Earticle

Purpose: To compare patient care multipliers estimated from subjective evaluation against work sampling (WS) techniques in genetic nursing activities. Methods: An observational WS technique was conducted from November to December 2019 with nine genetic nurses in a tertiary referral center in Malaysia. The WS activity instrument was devised, validated, and pilot tested. All care- and non-care-related activities were sampled at 10-minute intervals within 8 hours of working over 14 days, followed by a subjective evaluation of activities survey over the same period. Bonferroni correction was undertaken for multiple testing with a p value of 0.0025. Results: The two techniques produced significant differences in genetic nurses’ activities categorization. The WS showed that compared with subjective evaluation, direct care (19.3% vs. 45.0%; p < .001) was estimated to be significantly lower, and indirect care (40.4% vs. 25.6%; p < .001) and unit-related activity (28.5% vs. 16.9%; p < .001) were higher. Both techniques produced a similar proportion of time spent in other non-care activities (12.0%) but differed in genetic meetings and information-gathering activities. While the multipliers for patient face-to-face contact were significantly larger between WS (4.57) and the survey (1.94), the multipliers for patient care time were smaller between WS (1.47) and the survey (1.24), indicating that caution should be taken when multiplying for patient contact time compared to patient care activity to determine the cost of care provision. Conclusion: A considerable proportion of time spent away from the patient needs to be allocated to patient-related care time. Thus, estimating the paid cost solely based on direct time with patients considerably underestimates the cost per hour of nurses' care. It is recommended to employ ‘patientrelated activity’ instead of the ‘face-to-face contact’ multiplier because the former did not significantly differ from the one estimated using WS.

In the recent years, non-preemptive job shop scheduling problems have been applied to a wide variety of academic and industrial fields. In comparison, preemptive job shop scheduling problems have received almost no attention in the both fields. Motivated by the needs of a specific application, we presented an algorithm for dealing with preemptive job shop scheduling problem. First, we considered constraint programming techniques to preemptive scheduling problems. Second, we applied genetic algorithm to these problems. In proposed genetic algorithm, we developed a new concept for representing of genetic algorithm. In case study, we applied the proposed algorithm to several job shop problems. Experiment results show that the proposed algorithm considered by preemptive problems outperforms non-preemptive case and other conventional algorithms.

The fact that signature is widely used means of person authentication emphasizes the need for automatic verification system. This paper presents a new approach for signature verification. The proposed system aims to collect representative and unique features to distinguish between digital signature images. The comparison process between testing signature and the stored signatures is optimized using Genetic Algorithm. Our experimental results give a good balance between False Acceptance Rate (FAR) and False Rejection Rate (FRR).

Note that k-anonymity algorithm has been widely discussed in the area of privacy protection. In this paper, a new search algorithm to achieve k-anonymity for database application is introduced. A lattice is introduced to form a solution space for a k-anonymity problem and then a hybrid search method composed of tabu search and genetic algorithm is proposed. In this algorithm, the tabu search plays the role of mutation in the genetic algorithm. The hybrid method with independent tabu search and genetic algorithm is compared, and the hybrid approach performs the best in average case.

본 논문에서는 파킨슨 병 진단 및 바이오 표지자 검출을 위한 극한 기계학습을 결합하는 새로운 균형 표본 유전 알고리즘(SBGA-ELM)을 제안하였다. 접근법은 정확한 파킨슨 병 진단 및 바이오 표지자 검출을 위해 공개 파킨슨 병 데이터베이스로부터 22,283개의 유전자의 발현 데이터를 사용하며 다음의 두 가지 주요 단계를 포함하였다 : 1. 특징(유전자) 선택과 2. 분류단계이다. 특징 선택 단계에서는 제안된 균형 표본 유전 알고리즘에 기반하고 파킨스병 데이터베이스(ParkDB)의 유전자 발현 데이터를 위해 고안되었다. 제안된 제안 된 SBGA는 추가적 분석을 위해 ParkDB에서 활용 가능한 22,283개의 유전자 중에서 강인한 서브셋을 찾는다. 특징분류 단계에서는 정확한 파킨슨 병 진단을 위해 선택된 유전자 세트가 극한 기계학습의 훈련에 사용된다. 발견 된 강인한 유전자 서브세트는 안정된 일반화 성능으로 파킨슨 병 진단을 할 수 있는 ELM 분류기를 생성하게 된다. 제안된 연구에서 강인한 유전자 서브셋은 파킨슨병을 관장할 것으로 예측되는 24개의 바이오 표지자를 발견하는 데도 사용된다. 논문을 통해 발견된 강인 유전자 하위 집합은 SVM이나 PBL-McRBFN과 같은 기존의 파킨슨 병 진단 방법들을 통해 검증되었다. 실시된 두 가지 방법(SVM과 PBL-McRBFN)에 대해 모두 최대 일반화 성능을 나타내었다.

A novel Samples Balanced Genetic Algorithm combined with Extreme Learning Machine (SBGA-ELM) for Parkinson's Disease diagnosis and detecting bio-markers is presented in this paper. Proposed approach uses genes' expression data of 22,283 genes from open source ParkDB data base for accurate PD diagnosis and detecting bio-markers. Proposed SBGA-ELM includes two major steps: feature (genes) selection and classification. Feature selection procedure is based on proposed Samples Balanced Genetic Algorithm designed specifically for genes expression data from ParkDB. Proposed SBGA searches a robust subset of genes among 22,283 genes available in ParkDB for further analysis. In the "classification" step chosen set of genes is used to train an Extreme Learning Machine (ELM) classifier for an accurate PD diagnosis. Discovered robust subset of genes creates ELM classifier with stable generalization performance for PD diagnosis. In this research the robust subset of genes is also used to discover 24 bio-markers probably responsible for Parkinson's Disease. Discovered robust subset of genes was verified by using existing PD diagnosis approaches such as SVM and PBL-McRBFN. Both tested methods caused maximum generalization performance.

본 연구는 부산 북구 화명동 조선시대 분묘군에서 출토된 사람뼈를 대상으로 분자유전학적 분석을 수행한 결과이다. 실리카 추출법을 사용하여 토광묘에서 출토된 사람뼈 8개체의 DNA를 분리하였고, 미토콘드리아 DNA 과변이부위 분석을 통해 모계 유연관계 여부를 확인하였다. 분석 결과 토광묘 11호, 21호, 26호에서 출토된 피장자 3명의 하플로타입 이 동정되었으며 HaploGrep 2 프로그램에서 A5a, D4a와 M4"67+16311 하플로그룹으로 분류되었다. 하플로그룹이 동정된 3개체는 같은 변이형을 공유하지 않으므로 피장자 간 모계 친연관계는 없는 것으로 확인되었다 . 이번 연구는 영남지역에서 출토된 조선시대 사람뼈의 분자유전학적 분석의 첫 사례로서 과거 한반도에 살았던 옛사람들과 현대인들 의 유전학적 관계를 이해하기 위한 기초 자료로 활용할 수 있을 것으로 기대된다 .

The analysis of ancient DNA extracted from archaeological bones has become an important research tool in palaeogenetics and anthropology. Eight human skeletal remains of the Joseon dynasty, excavated from Hwamyeong-dong, were used in this study. DNA was extracted from bone powder using a silica-based protocol. The isolated DNA was analyzed by the sequencing variation of hyper-variable region of the mitochondrial DNA. In the present study, 3 human remains were identified into mtDNA haplogroups including the A 5a, D4a, and M4"67+16311 groups, using HaploGrep 2 program. The identified haplotypes of the 3 samples have been confirmed that the specimens in the tombs were not related by the maternal line. This is the first analysis of human skeletal remains of the Joseon dynasty excavated in Busan. Date from the analysis of human remains from the Joseon dynasty are considered as the basis for understanding the genetic relationship between modern and ancient humans of the Korean peninsula.

This study aimed to enhance the gross motor function, gait, balance, and quality of life of school-aged children using task-oriented approach based on the school function assessment (SFA). Total of three school-aged children who were diagnosed with genetic developmental disabilities (GDD) participated in the experiment. In a pre-test, the gross motor function measure (GMFM), pediatric evaluation of disability inventory (PEDI), pediatric balance scale (PBS), and GaitRite was evaluated. Then, 12 weeks of task-oriented approach based on SFA was performed. The treatment consisted of ‘traveling’, ‘maintaining and changing positions’, ‘manipulation with movement’, ‘using materials’, ‘recreational movement’, ‘hygiene’, ‘clothing management’, ‘up/down stairs’. After the treatment, a post-test followed. Gait analysis revealed increased symmetry in both time and distance of a single step. Furthermore, GMFM and PBS measures increased for every participant. Finally, daily life dependence in PEDI decreased for every participant. We demonstrated effect of task-oriented approach in three children with GDD. The proposed treatment effectively enhanced overall GMFM, ratio between swing and stance phases, and PBS. Especially, the ratio between swing and stance phases shifted close to 4:6, which is the presumed normal ratio. Also, daily life dependence in PEDI decreased for every participant, which indicates that it has a significant effect on physical function and participation. Increasing number of participants and applying the suggested protocol to patients with GDD could be future topics.

전력은 모든 나라에서 사회 발전과 경제 성장에 가장 기본적인 자원이다. 산업이 고도화 되고 경제의 규모가 발전하면서 전력의 소비량은 점점 증가하고 있다. 전력을 공급하는 쪽에서는 전력을 생산할 때 자원의 낭비 를 줄이기 위해 전력 사용량을 예측하는 것은 중요한 일이다. 또한 전력 수요 예측을 통해 여름과 겨울의 피크 타임 에서의 전력 수요를 분산하는 것이 가능하다. 그리고 소비 전력의 예측은 국내에서 수요자원 거래시장(Negawatt market)이 본격화되면서 더욱 중요하게 되었다. 더구나 전력 소비량 예측은 소비자가 전력 시장에 직간접적으로 참여하는 수요관리 방법을 제공해준다. 본 연구에서는 1999년부터 2011년까지의 국내총생산, 1인당 국민총소득, 부 가세, 국내전력소비량을 이용하여 제주도의 어업 전력 사용량을 예측하는데 유전자 알고리즘을 사용하고 있다. 유전 자 알고리즘은 다양한 조합 최적화 분야에서 최적해를 찾는데 유용하게 사용되는 알고리즘이다. 본 논문에서 유전 자 알고리즘에서 최적의 동작을 위한 파라미터들을 찾는다. 그리고 실제 전력 소비량 예측을 위해 사용되는 계수 (coefficient)들의 최적값을 찾아 예측값과 실제 전력 소비량의 오차를 최소화하는데 목적이 있다.

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

정보보호는 기업의 운영과 고객의 신뢰를 보장하는 필수요소이며 침해사고 예방을 통해 불확실한 피해를 완화시킬 수 있기 때문에 적절한 정보보호 대책의 선택과 적정투자 수준을 결정하는 것이 중요하다. 본 연구는 다양한 산업군에 속해있는 기업에서 정보보호 대책 투자에 활용할 수 있는 예산 범위에서 구성할 수 있는 최적의 정보보호 대책 포트폴리오 구성뿐만 아니라, 각 대책의 적절한 투자 수준에 대한 의사결정 지원 모델을 제시한다. 이를 위하여 산업군별 침해사고 유형 통계를 분석하고 유전자 알고리즘을 활용하여 최적 정보보호 대책 투자 포트폴리오를 도출한다. 도출된 모델을 기존 유전자 알고리즘을 이용한 정보보호 대책 투자 최적화 모델과 비교하고 기업 대상 설문 조사를 통하여 실제 사례를 분석한다.

Information security is an essential element not only to ensure the operation of the company and trust with customers but also to mitigate uncertain damage by preventing information data breach. Therefore, It is important to select appropriate information security countermeasures and determine the appropriate level of investment. This study presents a decision support model for the appropriate investment amount for each countermeasure as well as an optimal portfolio of information countermeasures within a limited budget. We analyze statistics on the types of information security breach by industry and derive an optimal portfolio of information security countermeasures by using genetic algorithms. The results of this study suggest guidelines for investing in information security countermeasures in various industries and help to support objective information security investment decisions.

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

유전자정보 활용을 위한 법․제도적인 노력은 범죄발생 및 사건의 조기해결에 따른 사회적 비용의 절감으로 보다 효율적인 민생치안 확보를 가능하게 할 뿐만 아니라 국민 불안을 해소시키는 데에도 크게 기여할 것으로 기대된다. 또한 유전자감식 기술의 활용은 초동수사에서의 유용성뿐만 아니라 장기미제 사건의 해결에도 기여할 것이며, 특히 인권에 대한 높은 사회적 관심과 더불어 과거 자백위주의 수사 관행에서 벗어나 범행현장에서 발견된 증거에 의해 사건 전체를 과학적으로 해석하고, 그동안 묻혀 있었을지도 모르는 사건의 실체적 진실을 파헤치는 중요한 수단을 제공할 것이다.이러한 유전자정보의 효율적 활용을 위해서는 유전자정보의 수집․관리와 관련된 법․제도적 여건이 우선적으로 마련되어야 할 것이다. 먼저 범죄사건의 초동수사과정에서 범인의 유전자를 발견할 수 있는 전문화된 현장감식 역량이 조기에 확보되어야 하며 현장감식업무의 전문화를 위해 다양한 전문적 교육과정 및 대학과의 산학연계가 필요하다. 뿐만 아니라 개개인의 전문성 강화를 위해 단계별 범죄현장조사관 또는 혈흔조사관 등 자격증 제도를 도입하고, 자격을 획득한 현장감식전문가에게 전문성의 인정과 더불어 적절한 업무권한과 수당을 지급하여 동기를 부여할 필요가 있다. 특히 수집된 유전자 시료는 신뢰성 있는 감정기관에서 전문적인 분석요원, 공정한 처리절차, 객관적인 분석기법과 그 성능이 입증된 장비에 의해 분석되어져야 하며 어렵게 수집된 증거물이 증거로서의 가치를 제대로 보존하기 위해서 수집뿐 아니라 이동이나 장기보관 등 모든 과정에서 오염이나 멸실을 막기 위한 엄격한 절차와 기준을 마련하고 철저히 교육 시키는 등 절차와 제도면에서 체계적인 노력을 기울여야 할 것이다.

This study aims to suggest how to take advantage of genetic information for efficient preliminary investigations which enables to deal rapidly with criminal cases and to prevent unnecessary time, human resources and expenses from wasting at the levels of both victims and the government. In this light, it is expected that the application of scientific investigation techniques using DNA typing will contribute greatly to improving the current criminal investigations. In particular, collected DNA samples should be analyzed by highly reliable and authorized laboratories employing DNA analysis specialists, fair procedures, objective analysis techniques and established, high-performance equipments, and in order to preserve the legal value and validity of such the biological evidence collected, all the processes including collection, transfer and long-term storage should comply with strict procedure standards and regulations to avoid any contamination or loss. In this connection, thorough personnel training about the above-mentioned standards and regulations is required. In conclusion, if genotype database is maintained as mentioned above, a quiet number of violent crimes can be prevented because it functions as a deterrent factor when criminals decide to commit crimes. Solving criminal cases early and reducing crime rates will lead to saving investigation expenses and human resources, and ultimately contribute significantly to protecting national competitiveness against loss factors.

최근 기업의 경쟁력 강화를 위하여 기업내의 지식을 중요한 자원으로 인식하고 활용하는 지식경영의 필요성이 강력히 대두되고 있다. 이러한 지식경영의 주요 활동을 지원할 구체적인 방법론으로 정보기술의 활용 방안이 다각도로 제시되고 있으나, 실제적인 연구는 아직 초보단계에 있다고 하겠다. 본 연구에서는 지식의 생성, 저장, 그리고 추출 및 활용이라는 지식경영의 주요 과제를 효과적으로 해결하는 방안으로써 인공지능기법인 사례기반추론과 유전자 알고리즘을 이용한 통합방법론을 제시한다. 본 연구에서 제시하고 있는 방법론은 생성된 지식의 표현, 저장, 그리고 추출에 사례기반추론기법을 활용하였다는 점 이외에 다음과 같은 두 가지 특징을 가지고 있다. 첫째로는, 해결하고자 하는 문제에 가장 적절한 과거 지식이 추출되도록 함으로써 활용 효과를 높일 수 있도록 하였다는 점이다. 둘째로는, 환경의 변화를 반영할 수 있는 방안을 제시하고 있다는 점이다. 본 인공지능 통합방법론은 신용평가부서의 지식관리모형을 통해 검증해 본 결과 그 효과가 입증되었다.

The efficient management and sharing of the radio spectrum among numerous users become an important issue. The frequency channels are a scarce resource in a cellular mobile system. Thus, many schemes have been proposed to assign frequencies to the cells such that the available spectrum is efficiently used. In this paper, we propose a channel allocation mechanism including migration concept using genetic algorithm in cellular mobile computing environments. Our simulation results indicate that the proposed algorithm could reduce a search time for an available channel.

In this paper, we propose a new user-adaptive pointing and correction algorithm applied in the field of smart sensing. The error from the accelerometer sensor’s output must be carefully managed as the sensor is more sensitive to data change compared to that of the gyroscope sensor. Thus, we minimize noise by applying the Kalman filtering to data for each axis from the accelerometer. In addition, we can also obtain effect compensating hand tremor by applying the Kalman filter to the data variation for x and y. In this study, we extract data through the Quaternion mapping process on data from the accelerometer and gyroscope. In turn, we can obtain a tilt compensation by applying the compensation algorithm with acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensors due to the rapid movement of a device, we propose and integrate a genetic approach.

Agile development is truly the need of the hour due to its numerous advantages which are in line with the present business trends. A successful requirement engineering serves as a foundation for success for any software development project. Functional requirements point towards the product services and non-functional requirements are related to the emergent properties of the system. Correct and speedy elicitation of functional and non-functional requirements contribute a great deal towards successful requirement engineering process. Many techniques have been proposed in the past for requirement elicitation for agile development, but they do not take into consideration a holistic automatic approach concerning functional and non-functional requirements. This paper proposes a supervised learning based automated (neural network with the genetic algorithm) approach for successfully classifying functional and non-functional requirements from multiple requirements documents in an agile environment. It is implemented on two data sets and further analysis, and comparison of this model is made with an another implemented model (SVM with RBF kernel) based on precision, recall, and accuracy. This paper contributes in simplifying and automating the requirement engineering process; thus, making the life easier for many stakeholders.

The web service developed by telecommunication domain is ineffective since they are demonstrated by syntactic description rather than semantic. The motivation of the research is to have semantic description with existing web services, and provides discovery, composition and invocation of web services automatically. The objective is to identify the discovery and composition concerns and devise a compositional approach that covers all concerns. So a new prototype named Semantic Web Service Engine for Telecommunication which automatically discover and composite a web service was proposed, enables semantic through upper ontology and maps Web Service Description Language to Ontology Web Language-Semantic. For composition, a genetic algorithm was proposed which can solve problems with great distinctiveness. This approach automatically discovers and generates the required composite semantic web services and considers all identified concerns concurrently, improves the accuracy for the service discovery and unifies the semantic representation of telecommunications without human intervention.

The main functions of glycosylation are stabilization, detoxification and solubilization of substrates and products. To produce glycosylated products, Escherichia coli was engineered by overexpression of UDPxylose, UDP-glucoronic acid, TDP-L-rhamnose and TDP-6-deoxy-Dallose biosynthetic gene clusters, and flavonoids were glycosylated by the overexpression of the glycosyltransferase gene from Arabidopsis thaliana. For the glycosylation, these flavonoids (ficetin, quercetin, kaempferol and other one) were exogenously fed to the host in a biotransformation system. The products were isolated, analyzed and confirmed by HPLC, LC/MS, NMR and ESI-MS/MS analyses. Several conditions, substrate concentration, incubation time) were optimized to increase the production level. We successfully isolated approximately 24 mg/L 3-O-rhamnosyl quercetin and 12.9 mg/L 3-O-rhamnosyl kaempferol upon feeding of 0.2 mM of the respective flavonoids and were also able to isolate 3-O-allosyl quercetin. Thus, this study reveals a method that might be useful for the biosynthesis of rhamnosyl and allosyl flavonoids and for the glycosylation of related compounds.