The purpose of this paper is to explore which financial performance indicators (FPIs) evaluate the level of supply chain capability (SCC) that explicitly touches all of the business functions and processes within and beyond the company. The authors investigated nine FPIs that were selected from the financial statements of 155 companies within nine industries. The authors find that suitable FPIs to measure SCC for shareholders’ value are return-on-assets (ROA), days-sales-outstanding (DSO), and current ratio (CR). This means that higher ROA, shortened DSO, and an appropriate level of CR could reach a sustainable supply chain. These results will help the industry to avert a major disruption in supply chain processes and activities using suitable financial performance indicators.

The ionospheric scintillation is a rapid phase and amplitude fluctuation of satellite signals due to the small-scale irregularity of electron density in the ionosphere. The characterization of the scintillation index in a proper way is a crucial aspect of the Global Positioning System (GPS) satellite signals for the purpose of space-based navigation, satellite communication, space weather as well as earth observation applications. In the current study, we analyzed the ionospheric scintillation index during the year of 2018 to 2019 over the Nepal region which locates itself almost being sandwiched between India and China and in the vicinity of low latitudes. The characteristic variations of scintillation occurrence are studied during the several geomagnetic storm and quiet days. The results show that the S4 indexes are varying from the 0.05 to 0.45 during the whole year. The S4 indexes behave higher variations during the whole day in the starting of the year and start to decrease at end of the day as well as at the ending months of the year 2019. The S4 values become completely less during the sunset time, while they have higher values during the sunrise. Especially, the S4 index during the storm days are larger than during the quiet days. It is worthy to note that the variations of S4 index studied in this current study do not follow the sunset property during the year of 2019. Consequently, the causes should be discovered and discussed additionally in the next research paper.

The post-pandemic has forced it Indonesian cloth sellers in Indonesia are facing changes in consumer behavior who tend to shop online. They need to find innovative ways to market products and reach potential customers. One of the problems faced is how to present traditional fabrics effectively to potential buyers. Therefore, a more creative and interactive marketing approach is needed. Using the needs analysis method to design innovative augmented reality (AR) integration in the sale of Indonesian fabrics could be an interesting solution. With AR, which will later be called PARTIKA, sellers can show potential buyers how the fabric looks in various situations, provide virtual interaction with the product, and provide detailed information. This application can thus provide a more in-depth and convincing experience, helping customers make better purchasing decisions.

In this study, the author proposes to evaluate the effect of sales growth, Receivable Turnover and operating cash flow on the liquidity of PT. Unilever Indonesia Plc. The research method used is descriptive method with a quantitative approach. In this statement, the population used in this study is the financial statement data from PT. Unilever Indonesia Plc. from 2010 to 2018, the technique of determining the sampling uses Purposive Sampling. This research data uses secondary data from PT. Unilever Indonesia Plc financial statements from 2010 to 2018. All data sources were obtained from the website of the Indonesia Stock Exchange at https://www.idx.co.id, the company's website and Google search. Our analysis reveals that sales growth and accounts receivable turnover from PT. Unilever Indonesia Plc. has no influence on the liquidity of PT. Unilever Indonesia Plc, while operating cash flow has an influence on the Liquidity of PT Unilever Indonesia Plc. This means the ups and downs of the value of sales and accounts receivable turnover of a company has no influence on the liquidity of PT. Unilever Indonesia Plc, while operating cash flow has increased or decreased has an influence on the liquidity of PT Unilever Indonesia Plc. The value of sales growth, accounts receivable turnover and operating cash flow can explain the liquidity of PT Unilever Indonesia Plc. by 78%, while 22% is explained by other factors which are not included in this study.

The aim of the study is to characterize the U.S. and Lithuanian defense cooperation from 1990–2020 including arms procurements. Findings suggest close defense cooperation between countries at Presidential, Congressional, and Defense Ministry/Secretary levels. The most developed cooperation areas are joint participation in international operations, improvements of the Lithuanian Armed Forces’ capabilities, and an increase of the U.S. military presence in the Baltic region. An analysis of Lithuanian defense procurements between 1990–2020 indicates that the majority of defense armaments were acquired from the U.S. using the Foreign Military Sales venue while other options to obtain armaments were not used.

With the rapidly increasing of people’s purchasing power, the fast moving consumer goods (FMCG) industry is supposed to grow dramatically. In order to gain more market access and profile, it is important for the FMCG manufacturers and retailers to find the preferences and provincial characteristics of consumers, to develop more suitable goods distribution strategy. Based on retails marketing data with geographic characteristics, this paper proposes a new combination of geography methods to solve the problems in distribution of FMCG. Via multiple K-means clustering and cross validation of KNN half off, the mesoscopic sales features are extracted through the classification of retails, which can indirectly grasp the consumer behavior characteristics. Based on space division and Moran’ I spatial autocorrelation arithmetic, two strategies are developed to satisfy consumer’s needs and promote sales, including conservative and positive strategies. According to our analysis, the total sales volume of the regions will increase by 5.1% and 10.3%. This study can be applied to the provide purchase strategies for FMCG retails according to their locations. The research can explore the consumption potential of different areas, thus improving the profile of retails and the development of economy in more mesoscopic scale.

Recently, during the last century, Communication & Information revolution caused an essential dynamic in society, especially in legal issues, This dynamic development of information data through this web, it has an essential effect upon the way of civil treatment, which then called "electronic agreements". The industrial and technological development lad to obvious changes in producing processes either for goods or services and then its distributing methods, through using modern media channels, but this factor has a negative side because there is no legal protection for the contractors. This fact leads to the necessity of publishing certain conditions to protect consumers.

A general methodology for loan amortization under arbitrary discount functions is discussed. It is shown that it is always possible to uniquely define a scheme for constructing the loan amortization schedule with an arbitrary assigned discount function. It is also shown that even if the loan amortization is carried out from the sequence of principal payments and the sequence of accrued interest, the underlying discount function can be uniquely determined at the maturities corresponding to the installment payment dates. As a special case of the proposed approach, we derive the amortization method according to the law of simple interest.

The fall armyworm, Spodoptera frugiperda (Lepidoptera, Noctuidae), is a notorious migratory pest. After invading China in 2019, the species has established as a year-round breeding population in most of the southern provinces. The area of winter corn in this region has been increasing due to the huge demand of fresh corn consumption which is potentially at risk from this invasive, although S. frugiperda growth and development under changing climate in this region is unclear, particularly the rising temperature during night period. Here we used highest daily temperatures of 27℃, 24℃, 20℃ and night temperatures that were 2℃, 4℃, 6℃ lower giving three daily temperature treatments reflective of winter conditions; GroupⅠ: 27-21℃, 27-23℃, 27-25℃, Group Ⅱ: 24-18℃, 24-20℃, 24-22℃, and Group Ⅲ: 20-14℃, 20-16℃, 20-18℃; to evaluate the effect of increasing night temperature on growth and development of S. frugiperda. Results show that the survival rate of larvae and pupae significantly declined with the daily temperature declining and the night temperature range increasing. Significant developmental effects were observed in all day-night temperature treatment, except for adults, with significant interaction effects for all stages except egg and generation time. Development rate accelerated along with the increasing daily temperature and night temperature, except for the intermediate treatments (Group Ⅱ). The uniformity of pupation and emergence times are higher in high daily temperatures and night temperature. Predictions of FAW development and warnings to local farmers need to be adjusted to take into account the more rapid development. These results will support decision makers to develop long-term management strategies for FAW in southern China.

In this paper, we study the usage of machine learning models for sales time series forecasting. The effect of machine learning generalization has been considered. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking technics, we can improve the performance of predictive models for sales time series forecasting.

Background. The main impetus behind the worldwide Covid-19 vaccination campaign in 2021 was to reduce the mortality attributed to SARS-CoV-2 infection in the preceding year. Nevertheless, rigorous analyses of the mortality benefits conferred by this massive vaccination effort have been lacking. Methods Statistics offers us an essential methodological approach for measuring the impacts of Covid-19 vaccination on public health. The mathematical relation between vaccinated-alive groups can be repeated between vaccinated-dead groups with relatively high statistical reliability because of the large population numbers involved. This method also confers greater statistical usefulness because it eliminates the Simpson effect. Results Calculations were performed for each of the following five(5) four-week intervals: weeks 35-38 (2021), weeks 39-42 (2021), weeks 43-46 (2021), weeks 47-50 (2021), and weeks 50(2021)-1(2022). The results obtained confirm that the mortality of the vaccinated coronavirus-infected groups was 14.5% higher on average than the mortality of non-vaccinated coronavirus-infected groups. Conclusions Vaccinated infected groups appear to have higher average mortality than their non-vaccinated infected counterparts. The findings suggest the legitimacy of extending the statistics between vaccinated living and vaccinated dead individuals for different age groups. Calculating the impact of Covid-19 vaccination on the mortality rate is a necessary step towards satisfying the first principle of medicine: “Primum non nocere”, “Do no harm”.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

Protected areas, such as natural World Heritage sites, RAMSAR wetlands, and Biosphere reserves are ecosystems within landscapes. Each site meets certain criteria that allow it to qualify as heritage or protected. Both climate change and human influence (e.g., incursion, increased tourist visitation) are altering biophysical conditions at many such sites. As a result, conditions at many sites are falling outside the criteria for their original designation. The alternatives are to change the criteria, remove protection from the site, change site boundaries such that the larger or smaller landscape meets the criteria, or manage the existing landscape in some way that reduces the threat. This paper argues for adaptive heritage, an approach that explicitly recognizes changing conditions. We discuss the need to view heritage areas as parts of a larger landscape, and to take an adaptive approach to management of that landscape. We offer five themes of adaptive heritage: 1) treat sites as living heritage, 2) employ innovative governance, 3) embrace transparency and accountability, 4) invest in monitoring and evaluation, and 5) manage adaptively. We offer the Australian Wet Tropics as an example where aspects of adaptive heritage currently are practiced, highlighting the tools being used. This paper offers guidance supporting decisions about natural heritage in the face of climate change and non-climatic pressures. Rather than delisting or lowering standards, we argue for adaptive approaches.

In the Global North, there is an increasing interest in pulses both for their beneficial effects in cropping systems and for human health. However, despite these advantages, the acreage dedi-cated to pulses has been declining and their diversity reduced, particularly in European temperate regions, due to several social and economic factors. This decline has stimulated a political debate in the EU on the development of plant proteins. By contrast, in the Global South, a large panel of minor pulses is still cropped in regional patterns of production and consumption. The aim of this paper is to investigate the for cultivation of potential minor pulses in European temperate regions as a complement to common pulses. Our assumption is that some of these crops could adapt to different pedo-climatic conditions, given their physiological adaptation capacity, and that these pulses might be of interest for the development of innovative local food chains in an EU policy context targeting protein autonomy. The research is based on a systematic review of 269 papers retrieved in the Scopus database (1974–2019), which allowed us to identify 41 pulses as candidate species with a protein content higher than 20% that are already consumed as food. For each spe-cies, the main agronomic (e.g. temperature or water requirements) and nutritional characteristics (e.g. proteins or antinutritional contents) were identified in their growing regions. Following their agronomic characteristics, the candidate crops were confronted with variability in the annual growing conditions for spring crops in European temperate areas to determine the earliest poten-tial sowing and latest harvest dates. Subsequently, the potential sum of temperatures was calcu-lated with the Agri4cast database to establish the potential climatic suitability. For the first time, 21 minor pulses were selected to be grown in these temperate areas and appear worthy of inves-tigation in terms of yield potential, nutritional characteristics or best management practices.

The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau's Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF) and Poor Man's Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall-runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using the Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts and NSE, PCC, MAE, KGE, PBias and RMSE and three categorical metrics CSI, FAR and POD for deterministic forecasts. The skill scores CRPS, relative CRPS, CRPSS and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE and RMSE as well as the categorical metrics. Forecast performance also progressively decreased with the higher streamflow regime. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country – and were generally better in the high runoff generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia and mid-west of Western Australia. We did not find any association between model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resources management.

Background. To improve maternal and child health, it is essential to adhere to health-promoting and preventive measures. However, reliable information as well as effective tools are not easy to identify in this field. Our cross-sectional study investigated the needs and expectations of expectant and new mothers and fathers -primary users of an app supporting the first 1000 days of life. Methods. Between May and August 2022, we recruited expectant and new parents by administering a 71-item 5-point Likert scale questionnaire related to content, functionalities, and technical features of a hypothetical app. We stratified responses by sociodemographic characteristics and then performed ward hierarchical clustering. Results. The 94 women and 69 men involved in our study generally agreed with the proposed content, but expressed low interest in certain app functionalities, especially those related to interaction mechanism and interactivity. Women were generally more demanding than men, and family income declined as the needs and expectations increased. Conclusions. Our findings, resulting from the engagement of end-users, may be useful for designers and technology providers to implement mHealth solutions that, in addition to conveying reliable information, are tailored to the needs and preferences of end-users in the first 1000 days of life.

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe, equipped with a novel formulation for storage in model versions with reduced temporal representation. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (~5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (~20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (>50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects.

This study aims to investigate how the adoption of an online to offline (O2O) transition strategy impacts the sales volume of Kampoeng Merdeka Clothing. The O2O strategy includes various components, such as establishing O2O platforms, delivering personalized services, ensuring secure transactions, and engaging in offline business activities. The research employs a quantitative approach, focusing on consumers who have made clothing purchases from Kampoeng Merdeka Clothing in Macassar. The sample size comprises 83 respondents, deliberately selected using the Slovin approach. The data analysis and methodology involve the application of Partial Least Squares (PLS) structural equation modeling to assess both the external and internal models. The findings of this study emphasize the significant and positive influence of the variables "Build O2O Network Platform," "Provide Personalized Experience," "Safeguard Transactions Security," and "Offline Business" on driving the sales growth of Kampoeng Merdeka Clothing. These results indicate that the O2O transition strategy has a substantial impact on the brand's sales performance, with the establishment of O2O platforms and personalized services playing crucial roles. Additionally, ensuring secure transactions and engaging in offline business activities further contribute to the overall success of Kampoeng Merdeka Clothing in the market. This research sheds light on the effectiveness of the O2O approach in boosting sales for clothing brands and underscores the importance of integrating online and offline elements to maximize business outcomes.

The South African government introduced a nationwide lockdown in March 2020 to mitigate the spread of Covid-19. Among other restrictions, the government banned the sale of tobacco products, which lasted for nearly five months. We performed a Google search using the keywords smok*, puff*, lockdown, tobacco, and cigarette*, for articles published in English from 23 March 2020to 18 December 2020 and analysed 441 online media articles. We identified the main arguments made by proponents and opponents of the tobacco sales ban, which were categorised into themes. Three themes were prominent: medical, legal, and economic/financial. Legal aspects were covered in 48% of articles, followed by economic (34%), and medical aspects (18%). The media was generally ambivalent about the tobacco sales ban during the first five weeks of lockdown. Sentiment then turned against the ban. because the medical rationale was not well communicated by the government, there was limited empirical evidence of a link between smoking and contracting Covid-19, and the sales ban was ineffective, since most smokers still purchased cigarettes. Policy framing in the media plays an important role in how the public receives the policy. Any future tobacco control policy intervention should be well-considered and appropriate cessation support services offered.

This research studies the impact of shopping tourism on retail sales and rents, using the Covid-19 pandemic as a quasi-experiment. Shopping tourism refers to individuals who travel primarily for shopping purposes, and their spending patterns can have significant effects on the retail sector. The Covid-19 pandemic disrupted global travel and resulted in a decline in shopping tourist arrivals, leading to a downturn in sales for retailers dependent on shopping tourism. Additionally, the decline in shopping tourism affected retail rents, as the reduced demand for retail spaces posed challenges for property owners in attracting tenants. The study focuses on Hong Kong, a prominent shopping destination, which experienced a significant decline in tourist arrivals and retail sales during the pandemic. The research analyzes the relationship between tourist arrivals, retail sales, and rents using time series analysis and identifies the impact of shopping tourism on retail spaces and rents. The findings suggest a positive association between tourist arrivals and retail sales, particularly during the period of shopping tourism growth. However, the pandemic severely reduced this effect, revealing the impact of shopping tourists on the retail sector. The study concludes by discussing the implications on retail resilience and highlighting the need for further research on the impacts of shopping tourists on retail sales and rents.

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chain (CLSC), and shortages and channels are considered to be two important issues in CLSC. This paper explores ordering and channel decisions in CLSC with new and differentiated remanufactured products, and considers the price and sales effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses, and establishes an integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. Numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. Wherein, the upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, decisions of integrated channel are better than those of decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out of stock costs. According to the research in this article, some management insights are also provided.

Delayed discharge for non-clinical reasons is defined as a period of stay that continues after a patient has been deemed medically fit to leave the hospital but is unable to do so for non-medical reasons. This circumstance overburdens the healthcare system and constitutes a major problem for healthcare systems and the patients themselves in this situation.The aim of this study was to evaluate the delay in effective discharge for non-medical reasons in patients admitted to acute care hospitals in Spain. A scoping review was conducted in order to revise and obtain an up-to-date review. The initial search strategy identified a total of 123 references, which were successively screened to a final selection of 13 studies. To conclude, delayed discharge from hospital for non-clinical reasons is a multifactorial problem. It may be due to factors internal or external to the hospital, as well as personal factors. The main causes of the delay are similar among the studies found, as are the clinical characteristics of the patients, most of whom are elderly, frail, and more dependent due to declining functional capacities. Further studies addressing the socio-familial characteristics of the patients and the perspective of the patient and families would be necessary.

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

Bovine mastitis is the most impacting disease of dairy industry, and it is characterized by a complexity of causal agents, which have revealed a geographical variation among regions and countries. The mastitis-related pathogens have been traditionally classified as contagious or environmental, based on habits of the microorganisms and transmission routes. In addition, the severity of mammary infections has been associated with the virulence of the pathogens, and immune and nutritional status of the hosts. Considering this scenario, we investigated the etiological nature, clinical severity scores, and days in milk (DIM) data in 4,273 clinical cases of bovine mastitis among ten large-dairy farms located in the Southeast region of Brazil. Streptococcus dysgalactiae (283/4,273=6.6%), Escherichia coli (190/4,273=4.4%), Prototheca spp. (112/4,273=2.6%), and Streptococcus uberis (95/4,273=2.2%) were the predominant pathogens isolated, all from the environmental origin. Among 4,273 clinical cases, clinical gravity score was available in 43.8% (1,871/4,273) animals. From these, 69.8% (1,306/1,871), 27.3% (510/1,871) and 2.9% (55/1,871) were scored as mild, moderate, and severe, respectively. Most of isolation of pathogens were observed in the first 100 days in milk, and their clinical severity scored as mild (3,612/4,273=84.5%). Our results contribute to the etiological identification, clinical severity scoring, and milking aspects of bovine clinical mastitis in dairy farms with a history of clinical mammary infections.

Cameroon's national vaccination campaign was launched on April 12, 2021, amid a nationwide outbreak of COVID-19 with two types of vaccines. This study provides preliminary evidence of the level of coverage of the population and gives an early overview of the challenges, the achievements and the lessons learned. COVID-19 vaccine administration data were obtained from data of the Cameroon Ministry of Public Health. Descriptive statistical analyses were conducted. Thirty days after the introduction of COVID 19 vaccines, five percent of the target population was vaccinated. Women represented one third of the people vaccinated regardless of age and health conditions. Although AEFI reported were minor and scanty with both vaccines, the majority of the vaccinated did not come back for their second dose. There is a need to build confidence among eligible beneficiaries in order to expand the benefits of vaccination to control the current pandemic. The country is still far below the target which could be worrisome given that the uptake is slow and, the 391 200 doses of the AstraZeneca are going to expire in August 2021. This study offers insights into those early efforts as a contribution to significant discussions about upcoming approach to improve service delivery and uptake.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

Abstract: Earth’s Phanerozoic history is marked by about 100 major events (“golden spikes”) indicating the beginnings of new stages in the Geologic Time Scale (GTS). Stage boundaries signify major deterministic events that are relatively well-known and can be correlated worldwide, mostly on the basis of appearances or disappearances of fossil species. The latest stage (Anthropocene) is human-made and currently involves rapid (approximately linear) increase in average atmospheric temperature. The main cause of Earth’s past and current temperature increases is addition of CO2 to the atmosphere. There is continuous exchange of greenhouse gases between atmosphere and oceans, which (per unit of volume) contain 50 to 60 times as much CO2 as the atmosphere. Abundance of new very precise worldwide observations is allowing multifractal modeling of weather and climate during the latest Quaternary stages during which some variables like temperature of the atmosphere and frequency of forest fires can be described by using the Pareto-lognormal frequency distribution model with parameters that are subject to continuous (deterministic) changes, similar to those in the earlier Phanerozoic.

Objectives: There are limited data regarding the efficacy of methylprednisolone in patients with acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) requiring invasive mechanical ventilation. We aimed to determine whether methylprednisolone increases the number of ventilator-free days (VFDs) among these patients. Design: Retrospective single-center study Setting: Intensive care unit Patients: All patients with ARDS due to confirmed SARS-CoV-2 infection and requiring invasive mechanical ventilation between 1 March and 29 May 2020 were included Interventions: None Measurements and Main Results: The primary outcome was ventilator-free days (VFDs) during the first 28 days, defined as being alive and free from mechanical ventilation. The primary outcome was analyzed with competing-risks regression based on Fine and Gray’s proportional subhazards model. Death before day 28 was considered to be the competing event. A total of 77 patients met the inclusion criteria. Thirty-two patients (41.6%) received methylprednisolone. The median dose was 1 mg.kg-1 (IQR: 1-1.3 mg.kg-1) and median duration of 5 days (IQR:5-7 days). Patients who received methylprednisolone had a mean 18.8 VFDs (95% CI, 16.6-20.9) during the first 28 days vs. 14.2 VFDs (95% CI, 12.6-16.7) in patients who did not receive methylprednisolone (difference, 4.61; 95% CI, 1.10-8.12; P = 0.001). In the multivariable competing-risks regression analysis and after adjusting for potential confounders (ventilator settings, prone position, organ failure support, severity of the disease, tocilizumab, and inflammatory markers), methylprednisolone was independently associated with a higher number of VFDs (subhazards ratio: 0.10, 95%CI: 0.02-0.45; p=0.003). Hospital mortality did not differ between the two groups (31.2% vs. 28.9%, p=0.82). Hospital length of stay was significantly shorter in the methylprednisolone group (24 days [IQR:15-41 days] vs. 37 days [IQR:23-52 days], p=0.046). The incidence of positive blood cultures was higher in patients who received methylprednisolone (37.5% vs. 17.8%, p=0.052). However, 91% of patients who received methylprednisolone also received tocilizumab. The number of days with hyperglycemia was similar in the two groups. Conclusions: Methylprednisolone was independently associated with increased VFDs and shortened hospital length of stay. The combination of methylprednisolone and tocilizumab was associated with a higher rate of positive blood cultures. Further trials are needed to evaluate the benefits and safety of methylprednisolone in moderate or severe COVID-19 ARDS.

Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.

Background: allergic diseases and, in particular food allergy (FA), have been identified as one of the major diseases in the world. The first 1000 days of life can be a “window of opportunity” or a “window of susceptibility” during which several factors can predispose children to FA development. Changes in gut microbiota composition, from pregnancy to infancy, could have a pivotal role in this regard: some bacterial genus such as Lactobacillus or Bifidobacterium seem to be protective from FA development, on the contrary Clostridium and Staphylococcus appear to be unprotective. Methods: we conducted a research through the most recent literature (2013-2023), using Pubmed and Scopus as databases. We included English original papers, clinical trials, metanalysis and reviews. Case reports, series and letters have been excluded. Results: during pregnancy, maternal diet can play a fundamental role in influencing gut microbiota composition of newborns. After birth, human milk can promote the development of protective microbial species by human milk oligosaccharides (HMOs), that play a prebiotic role. Also complementary feeding can modify gut microbiota composition. Conclusions: the first two years of life are a critical period where several factors, in genetic predisposed children, can increase the risk of FA development.