The snow cover in Jayawijaya, Papua, Indonesia, has been rapidly declining due to various climatic factors, posing significant threats to both the ecosystem and local culture. This research focused on the analysis of the impact of weather factors (temperature, relative humidity, wind direction, and wind speed) on the decrease in snow cover in Mt. Jayawijaya. Using the datasets from 2013 to 2022, a stepwise multiple regression analysis was performed to ascertain the predictors for snow cover loss. The findings indicated that out of all the weather factors, relative humidity and wind direction were the most important, with a p-value of 0.005 and 0.032, respectively. The regression model indicates that higher humidity increases snow sublimation, while wind direction brings warm air that accelerates snow melting. Pearson correlation analysis showed a strong correlation (r = 0.81) between the observed snow cover decline and the model, with an RMSE of 20.70 ha. These findings contribute to the understanding of how atmospheric factors interact with snow dynamics in tropical regions and can aid in future conservation efforts for Jayawijaya’s snow cover.

Abstract The covid-19 pandemic badly affects most countries in the world both sociologically and economically. Taiwan, however, manages to handle its pandemic remarkably well before and after its Covid-19 cases spike. This paper aims to describe Taiwan's strategy for keeping the cases low and to identify significant factors related to this spike. These factors are found through the use of a stepwise regression model. The model inputs are 15 daily data sets that belong to the three grouped indicators: Containment and closures, Economic responses, and Health systems. In addition, the number of Covid-19 tests and changes in the number of people attending indoor and outdoor activities are also given as model inputs. The model output is the daily covid-19 confirmed cases. It is found that restrictions on the internal move, government campaign, debt or contract relief, indoor activities changes, work closures, and their interactions are amongst the most significant factors leading to the sharp jump in Covid-19 cases. The model is able to explain almost 88% of the cases. It is important that these factors are taken into consideration by any government in its preparation for an upcoming pandemic.

Abstract Covid-19 pandemic badly affects most countries in the world both sociologically and economically. Taiwan, however, manages to handle its pandemic remarkably well before and after its Covid-19 cases spike. This paper aims to describe Taiwan’s strategy on keeping the cases low and to identify significant factors related to this spike. These factors are found through the use of a stepwise regression model. The model inputs are daily data sets that belong to the three grouped indicators: Containment and closures, Economic responses and Health systems. In addition, the number of Covid-19 tests and changes in the number of people attending indoor and outdoor activities are also given as model inputs. The model output is the daily covid-19 confirmed cases. It is found that restriction in internal move, government campaign, debt or contract relieved, indoor activities changes, work closures and their interactions are amongst the most significant factors leading to the sharp jump in Covid-19 cases. The model is able to explain almost 88% of the cases. It is important that these factors are taken into consideration by any government in its preparation for an upcoming pandemic.

Abstract Coral bleaching events could be viewed as a resultant of complex interactions involving various environmental factors. Just recently, a TEL (seawater Temperature-ENSO-Longitude) model is developed to predict the global bleaching events during the single 2016 El Niño episode. The model has a modest skill of approximately 50% predictability. In an attempt to improve coral bleaching prediction skill, here, two models are developed: an extended TEL model and a statistical model that combines multivariate and supervised classification techniques. The skills of both models are assessed in predicting bleaching events from three coral regions during several El Niño episodes. Two important findings are found. First, it is revealed that seawater salinity, ENSO, and Madden Julian Oscillation have stronger effect than seawater temperature in coral bleaching events. This has an important consequence to the fate of coral ecology when these phenomena are also subjected to change under global warming. Second, the predictability of our developed is 74% which is higher than that of the TEL model. Our model could serve as a tool for reef managers on communicating bleaching risk earlier. It could also be used for monitoring and reservation plans to mitigate the detrimental effect of various environmental stressors on corals.

The Maros-Pangkep karst in southwest Sulawesi, Indonesia, contains some of the world's oldest rock art. However, the Pleistocene images survive only as weathered patches of pigment on exfoliated limestone surfaces. Salt efflorescence underneath the case-hardened limestone substrate causes spall-flaking, and it has been proposed that the loss of artwork has accelerated over recent decades. Here, we utilise historical photographs and superposition constraints to show that the bulk of the damage was present before 1950 CE, and describe the role of anthropogenic sulphur emissions in promoting gypsum-salt efflorescence and rock art decay. The rock art shelters have been exposed to domestic fire-use and intensive rice cultivation with post-harvest burning of straw for hundreds (if not thousands) of years, both of which release chemically reactive sulphur oxides for gypsum formation, with cumulative effects. Analysis of time-lapse photography indicates that the rate of rock art loss may be on the decline, consistent with the history of fire-use in southwest Sulawesi. At present, vandalism and sulphur emissions from diesel-powered traffic and cement-based infrastructure development constitute localised threats. Our findings indicate that there are grounds for being cautiously optimistic that targeted conservation measures will ensure the longevity of some of our oldest artistic treasures.

Penelitian ini bertujuan untuk membuat pemodelan prediksi titik panas (hotspot) di wilayah Asia Tenggara bagian Selatan dengan sejumlah prediktor signifikan menggunakan Model Multiple Regression (MR) dan untuk melakukan verifikasi prediksi model tersebut. Data yang digunakan dalam penelitian ini yaitu data observasi titik panas (hotspot) di Wilayah Indonesia yakni di Pulau Kalimantan dan Sumatera dan di Wilayah Semenanjung Malaysia serta Sabah-Sarawak. Kemudian data indeks El Nino Southern Oscillation (ENSO), Madden-Julian Oscillation (MJO), Indian Ocean Dipole (IOD) dan Monsun selama 6 tahun mulai dari tahun 2013 hingga 2018 sebagai data prediktor. Metode yang digunakan yaitu Model Multiple Regression dengan Metode Regresi Stepwise dan verifikasi skill model prediksi yang digunakan yaitu Korelasi Pearson dan RMSE. Berdasarkan hasil pemodelan dan verifikasi prediksi terbaiknya, diperoleh nilai Korelasi Pearson sebesar 0,698 dan nilai RMSE-nya sebanyak 908 hotspot. Untuk model prediksi di wilayah Sumatera oleh 7 prediktor signifikan yang terkait dengan kejadian hotspot yaitu, IOD 0 (IOD pada bulan munculnya hotspot), MJO 0, MJO 9, MJO 10, Mons 1, MJO 8, dan MJO 5. Untuk wilayah Kalimantan nilai Korelasi Pearson sebesar 0,795 dan nilai RMSE-nya sebanyak 1150 hotspot oleh 4 prediktor signifikan, MJO 9 (MJO pada 9 bulan sebelum munculnya hotspot), Mons 1, Mons 0, dan ENSO 3. Untuk wilayah Semenanjung Malaysia diperoleh nilai Korelasi Pearson sebesar 0,145 dan nilai RMSE-nya sebanyak 135 hotspot oleh 2 prediktor signifikan, Mons 2 (Mons pada 2 bulan sebelum munculnya hotspot) dan MJO 0. Kemudian untuk wilayah Sabah dan Sarawak diperoleh nilai Korelasi Pearson sebesar 0,242 dan nilai RMSE-nya sebanyak 113 hotspot oleh 2 prediktor signifikan, IOD 2 (IOD pada 2 bulan sebelum munculnya hotspot) dan MJO 0. Untuk wilayah Sumatera prediktor yang paling berpengaruh yaitu IOD 0, yakni fenomena IOD khususnya fenomena IOD (+) penyebab terjadinya musim kering ini beberapa kali terjadi di wilayah Pulau Sumatera karena letaknya berdekatan langsung dengan Samudera Hindia sehingga iklimnya juga dipengaruhi oleh lautan di dekatnya. Untuk fenomena MJO dan Monsun yang paling berpengaruh di Wilayah Kalimantan (MJO 9), Semenanjung Malaysia (Mons 2) serta Sabah - Sarawak (MJO 0). Kedua fenomena tersebut secara periodik selalu melintas di ketiga wilayah tadi khususnya berkontribusi pada bulan-bulan terjadinya musim kering, sehingga diindikasikan dapat mempengaruhi munculnya hotspot.

Objective: Global society pays huge economic toll and live loss due to COVID-19 (Coronavirus Disease 2019) pandemic. In order to have a better management of this pandemic, many institutions develop their own models to predict number of COVID-19 cases, hospitalizations and mortalities. These models, however, are shown to be unreliable and need to be revised on a daily basis. Methods: Here, we develop a Bose-Einstein (13E) -based statistical model to predict daily COVID-19 cases up to 14 days in advance. This fat-tailed model is chosen based on three reasons. First, it contains a peak and decaying phase. Second, it also has both accelerated and decelerated phases which are similarly observed in an epidemic curve. Third, the shape of both the BE energy distribution and the epidemic curve is controlled by a set of parameters. The BE model daily predictions are then verified against simulated data and confirmed COVID-19 daily cases from two epidemic centres, i.e. New York and DKI Jakarta. Result: Over- predictions occur at the earlier stage of the epidemic for all data sets. Models parameters for both simulated and New York data converge to a certain value only at the latest stage of the epidemic progress. At this stage, model's skill is high for both simulated and New York data, i.e. the predictability is greater than 80% with decreasing RMSE. On the other hand, at that stage, the DKI's model's predictability is still fluctuating with increasing RMSE. Conclusion: This implies that New York could leave the stay-at-home order, but DKI Jakarta should continue its large-scale social restriction order. There remains a great challenge in predicting the full course of an epidemic using small data collected during the earlier phase of the epidemic. (C) 2021 SESPAS. Published by Elsevier Espafia, S.L.U.

Objective: The COVID-19 pandemic put enormous socio-economic pressures on most countries all over the world. In order to contain the spread of the coronavirus, governments implemented both pharmaceutical and non-pharmaceutical interventions. This simple modeling work aims to quantify the effect of three levels of social distancing and large-scale testing on daily COVID-19 cases in Malaysia, Republic of Korea, and Japan. Method: The model uses a Stepwise Multiple Regression (SWMR) method for selecting lagged mobility index and testing correlated with daily cases based on a 0.05 level of significance. Result: The models's predictability ranges are from 75% to 92%. It is also found that the mobility index plays a more important role, in comparison to testing rates, in determining daily confirmed cases. Conclusion: Behavioral changes that support physical distancing measures should be practiced to slow down the COVID-19 spreads.

Objective: The COVID-19 pandemic has disrupted people's normal life as a result of strict policies applied to slow down the pandemic. To find out how extensive the virus spread is, most countries increase their daily testing rates. Method: This simple modelling work uses stringency index and daily testing (including the lagged version up to the previous 14 days) to predict daily COVID-19 cases in India and Indonesia. A Stepwise Multiple Regression (SWMR) subroutine is used in this modelling to select factors based on a 0.01 significant level affecting daily COVID-19 cases before the epidemic peaks. Result: The models have high predictability close to 94% (Indonesia) and 99% (India). Increasing number of daily COVID-19 cases in Indonesia is associated with the country's increased testing capacity. On the other hand, stringency indices play more important role in determining India's daily COVID-19 cases. Cloclusion: Our finding shows that one question remains to be answered as to why testing and strict policy differ in determining daily cases in both Asian countries.

Groundwater Conservation in Mappakasunggu and Manggarabombang, District TakalarAbstract. Groundwater is water that is contained in layers of soil or rocks below the surface. Many damages are caused by excessive groundwater extraction. For example, one of the residents' wells in Tamaona Lengkese that had been closed because the water had turned to salt water after being used for 10 years. This phenomenon shows that there is sea water intrusion because the rate of groundwater exploitation is greater than the rate of recharge. Besides that, every year there is a drought in the dry season and flooding in the rainy season. Therefore, to avoid a prolonged water crisis, there must be efforts from the government and all levels of society to conserve groundwater. To overcome the various problems of the partners, the Unhas PPMU-PKM team conducted groundwater conservation counseling and training. Counseling is done to the community to understand the existence of ground water and how its conservation. While training was given to improve the skills of the community to conserve groundwater. The results of this education and training are that more than 80% participants have understood how the presence of ground water and its conservation and are able to conserve groundwater with infiltration holes and injection wells.Keywords: Biopore, permeability, ground water, conservation.Abstrak. Air tanah adalah air yang terdapat dalam lapisan tanah atau bebatuan di bawah permukaan tanah. Banyak dampak kerusakan yang ditimbulkan akibat pengambilan air tanah yang berlebihan. Sebagai contoh, salah satu sumur warga di Tamaona Lengkese yang telah ditutup karena airnya sudah berubah menjadi air asin setelah digunakan 10 tahun. Fenomena ini menunjukkan adanya intrusi air laut karena laju pengambilan air tanah jauh lebih besar dibandingkan dengan laju pengimbuhan. Selain itu setiap tahun di daerah tersebut terjadi kekeringan pada musim kemarau dan banjir pada musim hujan. Oleh karena itu, harus ada upaya pemerintah dan lapisan masyarakat untuk melakukan konservasi air tanah untuk menghindari krisis air berkepanjangan. Untuk mengatasi berbagai persoalan mitra tersebut tim PPMU-PKM Unhas melakukan penyuluhan dan pelatihan konservasi air tanah. Penyuluhan dilakukan kepada masyarakat untuk memahami keberadaan air tanah dan bagaimana konservasinya. Sedangkan pelatihan diberikan untuk meningkatkan keterampilan masyarakat untuk melakukan konservasi air tanah. Hasil dari penyuluhan dan pelatihan ini adalah peserta telah memahami bagaimana keberadaan air tanah dan konservasinya diatas 80% dan mampu melakukan konservasi air tanah dengan lubang resapan dan sumur injeksi.Kata kunci: Biopori, permeabilitas, air tanah, konservasi.

Abstract Analysis of groundwater conservation with cube biopore infiltration holes using the value of soil permeability, the amount of rainfall, and the depth of the groundwater level. The groundwater conservation research method is carried out by making the cavity biopore infiltration holes to absorb the volume of rainwater and domestic waste water into the soil. Conservation is suitable for homes that have enough yard. The main reason is to use cube infiltration holes to accommodate large and small organic waste. Organic waste is trees, grass and domestic waste. For a small house yard, conservation of ground water enough with a cylindrical hole diameter of 10 cm. Benefits of biopore infiltration holes are reducing surface runoff, producing manure, fertilizing soil, reducing waste piles, and conserving ground water. The research objective was to analyze the cube biopore infiltration holes based on soil permeability, rainfall intensity, depth of groundwater level, and volume of organic waste. The results of the analysis can be obtained from the cube biopore infiltration holes which can absorb all the rainwater which can accommodate all organic waste. The results of permeability testing show that the average soil permeability is 0.00112 cm/s and the yield of maximum discharge runoff water in January is 97.57 cm3/second. The ground water level is two meters. While the volume of organic waste is one cubic per month. The result of the analysis showed that the area of cake absorption was 8.73 m2. When converted to a cube absorption field it takes approximately 8 meters wide by 0.5 meters and a depth of 0.5 meters. When divided into four cavity recharge holes LRB1 obtained area of 4 m2, LRB2 area of 2.7 m2, LRB3 area 2 m2, and LRB4 as reserve.

Penelitian ini bertujun mengestimasi dosis efektif yang diterima pasien pada bagian kepala (head). Penelitian menggunakan hasil citra dari CT Scan Siemens tipe Somatom 128 slice dengan ketebalan slice 5mm dan tegangan 100 volt untuk data 100 pasien yang menajalani eksaminasi CT kepala non kontras. Hasil penelitian menunjukkan bahwa dosis efektif yang diterima pasien berada pada kisaran 1,45-1,80 mSv untuk pasien laki-laki dan 1,22 -1,61 mSv untuk pasien perempuan. Dari kisaran data, secara umum dosis yang diterima perempuan lebih kecil dibandingkan pasien laki-laki.

Penelitian ini bertujuan untuk menghitung dosis efektif yang diterima pasien yang mengalami eksaminasi CT pada bagian perut (abdomen). Data yang digunakan pada penelitian ini adalah data pasien yang dikumpulkan dari Departemen Radiologi di salah satu rumah sakit di Makassar dan merupakan hasil eksaminasi CT-Scan Simens tipe SOMATOM bagian abdomen 80 pasien. Hasil penelitian menunjukkan bahwa nilai rata-rata CTDIvol dan dosis efektif yang diterima pasien laki-laki masing-masing 7,87 mGy dan 5,52 mSv, sedangkan untuk pasien perempuan masing-masing 7,53 mGy dan 4,97 mSv Dosis yang diterima pasien tersebut masih dalam ambang batas yang telah ditetapkan oleh BAPETEN.

We apply a simple linear multiple regression model called IndOzy for predicting ENSO up to 7 seasonal lead times. The model still used 5 (five) predictors of the past seasonal Nino 3.4 ENSO indices derived from chaos theory and it was rolling-validated to give a one-step ahead forecast. The model skill was evaluated against data from the season of May-June-July (MJJ) 2003 to November-December-January (NDJ) 2015/2016. There were three skill measures such as: Pearson correlation, RMSE, and Euclidean distance were used for forecast verification. The skill of this simple model was than compared to those of combined Statistical and Dynamical models compiled at the IRI (International Research Institute) website. It was found that the simple model was only capable of producing a useful ENSO prediction only up to 3 seasonal leads, while the IRI statistical and Dynamical model skill were still useful up to 4 and 6 seasonal leads, respectively. Even with its short-range seasonal prediction skills, however, the simple model still has a potential to give ENSO-derived tailored products such as probabilistic measures of precipitation and air temperature. Both meteorological conditions affect the presence of wild-land fire hot-spots in Sumatera and Kalimantan. It is suggested that to improve its long-range skill, the simple INDOZY model needs to incorporate a nonlinear model such as an artificial neural network technique.

This study presents the chemical bonding and the structural properties of composites from accelerator chloride test migration (ACTM). The volume fractions between binder (cement and starch) and charcoal in composites are 20: 80 and 60:40. The effect of the binder to the chemical composition, chemical bonding, and structural properties before and after chloride ion passing through the composites was determined by X-ray fluorescence (XRF), by Fourier transform infra-red (FTIR), and x-ray diffraction (XRD), respectively. From the XRD data, XRF data, and the FTIR data shows the amount of chemical composition, the type of binding, and the structure of composites are depending on the type of binder. The amount of chloride migration using starch as binder is higher than that of cement as a binder due to the density effects.