ANALISIS DAYA DUKUNG DAN DAYA TAMPUNG LAHAN PERMUKIMAN KOTA DENPASAR

Denpasar City is the capital city of Bali Province which has a rapid population growth rate every year. Along with the rapid population growth, this has an impact on meeting the needs of housing and other regional service facilities. The increasing number of population automatically causes an increase in the need for housing. Analysis of residential land supporting capacity and supply capability needed to preserve the environment. This research was conducted with a quantitative approach which describes and describes the results in the form of numbers or nominal values by explaining clearly either with the help of pictures, tables, or graphs. The quantitative data described are the population, land area and land capability area to support and accommodate housing as a place to live in Denpasar City. Based on the results of the analysis using the overlapping method and scoring using a GIS application. Denpasar City potential land to be developed as a residential area after being adjusted to the settlement plan in the Denpasar City Spatial Plan is available for 454,73 hectares of the total area of Denpasar City of 12,521 hectares or about 3.63% of land in Denpasar city. The capacity of Denpasar City houses is able to accommodate around 44.736 housing units and 178.943 residents. There are 2 sub-districts that are not able to accommodate the population in 2030, namely South Denpasar and North Denpasar. However, cumulatively, Denpasar City is still able to accommodate population growth in 2030 in obtaining residential land. Keywords: Supporting Capacity and Supply Capability; Residential Land; Denpasar City.

Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.

A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems

Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

Predicting Land Cover Change in the Mamminasata Area, Indonesia, to Evaluate the Spatial Plan

The spatial plan program for Makassar City and the surrounding area called Mamminasata (Makassar, Maros, Sungguminasa, and Takalar) was created by the Indonesian Government. The program regulates the proportion of land cover, but predictions about land cover changes were not considered. Therefore, in this study, we predict what the land cover may be in 2031 using the multi-layer perceptron neural network and the Markov chain methods. For this purpose, image composite, support vector machine classifier, and change detection were applied to a time series of satellite data. Visual validation showed the hot-spots of land cover changes related to population density, and statistical validation scored 0.99 and 0.78 in no information kappa and grid-cell level location kappa, respectively. The model was performed to predict land cover in 2031, and the predicted result was then compared with the spatial plan using an overlapping method. The results showed that built-up area, dryland agriculture, and wetland agriculture occupied two, twenty, and eight percent of the protected zone, respectively. Meanwhile, fifteen percent of the development zone was covered by forest, mainly in the eastern part of Mamminasata. The result can be used to help the Government decide future plans for the Mamminasata area.

Analysis of the effects of auroral substorms on the data of ionosphere vertical sounding in the Arctic

The problem of diagnosing and predicting the characteristics of the polar ionosphere can be investigated by studying the effect of magnetospheric disturbances on the high-latitude ionosphere. Our task is to investigate the dependence of the variations in the electron concentration F of the ionosphere region at the subauroral stations of vertical sounding (VS) Sodankyla, Lovozero and Gorkovskaya on variations in the geomagnetic field. The data of AE and PC of geomagnetic indices were used during substorms in the winter of 2011–2012. For analysis, the epoch overlapping method was used. As a result, it was shown that the perturbation in the electromagnetic field is accompanied by a subsequent amplification of variations during the critical frequencies fоF2, and hence the electron concentration, auroral and subauroral ionosphere.We conclude that the geomagnetic indices AE and PC can serve as predictors of disturbances during the ionospheric parameter fоF2 of the high-latitude ionosphere in the winter season.An increase in the amplitude level of AE from 100 to 350 nT (and PC > 2) during the night hours of the winter season precedes an increase in the critical frequencies of the ionosphere F2 layer by an average of 30% of the median. An increase in the amplitude level of AE from 180 to 520 nT (and PC> 2) in the winter season in the afternoon precedes the positive or negative deviation of the critical frequencies of the ionosphere F2 layer from the median by a mean of 10%. The response of the high-latitude ionosphere of the F2 layer to variations in the AE and PC indices appears in the first hour after the maximum during geomagnetic indices, the delay of the maximum deviation from the median 1 hour at night and 3 hours in the afternoon at Lovozero station, at Sodankyla and Gorkovskaya is about 3 hours at night and weakly expressed during the day.I declare I have no competing interests.

Is there any connection between solar activity and earthquakes?

A possible relationship between solar activity and the seismic activity of the Earth is considered. We analyzed the frequency of occurrence of earthquakes of various magnitudes with the Fourier transform: for M ≥ 7 over the period 1900—2019 and for 2.5 ≤ M ≤ 7 over the period 1973–2019. The average annual, monthly, and daily values of the solar-terrestrial variables, the number of earthquakes with intensities that fall within the specified boundaries are calculated. The epoch overlapping method was used to analyze the possible relationship between the Wolf numbers and the number of earthquakes at the corresponding moment in the cycle. 4 periods of each solar cycle were identified: the phase of ascending, maximum, descending, and minimum. Earthquakes over the entire globe and in the regions of extension and compression of the earth's crust were analyzed for each phase. No statistically significant dependencies between solar-terrestrial variables and earthquake initiation were found for all time intervals and all selected earthquake magnitudes. An interesting fact was established concerning the change in the number of earthquakes at different periods of the day. The number of earthquakes in the nighttime appreciably increases (by ~ 10 %) compared to the daytime. A slight increase in the number of earthquakes after local noon was also detected. We could not confirm the existence of a direct connection between solar activity and the seismic activity of the Earth, but we cannot also claim that such a connection does not exist.

Numerical Study of the Dynamic Compaction Process considering the Phenomenon of Particle Breakage

Dynamic compaction (DC) is commonly used to strengthen the coarse grained soil foundation, where particle breakage of coarse soils is unavoidable under high-energy impacts. In this paper, a novel method of modeling DC progress was developed, which can realize particle breakage by impact stress. A particle failure criterion of critical stress is first employed. The “population balance” between particles before and after crushing is guaranteed by the overlapping method. The performance of the DC model is successfully validated against literature data. A series of DC tests were then carried out. The effect of particle breakage on key parameters of DC including crater depth and impact stress was discussed. Besides, it is observed that the relationship between breakage amount and tamping times can be expressed by a logarithmic curve. The present method will contribute to a better understanding of DC and benefit further research on the macro-micro mechanism of DC.