Bird Responses to Habitat Change in the Karst Area of Bantimurung Bulusaraung National Park

Birds are useful bioindicators to habitat changes. This study aims to determine the responses of birds to habitat change at Maros-Pangkep karst area, Bantimurung-Bulusaraung National Park. The research was carried out in three disturbance degrees (core-zone, wilderness-zone, and the community-gardens), which represents minimal, middle, and high interference level. A modified square-line method was used to observe vegetation of bird habitat. Point count method was used to observe bird population. Data of the bird habitat vegetation was analyzed using vegetation density. The difference of vegetation composition was analyzed using Sorensen-similarity index. Data of the bird was analyzed using abundance, and indexes of Shannon-Weinner diversity, Simpson dominance, Pielou evenness, and Margalef species richness. Significant differences between the number of the individual bird were tested using one-way ANOVA, Tukey-Bonferroni test. The results showed that birds living in karst were sensitive to habitat changes. Birds responded through reducing the number of individuals and species, shifting the species of bird that has high importance value index from low tolerance species to high tolerance species. Birds also responded by shifting the feeding guild that has high important value index from frugivore to frugivore-insectivore and then to granivore, decreasing the number of bird species with large body size, reducing the number of bird species that need a special location to build nest. Considering that Maros-Pangkep Karst has vital roles, scientific values, and biodiversity richness, it is necessary to involve all stakeholders to maintain its sustainability, including the establishment of entire Maros-Pangkep Karst area as the karst-landscape area

State of air quality in and outside of hospital wards in urban centres – A case study in Lahore, Pakistan

Particulate pollution in healthcare facilities is a potential threat to healthcare workers, patients and visitors. A study was carried out to monitor particulate levels in and outside of five wards of Sheikh Zayed Hospital, a tertiary healthcare facility of Lahore. Measurements indicated that the hourly mean concentrations of PM2.5 in a medical, pulmonology (chest), surgical, pediatric and nephrology ward were 78 ± 37, 86 ± 46, 94 ± 48, 169 ± 122 and 488 ± 314 µg m-3 respectively. The outside levels of PM2.5 of the same wards were 69 ± 27, 81 ± 49, 178 ± 85, 282 ± 164 and 421 ± 240 µg m-3. Indoor levels were higher than outdoors in all the wards except surgical and pediatric ward. Such elevated levels of PM can result in aggravation of the poor health status of the patients as well as affecting the hospital staff and visitors

An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper

This short paper presents an architectural overview of an agent-based framework called iv4XR for automated testing that is currently under development by an H2020 project with the same name. The framework's intended main use case of is testing the family of Extended Reality (XR) based systems (e.g. 3D games, VR sytems, AR systems), though the approach can indeed be adapted to target other types of interactive systems. The framework is unique in that it is an agent-based system. Agents are inherently reactive, and therefore are arguably a natural match to deal with interactive systems. Moreover, it is also a natural vessel for mounting and combining different AI capabilities, e.g. reasoning, navigation, and learning

PENINGKATAN KEMAMPUAN BERPIKIR KRITIS MATEMATIS SISWA SMK MELALUI PENDEKATAN MATEMATIKA REALISTIK

Mathematically critical thinking skills are two very important capabilities owned by a student. It allows the students to reason and solve problems in everyday life. The ability to cultivate, then one relevant learning is realistic mathematics learning. This study aims to analyze the increasa in critical thinking skills students acquire mathematical learning of mathematics using realistic mathematics approach whit student who received conventional mathematics, as well as see the interaction by the level of prior knowledge of students. This study is a quasi experimental study research design Pretest Posttest Control Design. The population of this research is all class X students at SMK Negeri 1 Meulaboh by taking two classes of samples taken at random from the 7 classes available. Data analysis was performed using t-test and ANOVA two lanes with a significance level of 0.05, both based on the whole student or based on prior knowledge of students. Based on the results of the study concluded that: 1) peningkatan critical thinking skills mathematical students taught through realistic mathematics approach is better than the critical thinking skills of students taught by conventional, 2) terdapat interaction between realistic mathematics approach to the level of students' critical thinking skills mathematically

Possibility of Recovering Iron Fines from Tailings by Hydrocyclone

Although India is having vast reserves of iron ore, lack of consistency with respect to Si02 /Al2 03 ratio makes these unsuitable to use directly in the blast furnace without proper beneficiation. Beneficiation processes mostly applicable are sizing, washing and jigging in case of hematite and magnetic and gravity separation in case of magnetite to obtain acceptable grade lumps and fines for agglomeration for further use in blast furnace. During the washing operations, enrichment with respect to iron is marginal and gangue reduction with particular refere-nce to favourable Si02 Al2 03 ratio is limited

Fiber-Optic Imaging Probe Developed for Space Used to Detect Diabetes Through the Eye

Approximately 16 million Americans have diabetes mellitus, which can severely impair eyesight by causing cataracts, diabetic retinopathy, and glaucoma. Cataracts are 1.6 times more common in people with diabetes than in those without diabetes, and cataract extraction is the only surgical treatment. In many cases, diabetes-related ocular pathologies go undiagnosed until visual function is compromised. This ongoing pilot project seeks to study the progression of diabetes in a unique animal model by monitoring changes in the lens with a safe, sensitive, dynamic light-scattering probe. Dynamic light scattering (DLS), has the potential to diagnose cataracts at the molecular level. Recently, a new DLS fiber-optic probe was developed at the NASA Glenn Research Center at Lewis Field for noncontact, accurate, and extremely sensitive particle-sizing measurements in fluid dispersions and suspensions (ref. 1). This compact, portable, and rugged probe is free of optical alignment, offers point-and-shoot operation for various online field applications and challenging environments, and yet is extremely flexible in regards to sample container sizes, materials, and shapes. No external vibration isolation and no index matching are required. It can measure particles as small as 1 nm and as large as few micrometers in a wide concentration range from very dilute (waterlike) dispersions to very turbid (milklike) suspensions. It is safe and fast to use, since it only requires very low laser power (10 nW to 3 mW) with very short data acquisition times (2 to 10 sec)

Estimation method for determining surface film conductance during cooling of fish packages.

This paper presents an alternative method for determining the surface film conductance of an infinite fish slab subjected to the cooling process. Many methods have been published, but their solutions have inherent appreciable inaccuracy and limitations. The present authors used the temperature histories of five locations within a slab sample of fish, obtained by the experimental investigation part of this work, along with the inverse heat conduction problem (IHCP) technique to develop a correlation for variable surface film conductance. When the above correlation was used for temperature predictions, the predicted and experimentally measured temperature distribution profiles were compared numerically. Better agreement than that implemented by other investigators was achieved. This revealed the accuracy and superiority of the present method, and the limitations of other methods are overcome in this method

Full counting statistics of information content

We review connections between the cumulant generating function of full counting statistics of particle number and the R\'enyi entanglement entropy. We calculate these quantities based on the fermionic and bosonic path-integral defined on multiple Keldysh contours. We relate the R\'enyi entropy with the information generating function, from which the probability distribution function of self-information is obtained in the nonequilibrium steady state. By exploiting the distribution, we analyze the information content carried by a single bosonic particle through a narrow-band quantum communication channel. The ratio of the self-information content to the number of bosons fluctuates. For a small boson occupation number, the average and the fluctuation of the ratio are enhanced.Comment: 16 pages, 5 figure

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model