High Dependence Research Articles

Assessment of the efficiency of improved cooking stoves and their impact in reducing forest degradation and contaminant emissions in Eastern Rwanda

Biomass is a major source of cooking energy in Rwanda. This high dependence on biomass strongly contributes to forest degradation, biodiversity loss, and atmospheric pollution. Shifting from the traditional three-stone stove (TSS) to improved cooking stoves (ICS) is being promoted to reduce wood consumption and air pollutant emissions. This study assessed the emission reduction of adopted ICS and their impact on forest conservation through a combined laboratory and kitchen performance test (KPT).Out of 940 households surveyed in Eastern Province (EP) of Rwanda, 317 used ICS. Among these users, 180 were selected and revisited for an in-depth interview about the adoption process. The four most popular ICSs were selected for evaluating thermal efficiency and emissions of contaminating gases using laboratory test protocol ISO 19867-1. In addition, the avoided wood consumption of improved cooking stoves compared to the TSS was assessed using a KPT in 100 households.The survey findings indicate that 33.7 % of households use only ICS, 23.1 % only TSS, and 56.4 % ICS and TSS combined. The laboratory results show that the Save80 stove had the highest thermal efficiency (45.8 %) followed by Igisubizo (32.8 %), EcoZoom (32.6 %), and Songa (29.6 %). The emission results showed a significant difference in CO2 (carbon dioxide) emissions (p = 0.001) between the ICS models. However, the emissions of PM2.5 (fine particulate matter) and CO (carbon monoxide) of ICS were high compared to the WHO (World Health Organization) guidelines.The KPT results show that using ICS would result in an estimated annual saving of 149,313 tons of firewood corresponding to 0.7 m3 of wood, leading to an annual equivalent reduction of 0.02 ha of forest per household and 7167 ha of forest in the EP. The KPT results show that using ICS reduced CO2 emissions by 0.372 tons per household per year and by 111,088 tons of CO2 in the EP. ICSs have been shown to enhance fuelwood efficiency, leading to reduced forest degradation.

Platelet bioenergetic profiling uncovers a metabolic pattern of high dependency on mitochondrial fatty acid oxidation in type 2 diabetic patients who developed in-stent restenosis

Although platelet bioenergetic dysfunction is evident early in the pathogenesis of diabetic macrovascular complications, the bioenergetic characteristics in type 2 diabetic patients who developed coronary in-stent restenosis (ISR) and their effects on platelet function remain unclear. Here, we performed platelet bioenergetic profiling to characterize the bioenergetic alterations in 28 type 2 diabetic patients with ISR compared with 28 type 2 diabetic patients without ISR (non-ISR) and 28 healthy individuals. Generally, platelets from type 2 diabetic patients with ISR exhibited a specific bioenergetic alteration characterized by high dependency on fatty acid (FA) oxidation, which subsequently induced complex III deficiency, causing decreased mitochondrial respiration, increased mitochondrial oxidant production, and low efficiency of mitochondrial ATP generation. This pattern of bioenergetic dysfunction showed close relationships with both α-granule and dense granule secretion as measured by surface P-selectin expression, ATP release, and profiles of granule cargo proteins in platelet releasates. Importantly, ex vivo reproduction of high dependency on FA oxidation by exposing non-ISR platelets to its agonist mimicked the bioenergetic dysfunction observed in ISR platelets and enhanced platelet secretion, whereas pharmaceutical inhibition of FA oxidation normalized the respiratory and redox states of ISR platelets and diminished platelet secretion. Further, causal mediation analyses identified a strong association between high dependency on FA oxidation and increased angiographical severity of ISR, which was significantly mediated by the status of platelet secretion. Our findings, for the first time, uncover a pattern of bioenergetic dysfunction in ISR and enhance current understanding of the mechanistic link of high dependency on FA oxidation to platelet abnormalities in the context of diabetes.

Global low carbon transitions in the power sector: A machine learning clustering approach using archetypes

This study presents an archetype-based approach to designing effective strategies for low-carbon transitions in the power sector. To achieve global energy transition goals, a renewable energy transition is critical, and understanding diverse energy landscapes across different countries is essential to designing effective renewable energy policies and strategies. Using a clustering approach, this study identifies 12 power system archetypes based on several features, including energy and socio-economic indicators of 187 UN countries. Each archetype is characterised by distinct challenges and opportunities, ranging from high dependence on fossil fuels to low electricity access, low economic growth, and insufficient contribution potential of renewables. Archetype A, for instance, consists of countries with low electricity access, high poverty rates, and limited power infrastructure, while Archetype J comprises developed countries with high electricity demand and installed renewables. The study findings have significant implications for renewable energy policymaking and investment decisions, with policymakers and investors able to use the archetype approach to identify suitable renewable energy policies and measures and assess renewable energy potential and risks. Overall, the archetype approach provides a comprehensive framework for understanding diverse energy landscapes and accelerating decarbonisation efforts for the power sector.

АНАЛІЗ ПЕРСПЕКТИВ ФУНКЦІОНУВАННЯ ПІДПРИЄМСТВ З ІНОЗЕМНИМИ ІНВЕСТИЦІЯМИ В УКРАЇНІ

Formulation of the problem. The post-war recovery of Ukraine requires attracting foreign investment. Attracting foreign direct investments will make it possible to ensure capital investments, annual direct contributions to the economy, new jobs, exports, acquisition of new skills and technologies, innovativeness of economic sectors. The aim of the research is to analyze modern prospects for the development of the process of foreign investment and the functioning of enterprises with foreign investments in Ukraine. The оbject of the research is theoretical and practical approaches to the attraction of foreign investments and the functioning of enterprises with foreign investments in Ukraine. The methods of the research: logical method, method of comparison, methods of induction and deduction, analysis and synthesis, system analysis, correlation analysis. The hypothesis of the research. The volume of foreign investment is directly proportional to the volume of GDP, expenditure on education and labor force and inversely proportional to the income inequality level and unemployment level. The statement of basic materials. Correlation coefficients between the volume of foreign investment and the factors affecting this volume have been determined, which will allow influencing specific key indicators that most stimulate the inflow of foreign investment. The mechanism for attracting foreign investments is presented, covering strategic, intermediate and current goals, a list of forms, methods, objects and tools of influence. The practical significance of the research. The practical implementation of the proposed measures will make it possible to intensify the attraction of foreign investments in Ukrainian enterprises. Conclusions and perspectives of further research. The problem of attracting investments into the economy of Ukraine is related to the lack of motivation to invest, insufficient accumulation of resources by the main groups of investors, a wide outflow of capital abroad, etc. Unfavorable investment climate caused by military actions, occupation of territories and destruction of infrastructure, migration of experienced specialists abroad, extremely unstable economic situation in the country, slowdown of economic reforms, high level of corporate taxation, instability of legislation, corruption, low purchasing power of the population, low predictability of junctures of commodity and raw material markets, significant price fluctuations; imperfection of ownership and shareholding relations, becomes the main obstacle in attracting investments. However, there are undoubtedly attractive factors for investing in Ukraine's economy, including the attractive geographical position determined by the country's location in the center of Europe at the crossroads of trade routes, the presence of an educated workforce, as Ukraine has a very high intellectual and professional level of manufacturers. Also, labor is cheap. Ukraine has favorable conditions for the introduction of high technologies, which is facilitated by the presence of performers with a high intellectual level. Also, relatively cheap raw materials and energy resources, which contributes to the low cost of manufactured goods. The obtained results showed a high dependence between the amount of FDI and the level of GDP in the country (correlation coefficient is 0.82), and a direct interdependence was revealed, which means that with the growth of GDP, the amount of foreign investment will increase, which is logical, because with the growth of GDP, the country is investment attractive for a foreign investor. The high correlation between the volume of foreign investment and the availability of labor in the country (correlation coefficient is 0.79) means that the availability of labor, including skilled labor, is extremely important for the investor (this is also evidenced by the interdependence between FDI and state spending on education in the country ), since the ease of hiring employees and their productivity depend on it.

Mechanism of plant–soil feedback in a degraded alpine grassland on the Tibetan Plateau

Abstract Although biotic and abiotic factors have been confirmed to be critical factors that affect community dynamics, their interactive effects have yet to be fully considered in grassland degradation. Herein, we tested how soil nutrients and microbes regulated plant–soil feedback (PSF) in a degraded alpine grassland. Our results indicated that soil total carbon (STC; from 17.66 to 12.55 g/kg) and total nitrogen (STN; from 3.16 to 2.74 g/kg) exhibited significant (P < 0.05) decrease from non-degraded (ND) to severely degraded (SD). Despite higher nutrients in ND soil generating significantly (P < 0.05) positive PSF (0.52) on monocots growth when the soil was sterilized, a high proportion of pathogens (36%) in ND non-sterilized soil resulted in a strong negative PSF on monocots. In contrast, the higher phenotypic plasticity of dicots coupled with a higher abundance of mutualists and saprophytes (70%) strongly promoted their survival and growth in SD with infertile soil. Our findings identified a novel mechanism that there was a functional group shift from monocots with higher vulnerability to soil pathogens in the ND fertile soil to dicots with higher dependence on nutritional mutualists in the degraded infertile soil. The emerging irreversible eco-evolutionary in PSF after degradation might cause a predicament for the restoration of degraded grassland.

Subpixel corner detection algorithm based on the plane chessboard

In this paper, the pixel-level corner position extracted by the Harris algorithm is used as the initial value of sub-pixel corner detection, and then it is used to detect the sub-pixel corner detection. The sub-pixel corner detection algorithm based on the inner product of the gray gradient has weak anti-noise and poor anti-distortion ability, and the sub-pixel corner detection algorithm based on Gauss quadric surface fitting has a high dependence on the initial value. In this case, a quadric fitting based on an iterative search thought algorithm of subpixel corner detection is proposed in this paper. The experiment shows that the improved algorithm has a stronger ability in anti-noise and anti-distortion and low dependence on the initial value. Using this algorithm to calibrate the camera, the re-projection error is smaller and the calibration accuracy is higher.

Subsidizing the Shift to Renewable Energy in Korea: A Levelized Cost Analysis and Computation of Required Subsidies

This study addresses the pivotal challenge of transitioning from nuclear to renewable energy sources, considering the distinctive energy landscape of South Korea characterized by high land costs and high dependence on nuclear energy. Amidst global momentum toward sustainable and carbon-neutral energy, South Korea’s Renewable Energy 3020 Implementation Plan aims to achieve 20% of power generation from renewables by 2030. Shifting away from nuclear energy, however, may lead to rising energy costs, supply disruptions, and environmental ramifications. Furthermore, achieving grid parity between renewable energy and nuclear power by the late 2030s is unlikely, in contrast to earlier projections. This is primarily due to the comparatively lower production cost of nuclear energy. To expedite the shift toward renewable energy, our study suggests transitioning from the Renewable Energy Portfolio Standard (RPS) to a Feed-in-Tariff (FiT) system. This change would offer voluntary incentives for investment in renewable energy. Additionally, we compute Feed-in-Tariff subsidies required to achieve grid parity by 2030, concluding that the amount of subsidies required to reach grid parity by 2030 is 54% of retail electricity price. This research offers valuable insights for policy formulation and energy transition planning.

Renewable energy investments in South Africa: Potentials and challenges for a sustainable transition - a review.

The Republic of South Africa is one of the leading investors in renewable energy in Africa, despite the widespread perception that the country is trapped in the carbon age due to its high dependence on fossil fuels. Renewable energy, rooted in sustainable development, requires the creation of an appropriate framework and environmentally friendly technologies to support growth. Renewable energy sources are considered more environmentally friendly than fossil fuels, which most energy processes rely on. For this reason, the scope of this article has been narrowed to focus on the motivation for renewable energy and its potential in South Africa. Furthermore, the current developments in the South African renewable energy sector, as well as the challenges and prospects for a sustainable transition to a bioeconomy, were discussed. This study shows that a sustainable energy system can only promote the integration of renewable energy into the energy mix with the help of technology, policy, and infrastructure.

Male and female syringeal muscles exhibit superfast shortening velocities in zebra finches.

Vocalisations play a key role in the communication behaviour of many vertebrates. Vocal production requires extremely precise motor control, which is executed by superfast vocal muscles that can operate at cycle frequencies over 100 Hz and up to 250 Hz. The mechanical performance of these muscles has been quantified with isometric performance and the workloop technique, but owing to methodological limitations we lack a key muscle property characterising muscle performance, the force-velocity relationship. Here, we quantified the force-velocity relationship in zebra finch superfast syringeal muscles using the isovelocity technique and tested whether the maximal shortening velocity is different between males and females. We show that syringeal muscles exhibit high maximal shortening velocities of 25L0s-1 at 30°C. Using Q10-based extrapolation, we estimate they can reach 37-42L0s-1 on average at body temperature, exceeding other vocal and non-avian skeletal muscles. The increased speed does not adequately compensate for reduced force, which results in low power output. This further highlights the importance of high-frequency operation in these muscles. Furthermore, we show that isometric properties positively correlate with maximal shortening velocities. Although male and female muscles differ in isometric force development rates, maximal shortening velocity is not sex dependent. We also show that cyclical methods to measure force-length properties used in laryngeal studies give the same result as conventional stepwise methodologies, suggesting either approach is appropriate. We argue that vocal behaviour may be affected by the high thermal dependence of superfast vocal muscle performance.

Confirmation of correct placement of nasogastric / orogastric tube on Point of Care Ultrasound (POCUS) in pediatric critical care unit patients.

Objective: To determine frequency of correct placement of nasogastric /orogastric tube on POCUS in pediatric critical care unit patients. Study Design: Descriptive cross-sectional. Setting: High Dependency Unit and Pediatric Intensive Care Unit, Indus Hospital Health Network, Karachi, Pakistan. Period: November 2022 to April 2023. Methods: Patients of age 1 months to 15 years of age requiring NGT placement were enrolled into the study. Patients with nasal surgery, fracture of skull base/ face, bleeding, coagulopathy, esophageal varices, stricture, upper gastrointestinal surgery were excluded from this study. NG tube was passed by principal investigator after evaluating correct size and length measurement. After passing the NG tube, gastric aspiration and gastric auscultation were also performed by principal investigator. Results: In this study we enrolled 147children with median age of patient was 3 (IQR=0.75-07) years. Majority of patient in our study were male (63.9%). Gastric aspiration and gastric auscultation was seen in all of the study patients. Based on POCUS, frequency of correct placement of nasogastric tube in children admitted in PICU was 83%. Sensitivity of POCUS against gastric aspiration and gastric auscultation was 82.5% with 100% positive predictive value (PPV). Specificity was unable compute as there was no negative finding in reference standard and hence there was also no false positive case. Conclusion: POCUS seems to be helpful and could help with the placement of NGT in paediatric clinical settings. Nevertheless, NGT envision in the stomach was difficult, and if the operator is not skilled or there is abdominal distention, it may go unnoticed.

ECONOMIC SECURITY OF UKRAINE DURING MILITARY OPERATIONS: CHALLENGES AND WAYS OF COUNTERACTION

The article focuses on analyzing the state of economic security of Ukraine during the military operations. It identifies key challenges directly impacting the country's economic security and explores potential ways to overcome them. 
 The analysis established that the current state of the economic system of Ukraine was formed under the influence of destructive external and internal challenges that pose threats to economic security. The challenges associated with the protracted war remain crucial for Ukraine.
 The article highlights the challenges that threaten the economic security of Ukraine's external security environment such as high dependence of the domestic economy on foreign borrowings, deteriorating financial conditions of the export-oriented production sector, and inadequate government responses to conflicts in political, economic and social realms. There are still threats to the amount and timing of external financing for the state budget, and logistical difficulties are a significant risk, which negatively affects export earnings and the economic security of the state.
 Internal threats include high level of corruption in public authorities; ineffective control over compliance with the law; inefficient use of state and local budgets; relatively high level of shadow economy.
 The author substantiates the priority directions and ways of counteracting the challenges and threats to Ukraine's economic security during military operations. Ensuring the economic security of the state in a military conflict requires flexibility, predictability, and an integrated approach that includes social, economic, and defense aspects of national security. Key priorities include ensuring food security, supporting crucial economic sectors, especially those important for defense and essential goods production, implementing policies for financial aid from international partners, transparent and effective use funds received from international partners, implementing a policy to increase defense spending

Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method

Rice, as a key element of Indonesia's food security, plays a crucial role in agricultural ecosystems. Despite its high economic value, rice plants are susceptible to various diseases that can reduce productivity and harvest quality. Farmer's limited knowledge about disease types, identification, and proper handling poses a serious challenge to sustainable agriculture. Previous studies highlight farmers' inadequate understanding of pests and diseases in rice plants, leading to a high dependency on pesticides. Furthermore, lack of training data and a shallow understanding of rice diseases present significant challenges in disease management efforts. This research aims to develop an Android-based Smart Farm application. This application utilizes image processing and artificial intelligence technologies to assist farmers in identifying leaf diseases in rice plants. Requirements analysis involves literature review and field observations around Bandung Regency. It can be concluded; Smart Farm application has been successfully developed with three functional and two non-functional requirements. Validation testing indicates a 100% functionality rate and an 80% accuracy in disease detection. Nevertheless, further attention is required to enhance accuracy by providing more training data and improving image quality. The implications of this research extend to enhancing farmers' knowledge, reducing pesticide dependency, and supporting sustainable agriculture in the future.

Towards a sustainable energy future: Evaluating Arundo donax L. in continuous anaerobic digestion for biogas production

AbstractIn response to the EU's REPowerEU initiative (COM (2022) 108) which encourages an increase in biogas production by 20% in member states by 2030 to boost energy independence, it has become essential to identify sustainable alternatives to traditional feedstocks for biogas production, especially in the EU Member states where there is still high dependence on corn silage as the main raw material in biogas plants. While corn silage, predominantly used in the European biogas plants today, serves primarily for the livestock sector, alternative sources need to be explored. Therefore, this study aimed to evaluate the potential of Arundo donax, a perennial energy crop, as an alternative feedstock in a continuous anaerobic process. The biogas yield and its quality, characterized by CH4, CO2, H2S and O2 content, were determined during a continuous process with A. donax, compared with two mixed feedstocks of A. donax and corn silage over a 5‐month period in a continuous anaerobic digestion process. The results revealed that A. donax exhibits a biogas yield and methane content comparable to corn silage, indicating its potential as a viable and sustainable alternative feedstock for biogas production.

Evaluation of a smoking cessation program for adults with severe mental illness in a public mental health service.

Smoking rates have decreased in the general population but remain high among people with severe mental illness (SMI). An individualized smoking cessation program was tested with 99 adults with SMI. The program showed it is possible to help people with SMI smoke fewer cigarettes and reduce nicotine addiction. Customized smoking cessation programs are essential for those with high nicotine dependence and mental health challenges. Mental health services should offer tailored tobacco cessation programs because these programs can improve the health of people with mental illness who smoke. It is important for mental health services to follow government guidelines and provide evidence-based support. INTRODUCTION: Despite significant reductions in smoking rates in the general population over recent decades, smoking rates remain relatively unchanged among people with SMI. To evaluate the feasibility and preliminary effectiveness of the Keep Quitting in Mind pilot program, an individualized smoking cessation program for people experiencing SMI. In total, 99 adult participants with SMI and engaged with a community mental health service, participated in the intervention. The intervention included motivational interviewing and goal setting, in addition to provision of pharmaceutical aids (including nicotine replacement therapy). Analysis determined that the Keep Quitting in Mind pilot program was feasible in a public adult mental health service and participation in the program was associated with reductions in the number of cigarettes smoked daily and level of nicotine addiction. This real-world pilot program demonstrated feasibility and potential effectiveness in reducing smoking among adults with mental illness. Aligning with government guidelines, tailored smoking cessation programs are crucial due to high nicotine dependence and mental health complexities. Given the high rates of cigarette smoking by people with mental illness and the cardiometabolic health risks associated with this, mental health services should consider adding evidence-based and bespoke tobacco cessation programs as part of core business.

The importance of multi‐species grassland leys to enhance ecosystem services in crop rotations

AbstractThe ongoing simplification of agricultural production systems has resulted in several negative consequences, ranging from losses in soil organic carbon and biodiversity to a high dependency on external inputs to maintain high yields. We identify how grassland leys in crop rotations may help to mitigate these effects, by conserving soil organic carbon and enhancing nutrient efficiency. In particular, grasslands containing legumes enhance these benefits by providing nitrogen, and displacement of mineral N fertilizer. In crop rotations, these grasslands may transfer some of the acquired nitrogen to arable follow‐on crops, thereby reducing the necessity for external inputs, while at the same time providing additional benefits, such as improvement of soil quality and reduction of weed pressure. However, there are still considerable knowledge gaps about how to optimize the community composition of grassland leys to best enhance the supply of these ecosystem services. Although the benefits of multi‐species grasslands for the grassland crop have been shown repeatedly and across a large gradient of environments, further research is required to determine the benefits for follow‐on crops, particularly across different environmental conditions. Here, we emphasize the importance of multi‐site research, such as in the research network LegacyNet. Finally, we present management techniques that are optimized for both ecosystem services and agronomic performance in mechanically cut and grazed systems. For the latter, we consider how the inclusion of bioactive plant species can enhance animal health and lower methane emissions in grazing ruminants.

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.

Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

An analysis of fog and low stratus life‐cycle regimes over central Europe

AbstractA better understanding of fog and low stratus (FLS) life cycles, in particular the typical onset and dissipation times, can help traffic safety and solar power planning. Besides its high dependence on the prevalent meteorological conditions, the FLS life cycle is further determined by the FLS type as well as the underlying geography and climate. While satellite‐based FLS data sets with high temporal and spatial resolution are available, an objective, large‐scale, satellite‐based classification of FLS life‐cycle regions, which would allow for more targeted analyses of the FLS life cycle, is lacking. In this study, a hierarchical clustering algorithm is applied to identify regions of similar relationships of the FLS life cycle to environmental conditions over central Europe. The clustering reveals major FLS life‐cycle regimes with distinct differences in FLS occurrence, temporal FLS life‐cycle patterns, and climatic characteristics. Based on regime‐specific sensitivity analyses, the relationships of the FLS life cycle with near‐ground temperature and specific humidity are identified as the most relevant input relationships for the clustering algorithm, especially in the Mediterranean region. FLS life‐cycle regimes are further presented at a regional scale, outlining the applicability of the derived FLS life‐cycle regimes to future regional process‐oriented FLS life‐cycle studies.

Hierarchical online energy management for residential microgrids with Hybrid hydrogen–electricity Storage System

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.

Dependency on imported agricultural inputs—global trade patterns and recent trends

Abstract Industrial food production systems depend on inputs such as fertilisers, pesticides, and commercial animal feeds that are highly traded commodities in global markets. Disturbances in international trade can threaten the local food production if the imports of the key agricultural inputs were drastically reduced. However, despite the importance of the topic, a comprehensive analysis focusing on the import dependency of multiple agricultural inputs at the global level and thus revealing the vulnerability of regions and individual countries does not exist. Here, we analyse the temporal trends of agricultural input trade globally at the national scale from 1991 to 2020 by applying statistics of the use and trade of synthetic fertilisers (N, P, and K), pesticides and livestock and aquaculture feeds (grouped into oilseed feeds and other feed crops). The results show that the import dependency of agricultural inputs has increased over the past 30 years, but there is high variation between countries. Countries with high import dependency combined with high use of these inputs, such as many industrial agricultural producers in South America, Asia as well as Europe, show high vulnerability to trade shocks. Also, our findings highlight that potential agricultural intensification in Sub-Saharan African countries—currently with low use of the inputs per cropland area but high import dependency—can lead to higher dependency on imported agricultural inputs. Therefore, understanding of the past trends and current risks associated with the dependency on imported agricultural inputs should be highlighted to mitigate the risks and build more resilient and sustainable food systems.

Malicious Packet Detection Technology Using Reinforcement Learning

In the present day, the advancement of 5G and Internet of Things (IoT) technology has resulted in the interconnectedness of everyday objects through networks. However, attempts to exploit networked computers for malicious purposes continue to rise while the attacks utilizing malicious codes to compromise user information's confidentiality and integrity are becoming increasingly sophisticated and intelligent. To counter these evolving threats, researches have been conducted on a method to identify malicious network packets using a combination of security control systems and Artificial Intelligence (AI) technology, especially supervised learning. Unfortunately, the current cybersecurity control systems suffer from inefficiencies in terms of both manpower and cost. Moreover, the surge in remote work has created challenges in responding swiftly to security incidents. Furthermore, the existing AI technology based on supervised learning has limitations, particularly in detecting new variants of malicious code, and its accuracy in identifying malicious code depends heavily on the quantity and quality of available data. In light of these challenges, this study, reinforcement learning is employed to overcome the limitations of the original supervised learning-based malicious packet detection system, such as high dependency on training data and the failure to detect variant malicious packets. This research proposes a malicious packet detection technology capable of addressing new malicious packets or variant types effectively.