Storage Management Research Articles

Design and Implementation of an Emergency Environmental Monitoring System

The collection and real-time transmission of emergency environmental information are crucial for rapidly assessing the on-site situation of sudden disasters and responding promptly. However, the acquisition of emergency environmental information, particularly its seamless transmission, faces significant challenges under complex terrain and limited ground communication. This paper utilizes sensors, line-of-sight communication with unmanned aerial vehicles (UAVs), and LoRa long-distance communication to establish an integrated emergency environmental monitoring system that combines real-time monitoring, UAV-mounted LoRa gateway relaying, and backend data analysis. This system achieves real-time acquisition, seamless transmission, storage management, and visualization of environmental emergency information. First, a portable emergency environmental monitoring device was developed to collect and transmit environmental factor data. Second, a UAV-mounted LoRa gateway was designed to extend the data transmission coverage, ensuring seamless communication. Finally, multiple field experiments were conducted to evaluate the system’s performance. The experimental results indicate that the system possesses reliable capabilities for emergency data collection and transmission in complex environments, providing new technical solutions and practical support for developing and applying emergency environmental monitoring systems.

ANN-fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV System with Battery Storage under Fast-changing Atmospheric Condition

Background: The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Methods: Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANN-Fuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VmPP. Results: ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. Conclusion: For energy storage, we consider the utilization of lithium-ion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

A cross-entropy-based synergy method for capacity configuration and SOC management of flywheel energy storage in primary frequency regulation

A cross-entropy-based synergy method for capacity configuration and SOC management of flywheel energy storage in primary frequency regulation

Risk factors associated with leptospirosis in dual-purpose cattle based on the One Health approach in the department of Huila, Colombia

Leptospirosis significantly impacts beef and dairy cattle production, particularly in tropical regions, although it remains a disease of global relevance. This zoonotic disease primarily causes abortions during the final third of gestation. The present study aimed to analyze the risk factors associated with leptospirosis in the southern Andean region of Colombia. To this end, 360 cattle were sampled across 24 municipalities within the department of Huila. A seroprevalence of 49.2% was determined using enzyme-linked immunosorbent assays (ELISA). These results were subsequently correlated with various variables obtained through epidemiological surveys. The findings revealed that the primary risk factors for leptospirosis were linked to inadequate livestock management practices. These included improper methods of food storage, failure to implement quarantine protocols for infected animals, the introduction of new fattening cattle into the herd without proper precautions, infrequent weighing of animals, poor storage management of veterinary medications, lack of segregation of sick animals, insufficient estrus detection practices, inadequate hand hygiene when interacting with the herd, and the use of unclean instruments during routine procedures. Conversely, the presence of well-defined internal pathways and the separation of poultry production from cattle operations were identified as protective factors. Understanding these risk factors is critical from a One Health perspective, as it facilitates the implementation of preventive measures aimed at safeguarding both animal and human health.

Exploring the Pathways of Power Grid Marketing Management Based on Big Data

The electric power industry is undergoing profound transformations driven by big data, posing challenges to the traditional power grid marketing management model. These challenges include neglecting market demands, insufficient data support, and inadequate customer service. The application of big data technology offers innovative solutions for power grid marketing management, encompassing critical aspects such as data collection and integration, storage management, analysis, and mining. By leveraging these technologies, power grid enterprises can precisely understand customer needs, optimize marketing strategies, and enhance operational efficiency. This paper explores strategies for power grid marketing management based on big data, addressing areas such as customer segmentation and personalized services, as well as market demand forecasting and response. Furthermore, it proposes implementation pathways, including essential elements such as organizational structure and team building, data quality and governance systems, training, and cultural development. These efforts aim to ensure the effective application of big data technology and maximize its value.

Smart Agriculture Blockchain Applications

Smart Agriculture is a combination of AI, Cloud Computing, and IoT that revolutionizes farming efficiency and sustainability. It successfully addresses the developing worldwide request for food production through expanding crops, optimizing inventory management, minimizing food waste, and improving safe consuming of food. Precision agriculture, facility agriculture, and order agriculture form the core components of smart agriculture. Each one of them concentrates on specific areas of farming processes, altogether contributing to enhancing farm productivity and efficiency. However, the integration of blockchain technology further adds an intrinsic value to smart agriculture by delivering food traceability, increasing efficiency to supply chains, ensuring food sustainability, and eliminating the need for intermediaries. With the various merits that blockchain technology provides, the reliable controlling of land and farm and monitoring of livestock assets can be provided, and tracking the entire product lifecycle is ensured, which not only enables producing more food, but also provides sustainability and rises food safety standards. By minimizing fraudulent activities, facilitating data management, enhancing farm and stock management, blockchain technology can be used as a success factor in the agricultural industry. This paper thoroughly explores the numerous applications of blockchain technology in smart agriculture, including product supply chain and storage management, farm, and stock control. It also highlights the potential future applications and directions for the integration of those technologies into the field of agriculture to realize their full potential.

Microstructural Suitability and Stability of AlSi10Mg–Sn Plasma Coatings for Thermal Energy Storage Purposes

The study explored the possibility of producing thick coatings of fully metallic composite phase change materials with suitable microstructure for thermal energy storage or thermal energy management purposes. The composite materials are based on Al-Si-based alloys with Sn additions, potentially obtainable from scraps. This leads to an Sn-rich low-melting phase which is able to store/release heat when it melts/solidifies. The material can thus be considered as a composite phase change material (C-PCM). A thick coating was deposited on an Al alloy substrate by plasma spray, mixing AlSi10Mg and Sn powders in a 60:40% mass ratio. Optical scanning microscopy and X-ray diffraction revealed a microstructure suitable for a C-PCM, presenting Sn basins interrupted by a matrix made up of primary Al and Al–Si eutectic. Preliminary investigation into the reliability of the coating was conducted by performing up to 10 heat cycles across the melting temperature of the low-melting phase, simulating service in TES/TEM devices. No significant changes in its coating microstructure were observed. Minor surface leakage of molten Sn occurred, mainly during the first heat cycle. No detachment of the coating or cracks formed within the coating were observed, which could have been expected due to the mismatch in the coefficients of thermal expansion of the main phases and to the expansion/shrinkage due to Sn melting/solidification.

Data Migration and security in cloud

Data migration and security in the cloud" refers to the process of transferring data from an on-premises system to a cloud environment while prioritizing security measures to protect sensitive information during transit and once stored in the cloud, including implementing strong access controls, encryption, and regular monitoring to prevent data breaches and ensure compliance with relevant regulations. In this time of digital evolution, cloud migration and its security have become essential topics that cannot be avoided. With more companies turning to cloud services for support, the demand for large-scale data storage and data management has also increased. Along with this rising need, the risk of cyber attacks on cloud data has also increased. To better protect yourself from these risks, it’s essential to ensure that you and your organization are up-to-date on the latest security technologies and safety precautions. Keywords : Data Migration, Data Security, cloud, ETL, Data Integration

BSHPC: Improve Big Data Privacy Based on Blockchain and High-Performance Computing (HPC)

The vast expansion and sharp rise in data across many facets of society have made it increasingly difficult to manage big data effectively. Using traditional methods to ensure the security and privacy of users’ data is no longer sufficient. In keeping with this worry, massive data storage is still crucial. High-Performance Computing (HPC) is examined to determine the need for handling blockchain issues and protecting large data in a decentralized manner that strives for resilience. This study proposes the Big Data Storage High-Performance Computing (BSHPC) approach, which addresses big data considerations in storage management to maintain accuracy and enables the usage of blockchain. The best storage management is the primary benefit of BSHPC, as only critical data is kept on the blockchain, and other data may be kept in an off-chain database using the interplanetary file system (IPFS). Furthermore, the network's node authentication in this strategy depends on trustworthy nodes. On HPC computers, data authenticity and provenance tracking would be guaranteed, and managing large data across blockchains would be more secure. The proposed method is simulated using the Python-MPI version, and the results confirm the effectiveness of the proposed method based on performance and transactions. Moreover, the proposed method is evaluated with another study in the literature on MEC-based sharing, and it proves its effectiveness. Doi: 10.28991/ESJ-2024-08-06-011 Full Text: PDF

The energy management strategy of two-by-one combined cycle gas turbine based on dynamic programming

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

Advanced Data Modeling Techniques in SAP BW/4HANA: Optimizing for Performance and Scalability

The increasing demand for faster and more scalable data processing in business intelligence systems has led to the adoption of advanced data modeling techniques in SAP BW/4HANA. SAP BW/4HANA, built on the in-memory computing platform of SAP HANA, offers significant advantages in terms of performance and scalability. This paper explores the latest data modeling methodologies within SAP BW/4HANA to enhance system performance and accommodate the growing complexity of business data. Key strategies for optimizing data models include leveraging the power of HANA's in-memory capabilities, implementing optimized object structures, and utilizing data tiering for efficient storage management. The use of advanced modeling techniques such as composite providers, enhanced data flows, and the integration of SAP HANA's native capabilities for real-time analytics are also discussed. Furthermore, the paper addresses the challenges involved in managing large-scale data environments, ensuring data consistency, and implementing best practices for performance tuning. By focusing on these advanced approaches, organizations can achieve higher data throughput, reduced query response times, and greater scalability, enabling them to effectively handle large volumes of data across multiple systems. The implementation of these techniques not only enhances the operational efficiency of SAP BW/4HANA but also provides businesses with actionable insights that drive data-driven decision-making. The paper concludes by highlighting the future directions in data modeling within SAP BW/4HANA and the ongoing evolution of its features for emerging business needs.

Urban land use optimization prediction considering carbon neutral development goals: a case study of Taihu Bay Core area in China

BackgroundGiven the increasing commitment of numerous nations to achieving future carbon neutrality, urban development planning that integrating carbon storage considerations plays a crucial role in enhancing urban carbon efficiency and promoting regional sustainable development. Previous studies have indicated that optimizing land use structure and quality is essential for regional carbon storage management. Taking the core area of Taihu Bay as study area, this study innovatively combined high-precision urban 3D data to account for the whole urban carbon pools of buildings, vegetation, soils, water. Then, multi-objective linear programming model and PLUS (Patch-generating Land Use Simulation) model were applied at patch scale to assess and compare carbon storage in various scenarios, considering both carbon storage maximization and urban development requirements.ResultsThe results were presented as follows. (1) Urban woodland carbon pool accounts for only a fraction of total carbon pool, and the role of soil and building carbon pools cannot be ignored. (2) Compared with the current situation, the carbon-growth optimized scenario will lead to the increase of total carbon storage by 38,568.31 tons. (3) Carbon-growth optimized scenario has reduced carbon storage in Woodland, Cropland, Village, Water compared to the Natural growth scenario, but has increased carbon storage in Garden plots, Street, Urban district, Town and other areas.ConclusionsTherefore, we find that for fast-growing cities, rationally planning built-up areas and woodland areas can achieve the twin goals of economic development and maximizing regional carbon storage. Furthermore, the implementation of new energy policies and projects such as green roofs can help to achieve regional carbon neutrality. The study provides new insights into the accounting of carbon pools within cities and the simulation of fine-grained land use planning based on the dual objectives of carbon stock maximization and urban development.

Ag nanomaterials enabled simultaneous thermal storage and heat transfer enhancement of CH3COONa·3H2O/vermiculite composite phase change material

Filling thermally conductive materials (TCMs) is an effectivity way to enhance the thermal conduction of phase change materials (PCMs). However, it always has negative effects on heat storage behavior of PCMs. Balance of latent heat and thermal conduction capacity plays a vital role in practical application of thermal storage management. To study the effect of dimensional features of TCM on thermal characteristic of PCM, Ag nanomaterials (Ag NMs) which including 0-dimensional Ag nanoparticles (AgNP), 1-dimensional Ag nanowires (AgNW) and 2-dimensional Ag nanosheets (AgNS) acted as TCM, were used to modify the flaky expended vermiculate (EVM-Ag NMs), a series of CH3COONa·3H2O (SAT)/EVM-Ag NM composite PCMs (SAT/EVM-Ag NM CPCMs) were prepared. It was found that the Ag NMs contributed to the phase change behavior of hydrated salts. Wherein, the SAT/EVM-AgNW CPCM possessed high latent heat (∼200.9 J/g) and great thermal reliability (heat enthalpy loss was 6.9 % after 500 thermal cycles). Besides, Ag NMs also improved the thermal conductivity of the CPCMs. The Ag NWs formed interconnected thermal highways which was benefited to enhance thermal conductivity (0.8153 W·K−1·m−1). This work provides a new strategy for simultaneously improving the thermal conductivity and heat storage behavior of PCM, finds a new perspective to explore the mechanism of TCMs on phase change behavior and heat conduction of PCM.

Holistic approach to energy storage management aspects in sustainable community

Energy management is nowadays key topic for synchronic operation of renewable sources of energy and their recipients. Contemporary national electrical power grid systems more often cannot supply efficiently electrical energy and cannot receive energy produced by renewable sources. The common approach to the problem is to meet energy demands supplying from electrical grid and renewable power sources with energy storage feature. From the other side, off-grid solutions based on the co-generation biogas plants are commonly aimed on small local communities as power supply supported by renewable energy systems like photovoltaic (PV) systems, wind power plants or small water plants with energy storage to support self-consumption of electrical energy. Integration of intermittent renewable power sources, such as solar, wind and biogas plant, increases the difficulty of managing the electricity grid and maintaining the balance of electricity supply and demand, especially in small communities. The holistic approach to the energy storage management takes all above aspects and presents the concept where municipal waste is used to produce energy in biogas plant supported by PV systems and community shared electrical energy storage to provide uninterrupted power supply. The study also presents how energy storage management can be used in whole process to adjust the size and manage energy supply and demand within the community based on energy self-consumption optimization. It is also shown that by utilizing municipal waste produced by the community we can meet the goals of circular economy and sustainable development of local communities as the waste will be used in full without necessity of recycling it outside the community. The novelty of the study is the foundation for energy storage capacity and renewable energy sources size evaluation to balance energy management process without the need of on-grid power supply and with use only municipal biodegradable waste for biogas fuel supply and solar energy for energy production.

Poly(vinyl alcohol)-Assisted Exfoliation and Grafting Modification of Boron Nitride Nanosheets with a High Aspect Ratio via a Continuous Production Method for Energy Storage and Thermal Management.

Hexagonal boron nitride nanosheets (BNNS) integrating many extraordinary properties are usually combined with polymers to fabricate multifunctional dielectric materials for energy storage and thermal management. Unfortunately, the existing technologies for producing BNNS generally have the disadvantages of inefficiency and low product quality, which significantly hinder the wide application of high-value nanocomposites. Herein, a novel poly(vinyl alcohol) (PVA)-assisted pan-milling method based on the innovative solid-state shear milling (S3 M) technology is reported for the production of high-quality BNNS. The uniform three-dimensional (3D) shear force field generated by pan milling not only ensured the high aspect ratio of BNNS (the average lateral size of ∼954 nm and the average thickness of ∼3.7 nm) but also induced the mechanochemical reaction between PVA and BNNS to achieve in situ grafting modification on the BNNS surface. The simultaneous high-quality exfoliation and surface modification of BNNS positively contributed to the mechanical, dielectric, and thermal conduction properties of BNNS/PVA nanocomposites. At the BNNS content of 10 wt %, the BNNS/PVA composite film exhibited excellent robustness (Young's modulus of 5305 MPa and the tensile strength of 83 MPa), with a satisfactory breakdown strength (Eb of 134.7 MV/m) and dielectric constant (∼5 at 1000 Hz). In addition, the substantial improvement of thermal conductivity (from 0.88 to 5.59 W·m-1·K-1) guaranteed the stability of the dielectric properties of the nanocomposites at high temperatures. This pan-milling method characterized by green and efficiency is highly scalable to other two-dimensional (2D) layered materials, providing a practical strategy for the industrial fabrication of nanocomposites in elevated-temperature energy storage.

Impact of seed hardness on quality in variety VBN- 8 of black gram (Vigna mungo L. Hepper) during maturation

The present study was performed to record the formation of hard seeds during the development and maturation of Blackgram variety VBN-8 under field conditions, which were then assessed and evaluated under laboratory conditions. It also focused on analyzing the biochemical properties, seed quality parameters and structural characteristics using Scanning Electron Microscopy (SEM). The findings could help develop strategies to improve germination rates and overcome seed dormancy. The present study on complex seed formation in black gram variety VBN-8 during the development and maturation stage was carried out using seed samples collected from standing crops 3 to 38 Days after Anthesis. The formation of hard seeds was assessed and evaluated from the seed sample taken from the different stages of development and maturation (3 to 38 DAA) of the black gram variety VBN-8. The physical and physiological parameters were studied as per the ISTA rules. The biochemical properties were estimated using the fractionation method and histological studies were conducted using SEM analysis. The present investigation revealed that a higher percentage of complex seed formation was observed at 33 DAA when the seed attained physiological maturity. The biochemical analyses showed that hard seeds have much higher quantities of pectin (4.68%), cellulose (4.27%), hemicellulose (2.55%), lignin (2.30%) and phenolic contents (5.78%) as compared to non-hard seeds of lower quantities of pectin (2.01%), cellulose (1.27%), hemicellulose (1.87%), lignin (0.54%) and phenolic contents (3.68%). The SEM analysis revealed that hard seeds have a thick cuticle (60.96 µm), a waxy layer, a compartmentalized pattern, amorphous deposits and a rough texture, all hindering water uptake.In contrast, non-hard seeds exhibit a smoother surface with fewer deposits and thinner cuticles (33.81 µm), enhancing their permeability. Additionally, in hard seeds, water entry points like the micropyle and hilum are often obscured or blocked, making water absorption difficult. Conversely, non-hard seeds feature a more visible hilum and an open micropyle, readily allowing water to enter. Understanding these changes in seed coat qualities can help agricultural experts regulate dormancy, provide suitable germination conditions and plan for sowing seed storage and crop management techniques.

Demand-side shared energy storage pricing strategy based on stackelberg-Nash game

With the large-scale access of user-side energy storage devices, shared energy storage has emerged as a key mode of energy storage in distribution networks. This mode requires efficient management of energy storage devices that balances the interests of different entities such as power supply enterprises, shared energy storage operators, and prosumers. In this mode, the formulation of charging and discharging prices is crucial. This paper proposed a dual-layer pricing model for shared energy storage systems based on mixed-game theory and its solution method. First, this study developed an upper-level stackelberg game model between the power supply enterprise and the cooperative alliance. The power supply enterprise, acting as the leader, sought to minimize operational costs while negotiating transaction electricity prices with the cooperative alliance. Second, a cooperative game model was developed within the lower-level alliance. As followers, the cooperative alliance seeks to maximize the alliance’s overall benefits. Based on the upper-level transaction electricity price and Nash bargaining theory, the internal transaction electricity price within the alliance was determined through negotiation. Subsequently, charging and discharging strategies were formulated along with a profit distribution mechanism. Finally, case studies simulations were used to validate the feasibility and effectiveness of the proposed model.

Rapid detection of corn moisture content based on improved ICEEMDAN algorithm combined with TCN-BiGRU model

Rapid detection of corn moisture content(MC) during maturity is of great significance for field cultivation, mechanical harvesting, storage, and transportation management. However, cumbersome operation, time-consuming and labor-intensive operation were the bottleneck in the traditional drying process and dielectric parameter method. Thus, to overcome the above problems, a rapid detection method for corn MC based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) model. First, based on the 405 groups of NIR spectral data of corn seeds, the crested Porcupine Optimizer (CPO) algorithm was used to optimize ICEEMDAN to reduce the noise of the original spectral data. Then the Chaotic-Cuckoo Search (CCS) algorithm was applied to extract 203 characteristic wavenumbers from the original spectrum, which were input into the constructed TCN-BiGRU network model to realize corn MC detection. Finally, the CPO-ICEEMDAN-CCS-TCN-BiGRU corn MC classification detection model was constructed. The result showed that the model accuracy was 97.54 %, which was 9.22 %, 5.58 %, 2.34 %, 4.74 %, and 5.94 % higher than those of convolutional neural networks (CNN), long short-term memory networks (LSTM), temporal convolutional network (TCN), partial least squares (PLS), and support vector machine (SVM) models, respectively. The research results can provide a reliable basis for improving corn yield, quality and economic benefits.

Seed responses to change in ambient humidity and the consequences for storability

The study of seed moisture sorption isotherms helps us to understand the response of seeds to changes in moisture in their environment and how this might impact their longevity. A seed moisture isotherm shows the relationship between moisture content (MC) and water activity (aw) at a constant temperature. Seed MC is higher when it dries from a hydrated state (desorption) than when it rehydrates from a very dry state (adsorption) to the same RH, an effect known as hysteresis. Seeds on the desorption isotherm will age faster than seeds on the adsorption isotherm at the same RH due to higher MC, resulting in a more rapid decline in seed viability and vigour. In this study, moisture isotherms of seeds from diverse species were determined and modeled using the Guggenheim-Anderson-de Boer (GAB) model; the hysteresis effect was then quantified. The maximum hysteresis magnitude varied between 0.3 and 2.7% MC (occurring between 49 and 57% RH) when seeds were dried over silica gel (below 11% RH) and rehydrated. Cycling seeds between 30 and 50%, 20 and 50%, and 15 and 50% RH, it was found that seeds of barley, lupin, buckwheat, wheat and pea switch to the adsorption isotherm upon rehydration after drying at intermediate moisture levels. Thus, adsorbing seeds of these species have greater longevity than desorbing seeds at the same RH. In contrast, seeds of oilseed rape and yellow mustard do not show a hysteresis effect unless they are dried to very low MC (over silica gel), so the adsorbing seed longevity remains the same as desorbing seeds at a constant RH. It is important to consider the moisture history of seeds depending on species for better seed storage management.

How cloud services and other it tools affect enterprise management

The article covers the issues of transformation of management processes in enterprises under the influence of cloud technologies and modern IT tools. The author analyzes how cloud computing, providing an on-demand computing model, changes traditional approaches to data storage and IT infrastructure management. The evolution of IT infrastructure is considered, emphasizing the advantages of cloud solutions, such as scalability, cost reduction and simplification of collaboration, which are especially relevant in the context of the spread of new strains of COVID-19 and other infectious diseases.