain sentimental tendency information from text and then extract word embeddings from the BERT (Bidirectional Encoder Representations from Transformers) pretraining model as the embedding layer of IDAN. Next, we use two Bidirectional LSTM (BiLSTM) networks to learn the long-range dependencies of contextual semantics and sentimental tendency information, respectively. Finally, two types of attention mechanisms are implemented in IDAN. One is multihead attention, which is the next layer of BiLSTM and is used to learn the interactive relationship between contextual semantics and sentimental tendency information. The other is global attention that aims to make the model focus on the important parts of the sequence and generate the final representation for classification. These two attention mechanisms enable IDAN to interactively learn the relationship between semantics and sentimental tendency information and improve the classification performance. A large number of experiments on four benchmark datasets show that our IDAN model is superior to competitive methods. Moreover, both the result analysis and the attention weight visualization further demonstrate the effectiveness of our proposed method.
7
Forister, Matthew L., Zachariah Gompert, James A. Fordyce, and Chris C. Nice. "After 60 years, an answer to the question: what is the Karner blue butterfly?" Biology Letters 7, no. 3 (December 22, 2010): 399–402. http://dx.doi.org/10.1098/rsbl.2010.1077.
Abstract:
The Karner blue butterfly (KBB), Lycaeides melissa samuelis , is a federally protected taxon whose relationship to the Melissa blue, Lycaeides melissa , has been a point of contention during the 66 years since the KBB was first described. Using a large population-genomic dataset and a model of population divergence with migration, we investigated the relationship between the KBB and L. melissa , as well as the relationship between L. melissa and a third taxon, Lycaeides idas . We report that gene flow between the KBB and L. melissa is low, and comparable to gene flow between L. melissa and L. idas . Considering this population-genetic evidence, we conclude that the KBB is a unique evolutionary lineage that should be recognized as Lycaeides samuelis .
8
Bouma, Esther M. C., Arie Dijkstra, and Stella Arnt Rosa. "Owner’s Anthropomorphic Perceptions of Cats’ and Dogs’ Abilities Are Related to the Social Role of Pets, Owners’ Relationship Behaviors, and Social Support." Animals 13, no. 23 (November 24, 2023): 3644. http://dx.doi.org/10.3390/ani13233644.
Abstract:
Background: For sustainable and healthy relationships with pets, attributing some degree of human abilities to the pet (anthropomorphization) might be necessary. We hypothesize that the tendency to anthropomorphize pet animals is related to relationship behaviors (communication and making up) and the experience of social support. Perceiving the pet in a human social role (e.g., family member or friend) might mediate this relationship. Method: Associations were tested in a mixed sample of cat and dog owners by means of multiple linear regression, moderation, and (moderated) mediation analyses. The differences between cat and dog owners were examined with pet type as the moderator in a moderated mediation analysis. Results: Dog owners anthropomorphize their pets more than cat owners. The social role of the pet mediates the association between anthropomorphization and relationship behavior and social support. The mediation effects were stronger for cat owners than for dog owners. Moreover, our newly developed comparative anthropomorphism measure was a better predictor than the commonly used general anthropomorphism measure (IDAQ).
9
Ma, Saisai, Jiuyong Li, Lin Liu, and Thuc Duy Le. "Discovering context specific causal relationships." Intelligent Data Analysis 23, no. 4 (September 26, 2019): 917–31. http://dx.doi.org/10.3233/ida-184010.
10
Mercado-Gómez, Yarlenis L., Jorge D. Mercado-Gómez, and Carlos E. Giraldo-Sánchez. "What Do Butterflies Tell Us about an Intermediate Disturbance in a Dry Tropical Forest Context?" Diversity 15, no. 8 (August 14, 2023): 927. http://dx.doi.org/10.3390/d15080927.
Abstract:
Montes de María is the best-preserved tropical dry forest fragment in the Colombian Caribbean, making it an ideal location for studying the impacts of human disturbance on local ecosystems. In this study, we analyzed the community structure of diurnal butterflies in both forested and disturbed areas using 16 circular plots to identify relationships between alpha and beta diversity, and the geographic distance between disturbed areas and native forests, using a range of metrics, including range–abundance and rarefaction curves, nonlinear models, and the Bray–Curtis dissimilarity index. The results revealed three distinct species assemblages associated with forests, intermediate disturbed areas (IDAs), and disturbed areas (DAs). Nonlinear models show that IDAs are more diverse than forests and DAs. However, forests have more beta diversity in plots than IDAs and DAs. Indicator species for each butterfly assemblage were also identified. Thus, although new butterfly species assemblages emerge from a new human landscape, it is clear that species that only occur within dry forest fragments are lost when forest fragments disappear. Overall, these findings have important implications for conservation efforts and understanding how human disturbance affects biodiversity in tropical ecosystems.
11
Prasetiyo, Joko Roby. "The Role of Religion on Food Consuming Issue Developing Theological-Philosophy Concept of Food Through Al Qur’an." SHAHIH: Journal of Islamicate Multidisciplinary 5, no. 1 (June 10, 2020): 1–14. http://dx.doi.org/10.22515/shahih.v5i1.2450.
Abstract:
This paper discusses the role of religion in responding the negative effects that arise as a result of human relationships with eating activities. Food borne diseases, environmental damage, uneven food supply are some examples of the negative effects that arise as a result of human feeding activities. Religion as the dominant element in shaping human behaviour, is expected to provide solutions to above problems. The solutions are by providing a framework of meaning that can form the behavior of its adherents. So that in interaction with food produces a proportional relationship between God, humans, and nature.This research is descriptive-analytical using the interpretation approach of critical discourse analysis through Qur'anic verses that use the food term and its derivation, namely: Ṭa'Äm, ' Akl, GizÄun, MÄidah, and SyarÄb. At Last, it will be built anearthly concept of food that is theologically-philosophically.Â
12
Allahgholi, Milad, Hossein Rahmani, Delaram Javdani, Zahra Sadeghi-Adl, Andreas Bender, Dezsö Módos, and Gerhard Weiss. "DDREL: From drug-drug relationships to drug repurposing." Intelligent Data Analysis 26, no. 1 (January 14, 2022): 221–37. http://dx.doi.org/10.3233/ida-215745.
Abstract:
Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.
13
Ghoghre, Sharda, and Tara Singh. "Relationship of Maternal Depression during Pregnancy and Risk of Preeclampsia." IRA-International Journal of Applied Sciences (ISSN 2455-4499) 4, no. 3 (October 5, 2016): 541. http://dx.doi.org/10.21013/jas.v4.n3.p17.
Abstract:
<div><p><em>Present research paper investigates the relationship of maternal depression during pregnancy and risk of preeclampsia. It is a cross sectional study conducted on 200 pregnant women from different maternity homes in Betul & Bhopal District from Madhya Pradesh. IDAS scale was used to see the depression level among pregnant women having preeclampsia or without preeclampsia. For analysis of data One way analysis of variance was used to show the significant relationship between depression and risk of Preeclampsia among pregnant women having preeclampsia and without preeclampsia. The results of the study show significant relationship of depression among pregnant women having preeclampsia and without preeclampsia.</em></p></div>
14
Guo, Wei, Han Qiu, Zimian Liu, Junhu Zhu, and Qingxian Wang. "An integrated model based on feedforward neural network and Taylor expansion for indicator correlation elimination." Intelligent Data Analysis 26, no. 3 (April 18, 2022): 751–83. http://dx.doi.org/10.3233/ida-215955.
Abstract:
Existing correlation processing strategies make up for the defect that most evaluation algorithms do not consider the independence between indicators. However, these solutions may change the indicator system’s internal connection, affecting the final evaluation result’s interpretability and accuracy. Besides, traditional independent analysis methods cannot accurately describe the complex multivariate correlation based on the linear relationship. Aimed at these problems, we propose an indicators correlation elimination algorithm based on the feedforward neural network and Taylor expansion (NNTE). Firstly, we propose a generalized n-power correlation and a feedforward neural network to express the relationship between indicators quantitatively. Secondly, the low-order Taylor expression expanded at every sample is pointed to eliminate nonlinear relationships. Finally, to control the expansions’ accuracy, the layer-by-layer stripping method is presented to reduce the dimensionality of the correlations among multiple indicators gradually. This procedure continues to iterate until there are all simple two-dimensional correlations, eliminating multiple variables’ correlations. To compare the elimination efficiency, the ranking accuracy is proposed to measure the distance of the resulting sequence to the benchmark sequence. Under Cleveland and KDD99 two datasets, the ranking accuracy of the NNTE method is 71.64% and 96.41%, respectively. Compared with other seven common elimination methods, our proposed method’s average increase is 13.67% and 25.13%, respectively.
15
Guo, Wei, Han Qiu, Zimian Liu, Junhu Zhu, and Qingxian Wang. "An integrated model based on feedforward neural network and Taylor expansion for indicator correlation elimination." Intelligent Data Analysis 26, no. 3 (April 18, 2022): 751–83. http://dx.doi.org/10.3233/ida-215955.
Abstract:
Existing correlation processing strategies make up for the defect that most evaluation algorithms do not consider the independence between indicators. However, these solutions may change the indicator system’s internal connection, affecting the final evaluation result’s interpretability and accuracy. Besides, traditional independent analysis methods cannot accurately describe the complex multivariate correlation based on the linear relationship. Aimed at these problems, we propose an indicators correlation elimination algorithm based on the feedforward neural network and Taylor expansion (NNTE). Firstly, we propose a generalized n-power correlation and a feedforward neural network to express the relationship between indicators quantitatively. Secondly, the low-order Taylor expression expanded at every sample is pointed to eliminate nonlinear relationships. Finally, to control the expansions’ accuracy, the layer-by-layer stripping method is presented to reduce the dimensionality of the correlations among multiple indicators gradually. This procedure continues to iterate until there are all simple two-dimensional correlations, eliminating multiple variables’ correlations. To compare the elimination efficiency, the ranking accuracy is proposed to measure the distance of the resulting sequence to the benchmark sequence. Under Cleveland and KDD99 two datasets, the ranking accuracy of the NNTE method is 71.64% and 96.41%, respectively. Compared with other seven common elimination methods, our proposed method’s average increase is 13.67% and 25.13%, respectively.
16
Rajwade, Dr Anjali. "Assessment Of Relationship Between IDA And Personal Hygiene, Nutritional Knowledge And Dietary Practices In Adolescent Girls." Indian Journal of Applied Research 1, no. 5 (October 1, 2011): 113–14. http://dx.doi.org/10.15373/2249555x/feb2012/42.
17
Yang, Guang, Yan Fang Yue, Jin Ye Wang, and Yong Di Zhang. "A Machining Shop Scheduling Model Based on UML and IDEF." Applied Mechanics and Materials 26-28 (June 2010): 870–74. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.870.
Abstract:
Combining with production features of the machining shop in small and medium manufacturing enterprises and difficulties in their information construction, we studied deeply on the scheduling model suitable for complex information environment of the machining shop, and established the job planning and scheduling system model. The hybrid modeling method of UML and IDEF was adopted to describe the architecture of the job planning and scheduling system in machining shop. IDEF, which has good flexibility and logic, is used to describe the system's functions and needs, and then express an implementation scheme that can meet the needs and realize the functions. At the same time, UML is utilized to reflect the chronological relationship of the interaction between functional objects. From the perspectives of structure, function, information and control, both of the above advantages are combined to establish the flexible and reusable model for job planning and scheduling system in machining shop.
18
Purohit, Sumit, George Chin, and Lawrence B. Holder. "ITeM: Independent temporal motifs to summarize and compare temporal networks." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 1071–96. http://dx.doi.org/10.3233/ida-205698.
Abstract:
Networks are a fundamental and flexible way of representing various complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Temporal networks are a specialization of general networks where every relationship occurs at a discrete time. The temporal evolution of such networks is as important to understand as the structure of the entities and relationships. We present the Independent Temporal Motif (ITeM) to characterize temporal graphs from different domains. ITeMs can be used to model the structure and the evolution of the graph. In contrast to existing work, ITeMs are edge-disjoint directed motifs that measure the temporal evolution of ordered edges within the motif. For a given temporal graph, we produce a feature vector of ITeM frequencies and the time it takes to form the ITeM instances. We apply this distribution to measure the similarity of temporal graphs. We show that ITeM has higher accuracy than other motif frequency-based approaches. We define various ITeM-based metrics that reveal salient properties of a temporal network. We also present importance sampling as a method to efficiently estimate the ITeM counts. We present a distributed implementation of the ITeM discovery algorithm using Apache Spark and GraphFrame. We evaluate our approach on both synthetic and real temporal networks.
19
Hong, Tzung-Pei, Chi-Cheng Kao, Siang-Wei Chen, and Chun-Hao Chen. "Mining multiplex interaction relationships from usage records in social networks." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 993–1005. http://dx.doi.org/10.3233/ida-184107.
Abstract:
Social networks have become increasingly popular and are commonly used in everyday life. They also become the most convenient places to send information or receive advertisements. The multiplex network is an important study topic in social networks, in which many features could be appropriately represented in different layers. In this paper, we propose an approach to find the multiplex interaction relationships based on the action records of users on social networks. The multiplex user interactions are found and divided into three levels: high, normal and low. They are then used to check the friend and the follower relations such that users can find which friends or followers are active or not. In the experiments, the parameters are chosen based on Dunbar’s number, which is the number of social relationships that humans can have with high confidence. The results show the proposed approach is effective in helping users know the truly close friend relationships on a social network.
20
Shao, Fubo, Keping Li, and Yulin Dong. "Identifying multi-variable relationships based on the maximal information coefficient." Intelligent Data Analysis 21, no. 1 (January 10, 2017): 151–66. http://dx.doi.org/10.3233/ida-150354.
21
Gbadebo, Shakeerah Olaide, Gbemisola Aderemi Oke, and Oluwole Oyekunle Dosumu. "A study protocol for a single-centred randomized trial to investigate the effect of pre-treatment communication methods on dental anxiety among adult dental patients." SAGE Open Medicine 11 (January 2023): 205031212311719. http://dx.doi.org/10.1177/20503121231171995.
Abstract:
Objective: Optimal oral health is an essential component of good quality of life. However, this may be hindered by dental anxiety (DA), thereby, affecting the utilization of dental services. DA could be alleviated by pre-treatment information; however, the method of delivering this information is yet to be explored. It is, therefore, necessary to assess the modes of presenting pre-treatment information to ascertain the one with significant effect on DA. This will improve treatment outcomes and quality of life for individuals. Hence, the primary objective is to assess the effect of audiovisual and written forms of pre-treatment information on DA, while the secondary objective will compare the subjective and objective methods of assessing DA with psychometric anxiety scale (Index of Dental Anxiety and Fear (IDAF)-4C+) and salivary alpha-amylase respectively. Study design: Single-centred, single-blind, parallel-group, four-arm randomized clinical trial. Methods: The study will compare the effects of audiovisual and written forms of pre-treatment information on DA among adults. Patients 18 years and above scheduled for dental treatment will be screened for eligibility. Written informed consent will be sought before participation. Participants will be allocated randomly using block randomization, to the groups; G1: audiovisual and G2: a written form of pre-treatment information. At the visit, participants will complete the DA questionnaires (IDAF-4C+, Modified Dental Anxiety Scale and Visual Analogue Scale). Physiological anxiety-related changes of salivary alpha-amylase will be measured using a point-of-care kit (iPro oral fluid collector) at baseline, and 10 min after intervention. Furthermore, blood pressure will be taken at baseline and 20 min into the treatment. The mean changes of physiologic anxiety levels and 95% confidence intervals will be compared between the methods of pre-treatment information. Discussion: This study hopes to establish pre-treatment information as a method for reducing DA among the populace. Also, to assess the relationship between questionnaire-based and physiologic methods of assessing DA.
22
Mirajsul, Mirajsul, and Mariyani Mariyani. "Stimulasi Ibu dengan Perkembangan Anak Usia 1-2 Tahun di Posyandu Kelurahan Palmeriam." Malahayati Nursing Journal 6, no. 6 (June 1, 2024): 2293–302. http://dx.doi.org/10.33024/mnj.v6i6.11247.
Abstract:
ABSTRACT It is recorded that 52.9 million children are less than 5 years old, it is estimated that 5-25% of preschool-aged children experience minor brain dysfunction including 85,779 (62.02%) fine motor development (WHO, 2018). The Indonesian Pediatricians Association (IDAI) revealed data that it is estimated that 5-10% of children experience developmental delays and 1-3% of toddlers experience global development delays (IDAI, 2013 in Setyaningsih & Wahyuni, 2018) so to anticipate this, parents are expected to be stimulated and screened for growth and development as early as possible. To know the relationship Stimulation of mothers with the development of children aged 1-2 years at Posyandu in Palmeriam in 2023. Quantitative study using a cross-sectional method on children aged 1-2 years at posyandu in Palmeriam village. Samples were taken randomly using cluster sampling technique at 10 posyandu as many as 84 respondents. The results of the chi square test showed that the p-value of the relationship between stimulation given by the mother and child development was 0.019 (<0.05). The stimulation given by the mother has been shown to have a significant relationship with the development of children aged 1-2 years at Posyandu in Palmeriam Village (H0 is rejected). For further research, it is expected to increase the number of samples so that they can be more representative of the population and health workers can play an active role in providing interventions related to stimulation of development according to the child's age. Keywords: Children, Development, Stimulation ABSTRAK Tercatat 52,9 juta anak kurang dari 5 tahun, diperkirakan 5-25% anak usia prasekolah mengalami disfungsi otak minor termasuk perkembangan motorik halus sebanyak 85.779 (62,02%) (WHO,2018). Ikatan Dokter Anak Indonesia (IDAI) mengungkapkan data bahwa diperkirakan 5-10% anak mengalami keterlambatan perkembangan dan 1-3% balita mengalami keterlambatan perkembangan umum (global development delay) (IDAI, 2013 dalam Setyaningsih & Wahyuni, 2018) sehingga untuk mengantisipasi hal tersebut, diharapkan oran tua dapat stimulasi dan skrining pertumbuhan dan perkembangan sedini mungkin. Mengetahui hubungan Stimulasi Ibu dengan perkembangan Anak usia 1-2 tahun di posyandu Kelurahan Palmeriam tahun 2023. Penelitian kuantitatif menggunakan metode cross sectional pada anak usia 1-2 tahun di di posyandu Kelurahan Palmeriam. Sampel diambil secara random dengan teknik cluster sampling pada 10 posyandu sebanyak 84 responden. Hasil uji chi square menunjukkan bahwa nilai p-value hubungan Stimulasi yang diberikan ibu dengan perkembangan anak yaitu 0,019(<0,05). Stimulasi yang diberikan ibu terbukti memiliki hubungan yang bermakna dengan perkembangan anak usia 1-2 tahun di posyandu Kelurahan Palmeriam (H0 di tolak). Untuk penelitian selanjutkan diharapkan dapat menambahkan jumlah sampel agar dapat lebih mewakili populasi serta nakes dapat berperan aktif memberikan intervensi terkait Stimulasi perkembangan sesuai usia anak. Kata Kunci: Anak, Perkembangan, Stimulasi
23
Oyeyemi, J. A., R. A. Idoko, and O. Haruna. "Assessment of user satisfaction with library resources available at the Federal Polytechnic’s Central Library, Idah." Journal of Management and Technology 19, no. 1 (August 15, 2023): 58–68. http://dx.doi.org/10.62254/jmt.2023.19.1.5.
Abstract:
This paper assesses the user satisfaction with library resources in Federal Polytechnic Idah. Descriptive/survey research design was adopted for the study. The population consists the students of the six schools that make up the institution where about 500 of them patronize the library daily. 250 of the population were randomly selected as sample. Structured questionnaire was used to collect data for this study, and was analyzed with simple percentage and chi-square (x2). The study revealed that there is significant relationship between the level of awareness of resources available and user satisfaction. It also established that there is significant relationship between satisfaction and frequency of usage. Hypothesis 3, revealed the significant relationship between satisfaction and purpose of usage. Hypothesis 4, inferred that users enjoying some level of satisfaction as they use the library resources despite the condition placed on the uses of some of the resources. Hypothesis 5, shows clearly that users encountered some challenges when using the library. However, the study concluded that the users were highly satisfied with the resources. Based on the conclusion, some recommendations were made. Creating thorough awareness on the use of library resources to the fresher/new intake students to catch them young. Library staff should be encouraged to undertake training programmes to upgrade their knowledge of handling information consistently etc.
24
Kodama, Satoshi. "Shape classification based on solid angles by a support vector machine." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 933–57. http://dx.doi.org/10.3233/ida-215992.
Abstract:
In the field of computer-aided design (CAD) and three-dimensional (3D) modeling, constructive solid geometry (CSG) representations based on primitive 3D shapes and boundary representations (B-Rep) based on geometry and topology are widely used to represent complex shapes. Therefore, it is important to recognize primitive shapes such as cubes, cones, and cylinders and to accurately judge and classify the deformation of primitive shapes. For this purpose, various techniques have been studied, such as a vector-based determination method, a determination method using multiple images from various angles, and a determination method based on positional relationships between points. However, because large datasets are required to classify these shapes and it is difficult to respond to changes in shape due to rotation, the resulting recognition accuracy is not always high. In this work, we propose a method based on solid angles, which do not depend on the positional relationship of vectors, viewpoints, or changes due to rotation, as feature quantities. We demonstrate the effectiveness of primitive 3D figures using features based on solid angles. In addition, we show that the presence or absence of deformation can be determined when part of a primitive 3D figure is deformed.
25
Liu, Jia, Ya-Jun Du, Qing Li, and Chun-Long Fu. "Social community evolution by combining gravitational relationship with community structure." Intelligent Data Analysis 22, no. 5 (September 26, 2018): 1143–61. http://dx.doi.org/10.3233/ida-173561.
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Huang, Wei, Yoshiteru Nakamori, Shouyang Wang, and Tieju Ma. "Mining scientific literature to predict new relationships1." Intelligent Data Analysis 9, no. 2 (May 17, 2005): 219–34. http://dx.doi.org/10.3233/ida-2005-9207.
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Judit, Á., PS Sándor, and J. Schoenen. "Habituation of Visual and Intensity Dependence of Auditory Evoked Cortical Potentials Tends to Normalize Just Before and During the Migraine Attack." Cephalalgia 20, no. 8 (October 2000): 714–19. http://dx.doi.org/10.1111/j.1468-2982.2000.00122.x.
Abstract:
Between attacks, migraine with (MO) or without aura (MA) patients show deficient habituation of pattern-reversal visual evoked potentials (PR-VEP) and a strong intensity dependence of auditory evoked cortical potentials (IDAP). Clinical observations of migraine prodromes and previously published electrophysiological studies suggest that cortical information processing may vary in close temporal relationship to the attack. We studied PR-VEP and IDAP just before (11 MO pts), during (23 MO, 3 MA), 1 day following (27 MO, 1 MA) and 2 days following (14 MO) a migraine attack. The results were compared with a large group of MO patients recorded at a distance of at least 3 days from an attack ( n = 66 for IDAP; n = 39 for VEP). Patients recorded the day before the attack had on average an habituation of −13.6 ± 20.5% (mean ± sd) between the 5th and 1st block of 100 averaged VEP responses and a flat (0.38 ± 1.06 µV/10 dB) amplitude-stimulus intensity function (ASF) slope of the auditory evoked cortical potential. Both values were significantly different from those obtained in the attack interval ( P = 0.003; P = 0.020). During the attack, VEP habituation was less pronounced (−0.17 ± 26.2%) and ASF slopes remained flat (0.32 ± 1.44 µV/10 dB; P = 0.002 compared to interval). During the 2 days following the attack, VEP habituation was replaced by potentiation (+ 0.09 ± 29.1% the 1st day; 19.5 ± 45.7% the 2nd day) and ASF slopes increased markedly (0.87 ± 1.39 and 1.14 ± 1.12 µV/10 dB). The normalization of evoked cortical responses just before and during the attack, might reflect an increase in the cortical preactivation level due to enhanced activity in raphe-cortical serotonergic pathways.
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Wang, Limin, Hangqi Fan, and He Kong. "From undirected dependence to directed causality: A novel Bayesian learning approach." Intelligent Data Analysis 26, no. 5 (September 5, 2022): 1275–302. http://dx.doi.org/10.3233/ida-216114.
Abstract:
Bayesian network (BN) is one of the most powerful probabilistic models in the field of uncertain knowledge representation and reasoning. During the past decade, numerous approaches have been proposed to build directed acyclic graph (DAG) as the structural specification of BN. However, for most Bayesian network classifiers (BNCs) the directed edges in DAG substantially represent assertions of conditional independence rather than causal relationships although the learned joint probability distributions may fit data well, thus they cannot be applied to causal reasoning. In this paper, conditional entropy is introduced to measure causal uncertainty due to its asymmetry characteristic, and heuristic search strategy is applied to build Bayesian causal tree (BCT) by identifying significant causalities. The resulting highly scalable topology can represent causal relationship in terms of causal science, and corresponding joint probability can fit training data in terms of data science. Then ensemble learning strategy is applied to build Bayesian causal forest (BCF) with a set of BCTs, each taking different attribute as the root node to represent root cause for causality analysis. Extensive experiments performed on 32 public datasets from the UCI machine learning repository show that BCF achieves outstanding classification performance compared to state-of-the-art single-model BNCs (e.g., CFWNB), ensemble BNCs (e.g., WATAN, IWAODE, WAODE-MI and TAODE) and non-Bayesian learners (e.g., SVM, k-NN, LR).
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Chanza, Nelson, Pakama Q. Siyongwana, Leizel Williams-Bruinders, Veronica Gundu-Jakarasi, Chipo Mudavanhu, Vusomuzi B. Sithole, and Albert Manyani. "Closing the Gaps in Disaster Management and Response: Drawing on Local Experiences with Cyclone Idai in Chimanimani, Zimbabwe." International Journal of Disaster Risk Science 11, no. 5 (July 27, 2020): 655–66. http://dx.doi.org/10.1007/s13753-020-00290-x.
Abstract:
Abstract Cyclone Idai in Zimbabwe exposed deficiencies in the country’s disaster management system. This study uses a phenomenological case exploration of the experiences of local residents in Rusitu Valley following cyclone-induced floods that affected the area in March 2019. Through capturing narratives of participants who were recruited through chain referrals, the research intends to understand how local actors, utilizing their local-based response systems, managed to fill in the voids that characterize disaster management practice in Zimbabwe. Results show that the participation of local “heroes” and “Samaritans,” by deploying their social networks, norms, relationships, practices, and modest ingenuity, helped to speed up response times and minimize threats to lives and livelihoods. Documentation of the stories of local actors about their disaster experiences also gives a richer picture of the Cyclone Idai disaster. Although the community response system also facilitated the operation of external disaster management agencies, their premature withdrawal tended to weaken the trust and values existing in the area, and created tensions between the disaster-affected people and other villagers. Given the delays in formal responses by the government and other external relief agencies, the practices of local actors, although spontaneous and largely uncoordinated, offer rich insights into the design and development of disaster management regimes.
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Huang, Cheng-Lung. "Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationships." Intelligent Data Analysis 23, no. 3 (April 28, 2019): 623–39. http://dx.doi.org/10.3233/ida-183910.
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Omanayin, Y. A., N. M. Waziri, U. S. Onoduku, and A. A. Alabi. "Characterization of Pegmatites in Ogodo-Odobola Area of Idah Sheet 267NW, Central Nigeria." International Journal of Science for Global Sustainability 9, no. 2 (August 9, 2023): 73–84. http://dx.doi.org/10.57233/ijsgs.v9i2.461.
Abstract:
The pegmatites of Ogodo-Odobola area around Ajaokuta, Central Nigeria belong to the pegmatite belt of Central Nigeria. A detailed geological mapping of the pegmatites and host rocks was conducted with the aim of understanding their mode of occurrence and field relationship and to assess their mineralization potentials. Twelve (12) representative samples of rocks were selected for petrographic analysis. The results show that the area is underlain by migmatite-gneiss, schist, with intrusions of granite and pegmatite. The pegmatites occur in tabular form with varying widths (2 centimetres – 6 metres) and lengths (12 – 200 metres). Principal joint direction is NNE-SSW which is believed to have influenced the pegmatite emplacement. Petrography of the representative rock samples revealed an average mineralogical composition of biotite (23.90%), microcline (22.15%), hornblende (15.05%), quartz (10.65%), plagioclase (10.35%), muscovite (8.00%), myrmekite (0.20%), and opaque and accessory minerals (9.70%). The pegmatites were found to be dominated by microcline and plagioclase feldspars, and then muscovite, biotite, and accessory and opaque minerals. The pegmatite of the Ogodo-Odobola area is worth probing as the results have shown prospect for possible economic minerals.
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Li, Hui, Fuli Wang, Hongru Li, and Xu Wang. "The relationship determination between the Bayesian networks nodes based on the intuitionistic fuzzy set." Intelligent Data Analysis 23, no. 4 (September 26, 2019): 951–69. http://dx.doi.org/10.3233/ida-184200.
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Hu, Linfeng, Junhao Wen, Hanwen Zhang, Wei Zhou, and Hongyu Wang. "Modeling multi-attribute and implicit relationship factors with self-supervised learning for recommender systems." Intelligent Data Analysis 27, no. 3 (May 18, 2023): 691–708. http://dx.doi.org/10.3233/ida-226576.
Abstract:
Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users’ specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model’s accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called “Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering” (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling.
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Gandini, Andi Lis Arming, Umi Kulsum, and Emmy Putri Wahyuni. "The Relationship of Working Mothers and Dependency Level on Pre-School Children Gadgets’ Use." Indonesian Journal of Multidisciplinary Science 2, no. 4 (January 30, 2023): 2345–49. http://dx.doi.org/10.55324/ijoms.v2i4.423.
Abstract:
Currently, many housewives decide to work, with these conditions it has an impact on limited time to interact directly with children. This causes children to be further away from parental supervision and assistance, especially in using gadgets. This study aimed to determine the relationship between working mothers and the level of dependency on gadget use in preschool-aged children. The research method used is an analytic study with a cross-sectional design. A sample of 303 mothers with preschool-age children in all public kindergartens in Samarinda City. The level of dependency on gadget use in preschool-age children is based on the standard length of time using gadgets in a day according to the Indonesian Pediatrician Association (IDAI). Then the data obtained were analyzed using the Chi-Square test. The results of the analysis obtained-valuable <0,05 (0,001), which means that there is a relationship between working mothers and the level of dependence on gadget use in preschool-aged children. The use of gadgets at preschool age must be supervised and assisted, especially by parents, not only mothers but also families.
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Oparaku, L. Alimonu, and R. Terungwa Iwar. "Relationships between average gully depths and widths on geological sediments underlying the Idah-Ankpa Plateau of the North Central Nigeria." International Soil and Water Conservation Research 6, no. 1 (March 2018): 43–50. http://dx.doi.org/10.1016/j.iswcr.2017.12.003.
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Bilgin, Turgay Tugay, and Ali Yilmaz Camurcu. "An efficient preprocessing stage for the relationship-based clustering framework." Intelligent Data Analysis 14, no. 6 (November 5, 2010): 731–48. http://dx.doi.org/10.3233/ida-2010-0449.
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Aguiar Junior, Paulo Cesar, and Christovam de Castro Barcellos Neto. "Informação sobre o uso de agrotóxicos e seus efeitos a saúde no Espírito Santo: Até onde sabemos, o que não sabemos, o que precisamos saber?/The conservative modernization of agriculture and the pathogenic transformation of space: Poisoning and death by use of pesticides in Espírito Santo." Geografares, no. 25 (June 27, 2018): 354–73. http://dx.doi.org/10.7147/geo25.17940.
Abstract:
O presente artigo aborda a temática dos agrotóxicos no estado do Espírito Santo, dando ênfase as informações disponibilizadas pelos sistemas oficiais de informação (IBGE, TOXCEN, IBAMA, IJSN, IDAF, ALES, SEFAZ), que abordam partes de processos de produção, consumo e circulação dos agrotóxicos e de exposição da população a riscos. Destaca-se os tipos de dados referentes a cada órgão, incluindo notícias em jornais, apresentando os caminhos para acessá-las. Por fim, o artigo espacializa, na forma de mapas, as informações do Toxcen sobre intoxicações e mortes por agrotóxicos de uso agrícola nos municípios do Espírito Santo. AbstractThe present article aims to expose the essential relationship between the conservative modernization of agriculture, its system of techniques and the pathogenic transformation of space. Therefore, we seek to reveal the nuances of conservative modernization in Brazil and Espírito Santo, through the analytical categories of systems object and systems of action. From this perspective, we present an overview of the poisoning and death resulting from the use of pesticides, from 2007 to 2014, en Espírito Santo. For this, we used the spatialization of the data, made available by the official information systems, in order to construct a set of maps and graphs that evidenced the pathogenic content of the space.Keywords: Conservative modernization; systems object and actions; pathogenic space
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Olusegun, Adeyemi S. "Length-weight, Length-length Relationship and Condition Factor of Synodontis robbianus at Idah Area of River Niger, Kogi State, Nigeria." Pakistan Journal of Nutrition 10, no. 6 (May 15, 2011): 505–8. http://dx.doi.org/10.3923/pjn.2011.505.508.
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Shcherbyuk, A. N., S. V. Morozov, A. I. Lobakov, E. E. Kruglov, V. M. Manuylov, S. S. Dydykin, E. V. Blinova, et al. "Comparative evaluation of scales to determine severity of acute pancreatitis." Bulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH) 13, no. 2 (February 6, 2023): 49–55. http://dx.doi.org/10.20340/vmi-rvz.2023.2.clin.9.
Abstract:
Based on the method developed by the authors for quantifying the severity of patients with acute pancreatitis, a method for calculating the duration of therapeutic fasting in a particular patient has been created. We studied the medical histories of 500 patients with acute pancreatitis who were in two medical hospitals from 2010 to 2021. They underwent complex therapy of acute pancreatitis. Based on the dependence found using multivariate regression analysis using the universal statistical software package StatSoft Statistica for OC Windows, the authors previously developed and published a method for calculating the severity of a patient's condition with acute pancreatitis in the enzymatic toxemia phase. This method in the process of practical application was significantly improved by the authors and called the IDAP scale (ACUTE PANCREATITIS HAZARD INDEX). The advantages of this method: the calculation of the severity of the patient's condition can be made at any time of interest, the indicators are included in the Medical and Economic standards of the Russian Federation for acute pancreatitis, it is enough to use the standard office program MS Excel, which is available to any practicing physician. When comparing the results of assessing the severity of patients on the IDAP and APACH II scales, no discrepancies of more than 15% were found in 640 measurements in 50 patients, which allowed us to use the IDAP system in our study on a par with the generally accepted APACH II. At the second stage of the study, as a result of the analysis of 86 clinical observations using the technique of multidimensional regression analysis, a relationship was found between the clinical severity of the patient upon admission to the hospital for treatment, the severity at a given time and the required duration of therapeutic fasting (in days). At the second stage of the study, as a result of the analysis of 86 clinical observations using the technique of multidimensional regression analysis, a relationship was found between the clinical severity of the patient upon admission to the hospital for treatment, the severity at a given time and the required duration of therapeutic fasting (in days). When using this method of calculating the duration of fasting, only 7 (8%) patients had an exacerbation of the process after the start of feeding them through the mouth. Thus, taking into account the coefficient of severity of the patient's condition with acute pancreatitis, it is possible to reliably determine the timing of therapeutic fasting in this disease.
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Li, Jun, Chao Li, Bin Tian, Yanzhao Liu, and Chengxiang Si. "DAF: An adaptive computing framework for multimedia data streams analysis." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1441–53. http://dx.doi.org/10.3233/ida-194640.
Abstract:
We consider the problem of efficiently online computing/filtering or analysis multimedia streams. In this scenario, we register a large scale of continuous analysis queries to filter pornographic stream items. Each query is a conjunction of filters. For instance, the query “does this image contain a people basking in the beach?” can be resolved by applying the conjunction of water, people, sand, sea filters successively on the stream item. However, the online evaluation of multimedia filters is indeed very expensive, fortunately there usually exist multiple filters shared among a lot of queries. In other words, each filter may occur in multiple queries. An open problem in such a filtering scenario is how to order the filters in an optimal sequence to achieve significant performance. Existing methods are based on a greedy strategy which orders the filters according to three factors (selectivity, popularity, cost). Although all these methods achieve good results, there are still some problems that haven’t addressed yet. First, the selectivity factor is set empirically, which can not adaptively adjust with multimedia stream. Second, the proportion relationships among the three factors (selectivity, cost, popularity) were not considerably explored. Under these observations,in this paper, we propose a Dynamic-Analytic hierarchy process Framework (DAF) which use a time-based compositional forecasting method, which is based on the idea of exponential smoothing, to deal with the factors’ proportion relationships dynamics. Experiments on both synthetic and real lift multimedia streams demonstrate that our proposed framework (DAF) provides much great adaptability in modeling the factors proportion relationships changing over multimedia stream environment.
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Karunaratne, Thashmee, Henrik Boström, and Ulf Norinder. "Comparative analysis of the use of chemoinformatics-based and substructure-based descriptors for quantitative structure-activity relationship (QSAR) modeling." Intelligent Data Analysis 17, no. 2 (April 17, 2013): 327–41. http://dx.doi.org/10.3233/ida-130581.
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Garatti, Simone, Sergio M. Savaresi, Sergio Bittanti, and Luca La Brocca. "On the relationships between user profiles and navigation sessions in virtual communities: A data-mining approach." Intelligent Data Analysis 8, no. 6 (December 22, 2004): 579–600. http://dx.doi.org/10.3233/ida-2004-8605.
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Jusuf, Siti Khodijah Nur'Aini, Azizah Nurdin, Andi Irhamnia Sakinah, and Halimah Sa’diyah. "The Relationship between Parental Knowledge and Anxiety Levels towards COVID-19 Vaccination in Children." Journal Of The Indonesian Medical Association 73, no. 6 (January 27, 2024): 282–91. http://dx.doi.org/10.47830/jinma-vol.73.6-2024-987.
Abstract:
Introduction: The COVID-19 vaccine is crucial as a preventive measure against infection with the SARS-CoV-2 virus. The Indonesian Pediatric Association (IDAI) has approved the vaccination for children under 12 years, allowing them to receive up to the second dose. However, a lack of knowledge about the vaccine has led to hesitancy among parents, fueled by misinformation and concerns about potential side effects on their children. This study aims to explore the correlation between parental knowledge and their anxiety levels concerning the COVID-19 vaccination for their children.Methods: We conducted a cross-sectional survey with 207 parents of students from SDIT Nurul Fikri in Makassar City. The research instrument was a structured questionnaire. Data were analyzed through univariate analysis, followed by bivariate comparisons using the Chi-square test and multivariate regression.Results: Among the respondents, a majority (58%) exhibited limited knowledge of COVID-19, with 50.2% reporting varying degrees of anxiety about the vaccination for their children. Of the children, 46.3% had received the complete vaccination series. Statistical analysis revealed a significant correlation between parental knowledge and anxiety levels regarding their child’s COVID-19 vaccination (α<0.05; p=0.007).Conclusion: There is a significant correlation between knowledge and anxiety concerning children's COVID-19 vaccination, with knowledge being the most significant predictor of anxiety levels.
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Wang, Pu, Wei Chen, Jinjing Huang, Yuyang Wei, Junhua Fang, and Lei Zhao. "Location prediction for facility placement by incorporating multi-characteristic information." Intelligent Data Analysis 25, no. 5 (September 15, 2021): 1187–210. http://dx.doi.org/10.3233/ida-205420.
Abstract:
In the course of recommending locations for establishing new facilities on urban planning or commercial programming, the location prediction offers the optimal candidates, which maximizes the number of served customers or minimize customer inconvenience, therefore brings the maximum profits. In most existing studies, only the spatial-temporal features are recognized to evaluate the location popularity, where social relationships of customers, which are significant factors for popularity assessing, have been ignored. Additionally, current researches also fail to take capacities and categories of the facilities into consideration. To overcome the drawbacks, we introduce a novel model of Multi-characteristic Information based Top-k Location Prediction (MITLP), it captures the spatio-temporal behaviors of customers based on historical trajectories, exploits the social relevancy from their friend relationships, as well as examines the category competitiveness of specific facilities thoroughly. Subsequently, by drawing on the feature evaluation and popularity quantization, MITLP will be implemented within a hybrid B-tree-liked recommending framework, Constrained Location and Social-Trajectory Clustered forest (CLSTC-forest), which can not only produce better performance in practice but also address the facility service constraints. Finally, extensive experiments conducted on real-world datasets demonstrate the higher efficiency and effectiveness of the proposed model.
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Shobe, Marcia A., and Jacqueline Dienemann. "Intimate Partner Violence in the United States: An Ecological Approach to Prevention and Treatment." Social Policy and Society 7, no. 2 (April 2008): 185–95. http://dx.doi.org/10.1017/s1474746407004137.
Abstract:
Physical, sexual, verbal and economic abuse, also known as Intimate Partner Violence (IPV), comprise a major public health problem. IPV risk factors include poverty, stress, substance use, depression, and history of child maltreatment. Protective factors include human capital (functional health and work competencies/qualifications), social capital (formal/informal relationships and resources) and financial capital (income and assets). Traditional IPV initiatives focus on increasing social capital by changing the cognition and/or behaviour of victims or perpetrators and increasing legal sanctions and supportive resources. The proposed Asset Model of IPV Resolution extends the current model to include an ecological approach to the prevention and resolution of IPV. In addition to human and social capital development, the authors suggest financial capital development through Individual Development Accounts (IDAs) as one social policy initiative to support women at risk of or women who experience IPV.
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Zhu, Haizhen, Yan Yuan, Yongjun Lei, and Xiaoru Li. "Study on the Division Method of Pipeline Robot Module." Journal of Physics: Conference Series 2218, no. 1 (March 1, 2022): 012084. http://dx.doi.org/10.1088/1742-6596/2218/1/012084.
Abstract:
Abstract The working environment in the pipeline is getting worse, and there are more and more functional needs for the pipeline robots. The traditional pipeline robot structure is fixed and single, and the rapid design and assembly cannot be realized. There are still problems of repeated design between different types of robots.In view of these problems, a top-down modular design method—combining of axiom design and IDEF(ICAM DEFinition method) functional modeling is proposed to divide the pipeline robot system architecture.This method uses the mapping relationship between function and structure, and extracts the general modules and special modules in different types of pipeline robot systems, which realizes the modular design of the non-modular pipeline robot, so that the module structure at its component level can be universally exchanged to form resource sharing.
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Zhang, Chunying, Ruiyan Gao, Jiahao Wang, Song Chen, Fengchun Liu, Jing Ren, and Xiaoze Feng. "MD-SPKM: A set pair k-modes clustering algorithm for incomplete categorical matrix data." Intelligent Data Analysis 25, no. 6 (October 29, 2021): 1507–24. http://dx.doi.org/10.3233/ida-205340.
Abstract:
In order to solve the clustering problem with incomplete and categorical matrix data sets, and considering the uncertain relationship between samples and clusters, a set pair k-modes clustering algorithm is proposed (MD-SPKM). Firstly, the correlation theory of set pair information granule is introduced into k-modes clustering. By improving the distance formula of traditional k-modes algorithm, a set pair distance measurement method between incomplete matrix samples is defined. Secondly, considering the uncertain relationship between the sample and the cluster, the definition of the intra-cluster average distance and the threshold calculation formula to determine whether the sample belongs to multiple clusters is given, and then the result of set pair clustering is formed, which includes positive region, boundary region and negative region. Finally, through the selected three data sets and four contrast algorithms for experimental evaluation, the experimental results show that the set pair k-modes clustering algorithm can effectively handle incomplete categorical matrix data sets, and has good clustering performance in Accuracy, Recall, ARI and NMI.
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Zou, Muquan, Lizhen Wang, Pingping Wu, and Vanha Tran. "Efficiently mining maximal l-reachability co-location patterns from spatial data sets." Intelligent Data Analysis 27, no. 1 (January 30, 2023): 269–95. http://dx.doi.org/10.3233/ida-216515.
Abstract:
A co-location pattern is a set of spatial features that are strongly correlated in space. However, some of these patterns could be neglected if the prevalence metrics are based solely on the clique (or star) relationship. Hence, the l-reachability co-location pattern is proposed by introducing the l-reachability clique where the members of each instance pair can be reachable to each other in a given step length l. Because the average size of l-reachability co-location patterns tends to be longer, maximal l-reachability co-location pattern mining is researched in this paper. First, some sparsification strategies are introduced to shorten star neighborhood lists of instances in an updated graph called the l-reachability neighbor relationship graph, and then, they are grouped by their corresponding patterns. Second, candidate maximal l-reachability co-location patterns are iteratively detected in a size-independent way on bi-graphs that contain group keys and their intersection sets. Third, the prevalence of each candidate maximal l-reachability co-location pattern is checked in a binary search way with a natural l-reachability clique called the ⌊l/2⌋-reachability neighborhood list. Finally, the effectiveness and efficiency of our model and algorithms are analyzed by extensive comparison experiments on synthetic and real-world spatial data sets.
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Shi, Haoran, Lixin Ji, Shuxin Liu, Kai Wang, and Xinxin Hu. "Collusive anomalies detection based on collaborative markov random field." Intelligent Data Analysis 26, no. 6 (November 12, 2022): 1469–85. http://dx.doi.org/10.3233/ida-216287.
Abstract:
Abnormal collusive behavior, widely existing in various fields with concealment and synergy, is particularly harmful in user-generated online reviews and hard to detect by traditional methods. With the development of network science, this problem can be solved by analyzing structure features. As a graph-based anomaly detection method, the Markov random field (MRF)-based model has been widely used to identify the collusive anomalies and shown its effectiveness. However, existing methods are mostly unable to highlight the primary synergy relationship among nodes and consider much irrelevant information, which caused poor detectability. Therefore, this paper proposes a novel MRF-based method (ACEagle), considering node-level and community-level behavior features. Our method has several advantages: (1) based on the analysis of the nodes’ local structure, the community-level behavioral features are combined to calculate the nodes’ prior probability to close the ground truth, (2) it measured the behavior’s collaborative intensity between nodes by time and weight, constructing MRF by the synergic relationship exceeding the threshold to filter irrelevant structural information, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available. Through experiments in user-reviewed datasets where abnormal collusive behavior is most typical, the results show that ACEagle is significantly outperforming state-of-the-art baselines in collusive anomalies detection.
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Ayaz, Reyhan, Meryem Hocaoğlu, Taner Günay, Oğuz devrim Yardımcı, Abdulkadir Turgut, and Ateş Karateke. "Anxiety and depression symptoms in the same pregnant women before and during the COVID-19 pandemic." Journal of Perinatal Medicine 48, no. 9 (November 26, 2020): 965–70. http://dx.doi.org/10.1515/jpm-2020-0380.
Abstract:
AbstractObjectivesWith clinical experience from previous coronavirus infections, public health measures and fear of infection may have negative psychological effects on pregnant women. This study aimed to compare the level of anxiety and depression in the same pregnant women before and during the COVID-19 pandemic.MethodsThe pregnant women continuing pregnancy who participated in the first study which was undertaken to clarify the factors associated with mental health of pregnant women before the COVID-19 pandemic, were included for the current study during the outbreak. Anxiety and depression symptoms of the same pregnant women were evaluated by using the Inventory of Depression and Anxiety Symptoms II and Beck Anxiety Inventory twice before and during the pandemic.ResultsA total of 63 pregnant women completed questionnaires. The mean age of the women and the mean gestational age was 30.35±5.27 years and 32.5±7 weeks, respectively. The mean total IDAS II score was found to increase from 184.78±49.67 (min: 109, max: 308) to 202.57±52.90 (min: 104, max: 329) before and during the SARS-CoV-2 pandemic. According to the BAI scores the number of patients without anxiety (from 10 to 6) and with mild anxiety (from 31 to 24) decreased and patients with moderate (from 20 to 25) and severe anxiety (from 2 to 8) increased after SARS-CoV-2 infection. Multivariate linear regression analysis revealed that obesity and relationship with her husband are the best predictors of IDAS II scores.ConclusionsThis study indicated that COVID-19 outbreak affects the mental health of pregnant women negatively which leads to adverse birth outcomes. The level of anxiety and depression symptoms of pregnant women during the COVID-19 infection significantly increased. Healthcare professionals should establish comprehensive treatment plans for pregnant women who are highly vulnerable population to prevent mental trauma during the infectious disease outbreaks.