DAMA/LIBRA–phase2 results and implications on several dark matter scenarios

The first DAMA/LIBRA–phase2 model-independent results (exposure: [Formula: see text], and software energy threshold at 1 keV). They further confirm — with high confidence level — the evidence already observed by DAMA/NaI and DAMA/LIBRA–phase1 on the basis of the exploited model-independent dark matter (DM) annual modulation signature. The total exposure of the three experiments above the 2 keV software energy threshold is [Formula: see text]. Here, several DM candidate particles and related scenarios are analyzed including the latest results. Thanks to the increased exposure and to the lower software energy threshold, corollary model-dependent analyses permit to significantly restrict the allowed regions for the parameters spaces of various dark matter candidates and astrophysical, particle and nuclear physics scenarios.

Value Re-Orienation in the Era of Change

The issue of value re-orientation has come to a point of crescendo, value re-orientation of students in our traditional society has honesty, hard work, integrity. A survey research design was employed, the population is made up of all senior secondary three (3) in Obio/Akpor Local Government Area. Which amounted to two thousand eight hundred and ten (2810). Random sampling technique was employed in the process three hundred (300) were sampled through balloting. The instrument for data collection was developed by the researcher and titled “Value re-orientation questionnaire” (VRQ), Test generated from the classroom. In asserting the validity of the instrument, three experts in the area of test and measurement validated the instrument and the emphasis was on content and face validities. Reliability of the instrument was ascertained by the use of Cronbach Alpha which resulted to 0.85 this gave a high confidence level for the use of instrument., data adopted frequencies and percentages to answer the research questions while analysis of variance and t-test were used to test the hypotheses at 0.05 of level of significance Some recommendations were made, the right legislation should be enacted to enforce the right values on the students.

Utilizing Cerlis Model in Improving Creative Writing Skills

The objectives of this paper were to identify the effectiveness of Cerlis Model in assisting teachers to improve students’ creative writing skills such as descriptive and imaginative essays and to analyze whether the model can improve students’ ways in expressing and organizing ideas in composing essays accurately. Samples consisted of 5 students from a primary school in Seremban, Negeri Sembilan. This study was conducted using a narrative qualitative approach through observation, questionnaire survey and document analysis and the results were reported using relevant tables, photos and visual graphics. The study focused among students that lack of ideas in producing creative essays such as descriptive and imaginative types. The results yielded that the Cerlis Model was able to create fun in learning and generate high confidence level in producing ideas for essay writing. Indeed, Cerlis Model is able to increase students’ interests in writing essays and their comprehension level. This study also shows that games are generating students’ ideas for composing descriptive and imaginative essays.

Conformal Prediction Based on Raman Spectra for the Classification of Chinese Liquors

This work extends the conventional back-propagation neural network (BPNN) to the classification of Chinese liquors of different flavors according to their Raman spectra. Conformal prediction is applied to assign reliable confidence measures for each classification and support an effective framework to make the machine learning on classification trustable. The BPNN can be used to predict the flavors of Chinese liquors according to their Raman spectra, and a classification rate of 88.96% can be achieved. In order to evaluate each classification, a non-conformity score is defined to generate a P-value for each classification. Moreover, the validity of conformal prediction in online mode is discussed. The number of cumulative errors in the conformal prediction is much less than that without conformal prediction. The relationship between the cumulative error and confidence levels shows that a high confidence level leads to low cumulative errors, but many cumulative errors will occur under a very high confidence level. The result implies that conformal prediction is a useful framework, which can employ classification satisfying a certain level of confidence. Meanwhile, the conformal prediction can improve our classification using a BPNN, when the number of data points is limited.

Peningkatan Ketahanan Pangan Melalui Penguatan Kelompok Tani

The research is based on the low household food security in Banyumas regency. One way to improve it is by empowering farmer groups. The growing group of farmers is "Minasari". focuses on the analysis of farmer group strengthening aspects. Strengths: have fish seeding certificate, access to information, training on fish feed, high confidence level. Weaknesses: low education level, unstable members, lack of response to information, marketing difficulties. Opportunities: "Minapolitan" Policy, the existence of KSU "Ngudi Rahayu"; buffer Beji Village. Threats: fishponds are narrow, depending on the weather, no follow-up after training, no bargaining position, and no cooperation with other parties.

The Challenge of Developing Primary Standards Mixtures of Carbon Dioxide at Atmospheric Levels to Establish Traceability to GHG Monitoring Analysis

This article describes how in recent years the need for the quality control, reliability and traceability of analytical results has been strongly emphasised. The National Institute of Metrology, Quality and Technology, is involved in improving calibration and measurement capabilities to provide high confidence level in accordance with the latest regulation regarding greenhouse gases standards. This article describes the gravimetric production of certified gaseous reference materials of carbon dioxide in atmospheric synthetic air that are used to monitor its concentration in the atmosphere. Considering the contributions from gravimetry preparation and its verification analysis by cavity ringdown system (CRDS) the relative expanded uncertainty of the standard mixture of carbon dioxide was lower than 0.5% for the range studied from 370 to 550 μmol/mol, which is comparable to the average of 0,25% relative uncertainties presented on international standards mixtures.

A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

A SEGMENT-BASED APPROACH FOR DTM DERIVATION OF AIRBORNE LIDAR DATA

With the characteristics of LIDAR system, raw point clouds represent both terrain and non-terrain surface. In order to generate DTM, the paper introduces one improved filtering method based on the segment-based algorithms. The method generates segments by clustering points based on surface fitting and uses topological and geometric properties for classification. In the process, three major steps are involved. First, the whole datasets is split into several small overlapping tiles. For each tile, by removing wall and vegetation points, accurate segments are found. The segments from all tiles are assigned unique segment number. In the following step, topological descriptions for the segment distribution pattern and height jump between adjacent segments are identified in each tile. Based on the topology and geometry, segment-based filtering algorithm is performed for classification in each tile. Then, based on the spatial location of the segment in one tile, two confidence levels are assigned to the classified segments. The segments with low confidence level are because of losing geometric or topological information in one tile. Thus, a combination algorithm is generated to detect corresponding parts of incomplete segment from multiple tiles. Then another classification algorithm is performed for these segments. The result of these segments will have high confidence level. After that, all the segments in one tile have high confidence level of classification result. The final DTM will add all the terrain segments and avoid duplicate points. At the last of the paper, the experiment show the filtering result and be compared with the other classical filtering methods, the analysis proves the method has advantage in the precision of DTM. But because of the complicated algorithms, the processing speed is little slower, that is the future improvement which should been researched.

A Real-Time PCR Method Targeting Camel Ingredient for Food Authentication

The special nutritious value of camel showed high potential for market exploitation. In this paper, a real-time PCR method targeting camel ingredient in camel meat and milk is reported as an approach to fight against adulteration. To understand the impact of processing procedures on the amplifiability of cytb gene, four kinds of processed camel meat were investigated, and the rate of DNA breakage was explored. The method was able to detect 5 fg/μL camel DNA and highly processed food containing 0.01% camel meat with a high confidence level.

An Improved Data Possession Checking Scheme in Cloud Computing

Remotely data storage in cloud computing faces great challenge. Data possession checking ensures the data integrity and availability. In this paper, a cloud data possession checking model has been presented. Based on Paillier cryptosystem and elliptic curve cryptograph (ECC),we designed a data possession checking scheme, where the user asks the server to compute a checking value of randomly appointed data blocks, and then makes a decision by proof verification. The homomorphic verification tag (HVT) generated based on Paillier cryptosystem to achieve unlimited queried times. Extensive evaluation and analysis shows the proposed scheme is efficient and secure with the features: a high confidence level, un-forged HVTs, a small constant overhead, etc.