Development of the grain crops supply chain model

The possibility of optimization of the grain crops supply chain taking into account the interests of the farm is investigated in the work. The main trends in the agricultural sector of Ukraine, which directly or indirectly affect the activities of the farm, are analyzed in the work. It is established that the choice of the most optimal means of transport for grain crops transportation allows the farm to reduce the cost of the logistics supply chain, which in turn will lead to increased profitability and expansion of the agricultural sector. The solution to this problem is possible through the modeling of the grain crops logistics supply chain. To form a model of the grain crops supply chain the process of grain delivery as a system that contains many acceptable alternatives for making the most rational decision. The proposed model is presented in form of a "black box" model. The objective function of this model for farms is to make a rational decision on the process of grain delivery in order to maximize profits by optimizing the costs of such supply chain and ensuring the development of farming as an activity. The original variables of this model are: grain crops yields and grain crops humidity; farm’s sown area; harvesting equipment efficiency; own granaries capacity; cost for grain storage in own granary or for elevator service; grain share depending on the storage place; sales price of grain crops: to the internal buyer, at the grain reception center, at the port terminal, and also weather and climate conditions during harvest (favorable / unfavorable conditions). The initial indicators that will characterize the success of the grain delivery process will be the following one: harvest duration, harvest amount, trucking cost, average daily harvest duration, additional costs obtained by managing grain humidity, magnitude of crop losses due to the harvest duration, magnitude of losses due to the grain quality reduction (with mixing, without mixing). The proposed model will consider all these factors and their impact on harvesting, storage and transportation of grain crops, and will help to take into account the costs for farms depending on the situation.

A Non-Stationary Heat Spell Frequency, Intensity, and Duration Model for France, Integrating Teleconnection Patterns and Climate Change

The warming observed over the past summers since 2000 is unprecedented in climate records in Europe and especially in France. Extreme temperatures and heat spells were often analyzed in the literature by applying extreme value theory but rarely in a non-stationary (NS) framework and duration modeling is often excluded. For a modern risk-based approach, it is important to have knowledge of the duration, magnitude, and frequency of occurrence of heat spells in a climate variability and change context. Yet, despite their obvious importance, teleconnections and associated climate indices (CIs) have often been excluded from heat spell modelling. The notion of duration is also not easily interpretable in a frequency analysis and can even be subtle, especially in a NS context. In this study, we used time-varying statistical distributions with parameters conditional on covariates representing the time and CIs. The daily maximum temperatures (DMTs) observed at the Orange and Dijon stations in France were used as a case study. This paper highlights a possible relationship between some large-scale climate patterns and the heat spells in France. Overall, the results suggest that considering the combined effect of global warming and these patterns in NS models is useful for a more appropriate characterization of the hazard heat spells in France.

Using routine emergency department data for syndromic surveillance of acute respiratory illness before and during the COVID-19 pandemic in Germany, week 10-2017 and 10-2021

Introduction: To better assess the epidemiological situation of acute respiratory illness in Germany over time, we used emergency department data for syndromic surveillance before and during the COVID-19 pandemic. Methods: We included routine attendance data from emergency departments who continuously transferred data between week 10-2017 and 10-2021, with ICD-10 codes available for >75% of the attendances. Case definitions for acute respiratory illness (ARI), severe ARI (SARI), influenza-like illness (ILI), respiratory syncytial virus disease (RSV) and Coronavirus disease 2019 (COVID-19) were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age. Results: We included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/2021. A pattern of seasonality of acute respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017-2020 and the dynamics of the COVID-19 pandemic in 2020-2021 were apparent. The absence of a flu season during the fall and winter of 2020/2021 was visible, in parallel to the resurge of COVID-19 cases. The proportion of SARI among ARI cases peaked in April-May 2020 and November 2020-January 2021. Conclusion: Syndromic surveillance using routine emergency department data has the potential to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza and SARS-CoV-2.

Literature Review and Data Analysis on the Impacts of Prolonged Events on Transit System Ridership

The COVID-19 pandemic has drastically disrupted transit operations and induced significant transit ridership losses worldwide. Given its unprecedented duration, magnitude, and scale, the long-term effects are still unclear. Despite the differences, we can learn from previous disruptive events, such as terrorist attacks and epidemics, in the past 30 years and draw qualitative and quantitative insights about public reactions, ridership recovery periods, and transit agency responses during and after those events. This study sought to understand ridership variations during the current COVID-19 pandemic and inform transit agencies’ future decisions. This project’s research team therefore reviewed the impacts of selected historical events. They observed the following: (i) that most of the reviewed incidents (except for the 9/11 attacks) did not impose prolonged post-event effects on transit ridership for more than one year; (ii) that executive orders (e.g., school closures), transportation services (e.g., intensified airport safety screening and rail station closures), public fear, media reports, and reduced tourism were frequently mentioned as key factors that impacted transit ridership; and (iii) that measures, such as sanitizing vehicles and facilities, improving communications with the public, and promotions and advertisements, can potentially help restore transit ridership. The research team also developed a modeling framework that integrated a Bayesian structural time-series model, a dynamics model for daily transit ridership loss, a prediction module, and ordinary least squares regression to study COVID-19’s effects on the Chicago Transit Authority’s rail ridership. The researchers undertook a model of ridership on the CTA rail system as a potential first step to modeling COVID-19’s effects on transit ridership in northeastern Illinois. The researchers have not modeled ridership on any other transportation mode in northeastern Illinois at this time. The statistical analysis showed that remote learning/work policies and executive orders had answered for most of the ridership loss. Socioeconomic and land-use characteristics could effectively capture their effects. However, these characteristics could not explain people’s different reactions to reported deaths and media attention. Different population groups may have reacted differently to policy decisions, but their responses to reported deaths and media coverage seem random and independent of sociodemographic factors.

Future drought episodes over the North of Italy

<p>Droughts are a normal and recurrent climate feature, but they have the greatest consequences, compared to all other natural hazards and can produce severe effects on natural and socioeconomic systems. The northern part of Italy (Po Valley) is historically rich in water resources, and one of the most fertile and productive agricultural areas. Recently drought events increased affecting the hydrological behaviour of the Po river. In fact, since the turn of the century, an increase of the prolongation of the Po river lean period was recorded. In this work near future (2021-2050) and far future (2071-2100) weekly drought events for the Po Plain region, based on 12 daily EURO-CORDEX and MED-CORDEX Regional Climate Models (RCP 4.8 and RCP 8.5) at 12 km<sup>2 </sup>resolution were identified and mapped. First, the model validation was performed, 10 daily precipitation and temperature series (equally distributed in the plain/hill and mountain sector) were extracted and compared with the corresponding quality-controlled and homogenised gridded data obtained from ground stations. The statistical comparison between reference and candidate series for the control period (1971-2000) was performed using the Co.Temp software for temperature and Co.Rain software for precipitation. Then, on the more reliable models, the bias in the annual mean maximum and minimum temperatures between reference and Euro- and Med-CORDEX simulations was estimated. For precipitation, the ratio between reference and simulations was used. Subsequently, daily values were converted to weekly means and the reference evapotranspiration (ETo) was estimated by means of the Hargreaves ETo equation. Severe and extreme drought episodes were detected by mean of two drought indices (SPI and SPEI) calculated at 12-, 24- and 36- month time scales. Trends were analysed, and the main future drought events were characterised, identifying duration, magnitude and extent. For both RCP scenarios, the results have indicated an intensification of droughts in northern Italy for the period 2021-2050, with the Alpine chain being heavily affected by an increase of drought severity and duration. A North-to-South spatial gradient of drought duration was observed for the far future. This study indicates that at least two different triggering factors influence the characteristics of drought events, and it highlights the importance of using multiple drought indicators.</p>

Locating rock slope failures along highways and understanding their physical processes using seismic signals

Regional monitoring of rock slope failures using the seismic technique is rarely undertaken due to significant source location errors; this method also still lacks the signal features needed to understand events of this type because of the complex mass movement involved. To better comprehend these types of events, 10 known events along highways in Taiwan were analyzed. First, a hybrid method (GeoLoc) composed of cross-correlation-based and amplitude-attenuation-based approaches was applied, and it produced a maximum location error of 3.19 km for the 10 events. We then analyzed the ratio of local magnitude (ML) and duration magnitude (MD) and found that a threshold of 0.85 yields successful classification between rock slope failure and earthquake. Further, GeoLoc can retrieve the seismic parameters, such as signal amplitude at the source (A0) and ML of events, which are crucial for constructing scaling law with source volume (V). Indeed, Log(V) = 1.12 ML + 3.08 and V = 77 290 A00.44 derived in this study provide the lower bound of volume estimation, as the seismic parameters based on peak amplitudes cannot represent the full process of mass loss. Second, while video records correspond to seismic signals, the processes of toppling and sliding present column- and V-shaped spectrograms, respectively. The impacts of rockfall link directly to the pulses of seismic signals. Here, all spectrogram features of events can be identified for events with volumes larger than 2000 m3, corresponding to the farthest epicenter distance of ∼ 2.5 km. These results were obtained using the GeoLoc scheme for providing the government with rapid reports for reference. Finally, a recent event on 12 June 2020 was used to examine the GeoLoc scheme's feasibility. We estimated the event's volume using two scalings: 3838 and 3019 m3. These values were roughly consistent with the volume estimation of 5142 m3 from the digital elevation model. The physical processes, including rockfall, toppling, and complex motion behaviors of rock interacting with slope inferred from the spectrogram features were comprehensively supported by the video record and field investigation. We also demonstrated that the GeoLoc scheme, which has been implemented in Sinwulyu catchment, Taiwan, can provide fast reports, including the location, volume, and physical process of events, to the public soon after they occur.

Optimizing time-limited non-pharmaceutical interventions for COVID-19 outbreak control

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible–infectious–recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Infection-Related Glomerulonephritis

<b><i>Background:</i></b> There has been a long, storied relationship between various bacterial infections and glomerular injury, which is now encompassed under the term of infection-related glomerulonephritis (GN). The clinical and pathologic manifestations vary depending on the duration, magnitude, and underlying pathogen associated with the inciting infectious process. A brief and acute episode may lead to a self-limiting glomerular manifestation while a chronic or repetitive infection can result in persistent and irreversible injury. In this review, we will discuss the clinical and pathologic findings associated with the infection-related glomerulonephritides. <b><i>Summary:</i></b> An acute exudative GN with an influx of neutrophils is the most characteristic morphologic alteration associated with infection-related glomerular injury. The immunofluorescence staining pattern often reveals prominent complement component C3 deposition in both capillary walls and mesangial regions with or without accompanying immunoglobulin. Large subepithelial electron-dense deposits known as “humps” are the hallmark ultrastructural finding; however, these features can also be present in C3 glomerulopathies, which are often triggered by infections and may have similar underlying abnormalities in alternative pathway complement activation. In addition, other glomerular injuries can simultaneously be present along with infection-related GN, such as diabetic nephropathy, lupus nephritis, or immunoglobulin A nephropathy, constituting a true diagnostic challenge for the pathologist. <b><i>Key Messages:</i></b> Bacterial infection-related GN represents a spectrum of glomerular injury with variable clinical and pathologic presentations. The pathologic findings can show overlap with other glomerular diseases, and different forms of infection-related GN vary in terms of prognosis and treatment approach.

The drying regimes of non-perennial rivers

&lt;p&gt;The paradigm of surface water flow regimes is central to the aquatic sciences, where the timing, duration, frequency, magnitude, and rate of change of flow drive physical, chemical, and biological functions in aquatic systems. However, non-perennial streams comprise the majority of the global river network and there is a need to understand not just whether or not a stream periodically dries, but how it dries. Here, we propose to flip the script on flow regimes by presenting a comprehensive 'drying regime' framework to characterize stream drying.&amp;#160; We then identify similar drying characteristics in streams across watersheds with a broad range of climates, physiographic regions, and land uses. Using daily streamflow from 894 U.S. Geological Survey streamflow gages we isolated over 25,000 unique drying events over a period from 1979 - 2018. From these drying events we identified and calculated streamflow metrics that describe timing, duration, magnitude, frequency, and rate of change of stream drying. Using multivariate statistics, k-means clustering, and random forest analyses we grouped drying events into distinct drying regimes and determined the drivers of the clustered regimes. K-means clustering resulted in 4 distinct drying regimes characterized by (1) more frequent drying, (2) longer no-flow duration, (3) drying occurring following low antecedent flows, and (4) flashy high frequency drying, respectively. The majority of gages had more than one drying regime present at different times within each year, suggesting that dominant flow paths or drivers varied through time&amp;#160; Clustered drying regimes show low event-scale spatial coherence, and while drying regimes (1) and (2) show similar frequency throughout the year, (3) and (4) are substantially more frequent during summer months. Based on random forest analysis, land cover characteristics appear to drive drying event assignment to drying regimes more than climate variables. Furthermore, increased importance of individual watershed properties shows that the structural makeup of the watershed is notably more important to how an intermittent system dries than climate or physiographic characteristics. Non-perennial systems have unique functions due to the occurrence of both flowing and dry states, yet most of the past efforts rely on frameworks built around perennial streamflow behavior. Our work presents a novel drying regime framework to allow future studies to more effectively connect river drying to the physical, chemical, and biological functioning in these systems. This framework may also aid in current sustainable river management, including engineered flow regimes that are designed to balance water allocations with ecosystem requirements.&lt;/p&gt;

Using GRACE/GRACE-FO, Data-Driven, and Modeling to Assess the Twentieth and Twenty-first Century Water Storage Changes in the Nile River Basin

This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.