Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

In sustainable agriculture, plant nutrients are the most important elements. Biofertilizers introduce microorganisms that improve the soil nutrients and increase their accessibility to crops. In order to meet the demands of a growing population, healthy crops need to be produced using the right type of fertilizers to provide them with all the major nutrients they require. However, an increasing dependency on chemical fertilizers is destroying the environment and negatively af-fecting the health of humans. Thus, using microbes as bioinoculants as the best replacement of chemical fertilizers as eco-friendly way for plant growth and soil fertility is believed to be the best method for improving plant growth and soil fertility. In sustainable agriculture, these microbes provide significant benefits to crops. In addition to colonizing plant systems (epiphytic, endo-phytic, and rhizospheric), beneficial microbes play a key role in absorbing nutrients from surrounding ecosystems. Plant associate microbes can promote plant growth regardless of natural and extreme conditions. Plant growth promoting microbes promote plant growth through a variety of direct and indirect methods, such as nitrogen fixation, plant growth hormone production, siderophores, HCN, several hydrolytic enzymes, and potassium, zinc, and phosphorus solubilization. Research on biofertilizers has been extensive and even available, which demonstrates how these microbes can deliver nutrients to crops in sufficient quantities to enhance their yield. This review examines in detail the direct and indirect mechanisms of PGPR action and their interaction in plant growth and resistance.

As with every new technology, safety demonstration is a critical component of bringing products to market and gaining public acceptance for cultured meat and seafood. This manuscript develops research priorities from the findings from a series of interviews and workshops with governmental scientists and regulators from food safety agencies in fifteen jurisdictions globally.  The interviews and workshops aimed to identify the key safety questions and priority areas of research. Participants raised questions about which aspects of cultured meat and seafood production are novel, and the implications of the paucity of public information on the topic. Novel parameters and targets may require the development of new analytical methods or adaptation and validation of existing ones, including for a diversity of product types and processes. Participants emphasized that data sharing of these efforts would be valuable, similar to those already developed and used in the food and pharmaceutical fields. Contributions to such databases from the private and public sectors would speed general understanding as well as efforts to make evaluations more efficient. In turn, these resources, combined with transparent risk assessment, will be critical elements of building consumer trust in cultured meat and seafood products.

Byers Peninsula is considered one of the largest ice-free areas in Maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving effects of climate changes in lives cycles, limnology and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical and morphological characteristics strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. To mapping and monitoring those areas with high biological occupation, could be very useful to have models of edaphic properties prepared from satellite images. In this approach, Deep Learning and Linear Regression models of soil properties and spectral indexes as explicative variables were performed. We training models of soil properties closely related to biological activity such as Dissolved Organic Carbon (DOC) and the iron fraction associated with organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe and pH by training models of Linear Regression and Deep Learning over Sentinel2 and WorldView2 images. The most robust models were used to track possible areas with ornithogenic soils as well as areas of the Byers Peninsula that could support the highest biological development.

Spodoptera frugiperda (J.E. Smith) is a global agricultural pest that poses a threat to crop production, characterized by its refractory polyphagous nature that makes it difficult to control and may lead to damage to woody plants. However, research on its adaptability to woody plants remains limited. This study compares the feeding adaptations (survival rate, pupation time, pupation rate, weight, length, and feeding volume), protective enzyme activities, and feeding preferences of S. frugiperda on leaves of six economically significant tree species (Areca catechu, Aquilaria sinensis, Cocos nucifera, Camellia oleifera, Dalbergia odorifera and Hevea brasiliensis) and maize as a control treatment. The results indicate that A. sinensis as the most vulnerable trees in high risk, H. brasiliensis and C. nucifera presented varying degrees of susceptibility, A. catechu, C. oleifera, and D. odorifera were unsuitability for S. frugiperda. In conclusion, this report extensively explores the feeding effects of S. frugiperda on six economically important tree species, and provides insights into the feeding preferences on these plants, thereby informing the potential threat posed by S. frugiperda to economically vital trees.

This study focuses on understanding how global auto parts companies manage innovation by analyzing keywords in CEO messages from their reports. The study involved three key phases: (1) extraction of innovation-related keywords emphasized in CEO messages, (2) comparison of innovation-related keyword usage between high-performing (upper group) and low-performing (lower group) companies, and (3) analysis of the characteristics of innovation management via internal meaning and the structural interpretation of keywords. A comparison between the upper and lower groups revealed distinct differences in the emphasized keywords. The mapping of open innovation success factors, based on keywords extracted according to eigenvector centrality, identified Provision of Resources and Governance as central to innovation management in the upper group. These findings serve as a benchmark for establishing the direction, vision, and strategy of innovation management in the lower group.

Sensor enabled big data and Artificial Intelligence platforms has the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand while maximizing the efficiency of production systems on the other. To stimulate a sustainable, digital, and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and develop new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI) based data acquisition and processing platform, the unmet current needs can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. There is a need for a multimodal, digitized, automated biosensor and health monitoring platform containing AI algorithms for optimized stress and disease prediction in farm animals. An experimental development of non-invasive wearable and wireless physiological sensor networks that can be deployed in agricultural environments would allow for real-time extraction and integration of physiological data and display on decision support dashboards for livestock workers. By testing the effectiveness of the system in generating meaningful longitudinal data for further research into physiological and behavioral characteristics of farm animals, novel insights about animal welfare can be developed. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to the significant innovation potential. Continuous monitoring of animal health and physiological functioning promotes more environmentally and socially acceptable developmental pathways. Once demonstrated, the portable animal health monitoring platform will close a critical gap in digitized livestock farming and position the agricultural industry as a frontrunner in the sector of farm animal monitoring and measurement systems. The multimodal wearable sensor-driven AI approach is expected to significantly improve the effectiveness of livestock decision support systems and the selection of resilient animals for the next generation and provide predictive data for end-users in livestock farming.

Phosphorus (P) is the second-most important element after nitrogen that is required for plant growth. Although this element is abundant in most soils, it is rarely available in plant-accessible forms since most of it normally exists in soil in insoluble forms such as phosphates. In conventional agriculture, P is normally supplied as chemical fertilizer to satisfy plant P requirements. This, to a large extent, boosts plant production. However, chemical fertilizers are costly, have a huge carbon footprint, and are environmentally-unsustainable owing to the high energy requirements during their synthesis. Besides, P-containing agricultural run-offs contribute hugely to the eutrophication of water bodies and environmental degradation. Moreover, plants can consume only a small amount of chemically-supplied P since between 75 and 90% of this form of P normally get precipitated into complexes and rapidly become fixed in soil. These issues and concerns necessitate research into alternative and viable ways of supplying P to plants. Rhizobacteria have for decades been investigated in vivo and in planta as suitable tools in sustainable agriculture due to the plant-growth-promoting activities such as nutrients’ solubilization, nitrogen fixation, and production of phytohormones. Although a lot of research has been done on different nutrients-solubilizing rhizobacteria and their potential in sustainable agriculture, their mechanisms of action and prospects in sustainable agriculture remain to be fully understood. This review particularly focuses on the P solubilizing rhizobacteria and evaluates their diversity, mechanisms of action, and prospects in sustainable agriculture based on the present and future scenario of their application. Such information is useful in determining their potential and evaluating their prospects in promoting sustainable agricultural systems.

Climate change impacts threaten sustainable development efforts. The magnitude of the impacts, however, varies with socio-ecological characteristics of locations. This is the reason there is con-sensus on the necessity for climate change adaptive capacity building that is country driven, based on and responsive to local needs. However, information on context specific capacity building needs in developing countries is not readily available. The objective of this study was to establish location specific awareness, training, educational, research and technology capacity building needs for climate change adaptation among small-holder farmers in Uganda. Structured interviews were undertaken with 465 households from five agro-ecological zones selected based on the level of vulnerability of agricultural systems to the main climate variation and change hazards. Results reveal substantial capacity building needs in all the zones. Majority of the farmers needed capacity building for interventions on soil water conservation practices for adapting to drought and un-predictable rainfall. For all zones, education, research, and technology were perceived as key needs. However, the needs varied among zones. These results demonstrate the importance of context specificity in adaptation efforts. The study provides agro-ecological and social system specific in-formation for climate change adaptation planning and policy interventions for effective capacity building

Agroecology refers to the greening of agrosystems with the mobilization of ecosystem services in order to limit exogenous inputs, enhance biodiversity and moderate the exploitation of natural resources (Tibi A. and Therond O., 2017). Agroecological practices offer pathways for transformation and transition not only of agricultural systems but of entire food systems. Through its objectives, agroecology aims at both sustainable land management and the strengthening of the livelihoods of producers and rural people, and thus contributes to the fight against desertification. To date, there is little scientific literature on the characteristics of agroecology in the Maghreb region. However, we can cite the work of Lattre-Gasquet M. de et al. (2017), Ameur et al. (2020), Akakpo et al. (2021). These studies, although they provide important information on the problem of agroecology in the region, on its different forms and perspectives, do not allow us to draw up a global panorama. The proposed article highlights general characteristics of agroecology in North Africa from a review of 88 sustainable agriculture projects, which it analyzes, through an inventory of agroecological practices supported by these projects, from the frameworks of the High Level Panel of Experts on Food Security and Nutrition, (HLPE, 2019), on the principles of agroecology and the transition levels approach developed by Gliessman and colleagues (Gliessman, 2007; Gliessman and Rosemeyer, 2010; FAO, 2015; Gliessman, 2016). The results show significant differences and evolution in the practices observed at the plot and on the farm, depending on the agrosystems considered. The majority of the agroecological innovations identified are at the plot and farm scales, with the exception of those found in oasis and mountain agrosystems, where practices integrate the scales of the territory and value chains in a more complete way.

Background: Individuals living in rural areas often face challenges in accessing healthcare, increasing their risk of poor health outcomes. Farmers, a sub-population in rural areas, are particularly vulnerable to mental health issues and suicide, yet exhibit low rates of help-seeking behavior. The aim of our study was to develop an in-depth understanding of the issues influencing mental help-seeking among farmers living in rural areas from the perspectives of healthcare providers, as well as to explore the strategies providers use to navigate through these issues to effectively engage with this vulnerable population. Methods: We used a descriptive phenomenological approach to understand healthcare providers’ perspectives, experiences, and approaches to providing mental healthcare to farmer clients in rural areas. Semi-structured interviews were conducted with 21 participants between March and May 2023. Results: Our analysis yielded five thematic areas: (1) ensuring accessibility, (2) establishing relatability, (3) addressing stoicism and stigma, (4) navigating dual roles, and (5) understanding community trauma. Conclusions: Healthcare service delivery for farmers is multifaceted. This study fills a gap in knowledge by translating these the data to inform an evidence-based model and a list of recommendations for implementing agriculturally-informed practices in rural areas.

Biotic stressors pose serious threats to crop yield, jeopardizing food security and resulting in over US$220 billion per year losses to the agriculture industry. Plants activate innate defense mechanisms upon pathogen perception and invasion. The plant immune response comprises numerous concerted steps, including the recognition of invading pathogens, signal transduction, and activation of defensive pathways. However, pathogens have evolved various structures to evade plant immunity. Given these facts, genetic improvements of plants are required for sustainable disease management to ensure global food security. Advanced genetic technologies have provided new opportunities to revolutionize and boost plant disease resistance against devastating pathogens. Furthermore, targeting susceptibility (S) genes, such as OsERF922 and BnWRKY70, through CRISPR methodologies offers novel frontiers to disrupt the molecular compatibility of pathogens and introduce durable resistance against them in plants. Here, we provide a critical overview of advances in understanding disease resistance mechanisms. The review also critically examines management strategies under challenging environmental conditions and R gene-based plant genome engineering systems intending to enhance plant responses against emerging pathogens. This work underscores the transformative potential of modern genetic engineering practices in revolutionizing plant health and crop disease management while emphasizing the importance of responsible application to ensure sustainable and resilient agricultural systems.

Organic agriculture based on participatory guarantee system (PGS) has been promoted as a tool for sustainable ecosystem, self-reliance and poverty alleviation of smallholder farmers in Thailand. However, specific criteria are required to certify as organic products. In this paper, the researchers present the collaboration of smallholder farmers practicing organic farming based on participatory guarantee system in four Thai provinces: Nong Bua Lam Phu, Nakhon Phanom, Ubon Ratchathani and Nakhon Ratchasima. One hundred thirty-five smallholder farmers were purposively selected as the informants. In depth interviews were conducted to conceptualize. The participatory guarantee system management considering their diverse agricultural background and socio-economic conditions. Main findings concluded that motivations for PGS were restoration soil quality and decreasing yield. Key factors for sustainable adaptation are policy support in terms of financial measures and capacity building.

Climate change is rapidly exacerbating and adding to major-to-existential issues associated with fresh water availability and utilization. The massive thus far untapped saline/salt water - oceans/wastelands/deserts – Halophytes resources nexus can, at scale and profitably provide major climate change mitigation and greatly alleviate most extant fresh water issues. Approaches include ocean fertilization and saline/seawater agriculture on deserts and wastelands to sequester massive amounts of CO2 and methane and for food, freeing up the some 70% of the fresh water now utilized by current agriculture for direct human use. Also enables production of huge amounts of Bio/SAF fuels and bio mass based chemical feed stock employing the massive capacity of cheap saline/ seawater and cheap deserts, wastelands. Overall saline/ seawater can, uniquely, at the scale of the climate and fresh water issues, without desalinization, profitably, utilizing extant technologies and the some 44% of the land that is deserts/ wastelands, and the 97% of the water that is saline/ seawater rapidly, seriously, address land, fresh water, food, energy and climate.

Diversification in agroecosystems can increase the sustainability of agricultural production and reduce input dependence in agroecological transitions. This study evaluated the effects of intercropping and the introduction of flowering (aromatic) plant strips on the ratio of land use efficiency and crop damage for broccoli and lettuce association. Two experiments were conducted in 2022 and 2023 in experimental plots in southwestern Colombia. Crop association alone did not impact crop yield; the production was higher in the lettuce monoculture. However, in the presence of flowering plant strips, the land use equivalent ratio (LER) is >1 in the two years of study, with a high yield for broccoli. Regarding the damage by the silverback moth Plutella xilostella L on broccoli, no differences were found between crops established with or without flower strips. Similarly, flower strips did not affect the incidence of lettuce rot caused by Sclerotinia spp. In summary, our results support the idea that farmers can introduce flower strips into their horticultural fields to improve the eco-productive efficiency of their farms and reduce dependence on external polluting inputs. However, further research is needed to clarify the mechanisms underlying these results.

Population growth and the need for increased agricultural productivity pose a global problem. Therefore, the development of green compounds to ensure agricultural sustainability becomes an urgent necessity. Surfactant compounds hold significant commercial importance due to their diverse industrial uses. However, the synthetic origin of these agents limits their commercial application due to their toxicity. As a result, extensive research has focused on the production of microbial-originated green surfactants, known as biosurfactants, over the past fifteen years. These biomolecules not only offer a green alternative for agriculture but also exhibit reduced toxicity and excellent stability under specific environmental conditions. Biosurfactants can lower surface tension more effectively than synthetic surfactants. With properties such as detergency and foam formation, biosurfactants are suitable for various agricultural applications, particularly in pesticide and agrochemical formulations. They can function as biopesticides to manage pests, pathogens, phytopathogenic fungi, and weeds due to their antimicrobial activity. Moreover, plants can benefit from biosurfactant molecules and the microorganisms as nutrients. They can also aid efficiently in the distribution of micronutrients and metals in the soil. They also stimulate plant immunity and are utilized for soil hydrophilization to ensure proper moisture levels and uniform fertilizer distribution. This review aims to provide valuable insights into the role and properties of biosurfactants as agricultural adjuvants, fostering the development of sustainable formulations to replace chemical surfactants used in pesticides.

A detailed structural investigation of a promising bio-based polymer, polyglycerol citrate poly-ester, obtained by bulk polycondensation of glycerol (Gly) against citric acid (Cit) under mild reaction was performed. This polymer was suggested to be well suited for coating seeds or similar agricultural applications. The reaction in conditions with and without catalyst use (sulfuric acid, H2SO4) was investigated, showing evidence that it is possible to modify the polymer solubility according to the ratio and catalyst utilization. 13C and 1H NMR indicated that synthesis catalyzed with Cit excess leads to higher esterification degrees of citrates groups. In contrast, the Gly moi-eties are more prominent in catalyzed polymers regardless of the excess monomers. Overall, a successful conversion of Gly and Cit into polyesters was attained, even without catalysis, enabling a simple route for large-scale production of this green material to be used as coating material. Tests on soybean seed coating with a PGCit solution of 75% indicated that the seed quality and ger-mination rate was not affected by PGCit coating, concluding that this polymer is promising for this application.

Antimicrobial resistance (AMR), the capacity of microbial pathogens to survive in the presence of antimicrobials, is considered one of the greatest threats to human health worldwide and is growing rapidly in importance. AMR is thought to be driven in part by the use of antimicrobials (AMU) in livestock production. AMU reduction in agriculture is therefore important, but doing so may endanger farmers’ incomes and hamper broader food security. Understanding drivers for farmers' antibiotics use is essential to designing interventions which avoid harming agricultural output and safeguard farmers’ economic security. In this study, we analyse AMUSE survey data from poultry farmers in Senegal to explore the effects of vaccination, attitudes towards AMR, and biosecurity practices on: AMU, animal mortality, and farm productivity. We found that farmers with more “AMR-aware” attitudes may be less likely to use antibiotics in healthy birds. Stronger on-farm biosecurity was associated with less use of antibiotics in healthy birds, and in some specifications was linked to higher broiler productivity. Vaccination and AMU were both linked with higher disease prevalence, and both factors appeared conducive to higher broiler productivity. Overall, there is evidence that awareness-raising and biosecurity improvements could encourage prudent use of antibiotics, and that biosecurity and vaccination could to some extent replace antibiotic use as productivity-enhancing and disease-management tools in broiler farms. Finally, issues of farm antimicrobial stewardship must be considered at the structural level, with farm behaviours contingent on interaction with state and private stakeholders.

The multiple crises that the world is facing – climate change, COVID-19 and war have halted or reversed the progress of the world towards the achievement of Sustainable Development Goals. Using a case study of Dayalbagh, a locality in metropolitan Agra, India, and headquarters of the Radhasoami faith, we examine the potential benefits of employing agroecology to achieve the United Nations Sustainable Development Goals (SDGs). The active, disciplined and cooperative community-based lifestyle followed in Dayalbagh with a strong focus on agriculture and service demonstrates how most of the SDGs can be achieved. It offers lessons for policy makers in terms of focus areas for policy support and reaching the last, lowest, least and the lost.

Antimicrobial resistance (AMR) is an increasingly pressing threat to human, animal, and environmental health. Reducing the use of antibiotics in agriculture has been identified as a key way to curb the spread of AMR. However, the effect of such policies on AMR prevalence, and their broader impacts on agricultural, health, and economic outcomes at the population level have proven very difficult to estimate and compare. This paper draws on and formalises ideas presented at the JPIAMR New Perspectives on Bacterial Drug Resistance workshop in June of 2022. With reference to emerging literature on the topic, it proposes a quantitative framework for estimating the relevant causal relationships needed to quantify the cross-sectoral impacts of AMR policies in agriculture, and for comparing these outcomes in like terms in a way which can feed directly into policy decision-making, notably without prohibitive data requirements. The ability of researchers to apply frameworks such as this will be increasingly necessary in order to holistically capture the impacts of AMR policies and to situate them in the broader policy context; especially where the mechanisms of transmission are opaque or complex, where data availability is limited; and where policymakers must allocate scarce resources among many competing objectives.

The aim of the current study was to determine the relation between subclinical mastitis and rumination behavior registered with an online sensor system. According to the results of the general clinical examination of 650 milking cows, 10 cows with subclinical mastitis (SCM) and 10 clinical healthy cows (HG) were selected (without clinical signs of any diseases). Rumination behavior biomarkers were registered with RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Sensors were implanted on the first day after calving. Data from the RWS from 13th days before the diagnosis and 13 days after diagnosis of SCM were used to compare with HG data during the same period. The healthy cows were monitored during the same days as the SCM cows.

This paper aimed to assess climate-smart agricultural practices in Ethiopia, discuss the contribution of climate-smart agricultural practices for mitigation of greenhouse gas emissions, and examine determinant factors of climate-smart agricultural practices in mitigation of greenhouse gas emissions. Conservation agriculture, integrated soil fertility management, agroforestry, crop diversification, and improved livestock feed and feeding practices are among the best climate-smart agricultural practices in Ethiopia. Combination of the adoption of climate-smart agricultural practices such as no-tillage increased crop diversity and retaining crop residue on-farm have a mitigation potential of increased SOC in non-flooded crops that change in a significant ton of CO2e ha-1 year-1. In addition, a mitigation potential of CH4 in reduced irrigation of paddy rice farms was also changed in ton CO2e ha-1 year-1. It was found that productivity enhancing interventions in the tropics could reduce emission intensity in dairy systems by up to 0.9 t CO2e per milk. Agroforestry practices and the addition of organic fertilizers on the farm increased mitigation potential of 784093 t CO2e and 193050 t CO2e biomass of carbon and SOC per year respectively. Adoptions of climate-smart agricultural practices are affected by different factors such as farming factors, technology inaccessibility, environmental factors, policy design and social expertise, negative attitudes and motivations of farmers, farmers’ socio-demographic factors, and farmers' socioeconomic factors. To reverse the situation, preparation of targeted climate-smart agricultural practices to areas that are likely to provide the greatest GHG reduction potential and demonstration of these practices to other areas should be encouraged so that other farmers will learn for similar agro-ecologies.

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

This review aims to explore the intricate relationship between epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorders' occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay between genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

This study utilized panel data from 31 Chinese provinces over a period of nine years to investigate the impact of green finance on the upgrading of the agricultural industrial structure. A fixed-effect model was employed, and the findings indicate that green financing has a positive effect on the growth of China's agricultural industry. However, regional disparities exist, particularly in the uneven distribution of green financing across the eastern, central, and western regions. Moreover, it emphasizes the need to consider regional differences and tailor development strategies accordingly. To promote further development and transformation of China's agricultural industrial structure, the study recommends innovative green financial products, improved regulations and policies, and the integration of digital technologies.

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targets Ahmedabad city in India and employs the Seasonal Auto Regressive Integrated Moving Average with eXogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using the Augmented Dickey-Fuller test, and the Akaike Information Criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and predictive accuracy. The model achieved an RMSE of 1.0265, indicating high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model's applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This maximizing throughput attitude calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for the passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays. The optimization model is used to make better scheduling intervention decisions considering the airport resource constraints and safety of operation. We have also developed a simulation method to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those obtained by the optimization model. Computational results from a case study demonstrate that a reduction of 40.7% and 61.6% in total delay times for the arrival and departure flights can be achieved by the optimization model. It also facilitates implementing a collaborative decision-making system for better coordination with commercial airlines.

Sado Island in Niigata prefecture, Japan is one of the first Globally Important Agricultural Heritage Systems (GIAHS) among developed countries and has since been involved in environmental conservation agriculture (ECA). While ECA is still in its early stage in Japan, it has proven to be effective in mitigating climate change in the agricultural sector; hence, this study analyzed the factors which could contribute to the ECA continuation among Sado Island farmers. The data revealed the prevalence of farmers’ cognitive dissonance between ECA and its mitigating effects on climate change. Exploratory factor analysis and ordinal regression confirmed the importance of perceived GIAHS involvement in the continuation of ECA. In addition, other identified factors affecting ECA continuation fall either on a macro-level (i.e., farmers’ awareness of their role in improving their environment) or micro-level (i.e., farmers’ differing farm optimizations). These perspectives highlighted the altruistic nature of the Sado Island ECA farmers by valuing the improvement of their local and global environment as their main reason to continue ECA, whereas their various farm management optimizations support this observed farmer altruism by providing avenues to increase yield with only a moderate paddy land area. This study thereby highlights the need to continuously develop sustainable strategies to maintain and improve a positive farmer mindset towards ECA.

(1) Background: Multiple alterations of cellular metabolism have been documented in experimental studies of autosomal dominant polycystic kidney disease (ADPKD) and are thought to contribute to its pathogenesis. (2) Methods: To elucidate the molecular pathways and transcriptional regulators associated with the metabolic changes of renal cysts in ADPKD, we compared global gene expression data from human PKD1 renal cysts, minimally cystic tissues (MCT) from the same patients, and healthy human kidney cortical tissue samples. (3) Results: We found PKD1 renal cysts displayed the Warburg effect with gene pathway changes favoring increased cellular glucose uptake and lactate production, instead of pyruvate oxidation. Additionally, mitochondrial energy metabolism was globally depressed, associated with downregulation of gene pathways related to fatty acid oxidation (FAO), branched-chain amino acid (BCAA) degradation, the Krebs cycle, and oxidative phosphorylation (OXPHOS) in renal cysts. Activation of mTORC1 and its two target proto-oncogenes, HIF-1α and MYC, was predicted to drive the expression of multiple genes involved in the observed metabolic reprogramming (e.g., GLUT3, HK1/HK2, ALDOA, ENO2, PKM, LDHA/LDHB, MCT4, PDHA1, PDK1/3, MPC1/2, CPT2, BCAT1, NAMPT); indeed, their predicted expression patterns were confirmed by our data. Conversely, we found AMPK inhibition was predicted in renal cysts. AMPK inhibition was associated with decreased expression of PGC-1α, a transcriptional coactivator for transcription factors PPARα, ERRα, and ERRγ, all of which play a critical role in regulating oxidative metabolism and mitochondrial biogenesis. (4) Conclusions: These data provide a comprehensive map of metabolic pathway reprogramming in ADPKD and highlight nodes of regulation that may serve as targets for therapeutic intervention.

To assess the community health workers’ (CHWs) knowledge, attitude, and practices (KAP) in type-2 diabetes (T2D) and hypertension (HTN) management. We thus also sought to assess the potential health system barriers for incorporating CHWs in T2D and HTN management. Mixed-method study comprised (1) a survey among 153 active CHWs to assess their KAP of T2D and HTN, and (2) semi-structured interviews with key informants to understand challenges of the health system and proposed solutions in incorporating CHWs in T2D and HTN management. The majority of CHWs correctly answered general knowledge questions on NCDs, risk factors, and prevention; however, only one-quarter correctly answered questions on smoking, family history, or tobacco use as risk factors. Most respondents appeared to have positive attitudes and have been practicing some activities related to T2D and HTN. Both financial and non-financial resource constraints were cited as challenges of the health system; therefore, re-structuring the role definition of CHWs and task of responsibilities, and assessment the need and workload have been proposed as ways forwards to effectively incorporate CHWs in T2D and HTN care. CHWs in Cambodia have shown their potential in T2D and HTN management; however, a well-designed strategy including technical training, clearly defined roles and responsibilities, and strong supporting structure are important to maximize their potential in the health system.

Previous work has shown that perturbation analysis in algorithmic information dynamics can uncover generative causal processes of finite objects and quantify each of its element's information contribution to computably constructing the objects. One of the challenges for defining emergence is that the dependency on the observer's previous knowledge may cause a phenomenon to present itself as emergent for one observer at the same time that reducible for another observer. Thus, in order to quantify emergence of algorithmic information in computable generative processes, perturbation analyses may inherit such a problem of the dependency on the observer's previous formal knowledge. In this sense, by formalizing the act of observing as mutual perturbations, the emergence of algorithmic information becomes invariant, minimal, and robust to information costs and distortions, while it indeed depends on the observer. Then, we demonstrate that the unbounded increase of emergent algorithmic information implies asymptotically observer-independent emergence, which eventually overcomes any formal theory that any observer might devise. In addition, we discuss weak and strong emergence and analyze the concepts of observer-dependent emergence and asymptotically observer-independent emergence found in previous definitions and models in the literature of deterministic dynamical and computable systems.

Rapid peri-urbanization has resulted in increasing demand for and pressure on peri-urban lands at the expense of agricultural lands. Households’ decision to convert from agricultural land uses to residential and commercial land uses is driven by a myriad of factors, ranging from social to economic, in the Asante-Akim South district of the Asante region, Ghana. The paper examined the effects of agricultural land use trade-off on food production in the district. Using a triangulation of qualitative and quantitative methods, 115 household respondents were proportionately sampled from three selected communities in the district, for the collection of data through the administration of questionnaires. The data were subjected to the Pearson’s chi-square, embedded in the SPSS V.16, to test for association among the variables. We report that the increasing rate of agricultural land uses conversions was as a result of increasing demand for residential and commercial land usage at the expense of agricultural land uses. Converting prime agricultural lands into other land uses was seen as profitable to agricultural expansion. A re-examination of the district land use plans by the Ghanaian Physical (Town and Country) Planning Department in tandem with the Lands Commission is therefore recommended.

Background: Emerging and re-emerging infectious diseases that infect wildlife, such as African swine fever, avian influenza, and SARS-CoV-2, have highlighted the necessity for wildlife health surveillance (WHS) due to their direct and indirect impacts on wildlife species, ecosystems, domestic animals, and human health. While global policies and guidelines exist, a critical gap remains in local-to-national implementation of WHS systems. A group of local, national, and global actors in WHS have formed a working group to address this gap. Methods and Findings: The working group reports on a theory of change (ToC) developed to implement WHS from local to global scales. Through brainstorming, plenary exercise, and building on peer-reviewed science and existing surveillance systems, we identified six transformative pathways to be implemented via collaborations across scales and contexts: mindset change, policy and investment, user-driven science, user-driven technologies, capacity enhancement, and mobilization of a global community of practice. Interpretation: This ToC serves as a roadmap to develop effective WHS systems that support adaptive management and implementation. WHS is fundamental to understanding the impacts of health threats to biodiversity and human and domestic animal health. This ToC presents an approach to operationalize integration of wildlife health into collaborative One Health surveillance. Funding: The Science for Nature and People Partnership.

Tellurium exhibits exceptional intrinsic electronic properties. However, investigations into the modulation of tellurium's electronic properties through physical modification are notably scarce. Here, we present a comprehensive study focused on the evolution of the electronic properties of tellurium crystal flakes under plasma irradiation treatment by employing conductive atomic force microscopy and Raman spectroscopy. The plasma-treated tellurium experienced a process of defect generation through lattice broken. Prior to the degradation of electronic transport performance due to plasma irradiation treatment, a remarkable observation emerged: in the low-energy region of hydrogen plasma-treated tellurium, a notable enhancement in conductivity was unexpectedly detected. The mechanism underlying this enhancement in electronic transport performance was thoroughly elucidated by comparing it with the electronic structure induced by argon plasma irradiation. This study not only fundamentally uncovers the effects of plasma irradiation on tellurium crystal flakes, but also unearths an unprecedented trend of enhanced electronic transport performance at low irradiation energies when utilizing hydrogen plasma. This abnormal trend bears significant implications for guiding the prospective application of tellurium-based 2D materials in the realm of electronic devices.

Conjugated polymers (CPs) offer potentials for sustainable semiconductor devices due to their low cost, and inherent molecular self-assembly. Enhanced crystallinity and molecular orientation in thin films of solution-processable CPs have significantly improved organic electronic device per-formance. In this work, three methods namely spin-coating, dip-coating, and unidirectional float-ing-film transfer method (UFTM) were utilized with their parametric optimization for fabricating RR-P3HT films. These films were then utilized for their characterization via optical and micro-structural analysis to elucidate dominant roles of molecular orientation, and crystallinity in con-trolling charge transport in organic field-effect transistors (OFETs). OFETs fabricated by RR-P3HT thin films using spin-coating and dip-coating displayed field-effect mobility (μ) of 8.0 × 10-4 cm2V-1s-1 and 1.3 × 10-3 cm2V-1s-1, respectively. This two-time enhancement in (µ for dip-coated films was attributed to its enhanced crystallinity. Interestingly, UFTM films based OFETs demon-strated μ of 7.0 × 10-2 cm2V-1s-1, >100 times increment as compared to its spin-coated counterpart This superior device performance is attributed to synergistic influence of higher crystallinity and molecular orientation. Since the crystallinity of dip-coated and UFTM-thin films are similar ~50 times improved µ of UFTM thin films suggest a dominant role of molecular orientation as com-pared to crystallinity in controlling the charge transport.

Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale so-lar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits including increased yield for a wide range of crops, plant protection from excess solar energy and hail, improved water conservation while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada in general, and Ontario in particular, is at a strategic disadvantage in agricultural without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn and winter wheat) are analyzed for agrivoltaic potential in Ontario. These results are analyzed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics and using agrivoltaics as a potential source of trade surplus with the U.S.

The overuse of chemical fertilisers degrades the soil ecosystem and restricts the natural development of plants. Within the coffee industry, various by-products are produced throughout the process of coffee production and consumption, which are significant in terms of environmental waste. SCG (spent coffee grounds) contain a variety of bioactive compounds that have demonstrated potential applications in various fields. These compounds can enhance soil quality by improving its chemical, physicochemical, physical properties, and biological fertility, ultimately leading to improved plant growth. This study examines the impact of SCG, vermicompost, and chemical fertilisers on the growth of organic red radish (Ravanello Cherry Belle) and soil quality. The experiment, conducted in a greenhouse, tested various concentrations and applications of SCG. Results showed that the SCGT 0.5 gm treatment yielded the highest mean plant length (18.47 cm) and fresh weight (27.54 grammes), while the SCG 10% treatment resulted in the lowest mean plant length (4.54 cm) and fresh weight (0.07 grammes). Vermicompost at 50% concentration produced the highest mean leaf surface area (58.32). These findings suggest the potential of SCG as a sustainable fertiliser alternative, contributing to improved plant growth and soil quality, thus supporting sustainable agricultural practices and a circular economy.

The domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 hr, 48 hr and 5 days post infection condition. Total 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides corresponding to 129 proteins were identified. Functional annotation of proteome data analysis reveals activation of RIG-I-like receptor and Jak-STAT signaling pathways, that lead to induction of interferon stimulated genes (ISGs) expression. The pathway analysis of phosphoproteome datasets also confirms the activation of RIG I, Jak-STAT signaling, NF-kappa B signaling and MAPK signaling pathways in the lung tissues. The induction of ISGs proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5 and PKR) establishes protective antiviral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1 and MAPK1 as main hub proteins that might play important roles in disease progression in ducks. Together, functional annotation of proteome and phosphoproteome datasets reveals the molecular basis of the disease progression and disease resistance mechanism in ducks infected with HPAI H5N1 virus.

This report covers the analysis of Intrusion Detection Systems nowadays. Therefore, it focuses on the IDS classification, which includes not only a well-known explanation of Network/Host-Based IDS type but also cloud-based solutions. The paper explores ways of intrusion detection, providing examples of the best detecting tools (OSSEC, Snort or Bro (ZEEK)). Another aspect covered in this report is the juxtaposition of three systems: IDS, IPS and IRS and their response. Finally, there will be presented Intrusion Detection Systems' evasion techniques and challenges, followed by critical conclusions.

This paper utilizes a life cycle assessment to evaluate the energy resources and environmental impacts of three heating systems. In the first step, life cycle assessments (LCAs) are prepared for the renewable, conventional, and combined systems. The system boundary of the LCA method is "cradle to gate," which means it includes the entire life cycle of a product or process. This includes energy supply, transport, energy generation, and operational energy use. The system scenarios were compared based on ecological and energy loads in the second step. Primary energy, resources, emissions, and environmental potentials are calculated for the system scenarios using the life cycle assessment methods CML 2016 and ReCiPe 2016. Finally, models for environmental reliability and complex decision-support tools have been developed. This work addresses two main questions: how to characterize the environmental and energy impacts of the systems under study, and what is the optimal scenario for the system? The research results show that the gas boiler system has a greater environmental impact. The most significant differences between the systems relate to the degrada-tion of abiotic fossil fuels when using a gas boiler and the process of acidification when using an electric heat pump. The results can be used to develop sustainable heating systems with reduced environmental impacts and enhanced energy efficiency.

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

In this paper, we propose a quantitative definition of emergence from the density matrix framework as a state of knowledge of the system and its generalized probability formulation, in order to detect emergent properties. We propose the use of a descriptor based on the difference of von Neumann entropy, to calculate if subsystems of systems have inner correlations by defining emergent systems in terms of emergent information.

This study determines the threshold for the computational rate of actuator motor controllers for unmanned underwater vehicles necessary to accurately follow discontinuous square wave commands. Motors must track challenging square-wave inputs, and identification of key computational rates permit application of deterministic artificial intelligence (D.A.I.) to achieve tracking to a machine-precision degree of accuracy in direct comparison to other state-of-art approaches. All modeling approaches are validated in MATLAB simulations where the motor process is discretized at varying step-sizes (inversely proportional to computational rate). At a large step-size (fast computational rate), discrete D.A.I. shows a mean error more than three times larger than that of a ubiquitous model-following approach. Yet, at a smaller step size (slower computational rate), the mean error decreases by a factor of 10, only three percent larger than that of continuous D.A.I. Hence, the performance of discrete D.A.I. is critically affected by the sampling period for discretization of the system equations and computational rate. Discrete D.A.I. should be avoided when small step-size discretization is unavailable. In fact, continuous D.A.I. has surpassed all modeling approaches which makes it the safest and most viable solution to future commercial applications in unmanned underwater vehicles.

Microscale surgery on single cells and small organisms have enabled major advances in fundamental biology and in engineering biological systems. Examples of applications range from wound healing and regeneration studies to the generation of hybridoma to produce monoclonal antibodies. Even today, these surgical operations are often performed manually, but they are labor-intensive and lack reproducibility. Microfluidics has emerged as a powerful technology to control and manipulate cells and multicellular systems at the micro- and nanoscale with high precision. Here, we review the physical and chemical mechanisms of microscale surgery, and the corresponding design principles, applications, and implementations in microfluidic systems. We consider four types of surgical operations: 1) Sectioning, which splits a biological entity into multiple parts, 2) ablation, which destroys part of an entity, 3) biopsy, which extracts materials from within a living cell, and 4) fusion, which joins multiple entities into one. For each type of surgery, we summarize the motivating applications and the microfluidic devices developed. Throughout the review, we highlight existing challenges and opportunities. We hope that this review will inspire scientists and engineers to continue to explore and improve microfluidic surgical methods.

The Eliezer and Gray physical interpretation of the Ermakov-Lewis invariant is applied as a guiding principle for the derivation of the special relativistic analog of the Ermakov-Milne-Pinney equation and associated first integral. The special relativistic extension of the Ray-Reid system and invariant is obtained. General properties of the relativistic Ermakov-Milne-Pinney are analyzed. The conservative case of the relativistic Ermakov-Milne-Pinney equation is described in terms of a pseudo-potential, reducing the problem to an effective Newtonian form. The non-relativistic limit is considered as well. A relativistic nonlinear superposition law for relativistic Ermakov systems is identified. The generalized Ermakov-Milne-Pinney equation has additional nonlinearities, due to the relativistic effects.

Liquorpheresis (CSF filtration) comprises a therapeutical approach that has been proposed to treat several neurological conditions where antibodies, inflammatory mediators or abnormal peptides are the cause or play an important role in the pathogenesis of the disease. CSF replacement may be an alternative approach not explored so far. Here, we review previous experiences in the use of liquorpheresis in autoimmune and degenerative neurological diseases. Then we describe previous developments and provide some new technical innovations in order to design bidirectional CSF shunting systems. These systems can be complemented either with a deposit of artificial CSF or with a CSF filter, allowing CSF replacement or liquorpheresis respectively. Both options would lead to mechanical dilution of the patient’s CSF.

This research investigates the relationship among human knowledge, behavior, and risk as they relate to urban chicken husbandry in the U.S. Concern over zoonotic diseases has been on the rise, especially with increasing contact. In particular, avian influenza, or bird flu, Salmonella enterica (salmonella), and Escherichia coli (E. coli), can all cross species lines between people and poultry. This study analyzed knowledge and practices in urban chicken husbandry to assess how they relate to risk of disease acquisition, hypothesizing that certain practices associated with a lessened knowledge base may heighten the risk. This study used social surveys to examine the self-reported knowledge base of individuals involved in chicken husbandry as they relate to beliefs and behaviors associated with the care of these animals. These results identify key factors that may heighten the risk of disease transmission, and demonstrate that an increased knowledge base could act to lessen this risk.

An effective method to inspect inaccessible nuclear power facility by interface wave which propagate along the shrink fit boundary of reactor head is proposed in this study. Reactor head is relatively thick to inspect from the outside of reactor by conventional ultrasonic testing. The proposed interface wave can propagate a long distance from the fixed transducer position. The inside of nuclear reactor is limited to access due to the high radiation, so transducers are located at outside of nuclear facility and interface wave propagates into the nuclear reactor for defect detection. The numerical simulation and experiments were carried out to validate the effectiveness of the proposed interface wave inspection method. Various defect cases simulating field failures are also presented with satisfactory detectability by the proposed technique with the features for defect classification.

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

Lipid droplet (LD) accumulation in hepatocytes is one of the major symptoms associated with fatty liver disease. Mitochondria play a key role in catabolizing fatty acids for energy production through β-oxidation. The interplay between mitochondria and LD assumes a crucial role in lipid metabolism, while it is obscure how mitochondrial morphology affects systemic lipid metabolism in the liver. We previously reported that cilnidipine, an already existing anti-hypertensive drug, has potency to prevent pathological mitochondrial fission by inhibiting protein-protein interaction between dynamin-related protein 1 (Drp1) and filamin, an actin-binding protein. Here, we found that cilnidipine and its new dihydropyridine (DHP) derivative, 1,4-DHP, which lacks Ca2+ channel blocking action of cilnidipine, prevent the palmitic ac-id-induced Drp1-filamin interaction, LD accumulation and cytotoxicity of human hepatic HepG2 cells. Cilnidipine and 1,4-DHP also suppressed the LD accumulation accompanied by reducing mitochondrial contact with LD in obese model and high fat diet-fed mouse livers. These results propose that targeting Drp1-filimin interaction becomes a new strategy for the prevention or treatment of fatty liver disease.

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries. As a result, it is suggested that, in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A library was set-up consisting of 415 scientific references. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber producing countries, in particular in Indonesia, Thailand, China and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only 4 papers are devoted to plant physiology and breeding. The discussion section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops.

This study examined the relationship between financial sector liberalization and agricultural sector output in Nigeria using annual data spanning the period 1986-2020. Specifically, the objectives of the study are to examine the relationship between lending rate, exchange rate, commercial bank credit to agriculture, inflation rate and agricultural sector output in Nigeria. Ex-post facto research design was employed and the annual time series data were collated from Central Bank of Nigeria (CBN) Statistical Bulletin. The econometrics methods of unit root, co-integration and error correction mechanism were used for the analyses. The outcome of the ADF unit root test show that the variables were stationary. Also the co-integration result showed that there exist co-integration amongst the variables in the model. The results from Error Correction Model indicates that lending rate and inflation rate have a negative relationship on agricultural sector output while exchange rate and commercial bank credit to agriculture have positive relationship on agricultural sector output. Based on these results, this study recommends that government and policy makers in Nigerian should initial policies that will boost investments in the agricultural sector through direct provision of credits to agriculturist and banks should also lend at a very low and subsidized interest rate to enable farmers’ access agricultural loans that will boost agricultural productivity in the economy.

The paper analyses how between 1956 and 2009 the agrarian metabolism of the Barcelona Metropolitan Region (BMR) has become less functional, losing circularity in biomass flows and in relationship to its landscape. We do so by adopting a Multi-EROI and flow-fund (MuSIASEM) analyses and its nexus with landscape functional structure. The study of agricultural flows of Final Produce, Biomass Reused and External Inputs is integrated with that of land use, livestock, power capacity and population changes between 1956 (at the beginning of the Green Revolution) and 2009 (fully industrialized agriculture). A multi-scale analysis is conducted at the landscape scale (seven districts within the Barcelona metropolitan region) as well as for the functions deployed, within an agroecosystem, by the mutual interactions between its funds (land-uses, livestock and farming population). A complex nexus between land, livestock, dietary patterns and energy needs is shown; we conclude that from the perspective of the circular bioeconomy the agrarian sector has gone worse hand in hand with the landscape functional structure. Therefore, a novel perspective in landscape agroecology is opened

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Accurate estimation of livestock carrying capacity (LCC) and implementing an appropriate actual stocking rate (ASR) is key to sustainable management of grazing adapted alpine grassland ecosystems. Reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here we demonstrate limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in efforts to support more accurate and reliable estimation of LCC and ASR. Results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. Findings from a smaller-scale study that incorporate phenology timings into estimation of annual aboveground biomass reveal that Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000-2005. The methods proposed can be extended to map grassland grazing pressure by predicting LCC and tracking ASR, thereby improving sustainable resource use in alpine grasslands.

We say that a system {zmF(z)}m=0∞ is a Beurling system if F is an outer function. Beurling’s approximation theorem asserts that if F is an outer function from H2(D) then the system {zmF(z)}m=0∞ is complete in the space H2(D). We prove that a Beurling system with F∈Hp(D),1≤p<∞ is an M−bases in Hp(D) with an explicit dual system. Any function f∈Hp(D),1≤p<∞ can be expanded as a series by the system {zmF(z)}m=0∞. For different methods of summation we characterize outer functions F for which the expansion converges to f. Related results for weighted Hardy spaces in the unit disc are studied. Particularly we prove Rosenblum’s hypothesis.

Minimal rotating thermodynamic systems are addressed. Particle m placed into the rotating symmetrical double-well potential (bowl), providing binary logical system is considered. The condition providing the transfer of the particle from one frictionless half-well to another, and in this way possibility to record 1 bit of information is derived. The procedure of recording turns out to be irreversible; it is impossible to return the particle to its initial state under rotation about the same axis. The same rotating double-well system exerted to the thermal noise is considered. Minimal rotating thermal engine built of the rotating chamber, movable partition and the particle confined within the chamber is treated. Rotation of the system displaces the partition; thus, enabling erasing of one bit information. Erasing of 1 bit of information is due to the inertia (centrifugal force) acting on the partition. Isothermal expansion of the “minimal gas” expectedly gives rise to the Landauer bound. Compression of the “gas” with the rotation around the same axis is impossible and demands the additional axis of rotation. The interrelation between the possibility of recording/erasing information and the symmetry of the system is considered.

Warren Weaver, writing about the function that science should have in mankind’s developing future, ideas and ideals, proposed to classify scientific problems into ‘problems of simplicity’, ‘problems of disorganised complexity’, and ‘problems of organised complexity’ — the huge complementary class to which all biological, human, and social problems belong. Problems of simplicity have few components and variables and have been extensively addressed in the last 400 years. Problems of disorganised complexity have a huge number of individually erratic components and variables, but possess collective regularities that can be analysed by resourcing to stochastic methods. Yet, problems of organised complexity do not yield easily to classical or statistical treatment since interrelations among phenomenon elements change during its evolution alongside commonly used state variables, affecting behaviour and outcome. Moreover, organisation, the focal point in this complementary class, is still an elusive concept despite the gigantic efforts undertaken since a century ago to tame it. This paper addresses the description, representation and study of phenomena in the ‘problems of organised complexity’ class. Grounded on relational mathematical constructs, a theoretical framework providing operational definitions and schemes for formally modelling organisations and interaction of organisations is introduced. This framework extends the general systems’ concept and provides a novel perspective for addressing organised complexity phenomena as a collection of interacting organisations.

Within the past decade, the aerospace engineering industry has evolved outside the constraints of using single, large, custom satellites. Due to increased reliability and robustness of commercial off the shelf (COTS) printed circuit board (PCB) components, missions instead have transitioned towards deploying swarms of smaller satellites. This approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites are able to be quickly developed with a more modular design at lowered risk. The Alpha mission at Cornell Space Systems Studio is fabricated in this manner. However, for the purpose of this mission, only one satellite was initially developed. This manuscript will discuss a systems engineering approach to the development of this satellite. As a disclaimer, this manuscript is written from a systems perspective. Therefore it will follow many subsystems from a wide range of functionalities. The research in this manuscript was kept broad with the hope to contribute to the mission as a system, through a range of development phases including validation and verification of existing methods. The two systems that will be primarily focused on are the Attitude Control System (ACS) of the carrier nanosatellite (cubesat), and the RF communications on the ex-creted picosatellites (chipsat). Milestones achieved in chipsat RF include chipsat to chipsat communication, chipsat to SDR ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Achievements on the ACS side included controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper and IMU tuning. These developments matured the technological readiness level (TRL) of our systems in preparation for satellite deployment.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

This study investigates the impact of climate change on the efficiency of organic honey production in Türkiye using a machine learning approach. The eXtreme Gradient Boosting (XGBoost) algorithm is employed for the Machine Learning (ML) modelling. The analysis includes data on organic honey production performance from 2004 to 2023, spanning a period of 20 years, and 8 different climate variables (16 variables depending on daytime and nighttime values). The dataset created to investigate the impact of climate change on organic honey yield comprises 28 columns and 120,960 rows. The study aimed to determine the significance of different climatic variables on the efficiency of organic honey production. The study found that, in addition to daytime solar radiation and temperature, wind speed and direction were also important factors. The sensitivity analysis has shown that minor alterations in the variables have a significant impact on the model's predictions. Additionally, non-linear relationships between the variables and honey production were identified. These findings suggest that it may be beneficial to consider implementing climate change adaptation and mitigation strategies in agricultural policies and beekeeping practices. The study suggests that machine learning has the potential to provide valuable insights into the complex relationship between climate change and agricultural production. This could aid in the sustainability and economic optimization of the sector.

The traditional societies evolved locally with the realms of time in their calendars while modern Gregorian based on the solar movement and some others use lunar or the combination with solar. Unveiling some information from the old manuscripts to gain the realm of traditional Batak people since the ancient times in settlements surrounding the tropical Lake Toba, North Sumatera, Indonesia, revival of cosmos is delivered. Uniquely the ancient Batak count days in a month by observing lunar phase and the period of year is by looking the observable Constellation of Orion & Scorpius within the new phase of the moon. Thus there are years with 12 months and some leap years with 13 months, and we propose to categorize the calendar to be lunar-star, relative to other widely analyzed calendars. In the realization of the use of the cosmogram, we cross the period of the leap year to the examination of contemporary data on agriculture event, i.e.: crop failure, as well as some profiles on weather. The revival of the Batak Calendar opens further interesting conjectures based on the rich cultural and astronomical knowledge embedded in tradition with deep connection between celestial observations and timekeeping.

Agriculture and rural regions in Europe face a number of economic, social and environmental challenges. Rural areas are active players in the EU’s green transitions. Weeding is one of the most important factors of agricultural production. New weeding techniques are being developed to enhance sustainability. Among them laser based weeding seems a promising alternative for use of chemicals. WeLASER technique is a novel technique of weed control. Its successful implementation depends on many factors related to the innovation itself, policy context, farming conditions and users’ attitudes. Survey was carried out to provide the insight on the attitudes toward the innova-tive (laser) weed control tool (device - autonomous robot). The CATI method was selected for the survey of farmers opinion and carried out in three countries: Denmark, Spain and Poland. Statistical methods were applied to analyze the results. The study provided knowledge on how farmers see the barriers and opportunities of implementing the device in practice. Positive attitudes of farmers were observed but with high expectations related to quality of the technique and systemic conditions of its implementation.

Leaf area estimation is a critical component in the study of plant growth and productivity within agricultural systems. This research introduces the LeafArea package, a specialized tool designed to calculate the leaf area of six distinct Andean fruit species: S. quitoense, S. betaceum, P. peruviana, R. fruticosus, P. ligularis and P. edulis. Leveraging response variables such as species type, leaf length and width, the package employs advanced machine learning algorithms to estimate leaf area accurately. The primary focus of the study is to identify the most effective model for describing the relationship between leaf width, length, and area for each plant species. Currently, the LeafArea package utilizes four different machine learning algorithms, namely generalized linear model (GLM), generalized linear mixed model (GLMM), Random Forest and XGBoost. Among these, XGBoost stands out as a top-performing algorithm, exhibiting exceptional predictive accuracy. The evaluation metrics employed in the program provide valuable insights for researchers, aiding in informed decision-making. Specifically, XGBoost demonstrates significantly lower prediction errors and approaches a near-perfect R2 value, emphasizing its potential to enhance predictive accuracy. These results underscore the efficacy of machine learning techniques, as a compelling choice for researchers seeking precise and robust predictions in leaf area estimation. The LeafArea package thus represents a valuable tool for advancing our understanding of plant growth dynamics, resource allocation, and overall productivity within agricultural ecosystems.

Ammonia-nitrogen (NH3 -N) loss from agriculture decreases crop yield potential and environ-mental quality. Incorporating animal manures by chisel plowing (CP) can reduce NH3 loss but may increase erosion and compaction potential compared to lower disturbance methods. Vertical tillage (VT) is designed to reduce disturbance and conserve more crop residue than CP, however its effect on NH3 loss from manure application is largely unknown. Six trials in corn production systems were conducted to evaluate the impacts of manure incorporation method (CP, VT, or broadcast) and weather conditions on NH3-N concentrations during 2013 to 2016 at a research farm in central Wisconsin, USA. Passive samplers were used to measure NH3-N concentrations at 30-cm above the ground during the first 0 to 24 and 24 to 48 hr post-application/incorporation. Average NH3 -N concentrations for CP and VT were 44 to 86% of surface broadcast and similar for most trials, while crop residue coverage was greater for VT than CP (39 and 22% of control plots, respectively). Concentrations of NH3 -N were correlated with the amount of plot area covered by manure for the first (r = 0.56, P<0.0001) and second measurement periods (r = 0.85, P<0.0001). Results show that VT had comparable NH3-N concentration reductions to CP while conserving more crop residue.

Bangladesh is located in the alluvial floodplain of the Ganges–Brahmaputra–Meghna (GMB) river system and its numerous tributaries. In this country, the frequency, as well as severity, of riverbank erosion is high. To examine the impacts of riverbank erosion on rural food security, a baseline survey was conducted in Zajira, Bangladesh. The findings show that most people in the study area are affected by riverbank erosion. Moreover, agricultural production is gradually decreasing as a result of loss of farmland due to the catastrophe and its impacts. In some cases, people lose their entire homestead and are left with few income opportunities and low purchasing power. Consequently, they face the constant threat of a food crisis. The affected people follow some coping mechanisms to face these stressful situations, such as relying on less expensive or less preferred food items. While the local-government authorities have implemented some programmes to assist them, such as food aid and social-safety-net schemes, these are insufficient, due to the government's limited institutional resources and capacities. These findings suggest that for formulating effective rural-development plans, assessing the impact of riverbank erosion on the food security of people living in the affected areas is a prerequisite.

Climate change is currently one of the main problems facing agriculture to achieve sustainability. It causes situations such as drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that allow improving the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data and discuss the use of the architectures in the crop, water, climate, and soil management, especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. Despite the advances in this line of research, few papers were found that mention the components of the architectures, in addition to the lack of standards and the use of reference architectures, which allow the proper development of Agricultural Big Data in the context of climate change.

The role that Colombia will play in terms of food supply in the coming years has generated a discussion on the agricultural practices implemented, specifically in the use of pesticides. Therefore, this article aims to provide a comprehensive and complete review of the status of pesticide use in the agricultural sector, highlighting the agri-food industry in Colombia. Currently, the use of pesticides has intensified throughout the world, generating a call for attention by governmental entities in terms of regulation. Colombia for the year 2019 presented a rate of use of 7.8 kg/ha of pesticides, placing it among the twenty countries with the highest rate. On the other hand, the quantities and economic benefits of exports and imports are growing steadily. Several organizations have focused their efforts on determining the health consequences that people exposed to chronic and/or acute intoxication may suffer. This has led to cases of cytogenetic, neurocognitive and physical effects, prompting governmental organizations to regulate the use of pesticides. Finally, the need to design pesticide management and handling systems that respond to the needs of producers and the sector, integrating international food safety guidelines, is identified.

Plants especially in their natural habitat are considered part of a rich ecosystem that includes many various microorganisms in the soil. The current study aimed to identify the bacterial profile of agriculture-related soil samples using the metabarcoding technique to compare and explore relevant rhizosphere bacteria associated with plant cultivations in newly reclaimed land and habitual cultivated ones. Total environmental DNA was extracted from rhizosphere and non-cultivated samples derived from three land types in Egypt. The bacterial 16S rDNA was amplified and sequenced by NGS technology to profile each sample. The microbial profile was characterized by statistical and literature-based methods. Among all samples, the most identified phyla were Actinobacteriota (28%), followed by Proteobacteriota (26%), Firmicutes (14%), Ac-idobacteriota and Chloroflexi (7%), Gemmatimonadota (5%), Bacteriodota and Crenarchaeota (3%), and Myxococcota (2%), in addition to 37 other phyla with <1% counts. A total of 74 OTU was unique to the plant rhizosphere area and classified as Bacteriodota (5.1%:0.3%), Firmicutes (2.4%:0.1%), and Proteobacteria (3.5%:2%) phyla in agriculture and reclaimed lands, respectively. Moreover, the rhizosphere profile included a large portion of uncultured and unidentified bac-terial species, which opened a window to further analysis. Our analysis provides a key Knowledge about the rhizosphere microbiome and highlights its possible use to create microbial-based bi-ofertilizers targeting plant performance in contrast to traditional fertilizers and their side effect on the agriculture sector.

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

The latest World Atlas of Desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the Convergence of Evidence paradigm, which identifies socioeconomic and biophysical variables whose behavior allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back to 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish Government's recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements for a roadmap to be designed around the pressures, impacts, and drivers that define today's Spanish desertification landscapes to manage them effectively and avoid their further degradation.

Copernicus is Europe's space-based Earth monitoring asset, which consists of a complex set of systems that collect data from different sources: remote sensing satellites (RS) and in-situ sensors such as ground stations, airborne and marine sensors. This study was originally prepared for the needs of the Czech agricultural community, where we provided an in-depth analysis of articles related to Earth observation in precision agriculture. At a later stage, we extended this study by comparing the recommendations of the European EO4Agri project and scientific articles published in MDPI. We had two important objectives, one was to validate the results of the EO4Agri project and the other was to look for gaps in current research and community needs. To recognize the importance of using Sentinel 1 data, we also added a specific analysis of methods for data fusion of Sentinel 1 and Sentinel 2 data.

Rural landscapes all over the world are subject to great transformations, first of all the continuous and slow depopulation of land and villages. It is a dramatic phenomenon that causes devastating consequences for environmental systems and for the tangible and intangible heritage of entire territories. The situation becomes more ambiguous when it comes to cultural landscapes, especially those internationally recognized as exceptional (i.e. inscribed on the UNESCO World Heritage List). In this case, the risk is to abandon agricultural production in favor of consumerist tourist economies, which can damage the territorial authenticity. In this paper we question the role of the landscape project in strengthening territorial resilience. In particular, a composite and interdependent action is proposed between landscape design and implementation of a multifunctional agriculture model, oriented towards teaching and tourism. To undertake this investigation, a master's thesis work on Landscape Architecture is examined, as an opportunity for a research-by-design method. The application case is the Italian UNESCO site of Vignale Monferrato, a depopulated rural village, characterized by abandoned land and buildings. The paper concludes by outlining replicability application scenarios for the proposed model.

Precision agriculture (PA) is a holistic, sustainable, innovative systems approach that assists farmers in production management. Adopting PA could improve sustainable food security and community economic sustainability. Developing an understanding of PA adoption attributes is needed to assist extension practitioners to promote adoption and better understand the innovation adoption phenomena. A systematic review of literature was conducted to investigate PA adoption. Thirty-three publications were examined, and four themes were found among the reviewed publications. The results were interpreted using Rogers’ diffusion of innovations framework to address the research objectives. Of the reviewed literature, we found relative advantage and compatibility were two dominant attributes to strengthen the adoption of PA, and the complexity attribute was rarely used to promote the adoption of PA. This study shows that change agents do not fully use five attributes of innovation when they promote PA technology to stakeholders to adopt. Thus, we recommend studies from the agricultural extension specialists’ perspectives in the future may determine contributions to motivate farmers’ adoption of PA, in particular related to complexity.

Concerns of food and environmental security have increased enormously in recent years due to the vagaries of climate change and variability. Efforts to promote food security and environmental sustainability often reinforce each other and enable farmers to adapt to and mitigate the impact of climate change and other stresses. Some of these efforts are based on appropriate technologies and practices that restore natural ecosystems and improve the resilience of farming systems, thus enhancing food security. Climate smart agriculture (CSA) principles, for example, translate into a number of locally-devised and applied practices that work simultaneously through contextualised crop-soil-water-nutrient-pest-ecosystem management at a variety of scales. The purpose of this paper is to review concisely the current state-of-the-art literature and ascertain the potential of the Pfumvudza concept to enhance household food security, climate change mitigation and adaptation as it is promoted in Zimbabwe. The study relied heavily on data from print and electronic media. Datasets pertaining to carbon, nitrous oxide and methane storage in soils and crop yield under zero tillage and conventional tillage were compiled. Findings show that, compared to conventional farming, Pfumvudza has great potential to contribute towards household food security and reducing carbon emissions if implemented following the stipulated recommendations. These include among others, adequate land preparation and timely planting and acquiring inputs. However, nitrous oxide emissions tend to increase with reduced tillage and, the use of artificial fertilizers, pesticides and herbicides is environmentally unfriendly.

Safety and health issues are growing concerns in the agricultural sector among farm-workers in South Africa. The current health pandemic arising from the corona virus has thrown these issues into the spotlight, and this study explored the perceived usefulness and perceived ease of using personal protective materials among farm-workers in the banana sector. Using a case study of 10 large farms in the lower south coast of South Africa, we utilized descriptive and inferential analysis to identify the demographic composition of farm-workers in the study area, examine their perception of specific personal protective materials, and determine the relationship between demographic characteristics and perception of personal protective materials. Farm-workers in the study area were found to be predominantly single black males aged between 36-55years, with no more than a primary education, with work experience of between 6-10 years and employed as unskilled farm-labourers. Perceived usefulness (83%) and perceived ease of use (79%) for personal protective materials was high. Respondents gender (p=0.012), marital status (p=0.029), level of education (p=0.035) and farm-work experience (p=0.008) were significant, while their age (p=0.057), population group (p=0.160) and work classification (p=0.203) were not found significant in determining perceived usefulness or perceived ease of use. Our study makes valuable contribution to the existing body of knowledge regarding farm-worker safety issues by exploring perception of personal protective materials.

In the last few decades, a lot has been written on the use of sustainable agriculture to improve ecosystem services for resilience to climate change. However, no tangible and systematic evidence exists on how this would participate in alleviating impacts on vulnerable rural communities. This paper provides a narrative systematic review (SR) integrated with a bibliometric analysis and a concept network analysis to understand how, in a changing climate, sustainable agriculture builds the resilience of agro-systems. The search was set from the date of the first relevant article until the end of 2018. Results generated have demonstrated that: a. Only single practices and methods have been studied to assess impacts on single ecosystem services. b. Soil quality and health are considered a key indicator of sustainable agriculture. c. Albeit the assessed practices and methods have shown to improve the biodiversity of agro-systems, which makes them more resilient to extreme climate events, we are still far from reaching interdisciplinary and multi-dimensional agriculture which integrates all management aspects and generates a full range of ecosystem services. In conclusion, the study addressed the following recommendations for the scientific community and for decision-makers to orient future research strategies and efforts: a. Integration of all agro-systems services into sustainable management using an ecosystem-based approach on a life-cycle basis using Life Cycle Assessment (LCA) method; b. Improving the scientific understanding of traditional knowledge for higher synergies and for further integration; c. Unification of assessment methods and indicators for the quantification of impacts; d. Creation of a platform to share, monitor, screen, and approve assessments and evaluations of sustainable agriculture by region.

A The agri-food sector is in full renovation, continuously demanding new systems that allow farmers to facilitate their work. Efficient agricultural practices are essential for increasing farm profitability. Efficient agricultural practices can be increased by understanding and managing water neeeds. However, real-time monitoring of water needs is currently slow, and irrigation decisions are based on previous data or physical evidence. Furthermore, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and the autonomy is very low. Emerging Internet of Things (IoT) technologies such as wireless sensor networks can be used to collect vast amounts of data recorded via apps. By means of an LPWAN communication the farmer can know in real time the state of his crops thanks to a large number of sensors connected wirelessly and distributed across his farm. This paper presents a low consumption solution based on LoRaWAN technology. The wireless sensor node, called BoX, exhibits very low power since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. That will allow the farmer to have all the information necessary to achieve an efficient management of his crops with full autonomy.

Conservation Agriculture (CA) alters soil properties and microbial processes compared to conventional agriculture. These changes can affect soil-atmosphere greenhouse gas (GHG) fluxes. In this overview, we summarized the results of global literature and the gaps in measuring and understanding of GHG fluxes in CA systems and conventional agriculture. Some studies compared soil carbon sequestration and soil respiration in conservation agriculture and no-tillage system with conventional agriculture and the results were not consistent in all experiments. Interactions between CA pillars and soil factors such as soil moisture, temperature, texture can determine the rate of respiration rate and soil-atmosphere CO₂ fluxes. The majority of studies reported larger N₂O emissions in no-tillage treatment compared with conventional tillage while some other studies reported no difference between no-tillage and conventional tillage systems. In the majority of CA studies, there is lack of required information which is necessary to understand the mechanisms and processes that affect soil GHG fluxes. Determining factors like climate, amount of plant residues, soil type, crop types included in crop rotation and cover crops and duration of the study are not considered. Static chamber method was used for measuring soil-atmosphere GHG fluxes in the majority of studies. Spatial and temporal changes in GHG flux rates are high and missing part of highly episodic events by using static chamber method may result over- or under-estimation in flux balance calculation. Applying standard techniques for measuring continuous fluxes can help to calculating accurate GHG balance.

This article presents a combined route and path planning strategy to guide Skid-Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity, and ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrated the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

The study of different forms of preconditioners for solving a system of nonlinear equations, by using Newton’s method, is presented. The preconditioners provide numerical stability and rapid convergence with reasonable computation cost, whenever chosen accurately. Different families of iterative methods can be constructed by using a different kind of preconditioners. The multi-step iterative method consists of a base method and multi-step part. The convergence order of base method is quadratic and each multi-step add an additive factor of one in the previously achieved convergence order. Hence the convergence of order of an m-step iterative method is m + 1. Numerical simulations confirm the claimed convergence order by calculating the computational order of convergence. Finally, the numerical results clearly show the benefit of preconditioning for solving system of nonlinear equations.

This study addresses the enhancement of material efficiency and reduction of brittleness in timber-to-concrete adhesive connections for beam-type timber and timber-concrete composite panels. The research explores the potential benefits of adding longitudinal timber ribs to Cross-Laminated Timber (CLT) beam-type panels. Three groups of flexure-tested specimens were analysed: (1) timber panels (1400 mm × 400 mm) with two 100 mm thick CLT panels and two 60 mm thick CLT panels reinforced with 150x80 mm timber ribs; (2) eight specimens (600 mm × 100 mm × 150 mm) with CLT members (600 mm × 100 mm × 100 mm) connected to a 50 mm concrete layer using granite chips and Sikadur-31 (AB) epoxy adhesive; (3) six CLT panels (1400 mm × 400 mm × 50 mm) bonded to a 50 mm concrete layer, with two panels containing polypropylene microfibers and two panels incorporating polyethene dowels for mechanical connection. Specimens were subjected to three-point bending tests and analysed using the transformed section method, γ-method, and finite element method with ANSYS 2023R2 software. Results indicated a 53% increase in load-carrying capacity for ribbed CLT panels with no additional material consumption, a 24.8%-41.1% increase for CLT panels strengthened with a concrete layer, and improved ductility and prevention of disintegration in timber-concrete composites with polypropylene microfibers.

Ultrasonic feedback energy is affected by the variety, planting and growth state of crops, it is difficult to fine application in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, jointing stage, booting stage, heading stage, filling stage and maturity stage. The relationship between the actual value of the height from the spray boom to the top of wheat (Habw) and the detected value of the height from the spray boom to the top of wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450 mm~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤5 cm during the full growth cycle. This study provides an ultrasonic detection model construction method, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages.

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

We consider an $N$ fermion system at low temperature $T$ in which we encounter special particle number values $N_m$ exhibiting special traits. These values arise in focusing attention upon the degree of mixture (DM) of the pertinent quantum states. Given the coupling constant of the Hamiltonian, the DMs stay constant for all $N$-values, but experience sudden jumps at the $N_m$. For a quantum state described by the matrix $\rho$, its purity is expressed by $Tr \rho^2$ and then the degree of mixture is given by $1 - Tr \rho^2$, a quantity that coincides with the entropy $S_q$ for $q=2$. Thus, Tsallis entropy of index two faithfully represents the degree of mixing of a state, that is, it measures the extent to which the state departs from maximal purity . Macroscopic manifestations of the degree of mixing can be observed through various physical quantities. Our present study is closely related to properties of many-fermion systemsn that are usually manipulated at zero temperature. Here we wish to study the subject at finite temperature. Gibbs' ensemble is appealed to. Some interesting insights are thereby gained.

In this study, the literature on varnish contamination is investigated among the various con-taminations of lubricant. As the period of use of lubricant increases, the lubricant deteriorates and becomes contaminated. The varnish causes filter plugging, sticking of hydraulic valves and fuel injection pumps, obstruction of flow, reduction of clearance, poor heater and cooler perfor-mance, and increase of friction and wear in various lubrication systems. These problems also cause mechanical system failure, performance degradation, and increased maintenance and re-pair cost. To improve the problems causes by the varnish, an adequate understanding of varnish is required. Therefore, in this literature review, definitions and characteristics, generating ma-chinery, generating mechanisms, causes, measurement methods, and prevention or removal methods are summarized. Most of the data are reports of companies related to lubricant and machine maintenance, and are included in some papers. We hope that this summary of varnish will be helpful to those who are engaged in reducing or preventing varnish problems.

Different hypotheses of carcinogenesis have been proposed based on local genetic factors and physiologic mechanisms. It is assumed that changes of the metric invariants of a biologic system (BS) determine general mechanisms of cancer development. Numerous data demonstrate an existence of three invariant feedback patterns of BS: negative feedback (NFB), positive feedback (PFB) and reciprocal links (RL). These base patterns represent basis elements of a Lie algebra sl(2,R) and imaginary part of coquaternion. Considering coquaternion as a model of a functional core of a BS, conditions of the system can be identified with the points of three families of hypersurfaces in R42: hyperboloids of one sheet, hyperboloids of two sheets and double-cones. Corresponding quadratic form relates negative and positive entropy contributions of base elements to the energy level of the system, so that anabolic states of the system will correspond to the points of a hyperboloid of one sheet, while catabolic conditions to the points of a hyperboloid of two sheets. Equilibrium states will lie in a double cone. Hypothetically anabolic and catabolic states dominate intermittently oscillating around the equilibrium. Deterioration of base elements increases positive entropy and causes domination of catabolic states which is the main metabolic determinant of cancer. Corresponding trajectory of a malfunctioning system will have a tendency to remain inside the double cone.

Different hypotheses of carcinogenesis have been proposed based on local genetic factors and physiologic mechanisms. It is assumed that changes of the metric invariants of a biologic system (BS) determine general mechanisms of cancer development. Numerous data demonstrate an existence of three invariant feedback patterns of BS: negative feedback (NFB), positive feedback (PFB) and reciprocal links (RL). These base patterns represent basis elements of a Lie algebra and imaginary part of coquaternion. Considering coquaternion as a model of a functional core of a BS, conditions of the system can be identified with the points of three families of hypersurfaces in R⁴₂: hyperboloids of one sheet, hyperboloids of two sheets and double-cones. Corresponding quadratic form relates negative and positive entropy contributions of base elements to the energy level of the system, so that anabolic states of the system will correspond to the points of a hyperboloid of one sheet, while catabolic conditions to the points of a hyperboloid of two sheets. Equilibrium states will lie in a double cone. Hypothetically anabolic and catabolic states dominate intermittently oscillating around the equilibrium. Deterioration of base elements increases positive entropy and causes domination of catabolic states which is the main metabolic determinant of cancer. Corresponding trajectory of a malfunctioning system will have a tendency to remain inside the double cone.

As the scarcity of potable water increases, recycling of treated wastewater is increasing. Small-scale, decentralized treatment can be implemented to serve local populations by keeping water within their boundaries and within reach for reuse, particularly in less dense, non-urban communities. Availability of cellular networks and high-speed internet connectivity, along with significant reduction in cost, allows ongoing monitoring of decentralized treatment systems at a central location. In this paper a decentralized treatment system using micro nanobubble aeration, moving media and membranes is able to produce reusable water with low energy consumption, thereby allowing the use of solar energy in places with unreliable electrical supply. The treatment system, which uses no external chemicals, is able to operate unattended and deliver clear, disinfected water with non-detect suspended solids, BOD5 less than 10 mg/L and nutrients below 5 mg/L.

The outbreak of COVID-19 started in mainland China and has rapidly spread to more than 200 countries and territories around the world (WHO, 2020). The new coronavirus is a respiratory virus and its transmission is known to occur by upper respiratory secretions, including airborne droplets after coughing or sneezing. There is no evidence to support the role of food in the transmission of COVID-19. However, sharing food in public places is not encouraged. Furthermore, standard operating procedures of food safety like thorough washing, avoid cross contamination, keeping raw and cooked foods separated, refrigeration and heating are highly suggested. The pandemic has dramatically increased food insecurity across the countries and threatens the food security and nutrition of millions of people. The review presents information about SARS-CoV-2 with respect to food, including a brief history of coronavirus, its classification and transmission. It further presents the role of food as a carrier of the virus, which looks unlikely so far but cannot be fully ruled out, food contamination and handling followed by prevention and safety measures as per the standard guidelines, and conclusive remarks.

In this paper, we identify issues and possible solutions in the key area of large-scale antenna systems, also know as Massive Multiple Input Multiple Output (MIMO) systems. Additionally, we propose the use of Massive MIMO technology as a means to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), the capacity of the MTC network and, in turn, that of the whole system, can be increased by grouping Machine-Type Communication (MTC) devices into clusters and letting each cluster share the same time-frequency physical resource blocks. We study the feasibility of applying sub-optimal linear detection to the problem of detecting a large number of MTC devices sharing the same time-frequency resources at the uplink of a base station (BS) equipped with a large number of antennas, M. In our study, we derive the achievable lower-bound rates for the studied sub-optimal linear detectors and show that the transmitted power of each MTC device can be reduced as M increases, which is a very important result for powerconstrained MTC devices running on batteries. Our simulation results suggest that, as M is made progressively larger, the performance of sub-optimal linear detection methods approach the matched filter bound, also known as perfect interference-cancellation bound.

A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g. ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

Cyber Physical Systems (CPS) is the integration of computation and physical process that makes a complete system such as the physical components, networked systems, embedded computers and software and linking together of devices and sensors for information sharing. Cyber Physical Systems are Smart Systems that comprises of the merging and integration of Industry Control Systems, Critical Infrastructures, Internet of Things (IoT) and Embedded Systems. Major industries such as the Chemical and Industrial Plants, Aviation Systems, National Grid, the Stock Exchange, Military Systems, and others depends heavily on these Cyber Physical Systems for financial and economic growth. The benefits of CPS nationally and globally are in the areas of Manufacturing, Energy, Transport, Healthcare and Communication. Cyber Physical Systems incorporates Physical systems, Digital systems and Human elements on network infrastructures to provide interactive systems. However, these three key components the Physical systems, Digital systems and Human elements may have inherent threats and vulnerabilities on them that may run the risk of being compromise, exploited, attacked or hacked. Cybercriminals in their quest to bring down these systems and may cause disruption of services either for fame, revenge, political motive, economic war, cyber terrorism and cyber war. The study seeks to review the risks that are associated with these three key components Physical systems, Digital systems and Human elements. The study considered four main risk mitigation goals for this purpose, and these are Business Value, Organizational Requirements, Threat Agent and Impact based on the review results. We used Analytical Hierarchical Process (AHP) to determine the relative importance of these goals that contributes to developing cybercrime and rich in CPS. For the results, the prioritized goals are then used to assess the risks using a semi-quantitative approach to determine the net threat level.

Vehicle to vehicle (V2V) and Vehicle to infrastructure (V2I) or briefly V2X communications are the one of hot topics in automotive industry. Therefore, this situation is providing many advantages of connected vehicles and infrastructures which bring to human life. For instance, vehicles and road infrastructures which shares information with each other, provides a neat flow regulation, more ordered traffic flow and therefore jammed traffic dependent accident’s percentage will be decreased. On the other hand, security is the most important issue for these systems because the operation of V2X networks is completely dependent on uninterrupted and accurate information sharing. In the light of these information, in this paper we review security issues and current solution architectures. We also propose some open problems in this lively field.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

Damage to crops from wildlife interference is a common threat to food security among rural communities in or near Game Management Areas (GMAs) in Zambia. This study uses a two-stage econometric model and cross-sectional data from a survey of 2,769 households to determine the impact of land use planning on the probability and extent of wildlife-inflicted crop damage. The results show that crop damage is higher in GMAs as compared to non-GMAs, and that land use planning could be an effective tool to significantly reduce the likelihood of such damage. These findings suggest that there is merit in the current drive to develop and implement land use plans as means to minimize human-wildlife conflict such as crop damage. This is especially critical as Zambian conservation policies do not have an explicit provision for compensation in the event of damage from wildlife.

(1) Agricultural production is one of the most important, fundamental and resilient sectors. The excessive and incorrect use of agricultural pesticides has caused environmental pollution, with final destination in aquatic environments. Once in the aquatic environment, the decontamination process aimed at removing the pesticide is not always a simple task. There are expensive and low-efficiency procedures in the literature. In this sense, it is necessary to develop low-cost, sustainable procedures with high remediation capacity. Among the widely used agricultural pesticides, glyphosate stands out for weed control, which according to the World Health Organization (WHO) is potentially carcinogenic in humans. (2) In this context, a nanocellulose-based biopolymer was developed to recover glyphosate from aquatic environments. Different methodologies have been developed, the most interesting being membrane immersion in aquatic systems. The analytical technique for quantifying glyphosate was molecular spectrophotometry in the ultraviolet – visible range (UV-VIS); (3) Nanocellulose showed a value of 4.7 mg of Glyphosate per g of nanocellulose, and organomodified nanocellulose showed the removal of 6.1 mg of Glyphosate per g of nanocellulose, evaluated in pseudo-first order kinetic models; (4) The biomaterial has a sustainable and renewable origin and has potential for contaminant removal and can be applied to contaminated aquatic systems.