Specific Period Research Articles

Revealing Coupled Periodicities in Sunspot Time Series Using Bispectrum—An Inverse Problem

Sunspot daily time series have been available for almost two centuries providing vast and complicated information about the behavior of our star and especially the interaction of the motion of the planets and other possible interstellar phenomena and their effects on the surface of the Sun. The main result obtained from the sunspot time series analysis is the imprint of various periodicities, such as the planets’ orbital periods and the planetary synodic periods on the sunspots signature. A detailed spectrum representation is achieved by means of a periodogram and a virtual extension of the time length segments with zeroed samples for longer representations. Furthermore, the dependence or coupling of these periodicities is explored by means of a bispectrum. We establish the exact interdependencies of the periodic phenomena on the sunspot time series. Specific couplings are explored that are proved to be the key issues for the coupled periodicities on the sunspot time series. In this work, contrary to what has been presented in the literature, all periodic phenomena are limited within the time period of an 11-year cycle as well as the periodicities of the orbits of the planets. The main findings are the observed strong coupling of the Mercury, Venus, and Mars periodicities, as well as synodic periodicities with all other periodicities that appear on the sunspot series. Simultaneously, the rotation of the Sun around itself (25.6 to 33.5 days) provides an extensive coupling of all recorded periodicities. Finally, there is strong evidence of the existence of a quadratic mechanism, which couples all the recorded periodicities, but in such a way that only frequency pairs that sum up to specific periods are coupled. The justification for this kind of coupling is left open to the scientific community.

Potential benefits of climate change on navigation in the northern sea route by 2050

Climate change has been inducing a continuous increase in temperatures within the Arctic region, consequently leading to an escalation in the rates of Arctic ice depletion. These changes have profound implications for navigation along the Arctic Northern Sea Route (NSR). However, access to the NSR is constrained to specific temporal intervals when the sea ice thickness reaches a threshold that permits safe passage of ships. This research employs climate change model simulations and the Polar Operational Limit Assessment Risk Indexing System framework to investigate the navigational feasibility of diverse ship types along NSR during the calendar years 2030, 2040, and 2050, under SSP2-4.5 and SSP5-8.5 scenarios. Different ship categories were analyzed within the context of these two scenarios. Results indicate considerable variation in the navigability of different ship categories across different scenarios and years. In general, polar ships demonstrate a higher navigational potential throughout most of the year, while pleasure crafts are constrained to specific periods. These findings bear significant implications for the future of shipping along the NSR. As Arctic ice continues to melt, NSR is anticipated to become more accessible to ships, albeit with navigational availability remaining contingent on the ship category and seasonal considerations.

“Some Sort of Machine without a Body”

AbstractFrom the mid-1950s, Basel harpsichordist Antoinette Vischer (1909–1973) promoted the harpsichord as a modern instrument, commissioning numerous composers to contribute to a new repertoire. To this end, she turned to György Ligeti, who completed Continuum for her in 1968. The composer had already used the harpsichord in ensembles several times, but now he had to think about it in a soloistic function for the first time, starting from a specific performer with her specific instrument. In this paper, I focus on this relationship between composer, performer-commissioner, and instrument, drawing primarily on the correspondence between Ligeti and Vischer preserved at the Paul Sacher Foundation in Basel. These unpublished letters document their collaboration and how both negotiated their artistic positions and aesthetic concepts of the harpsichord as “some sort of machine.” An in-depth analysis of Ligeti and Vischer's exchange about the instrument's peculiarities and performance issues allows us to better understand their self-conception as artists and their ideas of “modernity.” Furthermore, this case study sheds light on a specific period in the history of an instrument that through the efforts of performers like Vischer was transformed from an artifact of the past to an emblem of the present.

Estimation of oil palm’s leaf area index (LAI) using Unmanned Aerial Vehicle (UAV) images

Leaf Area Index (LAI) is a parameter commonly used to indicate oil palm growth and production. The destructive method is the standard method to estimate LAI. However, this method requires much effort and cost and can only be done at a specific period. Unmanned Aerial Vehicles (UAVs), which have been widely used for mapping and calculating trees in oil palm plantations, have the potential to estimate LAI values quickly, efficiently, and without disturbing the oil palm tree. This research was conducted to develop the model for predicting LAI based on UAV images. Data was collected from six plots of 12-year-old oil palm trees at Adolina Estate, Serdang Bedagai, North Sumatra, Indonesia. The research was conducted on six varieties released by IOPRI, consisting of DxP Yangambi, DxP Langkat, DxP Simalungun, DyP Dumpy, DxP LaMe, and DxP PPKS 540. The estimated canopy cover from the UAV images was employed as an independent factor (x) compared to the calculated LAI from the destructive method, which was used as a dependent factor (y). The results showed that the LAI estimation using UAV imagery followed a linear model with R-squared values ranging from 0.3874-0.9556. In conclusion, despite requiring further research, UAV images could be used as rapid tools to estimate oil palm LAI.

Firm Size, Market Risk, And Return Reversal Anomalies During The COVID-19 Pandemic

This research aims to prove whether firm size and market risk based on CAPM affect return reversal anomalies as indicators of market overreaction during the COVID-19 pandemic. This explanatory research used a sample of stocks on the Indonesia Stock Exchange (IDX) that could be profitable during the pandemic period up to the effective period of phase one and two vaccination. It was found that return reversal anomalies occurred in the short term on the IDX, and contrarian strategies resulted in profits. Factors of firm size and market risk affected the reversal of returns in specific periods but did not affect other periods. When firm size and market risk had no effect, the return reversal anomaly occurred entirely due to the investors' overreaction in response to the pandemic without regard to the size and market risk factors of companies whose stocks were the investment target.

Detailed Analysis of Energy Consumption for an Office Building

Modern buildings consume a significant share of global power production. The primary sources of the power consumption are heating, ventilation and air conditioning (HVAC), light, cooking equipment, technical equipment like elevators, office facilities. Optimization of the buildings power consumption requires real-life data both for strategic planing and operational control. Here we analyze detailed power consumption of a research institute campus which is a medium-sized office buildings. Our data contain detailed electric measurements of 100 three-phase power lines which power all facilities inside the building. Each line is associated with several power consumers like lights, HVAC and office equipment. We analyze weekly and monthly trends, typical patterns in power consumption, and provide rough analysis of what classes of power consumers are most active in specific periods of time.

Political Regime Types and Income Inequality in Pakistan

Rising income inequality is one of the major issues in the Pakistani economy. It has been investigated in different dimensions. Different cross-sectional and panel studies have extensively examined the connection between political regime types and income inequality over a specific period. The findings have displayed significant variations across countries and regions. This study delves into exploring the relationship between political regime types and income inequality, specifically in Pakistan, utilizing time series data spanning from 1972 to 2019. To measure political regime types, the Polity index is employed, while the Gini-Coefficient serves as the metric for income inequality. Additionally, this research takes into account various factors, such as GDP per capita, the ratio of direct taxes to GDP, the percentage of government expenditure on education relative to GDP, and the percentage of government expenditure on community and social services relative to GDP, in order to control for their direct effects. The stationarity of the data is assessed using the augmented Dickey-Fuller test, while the autoregressive distributive lag (ARDL) model is employed to examine the cointegrating relationship among the variables in the model. The empirical findings from the estimation affirm the presence of a lasting correlation between Pakistan's political regime types and wealth disparities. The findings show that different political regimes have a notable detrimental effect on the nation's economic disparity. To be more precise, a one percent increase in the Polity score is associated with a 0.45 percent long-term and a 0.25 percent short-term fall in the Gini Coefficient. This suggests that Pakistan should work toward a stable democratic political framework and put into place sustainable economic policies in order to successfully address income disparity and achieve a desirable level of income distribution

ANALISIS MARKOV CHAIN DALAM MEMPREDIKSI MARKET SHARE DAN EQUILIBRIUM PENGGUNA LAPTOP MAHASISWA AKTIF UNIVERSITAS UDAYANA

The goal of this research is to assess the shift in laptop brands among currently enrolled students at Udayana University and predict the market share when the system reaches a stable state for each switch made by these students. The study employs the Markov Chain method, a component of Stochastic Processes. This method generates probabilistic information that aids in decision-making. In Markov Chain analysis, the equilibrium condition refers to a state in a specific period where the transition probability matrix stabilizes. Data on laptop brand switches were collected from users who have utilized more than one laptop brand. The study provides in-depth insights into the behavior of laptop users in the academic environment of Udayana University. The results of this research provide deep insights into the behavior of laptop users in the academic environment of Udayana University, laying the groundwork for a better understanding of market trends and user preferences.

EVALUASI PENYIMPANAN DAN PENDISTRIBUSIAN VAKSIN COVID-19 DI DINAS KESEHATAN PROVINSI SULAWESI TENGAH

The Health Department is responsible for distributing COVID-19 vaccines in the region. This research was conducted to evaluate the suitability of storage and distribution in the warehouse of the Pharmaceutical Installation of the Central Sulawesi Health Department, following the guidelines set in the Director General of Disease Prevention and Control Decision Number Hk.02.02/4/1/2021. The research used observational and qualitative descriptive methods, collecting data within a specific period, using all COVID-19 vaccines stored in the Pharmaceutical Installation of the Central Sulawesi Health Department as the sample. The research results show 8 types of COVID-19 vaccines managed by the Central Sulawesi Health Department, divided into 3 groups: vaccines that must be stored at 2-8 ºC, vaccines that must be stored at -20 ºC, and vaccines that must be stored at -70ºC. All groups of vaccines comply with the guidelines at 100%. However, concerning distribution, the level of compliance hasn't reached 100%. The group of vaccines that must be stored at 2-8 ºC, the compliance percentage is 63.64%. Similarly, the group of vaccines that must be stored at -20 ºC, the compliance percentage is also 63.64%, and the group of vaccines that must be stored at -70 ºC, the compliance percentage reaches 69.23%

Changes in Atmospheric Dynamics Over Dansgaard‐Oeschger Climate Oscillations Around 40 ka and Their Impact on Europe

AbstractDansgaard‐Oeschger (D‐O) climate variability during the last glaciation was first evidenced in ice cores and marine sediments, and is also recorded in various terrestrial paleoclimate archives in Europe. The relative synchronicity across Greenland, the North Atlantic and Europe implies a tight and fast coupling between those regions, most probably effectuated by an atmospheric transmission mechanism. In this study, we investigated the atmospheric changes during Greenland interstadial (GI) and stadial (GS) phases based on regional climate model simulations using two specific periods, GI‐10 and GS‐9 both around 40 ka, as boundary conditions. Our simulations accurately capture the changes in temperature and precipitation as reconstructed by the available proxy data. Moreover, the simulations depict an intensified and southward shifted eddy‐driven jet during the stadial period. Ultimately, this affects the near‐surface circulation toward more southwesterly and cyclonic flow in western Europe during the stadial period, explaining much of the seasonal climate variability recorded by the proxy data, including oxygen isotopes, at the considered proxy sites.

Study of Effect of Temperature on Commercial Moisturising Cream by UV-Visible Spectroscopy

Abstract: Skin moisturisation has become a part of our daily skin care routine whether the skin is dry or normal. To keep the skin hydrated moisturising creams are widely used across the world. Antioxidants are important ingredients in commercial cosmetic skin creams. It helps in extending the shelf life of a cream and preventing them from becoming rancid. α-Tocopherol acetate is a popular antioxidant in moisturising skin creams whether it is a herbal moisturising cream or a synthetic moisturising cream. The matrix of cosmetic creams is very complex due to the presence of several ingredients and therefore their extraction needs a special process. In this paper concentration of antioxidant in herbal and synthetic moisturizing creams have been calculated at various temperatures and at a specific time period by UV-Visible spectroscopy. This method is simple, sensitive, cost effective and reliable

Endocannabinoids, endocannabinoid-like compounds and cortisone in head hair of health care workers as markers of stress and resilience during the early COVID-19 pandemic

The pandemic caused by SARS-CoV-2 impacted health systems globally, creating increased workload and mental stress upon health care workers (HCW). During the first pandemic wave (March to May 2020) in southern Germany, we investigated the impact of stress and the resilience to stress in HCW by measuring changes in hair concentrations of endocannabinoids, endocannabinoid-like compounds and cortisone. HCW (n = 178) recruited from multiple occupation and worksites in the LMU-University-Hospital in Munich were interviewed at four interval visits to evaluate mental stress associated with the COVID-19 pandemic. A strand of hair of up to 6 cm in length was sampled once in May 2020, which enabled retrospective individual stress hormone quantifications during that aforementioned time period. Perceived anxiety and impact on mental health were demonstrated to be higher at the beginning of the COVID-19 pandemic and decreased significantly thereafter. Resilience was stable over time, but noted to be lower in women than in men. The concentrations of the endocannabinoid anandamide (AEA) and the structural congeners N-palmitoylethanolamide (PEA), N-oleoylethanolamide (OEA) and N-stearoylethanolamide (SEA) were noted to have decreased significantly over the course of the pandemic. In contrast, the endocannabinoid 2-arachidonoylglycerol (2-AG) levels increased significantly and were found to be higher in nurses, laboratory staff and hospital administration than in physicians. PEA was significantly higher in subjects with a higher resilience but lower in subjects with anxiety. SEA was also noted to be reduced in subjects with anxiety. Nurses had significantly higher cortisone levels than physicians, while female subjects had significant lower cortisone levels than males. Hair samples provided temporal and measurable objective psychophysiological-hormonal information. The hair endocannabinoids/endocannabinoid-like compounds and cortisone correlated to each other and to professions, age and sex quite differentially, relative to specific periods of the COVID-19 pandemic.

Revised European Association for Palliative Care (EAPC) recommended framework on palliative sedation: An international Delphi study

Background: The European Association for Palliative Care (EAPC) acknowledges palliative sedation as an important, broadly accepted intervention for patients with life-limiting disease experiencing refractory symptoms. The EAPC therefore developed 2009 a framework on palliative sedation. A revision was needed due to new evidence from literature, ongoing debate and criticism of methodology, terminology and applicability. Aim: To provide evidence- and consensus-based guidance on palliative sedation for healthcare professionals involved in end-of-life care, for medical associations and health policy decision-makers. Design: Revision between June 2020 and September 2022 of the 2009 framework using a literature update and a Delphi procedure. Setting: European. Participants: International experts on palliative sedation (identified through literature search and nomination by national palliative care associations) and a European patient organisation. Results: A framework with 42 statements for which high or very high level of consensus was reached. Terminology is defined more precisely with the terms suffering used to encompass distressing physical and psychological symptoms as well as existential suffering and refractory to describe the untreatable (healthcare professionals) and intolerable (patient) nature of the suffering. The principle of proportionality is introduced in the definition of palliative sedation. No specific period of remaining life expectancy is defined, based on the principles of refractoriness of suffering, proportionality and independent decision-making for hydration. Patient autonomy is emphasised. A stepwise pharmacological approach and a guidance on hydration decision-making are provided. Conclusions: This is the first framework on palliative sedation using a strict consensus methodology. It should serve as comprehensive and soundly developed information for healthcare professionals.

Conflict between urbanization and water environmental protection: Lessons from the Xiangjiang River Basin in China

China, the largest developing country, has experienced rapid urbanization since its reform and opening-up. However, the increasing pollution load from urban areas has deteriorated urban river water quality, contradicting the concept of sustainable and green development promoted by the Chinese government. This situation elucidates governmental shortcomings in systematic environmental protection. Our study revealed that the current wastewater treatment plant (WWTP) discharge standards in urban areas are insufficient for attaining the desired urban river water quality and thus intensify the conflict between urbanization and water environmental protection. As urbanization continues, the urban population will grow, further exacerbating pollution and conflict. Our focus was the Xiangjiang River basin in Zunyi, a typical urbanized city in China. Using a validated one-dimensional mathematical model, we compared the water quality in the Xiangjiang River between current and upgraded WWTP discharge standards. The results showed that the water quality in the Xiangjiang River falls short of the standards, with more than 60 % of the river exceeding limits. However, upgrading WWTP discharge standards significantly reduces the proportion of river sections exceeding limits, with only 0.4 % exceeding standards during specific periods. This enhancement greatly improved the Xiangjiang River's water quality, aided in restoring the entire water environment in the basin, and supported water environmental protection goals. Our research findings offer crucial support for local governments in shaping comprehensive water environmental protection policies and insights for addressing similar environmental challenges caused by rapid urbanization in other developing regions.

Spectrum-consistent [formula omitted]-based fragility curves

In the field of earthquake engineering, Fragility Curves (FCs) have become indispensable for regional risk assessments due to their efficacy in estimating the failure probabilities of diverse structures, including buildings and bridges, based on certain intensity measures. FCs are typically derived from either mechanical simulations or on-site observations, encapsulating crucial information about local stratigraphy and topography. The dependency of FCs on these local parameters, such as soil and topographic conditions, may vary depending on the selected intensity measure. When a spectral ordinate at a specific period is used as an intensity measure, FCs tend to be largely site-independent. On the other hand, if the intensity measure is chosen to be the acceleration ag at the bedrock substrate, as often occurs, then the FC is site-dependent. This distinction implies that identical structures at different locations, or even the same location with different soil and topographic conditions, have different FCs. This can significantly influence the results of regional risk studies, as using FCs for a specific building type at diverse locations with different hazards, stratigraphy, and topography can result in significantly different failure probabilities. The goal of this study is to develop a method so that ag-based FCs, developed at a certain location, can be used at any other location and on any other soil, preserving consistency in terms of spectral ordinate. This is done by a fully analytical approach, using a spectrum-consistent transformation. As an application, risk maps for Italy are generated and compared with an alternative method based on shifting the hazard curve.

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or minimize risks when investing or doing business in a country. This research aims to forecast Indonesia's GDP for the second, third, and fourth quarters of 2023 using the best models from machine learning or deep learning methods. Forecasts are made for each method with and without additional variables. The results indicate that the SimpleRNN algorithm from deep learning without additional variables is the method with the smallest RMSE and MAPE for GDP forecasting. Therefore, the best method used to forecast GDP is the SimpleRNN algorithm, and the forecasted GDP values for Indonesia's second, third, and fourth quarters are 5.350.840,00, 5.483.895,00, and 5.610.077,50 billion rupiah.

The Early Warning of Financial Failure for Iraqi Banks Based on Robust Adaptive Lasso Logistic Regression

It is well known that there are many mathematical financial failure models that have been proposedin the financial literature for specific stock markets. Some researchers are not aware these mathematical modelswere constructed to be fitted for that market data, not for other markets. Iraq stock market exchange is one of thesemarkets in which the researchers used imported models such as Kida, Sherrod, Altman, and others to predictfinancial failure. Therefore, the development of a financial failure warning model for banks has become verycrucial for the Iraqi bank sector in the stock market exchange. Unfortunately, there is no clear information aboutthe financial failure of Iraqi banks as a response variable, and the financial indicators contain outliers. Theobjective of this paper is to propose an algorithm to know the performance of efficient and inefficient banks basedon their indicators during specific time periods. The output of this algorithm will be considered as responsevariables. Then, a weighted adaptive lasso logistic regression algorithm that has a high breakdown point is used totackle outliers’ problem. Thirteen banks have been chosen as the most traded during the period (2010-2017), andfor each bank (27) financial indicators were collected. Our proposed model is compared with adaptive lassologistic regression by using Deviance, Misclassification, Area Under Curve, Mean Square Errors, and MeanAbsolute Errors. Consequently, the results showed that the weighted Adaptive Lasso Logistic Regression model ismore robust and relevant than others to be a financial model to warn of the failure of the banks in Iraq's stockmarket.

Digitalization and Firm Performance: Empirical Evidence from Forbes-listed Companies

As the world goes digital, many companies have found that their performance improves when they digitize their operations. This is especially true in light of the COVID-19 pandemic, as companies must rely more on digital tools to survive. Our paper aims to examine the connection between digital competitiveness and the financial performance of 86 digital businesses at the country level before, during, and after the COVID-19 pandemic over the period 2017--2021. Unlike previous findings that relied on qualitative methods, our research relies on quantitative methods. More precisely, we empirically examine the impact of digital competitiveness (i.e., an index) on financial performance (i.e., measured by return on assets) through linear regression models and panel data regressions for three specific periods (2017-2019 pre-pandemic, 2020 pandemic, and 2021 post-pandemic). We found that COVID-19 allows firms to adopt digitalization. More specifically, comparing the three periods, we discovered that digital competitiveness positively influences business financial performance in the post-Covid era. Looking ahead to the post-Covid world, it is clear that companies must prioritize digital competitiveness to ensure their long-term success. Governments can learn important lessons from this research on how to help companies digitize."

Impact-based flood forecasting in the Greater Horn of Africa

Abstract. Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods, and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup and the methodological approach of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 d. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the Disaster Operation Center of the Intergovernmental Authority on Development (IGAD) in the daily monitoring and early warning from hydro-meteorological disasters in eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River basin in summer 2020. More extensive quantitative evaluation of the system performance is envisaged to provide its users with information on the model reliability in forecasting extreme events and their impacts.

Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.