Structural choice based on knowledge discovery system

Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.

Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data.This data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the quality.Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours.This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints.By investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance analysis.Furthermore,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the system.The configuration of the system and the conformance checking for further enhancement is also addressed in this research.To achieve the objectives,this research uses process mining techniques,i.e.process discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and enhancement.Initially,the identified executed process is reconstructed by using the process discovery techniques.Following the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and redesigning.Finally,some recommendations are made to improve the enterprise management system processes.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.

Towards cultivar-oriented gene discovery for better crops

The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars.Many genes have been described in papers as being beneficial for yield increase.However,few of them have been translated into increased yield on farms.In contrast,commercial breeders are facing gene decidophobia,i.e.,puzzled about which gene to choose for breeding among the many identified,a huge chasm between gene discovery and cultivar innovation.The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation.The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds,experimental validation in relevant environments,appropriate crop management,and data reusability.The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.

Comprehensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer:A systematic review

BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies.Ubiquitin-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various cancers.There have been numerous studies on the role of USPs in CRC;however,a comprehensive view of this role is lacking.AIM To provide a systematic review of the studies investigating the roles and functions of USPs in CRC.METHODS We systematically queried the MEDLINE(via PubMed),Scopus,and Web of Science databases.RESULTS Our study highlights the pivotal role of various USPs in several processes implicated in CRC:Regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug resistance.The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC.The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms.CONCLUSION Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.

Data-Driven Modeling of Partially Observed Biological Systems

We present a numerical approach for modeling unknown dynamical systems using partially observed data,with a focus on biological systems with(relatively)complex dynamical behavior.As an extension of the recently developed deep neural network(DNN)learning methods,our approach is particularly suitable for practical situations when(i)measurement data are available for only a subset of the state variables,and(ii)the system parameters cannot be observed or measured at all.We demonstrate that,with a properly designed DNN structure with memory terms,effective DNN models can be learned from such partially observed data containing hidden parameters.The learned DNN model serves as an accurate predictive tool for system analysis.Through a few representative biological problems,we demonstrate that such DNN models can capture qualitative dynamical behavior changes in the system,such as bifurcations,even when the parameters controlling such behavior changes are completely unknown throughout not only the model learning process but also the system prediction process.The learned DNN model effectively creates a“closed”model involving only the observables when such a closed-form model does not exist mathematically.

Discovery of Lorandite TlAsS_2 at the Distal Au-Tl Deposit in a Skarn system,Fengshan Area,Middle-Lower Yangtze River,Eastern China

Thallium has been used geochemical exploration of gold deposits. However, as an indicator element in searching for hydrothermal the T1 minerals and mineralization are rare in nature. Lorandite T1AsS2, a relatively uncommon mineral, has been dominantly discovered in some Carlin gold deposits, and minor Sb- Hg, U and Pb-Zn-Ag deposits.

Identify Can didate Genes in the Interactio n betwee n Abdominal Aortic Aneurysm and Type 2 Diabetes Mellitus by Using Biomedical Discovery Support System

Objective To explore the candidate genes that play significant roles in the interconnection between abdominal aortic aneurysm(AAA)and type 2 diabetes mellitus(DM).Methods We used the Biomedical Discovery Support System(BITOLA)to screen out the candidate intermediate molecular(CIM)"Gene or Gene Product”that are related to AAA and DM.The dataset of GSE13760,GSE7084,GSE57691,GSE47472 were used to analyze the differentially expressed genes(DEGs)of AAA and DM compared to the healthy status.We used the online tool ofVenny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs.The Human eFP Browser was applied to examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue.Results There were 86 CIMs suggested by the closed BITOLA system.Among all the DEGs of AAA and DM,8 genes in GSE7084(ISG20,ITGAX,DSTN,CCL5,CCR5,AGTR1,CD19,CD44)and 2 genes in GSE 13760(PSMD12,FAS)were found to be overlapped with the 86 CIMs.By manual checking and comparing with tissuespecific gene data through Human eFP Browser,the gene PSMD12(proteasome 26S subunit,non-ATPase 12)was recognized to be strongly expressed in both the aorta and pancreatic tissue.Conclusion We proposed a hypothesis through text mining that PSMD12 might be involved or potentially involved in the interconnection between AAA and DM,which may provide a new clue for studies on novel therapeutic strategies for the two diseases.

ESKD-A New Structure of Expert System Based on Knowledge Discovery

A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experi- ment, the structure of ESKD is effective and feasible.

Knowledge Discovery from Dynamic Data on a Nonlinear System

A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Metabolomics in drug discovery: Restoring antibiotic pipeline

Metabolomics has emerged as a valuable tool in drug discovery and development,providing new insights into the mechanisms of action and toxicity of potential therapeutic agents.Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites,within biological systems.Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents,thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention.This approach can also uncover key metabolic pathways essential for survival,which can serve as potential targets for novel antibiotics.By analyzing the metabolites produced by diverse microbial communities,metabolomics can guide the discovery of previously unexplored sources of antibiotics.This review explores some examples that enable medicinal chemists to optimize drug structure,enhancing efficacy and minimizing toxicity via metabolomic approaches.

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.

The role of SLC12A family of cation-chloride cotransporters and drug discovery methodologies

The solute carrier family 12(SLC12)of cation-chloride cotransporters(CCCs)comprises potassium chloride cotransporters(KCCs,e.g.KCC1,KCC2,KCC3,and KCC4)-mediated Cl^(-)extrusion,and sodium potassium chloride cotransporters(N[K]CCs,NKCC1,NKCC2,and NCC)-mediated Cl^(-)loading.The CCCs play vital roles in cell volume regulation and ion homeostasis.Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues.In recent years,there have been considerable advances in our understanding of CCCs'control mechanisms in cell volume regulations,with many techniques developed in studying the functions and activities of CCCs.Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs.These techniques include the ammonium pulse technique,radioactive or nonradioactive rubidium ion uptakeassay,and thallium ion-uptake assay.CCCs'activity can also be indirectly observed by measuring gaminobutyric acid(GABA)activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes,radiotracer^(36)Cl^(-),and fluorescent dyes.Other techniques include directly looking at kinase regulatory sites phosphorylation,flame photometry,22Nat uptake assay,structural biology,molecular modeling,and high-throughput drug screening.This review summarizes the role of CCCs in genetic disorders and cell volume regulation,current methods applied in studying CCCs biology,and compounds developed that directly or indirectly target the CCCs for disease treatments.

Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.

Knowledge Discovery in Learning Management System Using Piecewise Linear Regression

Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be anessential part of 5G cellular networks. D2D communication enables closeproximitydevices to establish a direct communication session. D2D communicationoffers many advantages, such as reduced latency, high data rates,range extension, and cellular offloading. The first step to establishing a D2Dsession is device discovery;an efficient device discovery will lead to efficientD2D communication. D2D device further needs to manage its mode of communication,perform resource allocation, manage its interference and mostimportantly control its power to improve the battery life of the device. Thiswork has developed six distinct scenarios in which D2D communication canbe initiated, considering their merits, demerits, limitations, and optimizationparameters. D2D communication procedures for the considered scenarioshave been formulated, based upon the signal flow, containing device discovery,resource allocation, and session teardown. Finally, latency for each scenariohas been evaluated, based on propagation and processing delays.

A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models.

Truth Discovery from Conflicting Data: A Survey

With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.

Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As a result,we build an Enhanced Opportunistic Routing(EORP)protocol architecture in order to address the issues raised before.This proposed routing protocol goal is to manage the routing cost by employing power,load,and delay to manage the routing energy consumption based on theflooding of control pack-ets from the target node.According to the goal of the proposed protocol techni-que,it is possible to manage the routing cost by applying power,load,and delay.The proposed technique also manage the routing energy consumption based on theflooding of control packets from the destination node in order to reduce the routing cost.Control packet exchange between the target and all the nodes,on the other hand,is capable of having an influence on the overall efficiency of the system.The EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adja-cent nodes for each node in the routing route as part of the routing path discovery process,which occurs during control packet transmission.While control packet transmission is taking place during the routing path discovery process,the EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adjacent nodes for each node in the routing.Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate(CBR).When the packet rate of the CBR is 20 packets per second,the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols,according to thefindings.Assuming that the CBR packet rate is 20 packets per second,the EORP-MCCND achieves 0.6 s of End 2 End Delay,0.05 s of Routing Overhead Delay,120 s of Network Lifetime,and 20 J of Energy Consumption efficiency,which is much better than that of the state-of-the-art protocols.