Search results for: causal discovery

Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: data mining, k-means, road traffic accidents, Waze, Weka

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Abdullah M. Algarni, Yashwant K. Malaiya

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Some of the key aspects of vulnerability-discovery, dissemination, and disclosure-have received some attention recently. However, the role of interaction among the vulnerability discoverers and vulnerability acquirers has not yet been adequately addressed. Our study suggests that a major percentage of discoverers, a majority in some cases, are unaffiliated with the software developers and thus are free to disseminate the vulnerabilities they discover in any way they like. As a result, multiple vulnerability markets have emerged. In some of these markets, the exchange is regulated, but in others, there is little or no regulation. In recent vulnerability discovery literature, the vulnerability discoverers have remained anonymous individuals. Although there has been an attempt to model the level of their efforts, information regarding their identities, modes of operation, and what they are doing with the discovered vulnerabilities has not been explored. Reports of buying and selling of the vulnerabilities are now appearing in the press; however, the existence of such markets requires validation, and the natures of the markets need to be analysed. To address this need, we have attempted to collect detailed information. We have identified the most prolific vulnerability discoverers throughout the past decade and examined their motivation and methods. A large percentage of these discoverers are located in Eastern and Western Europe and in the Far East. We have contacted several of them in order to collect first-hand information regarding their techniques, motivations, and involvement in the vulnerability markets. We examine why many of the discoverers appear to retire after a highly successful vulnerability-finding career. The paper identifies the actual vulnerability markets, rather than the hypothetical ideal markets that are often examined. The emergence of worldwide government agencies as vulnerability buyers has significant implications. We discuss potential factors that can impact the risk to society and the need for detailed exploration.

Keywords: risk management, software security, vulnerability discoverers, vulnerability markets

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Authors: S. Niamkaeo, O. Robert, O. Chaowalit

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In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.

Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system

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Authors: Ayan Giri, Lukaranjan Phukan, Shantanu Karmakar

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Structure and tectonics play a vital role in ore genesis and deposition. The existence of a swelling structure below the current level of a mine leads to the discovery of ores below some permeant developments of the mine. The discovery and the extraction of the ore body are very critical to sustain the business requirement of the mine. The challenge was to extract the ore without hampering the global stability of the mine. In order to do so, different mining options were considered and analysed by numerical modelling in FLAC3d software. The constitutive model prepared for this simulation is the improved unified constitutive model, which can better and more accurately predict the stress-strain relationships in a continuum model. The IUCM employs the Hoek-Brown criterion to determine the instantaneous Mohr-Coulomb parameters cohesion (c) and friction (ɸ) at each level of confining stress. The extra swelled part can be dimensioned as north-south strike width 50m, east-west strike width 50m. On the north side, already a stope (P1) is excavated of the dimension of 25m NS width. The different options considered were (a) Open stoping of extraction of southern part (P0) of 50m to the full extent, (b) Extraction of the southern part of 25m, then filling of both the primaries and extraction of secondary (S0) 25m in between. (c) Extraction of the southern part (P0) completely, preceded by backfill and modify the design of the secondary (S0) for the overall stability of the permanent excavation above the stoping.

Keywords: extraction, IUCM, FLAC 3D, stoping, tectonics

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Authors: Majid Farsadrooh, Mehran Feizi-Dehnayebi

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Serum albumin is the most abundant protein that is present in the circulatory system of a wide variety of organisms. Although it is a significant macromolecule, it can contribute to osmotic blood pressure and also, plays a superior role in drug disposition and efficiency. Molecular docking simulation can improve in silico drug design and discovery procedures to propound a lead compound and develop it from the discovery step to the clinic. In this study, the molecular docking simulation was applied to select a lead molecule through an investigation of the interaction of the two anticancer drugs (Alitretinoin and Abemaciclib) with Human Serum Albumin (HSA). Then, a series of new compounds (a-e) were suggested using lead molecule modification. Density functional theory (DFT) including MEP map and HOMO-LUMO analysis were used for the newly proposed compounds to predict the reactivity zones on the molecules, stability, and chemical reactivity. DFT calculation illustrated that these new compounds were stable. The estimated binding free energy (ΔG) values for a-e compounds were obtained as -5.78, -5.81, -5.95, -5,98, and -6.11 kcal/mol, respectively. Finally, the pharmaceutical properties and toxicity of these new compounds were estimated through OSIRIS DataWarrior software. The results indicated no risk of tumorigenic, irritant, or reproductive effects and mutagenicity for compounds d and e. As a result, compounds d and e, could be selected for further study as potential therapeutic candidates. Moreover, employing molecular docking simulation with the prediction of pharmaceutical properties helps to discover new potential drug compounds.

Keywords: drug design, anticancer, computational studies, DFT analysis

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Authors: Junie B. Billones, Maria Constancia O. Carrillo, Voltaire G. Organo, Stephani Joy Y. Macalino, Inno A. Emnacen, Jamie Bernadette A. Sy

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The drug discovery and development process is generally known to be a very lengthy and labor-intensive process. Therefore, in order to be able to deliver prompt and effective responses to cure certain diseases, there is an urgent need to reduce the time and resources needed to design, develop, and optimize potential drugs. Computer-aided drug design (CADD) is able to alleviate this issue by applying computational power in order to streamline the whole drug discovery process, starting from target identification to lead optimization. This drug design approach can be predominantly applied to diseases that cause major public health concerns, such as tuberculosis. Hitherto, there has been no concrete cure for this disease, especially with the continuing emergence of drug resistant strains. In this study, CADD is employed for tuberculosis by first identifying a key enzyme in the mycobacterium’s metabolic pathway that would make a good drug target. One such potential target is the lipoate protein ligase B enzyme (LipB), which is a key enzyme in the M. tuberculosis metabolic pathway involved in the biosynthesis of the lipoic acid cofactor. Its expression is considerably up-regulated in patients with multi-drug resistant tuberculosis (MDR-TB) and it has no known back-up mechanism that can take over its function when inhibited, making it an extremely attractive target. Using cutting-edge computational methods, compounds from AnalytiCon Discovery Natural Derivatives database were screened and docked against the LipB enzyme in order to rank them based on their binding affinities. Compounds which have better binding affinities than LipB’s known inhibitor, decanoic acid, were subjected to in silico toxicity evaluation using the ADMET and TOPKAT protocols. Out of the 31,692 compounds in the database, 112 of these showed better binding energies than decanoic acid. Furthermore, 12 out of the 112 compounds showed highly promising ADMET and TOPKAT properties. Future studies involving in vitro or in vivo bioassays may be done to further confirm the therapeutic efficacy of these 12 compounds, which eventually may then lead to a novel class of anti-tuberculosis drugs.

Keywords: pharmacophore, molecular docking, lipoate protein ligase B (LipB), ADMET, TOPKAT

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Authors: Rezky Rifaini, Raden Bagus Fajriya Hakim

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This research was conducted at the Bolo primary health Care in Bima Regency. The purpose of the research is to find out the association pattern that is formed of medical record database from Bolo Primary health care’s patient. The data used is secondary data from medical records database PHC. Sequential pattern mining technique is the method that used to analysis. Transaction data generated from Patient_ID, Check_Date and diagnosis. Sequential Pattern Discovery Algorithms Using Equivalent Classes (SPADE) is one of the algorithm in sequential pattern mining, this algorithm find frequent sequences of data transaction, using vertical database and sequence join process. Results of the SPADE algorithm is frequent sequences that then used to form a rule. It technique is used to find the association pattern between items combination. Based on association rules sequential analysis with SPADE algorithm for minimum support 0,03 and minimum confidence 0,75 is gotten 3 association sequential pattern based on the sequence of patient_ID, check_Date and diagnosis data in the Bolo PHC.

Keywords: diagnosis, primary health care, medical record, data mining, sequential pattern mining, SPADE algorithm

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Authors: Noa Nelson, Orit Appel, Rachel Ben-ari

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Organizational Citizenship Behavior (OCB) is voluntary employee behavior that contributes to the organization beyond formal job requirements. It can take different forms, such as helping teammates (OCB toward individuals; hence, OCB-I), or staying after hours to attend a task force (OCB toward the organization; hence, OCB-O). Generally, OCB contributes substantially to organizational climate, goals, productivity, and resilience, so organizations need to understand what encourages it. This is particularly challenging in security organizations. Security work is characterized by high levels of stress and burnout, which is detrimental to OCB, and security organizational design emphasizes formal rules and clear hierarchies, leaving employees with less freedom for voluntary behavior. The current research explored the role of Perceived Procedural Justice (PPJ) in enhancing OCB in a security organization. PPJ refers to how fair decision-making processes are perceived to be. It involves the sense that decision makers are objective, attentive to everyone's interests, respectful in their communications and participatory - allowing individuals a voice in decision processes. Justice perceptions affect motivation, and it was specifically suggested that PPJ creates an attachment to one's organization and personal interest in its success. Accordingly, PPJ had been associated with OCB, but hardly any research tested their association with security organizations. The current research was conducted among prison guards in the Israel Prison Service, to test a correlational and a causal association between PPJ and OCB. It differentiated between perceptions of direct commander procedural justice (CPJ), and perceptions of organization procedural justice (OPJ), hypothesizing that CPJ would relate to OCB-I, while OPJ would relate to OCB-O. In the first study, 336 prison guards (305 male) from 10 different prisons responded to questionnaires measuring their own CPJ, OPJ, OCB-I, and OCB-O. Hierarchical linear regression analyses indicated the significance of commander procedural justice (CPJ): It associated with OCB-I and also associated with OPJ, which, in turn, associated with OCB-O. The second study tested CPJ's causal effects on prison guards' OCB-I and OCB-O; 311 prison guards (275 male) from 14 different prisons read scenarios that described either high or low CPJ, and then evaluated the likelihood of that commander's prison guards performing OCB-I and OCB-O. In this study, CPJ enhanced OCB-O directly. It also contributed to OCB-I, indirectly: CPJ enhanced the motivation for collaboration with the commander, which respondents also evaluated after reading scenarios. Collaboration, in turn, associated with OCB-I. The studies demonstrate that procedural justice, especially commander's PJ, promotes OCB in security work environments. This is important because extraordinary teamwork and motivation are needed to deal with emergency situations and with delicate security challenges. Following the studies, the Israel Prison Service implemented personal procedural justice training for commanders and unit level programs for procedurally just decision processes. From a theoretical perspective, the studies extend the knowledge on PPJ and OCB to security work environments and contribute evidence on PPJ's causal effects. They also call for further research, to understand the mechanisms through which different types of PPJ affect different types of OCB.

Keywords: organizational citizenship behavior, perceived procedural justice, prison guards, security organizations

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Authors: Karima Sekkoum Abdelkrim Cheriti, Leila Feguigui

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Algeria has a large plant biodiversity accounting more than 4125 species (123 Families) and is endowed with resources of medicinal plants growing on various bioclimatic zones from subhumide to semi-arid and Saharan. On the other hand, the ethnopharmacology investigation remains the principal way to improve, evaluate, and finding bioactive substances derived from medicinal plants. In continuation of our works in Saharan ethpharmacopeae and phytochemistry of Saharan medicinal plants, we focus our attention on the importance of local ethnopharmacology especially to treat gastro-intestinal disorders in the south west of Algeria (El Baydh, Naama and Bechar region) as platform for bioactive substances discovery and further development. Our present investigation deals with an ethnopharmacological study on medicinal plants used for the treatment of gastro-intestinal disorders in the south west of Algeria. The study presents the uses of plants in local traditional herbal medicines, determines the homogeneity of informant traditional knowledge and the preferred medicinal plants used to treat gastro-intestinal disorders. The results indicated that Asteraceae and Lamiaceae are the most locally used families and medicines were prepared in the form of powder or infusion and used orally. Aerial parts were the most frequently used plant part. Thus, the results can be used as platform for bioactive substances discovery and further development especially for the preferred plant species used in the treatment of gastro-intestinal disorders.

Keywords: ethnopharmacology, gastro-intestinal, phytochemical, South Algeria, Sahara, endemic species

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Authors: Yaara Oppenheimer-Shaanan, Nimrod Nachmias, Marina Campos Rocha, Neta Schlezinger, Noam Dotan, Asaf Levy

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Fusarium oxysporum, a fungus that attacks a broad range of plants and can cause infections in humans, operates across different kingdoms. This pathogen encounters varied conditions, such as temperature, pH, and nutrient availability, in plant and human hosts. The Fusarium oxysporum species complex, pervasive in soils globally, can affect numerous plants, including key crops like tomatoes and bananas. Controlling Fusarium infections can involve biocontrol agents that hinder the growth of harmful strains. Our research developed a computational method to identify toxin domains within a vast number of microbial genomes, leading to the discovery of nine distinct toxins capable of killing bacteria and fungi, including Fusarium. These toxins appear to function as enzymes, causing significant damage to cellular structures, membranes and DNA. We explored biological control using bacteria that produce polymorphic toxins, finding that certain bacteria, non-pathogenic to plants, offer a safe biological alternative for Fusarium management, as they did not harm macrophage cells or C. elegans. Additionally, we elucidated the 3D structures of two toxins with their protective immunity proteins, revealing their function as unique DNases. These potent toxins are likely instrumental in microbial competition within plant ecosystems and could serve as biocontrol agents to mitigate Fusarium wilt and related diseases.

Keywords: microbial toxins, antifungal, Fusarium oxysporum, bacterial-fungal intreactions

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Authors: Camila Plaza, Stefan Felder

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In October 2014, the TARMED reform was implemented in Switzerland. In an effort to even out the financial standing of general practitioners (including pediatricians) relative to that of specialists in the outpatient sector, the reform tackled two aspects: on the one hand, GPs would be able to bill an additional 9 CHF per patient, once per consult per day. This is referred to as the surcharge position. As a second measure, it reduced the fees for certain technical services targeted to specialists (e.g., imaging, surgical technical procedures, etc.). Given the fee-for-service reimbursement system in Switzerland, we predict that physicians reacted to the economic incentives of the reform by increasing the consults per patient and decreasing the average amount of time per consult. Within this framework, our treatment group is formed by GPs and our control group by those specialists who were not affected by the reform. Using monthly insurance claims panel data aggregated at the physician praxis level (provided by SASIS AG), for the period of January 2013-December 2015, we run difference in difference panel data models with physician and time fixed effects in order to test for the causal effects of the reform. We account for seasonality, and control for physician characteristics such as age, gender, specialty, and physician experience. Furthermore, we run the models on subgroups of physicians within our sample so as to account for heterogeneity and treatment intensities. Preliminary results support our hypothesis. We find evidence of an increase in consults per patients and a decrease in time per consult. Robustness checks do not significantly alter the results for our outcome variable of consults per patient. However, we do find a smaller effect of the reform for time per consult. Thus, the results of this paper could provide policymakers a better understanding of physician behavior and their sensitivity to financial incentives of reforms (both past and future) under the current reimbursement system.

Keywords: difference in differences, financial incentives, health reform, physician behavior

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Authors: Matthias Stange

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In the early modern period, a different image of man began to prevail in Europe. The focus was on the self-determined human being and his abilities. At first, these developments could be seen in Italian painting and architecture, which again oriented itself to the concepts and forms of antiquity. For example, through the discovery of perspective representation by Brunelleschi or later the orthogonal projection by Alberti, after the ancient knowledge of optics had been forgotten in the Middle Ages. The understanding of reality in the Middle Ages was not focused on the sensually perceptible world but was determined by ecclesiastical dogmas. The empirical part of this study examines the rediscovery and development of perspective. With the paradigm of antiquity, the figure of the architect was also recognised again - the cultural man trained theoretically and practically in numerous subjects, as Vitruvius describes him. In this context, the role of the architect, the influence on the painting of the Quattrocento as well as the influence on architectural representation in the Baroque period are examined. Baroque is commonly associated with the idea of illusionistic appearance as opposed to the tangible reality presented in the Renaissance. The study has shown that the central perspective projection developed by Filippo Brunelleschi enabled another understanding of seeing and the dissemination of painted images. Brunelleschi's development made it possible to understand the sight of nature as a reflection of what is presented to the viewer's eye. Alberti later shortened Brunelleschi's central perspective representation for practical use in painting. In early modern Italian architecture and painting, these developments apparently supported each other. The pictorial representation of architecture initially served the development of an art form before it became established in building practice itself.

Keywords: Alberti, Brunelleschi, central perspective projection, orthogonal projection, quattrocento, baroque

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Authors: Murad Ali, Anderson Konan Seny Kan, Khalid A. Maimani

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Based on the theory of organizational design and the theory of knowledge, this study uses complexity theory to explain and better understand the causal impacts of various patterns of organizational structural factors stimulating absorptive capacity (ACAP). Organizational structure can be thought of as heterogeneous configurations where various components are often intertwined. This study argues that impact of the traditional variables which define a firm’s organizational structure (centralization, formalization, complexity and integration) on ACAP is better understood in terms of set-theoretic relations rather than correlations. This study uses a data sample of 347 from a multiple industrial sector in South Korea. The results from PLS-SEM support all the hypothetical relationships among the variables. However, fsQCA results suggest the possible configurations of centralization, formalization, complexity, integration, age, size, industry and revenue factors that contribute to high level of ACAP. The results from fsQCA demonstrate the usefulness of configurational approaches in helping understand equifinality in the field of knowledge management. A recent fsQCA procedure based on a modeling subsample and holdout subsample is use in this study to assess the predictive validity of the model under investigation. The same type predictive analysis is also made through PLS-SEM. These analyses reveal a good relevance of causal solutions leading to high level of ACAP. In overall, the results obtained from combining PLS-SEM and fsQCA are very insightful. In particular, they could help managers to link internal organizational structural with ACAP. In other words, managers may comprehend finely how different components of organizational structure can increase the level of ACAP. The configurational approach may trigger new insights that could help managers prioritize selection criteria and understand the interactions between organizational structure and ACAP. The paper also discusses theoretical and managerial implications arising from these findings.

Keywords: absorptive capacity, organizational structure, PLS-SEM, fsQCA, predictive analysis, modeling subsample, holdout subsample

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Yumeng Ma, Fang Wang, Jinxia Huang

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Researchers put forward higher requirements for efficient acquisition and utilization of domain knowledge in the big data era. As literature is an effective way for researchers to quickly and accurately understand the research situation in their field, the knowledge discovery based on literature has become a new research method. As a tool to organize and manage knowledge in a specific domain, the subject knowledge base can be used to mine and present the knowledge behind the literature to meet the users' personalized needs. This study designs the construction route of the subject knowledge base for specific research problems. Information extraction method based on knowledge engineering is adopted. Firstly, the subject knowledge model is built through the abstraction of the research elements. Then under the guidance of the knowledge model, extraction rules of knowledge points are compiled to analyze, extract and correlate entities, relations, and attributes in literature. Finally, a database platform based on this structured knowledge is developed that can provide a variety of services such as knowledge retrieval, knowledge browsing, knowledge q&a, and visualization correlation. Taking the construction practices in the field of activating blood circulation and removing stasis as an example, this study analyzes how to construct subject knowledge base based on literature knowledge extraction. As the system functional test shows, this subject knowledge base can realize the expected service scenarios such as a quick query of knowledge, related discovery of knowledge and literature, knowledge organization. As this study enables subject knowledge base to help researchers locate and acquire deep domain knowledge quickly and accurately, it provides a transformation mode of knowledge resource construction and personalized precision knowledge services in the data-intensive research environment.

Keywords: knowledge model, literature knowledge extraction, precision knowledge services, subject knowledge base

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Authors: Fahad Hanash Alzahrani

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This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

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Authors: Leena Korpinen, Rauno Pääkkönen, Fabriziomaria Gobba

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This paper is based on a large questionnaire study. The paper presents how all participants and subgroups (upper- and lower-level white-collar workers) answered the question, “Have you had an ache, pain, or numbness, which you associate with desktop computer use, in the different body parts during the last 12 months?’ 14.6% of participants (19.4% of women and 8.2% of men) reported that they had often or very often physical symptoms in the neck. Even if our results cannot prove a causal relation of symptoms with computer use, show that workers believe that computer use can influence their wellbeing: This is important when devising treatment modalities to decrease these physical symptoms.

Keywords: ergonomics, work, computer, symptoms

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Authors: Muhammad Ishaqa, Quanchao Zhanga, Qian Wangb

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The Indus Valley Civilization is predominantly found in the northeast of Afghanistan, Pakistan, and the northwest of India and is considered one of the four ancient civilizations of the Old World, as well as the first urban civilization in South Asia. In 1920, John Marshall and other archaeologists established the existence of this civilization. Over the course of a century, India and Pakistan have made significant advancements in their joint archaeological investigation and excavation, contributing to the study of the Indus Valley Civilization. Given the importance of early discovery and confirmation of this civilization, our research focuses on the academic history of its archaeology by gathering published research material. Our research begins by collecting research data associated with the Indus Valley Civilization and documenting the process of archaeological investigations and excavations from the 19th century until the present day. We also summarize the archaeological works conducted during different periods. Furthermore, we present the primary academic views on the Indus Civilization from the 19th century until the present, explaining their developmental process and highlighting recent research. This forms a foundation for further study. We discovered that the archaeological research of the Indus Civilization is significantly influenced by Western archaeology and has yet to establish an independent, local research system. We delve into the three primary sites of the Indus Valley Civilization - Harappa, Mohenjo-Daro, and Chanhudaro - discussing their history and archaeological excavation records. Our findings indicate that the Indus Civilization is solely dependent on archaeology, distinguishing it from the Sumerian Civilization and verifying that it originates from the Bronze Age of the Indus Valley. Lastly, we examine the primary academic issues associated with the Indus Civilization in greater depth. These issues include climate environment, political system, primitive religion, and academic contribution.

Keywords: Indus Valley civilization, archaeology, Harappa, Mohenjo-Daro

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Authors: Chih-Ping Chang

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Different from traditional objective evidence, social media evidence has its own characteristics with easily tampering, recoverability, and cannot be read without using other devices (such as a computer). Simply taking a screenshot from social network sites must be questioned its original identity. When the police search and seizure digital information, a common way they use is to directly print out digital data obtained and ask the signature of the parties at the presence, without taking original digital data back. In addition to the issue on its original identity, this conduct to obtain evidence may have another two results. First, it will easily allege that is tampering evidence because the police wanted to frame the suspect and falsified evidence. Second, it is not easy to discovery hidden information. The core evidence associated with crime may not appear in the contents of files. Through discovery the original file, data related to the file, such as the original producer, creation time, modification date, and even GPS location display can be revealed from hidden information. Therefore, how to show this kind of evidence in the courtroom will be arguably the most important task for ruling social media evidence. This article, first, will introduce forensic software, like EnCase, TCT, FTK, and analyze their function to prove the identity with another digital data. Then turning back to the court, the second part of this article will discuss legal standard for authentication of social media evidence and application of that forensic software in the courtroom. As the conclusion, this article will provide a rethinking, that is, what kind of authenticity is this rule of evidence chase for. Does legal system automatically operate the transcription of scientific knowledge? Or furthermore, it wants to better render justice, not only under scientific fact, but through multivariate debating.

Keywords: federal rule of evidence, internet forensic, printouts as evidence, social media evidence, United States v. Vayner

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Authors: Ali Aqa Mohammad Jawad

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Taking a relational approach, this paper analyzes the causal mechanisms associated with successful mobilization and rapid demobilization of the Enlightening Movement in post-2001 Afghanistan. The movement emerged after the state-owned Da Afghan Bereshna Sherkat (DABS) decided to divert the route for the Turkmenistan-Uzbekistan-Tajikistan-Afghanistan-Pakistan (TUTAP) electricity project. The grid was initially planned to go through the Hazara-inhabited province of Bamiyan, according to Afghanistan’s Power Sector Master Plan. The reroute served as an aide-mémoire of historical subordination to other ethno-religious groups for the Hazara community. It was also perceived as deprivation from post-2001 development projects, financed by international aid. This torched the accumulated grievances, which then gave birth to the Enlightening Movement. The movement had a successful mobilization. However, it demobilized after losing much of its mobilizing capabilities through an amalgamation of external and internal relational factors. The successful mobilization yet rapid demobilization constitutes the puzzle of this paper. From the theoretical perspective, this paper is significant as it establishes the applicability of contentious politics theory to protest mobilizations that occurred in Afghanistan, a context-specific, characterized by ethnic politics. Both primary and secondary data are utilized to address the puzzle. As for the primary resources, media coverage, interviews, reports, public media statements of the movement, involved in contentious performances, and data from Social Networking Services (SNS) are used. The covered period is from 2001-2018. As for the secondary resources, published academic articles and books are used to give a historical account of contentious politics. For data analysis, a qualitative comparative historical method is utilized to uncover the causal mechanisms associated with successful mobilization and rapid demobilization of the Movement. In this pursuit, both mobilization and demobilization are considered as larger political processes that could be decomposed to constituent mechanisms. Enlightening Movement’s framing and campaigns are first studied to uncover the associated mechanisms. Then, to avoid introducing some ad hoc mechanisms, the recurrence of mechanisms is checked against another case. Mechanisms qualify as robust if they are “recurrent” in different episodes of contention. Checking the recurrence of causal mechanisms is vital as past contentious events tend to reinforce future events. The findings of this paper suggest that the public sphere in Afghanistan is drastically different from Western democracies known as the birthplace of social movements. In Western democracies, when institutional politics did not respond, movement organizers occupied the public sphere, undermining the legitimacy of the government. In Afghanistan, the public sphere is ethicized. Considering the inter- and intra-relational dynamics of ethnic groups in Afghanistan, the movement reduced to an erosive inter- and intra-ethnic conflict. This undermined the cohesiveness of the movement, which then kicked-off its demobilization process.

Keywords: enlightening movement, contentious politics, mobilization, demobilization

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Authors: Adegoke Yinka Adebayo

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An understanding of the critical product attributes that impact on in vivo performance is key to the production of safe and effective medicines. Thus, a key driver for our research is the development of new basic science and technology underpinning the development of new pharmaceutical products. Research includes the structure and properties of drugs and excipients, biopharmaceutical characterisation, pharmaceutical processing and technology and formulation and analysis.

Keywords: drug discovery, drug development, drug delivery

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Authors: K. S. Suganya, R. Nedunchezhian

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In recent years, blockchain technology has been found to be the most significant discovery in this digital era, after the discovery of the Internet and Cloud Computing. Blockchain is a simple, distributed public ledger that contains all the user’s transaction details in a block. The global copy of the block is then shared among all its peer-peer network users after validation by the Blockchain miners. Once a block is validated and accepted, it cannot be altered by any users making it a trust-free transaction. It also resolves the problem of double-spending by using traditional cryptographic methods. Since the advent of bitcoin, blockchain has been the backbone for all its transactions. But in recent years, it has found its roots and uses in many fields like Smart Contracts, Smart City management, healthcare, etc. Identity management against digital identity theft has become a major concern among financial and other organizations. To solve this digital identity theft, blockchain technology can be employed with existing identity management systems, which maintain a distributed public ledger containing details of an individual’s identity containing information such as Digital birth certificates, Citizenship number, Bank details, voter details, driving license in the form of blocks verified on the blockchain becomes time-stamped, unforgeable and publicly visible for any legitimate users. The main challenge in using blockchain technology to prevent digital identity theft is ensuring the pseudo-anonymity and privacy of the users. This survey paper will exert to study the blockchain concepts, consensus protocols, and various blockchain-based Digital Identity Management systems with their research scope. This paper also discusses the role of Blockchain in COVID-19 pandemic management by self-sovereign identity and supply chain management.

Keywords: blockchain, consensus protocols, bitcoin, identity theft, digital identity management, pandemic, COVID-19, self-sovereign identity

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Authors: Innocent Uzougbo Onwuegbuzie

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Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.

Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan

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Authors: Peng Hou

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Natural products produced from marine bacteria serve as an immense reservoir for anti-infective drugs and therapeutic agents. Nowadays, heterologous expression of gene clusters of interests has been widely adopted as an effective strategy for natural product discovery. Briefly, the heterologous expression flowchart would be: biosynthetic gene cluster identification, pathway construction and expression, and product detection. However, gene cluster capture using traditional Transformation-associated recombination (TAR) protocol is low-efficient (0.5% positive colony rate). To make things worse, most of these putative new natural products are only predicted by bioinformatics analysis such as antiSMASH, and their corresponding natural products biosynthetic pathways are either not expressed or expressed at very low levels under laboratory conditions. Those setbacks have inspired us to focus on seeking new technologies to efficiently edit and refractor of biosynthetic gene clusters. Recently, two cutting-edge techniques have attracted our attention - the CRISPR-Cas9 and Gibson Assembly. By now, we have tried to pretreat Brevibacillus laterosporus strain genomic DNA with CRISPR-Cas9 nucleases that specifically generated breaks near the gene cluster of interest. This trial resulted in an increase in the efficiency of gene cluster capture (9%). Moreover, using Gibson Assembly by adding/deleting certain operon and tailoring enzymes regardless of end compatibility, the silent construct (~80kb) has been successfully refactored into an active one, yielded a series of analogs expected. With the appearances of the novel molecular tools, we are confident to believe that development of a high throughput mature pipeline for DNA assembly, transformation, product isolation and identification would no longer be a daydream for marine natural product discovery.

Keywords: biosynthesis, CRISPR-Cas9, DNA assembly, refactor, TAR cloning

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Authors: N. Taghinejad, F. Dortaj, N. Khodabandeh

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This research aimed at comparing resilience among male and female children with and without parental supervision in Bandar Abbas. The sample consists of 200 subjects selected through cluster sampling. The research method was comparative causal and Conner and Davidson’s questionnaire form resilience was used for data collection. Results indicated that there is no difference between children with and without parental supervision regarding their resilience capacity. These findings may be challenging and useful for psychologists, officials of children’s affairs and legislators.

Keywords: resilience, children , children with parental supervision, children without parental supervision

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Authors: Kanduru Lokesh, Venkitasamy Kesavan

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The unique structural features of spiro-oxindoles with diverse biological activities have made them privileged structures in new drug discovery. The key structural characteristic of these compounds is the spiro ring fused at the C-3 position of the oxindole core with varied heterocyclic motifs. Structural diversification of heterocyclic scaffolds to synthesize new chemical entities as pharmaceuticals and agrochemicals is one of the important goals of synthetic organic chemists. Nitrogen and oxygen containing heterocycles are by far the most widely occurring privileged structures in medicinal chemistry. The structural complexity and distinct three-dimensional arrangement of functional groups of these privileged structures are generally responsible for their specificity against biological targets. Structurally diverse compound libraries have proved to be valuable assets for drug discovery against challenging biological targets. Thus, identifying a new combination of substituents at C-3 position on oxindole moiety is of great importance in drug discovery to improve the efficiency and efficacy of the drugs. The development of suitable methodology for the synthesis of spiro-oxindole compounds has attracted much interest often in response to the significant biological activity displayed by the both natural and synthetic compounds. So creating structural diversity of oxindole scaffolds is need of the decade and formidable challenge. A general way to improve synthetic efficiency and also to access diversified molecules is through the annulation reactions. Annulation reactions allow the formation of complex compounds starting from simple substrates in a single transformation consisting of several steps in an ecologically and economically favorable way. These observations motivated us to develop the annulation reaction protocol to enable the synthesis of a new class of spiro-oxindole motifs which in turn would enable the enhancement of molecular diversity. As part of our enduring interest in the development of novel, efficient synthetic strategies to enable the synthesis of biologically important oxindole fused spirocarbocyclic systems, We have developed an efficient methodology for the construction of highly functionalized spirocarbocyclic oxindoles through [4+2] annulation of phthalides via Hauser annulation. functionalized spirocarbocyclic oxindoles was accomplished for the first time in the literature using Hauser annulation strategy. The reaction between methyleneindolinones and arylsulfonylphthalides catalyzed by cesium carbonate led to the access of new class of biologically important spiro[indoline-3,2'-naphthalene] derivatives in very good yields. The synthetic utility of the annulated product was further demonstrated by fluorination Using NFSI as a fluorinating agent to furnish corresponding fluorinated product.

Keywords: Hauser-Kraus annulation, spiro carbocyclic oxindoles, oxindole-ester, fluoridation

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Authors: Yulan Wu

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With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Amna J. Ghith, Khaled Abu Zid, Khairia Youssef, Nasser Saad

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Backgrounds: The multikinase and vascular endothelial growth factor (VEGF) receptor inhibitors interrupt the pathway by which angiogenesis becomes established and promulgated, resulting in the inadequate nourishment of metastatic disease. VEGFR-2 has been the principal target of anti-angiogenic therapies. We disclose the new thieno pyrimidines as inhibitors of VEGFR-2 designed by a molecular modeling approach with increased synergistic activity and decreased side effects. Purpose: 2-substituted thieno pyrimidines are designed and synthesized with anticipated anticancer activity based on its in silico molecular docking study that supports the initial pharmacophoric hypothesis with a same binding mode of interaction at the ATP-binding site of VEGFR-2 (PDB 2QU5) with high docking score. Methods: A series of compounds were designed using discovery studio 4.1/CDOCKER with a rational that mimic the pharmacophoric features present in the reported active compounds that targeted VEGFR-2. An in silico ADMET study was also performed to validate the bioavailability of the newly designed compounds. Results: The Compounds to be synthesized showed interaction energy comparable to or within the range of the benzimidazole inhibitor ligand when docked with VEGFR-2. ADMET study showed comparable results most of the compounds showed absorption within (95-99) zone varying according to different substitutions attached to thieno pyrimidine ring system. Conclusions: A series of 2-subsituted thienopyrimidines are to be synthesized with anticipated anticancer activity and according to docking study structure requirement for the design of VEGFR-2 inhibitors which can act as powerful anticancer agents.

Keywords: docking, discovery studio 4.1/CDOCKER, heterocycles based anticancer agents, 2-subsituted thienopyrimidines

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

Procedia PDF Downloads 443