PENGARUH PEMBERITAAN LARANGAN BERKENDARA ANAK DI BAWAH UMUR TERHADAP PERSEPSI ORANG TUA DI GAMPONG GAROT GEUCEU KECAMATAN DARUL IMARAH ACEH BESAR

ABSTRAKPenelitianiniberjudulPengaruhPemberitaanLaranganBerkendaraAnakDibawahUmurTerhadapPersepsiOrangTuadiGampongGarotGeuceuKecamatanDarulImarahAcehBesar.Permasalahandalam penelitianinibagaimanapengaruhpemberitaanlaranganberkendaraanakdibawahumurterhadappersepsiorangtuadiGampongGarotGeuceuKecamatanDarulImarah Aceh Besar.Penelitian inibertujuan untukmengetahuipengaruhorang tua mengenaipemberitaan yang melarang anak dibawah umurberkendara,diGampongGarotGeuceuKecamatanDarulImarahAcehBesar.Metodeyangdigunakandalam penelitianinimdeskriptifkuantitatif,yaitumetodeyang bertujuan mengembangkan dan menggunakan modelyangmatematis.PenelitianinidilakukandiDarulImarahAcehBesar,danyangdijadikan responden adalah masyarakat gampong Garot Geuceu.Teoripendukungyangdigunakandalam penelitianiniadalahteoriS-O-R.SampelpenelitiandiperolehdenganmenggunakanrumusTaroYamaneyaknimenjadi100 orang.Dengan teknik penarikan sample menggunakan Purposivesampling dengan teknik penarikan data menggunakan kuisioner.Hasilpenelitianmenunjukkanbahwahipotesisditerimadengannilaithitungadalah2,410.Sedangkanuntukttabeldapatdiketahui?=5%ataudengansignifikan=0,05padaujiduasisi,denganderajatkebebasan(df)diketahuin-2atau1002=98,makadiperolehttabel=1,660.Makanilaithitung>ttabelyaitu2,410>1,664dannilaisignifikan(lihattabelanalisisKoefisienKorelasiProductMoment)yaitu0,00

Tibial acceleration-based prediction of maximal vertical loading rate during overground running : a machine learning approach

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 +/- 2.04 BW.s(-1), mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 +/- 7.90 BW.s(-1) (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA

Unusual Open Water Grouping Behavior in Salish Sea Harbor Seals (Phoca vitulina richardii)

Most pinniped species are relatively solitary when in water, but some species, most notably the otariids, will form large groupings (referred to as rafts) in open water for thermoregulation or rest, as well as participating in group foraging behaviors. Alternatively, individuals of many species may concentrate in one area, forming foraging aggregations when prey are in high abundance. Open water grouping behavior that is distanced from haulout sites is less common in phocid species, and in particular has not been documented in the literature for harbor seals (Phoca vitulina richardii). In the Salish Sea, the inland waters of Washington, United States and British Columbia, Canada, harbor seals are the most abundant pinniped species. Recent observations in two locations in the south and central Salish Sea have documented large groupings ranging from 6 to 50 individuals (x=23.9) not located near haulout sites (more than at least 1 mile from known large group haulout locations). These observations occurred only in April/May 2019-2020 off Fidalgo Island (n=14) and in February 2017 and January 2019 in southern Puget Sound (n=2). These groups consisted of only adults/juveniles floating together within 1-2 body lengths of each other, unlike larger aggregations where individuals are in the same area, but not necessarily as a group. Some observations included systematic diving where individuals took turns periodically diving, appearing to be foraging, while others remained at the surface. This behavior appears to differ from the rafting behavior observed in otariids. Though harbor seals are known to habitually haul out together on beaches or islands, grouping behavior while in the water, like that observed here, has not been previously described. The purpose of such large groupings is unknown and continued monitoring of these occurrences and further analysis of behavior is needed

Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. Therefore, this paper investigates whether a structured machine learning approach can achieve a more accurate prediction of running gait event timings from tibial accelerometry. Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. Both a structured perceptron model (median absolute error of stance time estimation: 10.00 $\pm$ 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 $\pm$ 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 $\pm$ 9.52 ms) on data from 93 rearfoot runners. Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory

Music-based biofeedback to reduce tibial shock in over-ground running : a proof-of-concept study

Methods to reduce impact in distance runners have been proposed based on real-time auditory feedback of tibial acceleration. These methods were developed using treadmill running. In this study, we extend these methods to a more natural environment with a proof-of-concept. We selected ten runners with high tibial shock. They used a music-based biofeedback system with headphones in a running session on an athletic track. The feedback consisted of music superimposed with noise coupled to tibial shock. The music was automatically synchronized to the running cadence. The level of noise could be reduced by reducing the momentary level of tibial shock, thereby providing a more pleasant listening experience. The running speed was controlled between the condition without biofeedback and the condition of biofeedback. The results show that tibial shock decreased by 27% or 2.96 g without guided instructions on gait modification in the biofeedback condition. The reduction in tibial shock did not result in a clear increase in the running cadence. The results indicate that a wearable biofeedback system aids in shock reduction during over-ground running. This paves the way to evaluate and retrain runners in over-ground running programs that target running with less impact through instantaneous auditory feedback on tibial shock

Pertumbuhan Sengon dan Produksi Padi Gogo pada Taraf Pemupukan P yang Berbeda dalam Sistem Agroforestri

Agroforestry of upland rice (Oryza sativa L.) and sengon tree (Paraserianthes falcataria) could increase the growth of sengon trees; however, it would also increase the percentage of empty grain due to shade from the tree. Fertilization with P is expected to increase plant height, grain weight and weight of straw of upland rice and growth of sengon tree. The aim of this research is to analyze the growth and production of sengon and upland rice with agroforestry and P fertilizer application. Application in cultivation of upland rice using split-split plot design. The main plot is agroforestry and monoculture, subplot of Sintanur and Situ Bagendit varieties and split-split plot of P fertilization consisting of 4 levels, namely: P 0 = 0 g / plant, P50 = 3 g / plant, P100 = 6 g / plant and P150 = 9 g / plant. The application of P fertilizer showed that P 100% had high production on Sintanur varieties with monoculture. Agroforestry system can increase the growth of sengon plants.Keywords: agroforestry, P fertilizer, sengon, upland rice