The Gompertz Function And Its Applications In Management

Rza̧dkowski, Grzegorz; Głażewska, Iwona; Sawińska, Katarzyna

doi:10.1515/fman-2015-0035

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…In this study, we fitted curves growth models to the dataset. Predict the values of a time series, assuming a Gompertz or logistic trend, is not a new idea [23]. [2] fitted the Gompertz curve to predict the total number of cases of the COVID 19 in Iran.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian analysis of the total number of cases of the COVID 19 when only a few data is available. A case study in the state of Goias, Brazil

Silva

Velasco

Marques

et al. 2020

Preprint

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The outbreak of COVID 19 has been provoking several problems to the health system around the world. One of the concerning is the crash of the health system due to the increasing demand suddenly. To avoid it, knowing the total number and daily new cases is crucial. In this study, we fitted curves growth models using a Bayesian approach. We extracted information obtained from some countries to build the prior distribution of the model. The total number of cases of the COVID 19 in the state of Goias was analyzed. Results from analysis indicated that the date of the outbreak peak is between 51 and 68 days after the beginning. Moreover, the total number of cases is around 3180 cases. The analysis did not take into consideration possibles changes in government control measures. We hope this study can provide some valuable information to public health management.

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Section: Introductionmentioning

confidence: 99%

A Bayesian analysis of the total number of cases of the COVID 19 when only a few data is available. A case study in the state of Goias, Brazil

Silva

Velasco

Marques

et al. 2020

Preprint

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“…Distribution based on Gompertz function ( 217 ). As this function is widely used in the field of biology (description of growth of plants, animals, cells, bacteria, etc.…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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“…Wu et al (2014), mengatakan bahwa pertumbuhan kendaraan di Cina mengikuti pola S-Curve bedasarkan PDRB perkapita penduduk. Terakhir, Rządkowski et al (2015), mengaplikasikan Gomperzt Function untuk melihat pertumbuhan kendaraan berdasarkan PDRB di Polandia. Dari beberapa penelitian yang disampaikan dapat dikatakan bahwa ada hubungan yang signifikan antara pertumbuhan kendaraan dengan variabel sosio ekonomi terutama PDRB (income).…”

Section: Doi: Mktsv25i118585unclassified

“…Temuan hasil kalibrasi Model Gompertz, nilai awal koefisien  menggambarkan kondisi awal (initial condition) dicari dengan menggunakan pendekatan formulasi yang digunakan oleh (Rządkowski et al, 2015). Jika pada tahun ke t = 1, nilai RGDP adalah 0 dan * = 1, maka persamaan (1) akan menjadi: Hasil uji sensivitas fungsi menunjukkan bahwa wilayah dengan PDRB kurang dari 45 juta/pertahun, penduduknya cenderung memiliki keinginan tinggi untuk dapat memiliki kendaraan bermotor sepeda motor.…”

Section: Hasil Kalibrasi Model Gompertz Hubungan Kepemilikan Kendaraaunclassified

Model Pertumbuhan Sepeda Motor Berdasarkan Produk Dosmetik Regional Bruto (PRDB) Perkapita (Studi Kasus Pulau Jawa)

Kresnanto

2019

MKTS

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The number and growth of vehicles, is a separate issue associated with sustainable transportation. In 2014, recorded vehicle election in Indonesia reached more than 448 vehicles per 1,000 people. And specifically, for motorcycles reach 365 vehicles per 1,000 people. The growth of motor vehicles is significantly influenced by economic growth (measured by Gross Domestic Product -RGDP). When compared with motor vehicle growth, it can be concluded that the high growth rate of motor vehicles at the end of this decade actually impact on the decrease of RGDP. To see the trend between economic growth represented by RGDP and the growth of motor vehicle (motorcycle) needs a model. Motor vehicle growth model in Java can be approached with Gompertz function. This function is a negative exponential function with asymptote used is the highest value of motor vehicle ownership in DKI Jakarta Province is 1,299 motorcycles per 1,000 people. AbstrakJumlah dan pertumbuhan kendaraan, merupakan permasalahan tersendiri terkait dengan transportasi yang berkelanjutan. Tahun 2014, tercatat kepemilihan kendaraan bermotor di Indonesia mencapai lebih dari 448 kendaraan per 1.000 orang. Dan khusus untuk sepeda motor mencapai 365 kendaraan per 1.000 orang. Pertumbuhan kendaraan bermotor secara signifikan dipengaruhi oleh pertumbuhan ekonomi (diukur dengan Produk Domestik Regional Bruto -PDRB). Jika dibandingkan dengan pertumbahan kendaraan bermotor, dapat disimpulkan sementara bahwa laju pertumbuhan kendaraan bermotor yang tinggi di akhir dekade ini justru berdampak pada menurunnya PRDB. Untuk melihat trend antara pertumbuhan ekonomi yang diwakili oleh PRDB dengan pertumbuhan kendaraan bermotor (sepeda motor) perlu sebuah model. Hasil penelitian menunjukkan bahwa model pertumbuhan kendaraan bermotor (sepeda motor) di Pulau Jawa dapat didekati dengan fungsi Gompertz. Fungsi ini merupakan fungsi exponential negatif dengan asimtot yang digunakan adalah nilai tertinggi kepemilikan kendaraan sepeda motor di Provinsi DKI Jakarta yaitu 1.299 sepeda motor per 1.000 orang penduduk.Kata kunci: Produk domestik regional bruto, fungsi gompertz, pertumbuhan sepeda motor

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The Gompertz Function And Its Applications In Management

Cited by 12 publications

References 13 publications

A Bayesian analysis of the total number of cases of the COVID 19 when only a few data is available. A case study in the state of Goias, Brazil

A Bayesian analysis of the total number of cases of the COVID 19 when only a few data is available. A case study in the state of Goias, Brazil

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Model Pertumbuhan Sepeda Motor Berdasarkan Produk Dosmetik Regional Bruto (PRDB) Perkapita (Studi Kasus Pulau Jawa)

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