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The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI's role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI's potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI's potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness. [ABSTRACT FROM AUTHOR]

This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities. [ABSTRACT FROM AUTHOR]

The phenylpropanoid biosynthesis pathway is involved in the response of plants to stress factors, including microorganisms. This paper presents how free-living strains of rhizobacteria Pseudomonas brassicacearum KK5, P. corrugata KK7, Paenibacillus borealis KK4, and the symbiotic strain Sinorhizobium meliloti KK13 affect the expression of genes encoding phenylalanine ammonia-lyase (PAL), the activity of this enzyme, and the production of phenolic compounds in Medicago truncatula. Seedlings were inoculated with rhizobacteria, then at T0, T24, T72, and T168 after inoculation, the leaves and roots were analyzed for gene expression, enzyme activity, and the content of phenolic compounds. All bacteria affected PAL gene expression, in particular, MtPAL2, MtPAL3, and MtPAL4. Pseudomonas strains had the greatest impact on gene expression. The inoculation affected PAL activity causing it to increase or decrease. The most stimulating effect on enzyme activity was observed 168 h after inoculation. A varied effect was also observed in the case of the content of phenolic compounds. The greatest changes were observed 24 h after inoculation, especially with the KK7 strain. The influence of the studied rhizobacteria on the biosynthesis of phenolic compounds at the molecular level (expression of MtPAL genes) and biochemical level (PAL activity and content of phenolic compounds) was confirmed. The MtPAL3 gene underwent the most significant changes after inoculation and can be used as a marker to assess the interaction between M. truncatula and rhizobacteria. The Pseudomonas strains had the greatest influence on the biosynthesis pathway of phenolic compounds. [ABSTRACT FROM AUTHOR]

Drought is an increasingly critical global challenge, significantly impacting agricultural productivity, food security, and ecosystem stability. As climate change intensifies the frequency and severity of drought events, innovative strategies are essential to enhance plant resilience and sustain agricultural systems. This review explores the vital role of beneficial microbes in conferring drought tolerance, focusing on Plant Growth-Promoting Rhizobacteria (PGPR), mycorrhizal fungi, endophytes, actinomycetes, and cyanobacteria. These microorganisms mitigate drought stress through diverse mechanisms, including osmotic adjustment, enhancement of root architecture, modulation of phytohormones, induction of antioxidant defenses, and regulation of stress-responsive gene expression. Ecological and agricultural innovations leveraging these beneficial microbes have demonstrated significant potential in bolstering drought resilience. Strategies such as soil microbiome engineering, bioaugmentation, and the integration of microbial synergies within pest management frameworks enhance ecosystem resilience and agricultural sustainability. Additionally, advancements in agricultural practices, including seed coating, soil amendments, the development of microbial consortia, and precision agriculture technologies, have validated the effectiveness and scalability of microbial interventions in diverse farming systems. Despite promising advancements, several challenges hinder the widespread adoption of microbial solutions. Environmental variability can affect microbial performance, necessitating the development of robust and adaptable strains. Scale-up and commercialization hurdles, economic constraints, and regulatory and safety considerations also pose significant barriers. Furthermore, the complex interactions between microbes, plants, and their environments require a deeper understanding to optimize microbial benefits consistently. Future research should focus on integrating cutting-edge technologies such as genomics, synthetic biology, and precision agriculture to refine and enhance microbial interventions. Collaborative efforts among academia, industry, and government are essential to bridge the gap between research and practical implementation. By addressing these challenges and harnessing microbial innovations, it is possible to develop resilient and sustainable agricultural systems capable of thriving in an increasingly water-scarce world. [ABSTRACT FROM AUTHOR]

The maritime industry, central to global trade, faces critical challenges related to energy efficiency and environmental sustainability due to significant energy loss from waste heat in marine engines. This review investigates the potential of waste heat recovery (WHR) technologies to enhance operational efficiency and reduce emissions in marine systems. By analyzing major WHR methods, such as heat exchangers, Organic Rankine Cycle (ORC) systems, thermoelectric generators, and combined heat and power (CHP) systems, this work highlights the specific advantages, limitations, and practical considerations of each approach. Unique to this review is an examination of WHR performance in confined marine spaces and compatibility with existing ship components, providing essential insights for practical implementation. Findings emphasize WHR as a viable strategy to reduce fuel consumption and meet environmental regulations, contributing to a more sustainable maritime industry. [ABSTRACT FROM AUTHOR]

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä's Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI's potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber's fatal accident and Tesla's crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized. [ABSTRACT FROM AUTHOR]

The maritime industry, responsible for moving approximately 90% of the world's goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry's environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry. [ABSTRACT FROM AUTHOR]

The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Drought presents a critical challenge to global crop production, exacerbated by the effects of global warming. This study explores the role of rhizospheric bacteria (Bacillus, Pantoea, and Pseudomonas) in enhancing the drought resistance and nutrient absorption of strawberry plants. The experimental approach involved inoculating plant roots with various strains of rhizobacteria and assessing their impact under different water potential conditions in two substrates: optimal moisture and water deficit. The results showed significant changes in the nutrient content of strawberry plants, influenced by the type of bacterial strain and moisture conditions. Phosphorus and potassium content in the leaves varied considerably, with the highest levels observed in plants inoculated with specific bacterial strains under both optimal and water-deficit conditions. Similarly, calcium and magnesium content in the leaves also changed notably, depending on the bacterial strain and moisture level. The water deficit cluster, featuring the PJ1.1, DKB63, and DKB65 strains, showed PGPR's role in maintaining nutrient availability and plant resilience. The study demonstrates that inoculation with PGPR can markedly influence the nutrient profile of strawberry plants. These findings underscore the potential of using rhizobacteria to enhance crop resilience and nutritional status, especially in the context of increasing drought conditions due to climate change. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics. [ABSTRACT FROM AUTHOR]

Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook. [ABSTRACT FROM AUTHOR]

Simple Summary: The future of plant biology, particularly rapidly advancing precision horticulture and predictive breeding, will require the transformation of huge volumes of multi-omics data into structured information and valuable knowledge, representing a key challenge. This review aims to delve into the transformative potential of integrating multi-omics data and artificial intelligence (AI) for a more comprehensive, high-throughput approach to plant phenotyping in horticultural research. We argue that the union of these advanced techniques can provide a robust analytical framework that can handle the complexity of plant biology, thus surmounting the limitations of traditional phenotyping methods. Our discussion also acknowledges the technical and non-technical challenges associated with this integration, critically evaluating their advantages and limitations, proposing potential solutions, and outlining promising future prospects. This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations. [ABSTRACT FROM AUTHOR]

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security. [ABSTRACT FROM AUTHOR]

This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML's potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems. [ABSTRACT FROM AUTHOR]

The article describes a study of ship delays on the Świnoujście-Szczecin waterway observed by the VTS operator. The research has led to an understanding of the factors that affect delays of ships calling at the ports of Szczecin and Police, as well as the possibilities of predicting and preventing these delays. This article presents the results of the study on the traffic intensity on the investigated waterway and the process of identifying the port regulation that causes the most frequent delays. Based on the obtained results from the statistical analysis and from using multiple regression, a statistical model has been developed that has the ability to estimate expected delays. Additionally, the model has been expanded to calculate financial losses resulting from delays, taking into account the daily cost of maintaining the studied ships. The study took place during ongoing project "Modernization of the Świnoujście-Szczecin waterway to a depth of 12.5 m" but does not include delays resulting from this project. [ABSTRACT FROM AUTHOR]

An increasing number of scientists working to raise agricultural productivity see the potential in the roots and the soil adjacent to them, together with a wealth of micro-organisms. The first mechanisms activated in the plant during any abiotic or biotic stress concern changes in the oxidative status of the plant. With this in mind, for the first time, an attempt was made to check whether the inoculation of seedlings of the model plant Medicago truncatula with rhizobacteria belonging to the genus Pseudomonas (P. brassicacearum KK5, P. corrugata KK7), Paenibacillus borealis KK4 and a symbiotic strain Sinorhizobium meliloti KK13 would change the oxidative status in the days following inoculation. Initially, an increase in H2O2 synthesis was observed, which led to an increase in the activity of antioxidant enzymes responsible for regulating hydrogen peroxide levels. The main enzyme involved in the reduction of H2O2 content in the roots was catalase. The observed changes indicate the possibility of using the applied rhizobacteria to induce processes related to plant resistance and thus to ensure protection against environmental stress factors. In the next stages, it seems reasonable to check whether the initial changes in the oxidative state affect the activation of other pathways related to plant immunity. [ABSTRACT FROM AUTHOR]

One of the main causes of climate change is the emission of GHGs, and one of the sources for the generation of such gasses is agriculture via plant production. Considering the foregoing, a study was conducted to assess PGPRs in strawberry cultivation which were able to limit GHG emissions. The first experimental factor was the inoculation of plant roots with the Bacillus sp. strains DLGB3, DKB26, DKB58, and DKB 84; the Pantoea sp. strains DKB63, DKB64, DKB65, and DKB68; Azotobacter sp. AJ 1.2; and Pseudomonas sp. PJ 1.1. The second experimental factor constituted the different moisture levels of the growth substrate. In the experiment, emissions of NH3, CO2, N2O, and CH4 were measured. In light of the conducted research, five strains were selected (Azotobacter sp. AJ 1.2; Pantoea sp. DKB64, DKB63, and DKB68; and Pseudomonas sp. strain PJ 1.1) that showed the greatest potential for reducing GHG emissions depending on the prevailing environmental conditions. The application of the tested bacterial strains under different moisture conditions in the substrate either reduced or did not affect GWP. This research on PGPR, which was conducted to select strains of rhizosphere bacteria that would be able to reduce GHG emissions, may form the basis for creating an inoculum and can be employed as an effective strategy for mitigating certain abiotic stresses. [ABSTRACT FROM AUTHOR]

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions, and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences? [ABSTRACT FROM AUTHOR]

Due to the observed climate warming, water deficiency in soil is currently one of the most important stressors limiting the size and quality of plant crops. Drought stress causes a number of morphological, physiological, and biochemical changes in plants, limiting their growth, development, and yield. Innovative methods of inducing resistance and protecting plants against stressors include the inoculation of crops with beneficial microorganisms isolated from the rhizosphere of the plant species to which they are to be applied. The aim of the present study was to evaluate 12 different strains of rhizosphere bacteria of the genera Pantoea, Bacillus, Azotobacter, and Pseudomonas by using them to inoculate strawberry plants and assessing their impact on mitigating the negative effects of drought stress. Bacterial populations were assessed by estimates of their size based on bacterial counts in the growth substrate and with bioassays for plant growth-promoting traits. The physiological condition of strawberry plants was determined based on the parameters of chlorophyll fluorescence. The usefulness of the test methods used to assess the influence of plant inoculation with rhizosphere bacteria on the response of plants growing under water deficit was also evaluated. A two-factor experiment was performed in a complete randomization design. The first experimental factor was the inoculation of plant roots with rhizosphere bacteria. The second experimental factor was the different moisture content of the growth substrate. The water potential was maintained at −10 to −15 kPa under control conditions, and at −40 to −45 kPa under the conditions of water deficit in the substrate. The tests on strawberry plants showed that the highest sensitivity to water deficiency, and thus the greatest usefulness for characterizing water stress, was demonstrated by the following indices of chlorophyll "a" fluorescence: FM, FV, FV/FM, PI, and Area. Based on the assessment of the condition of the photosynthetic apparatus and the analysis of chlorophyll "a" fluorescence indices, including hierarchical cluster analysis, the following strains of rhizosphere bacteria were found to have favorable effects on strawberry plants under water deficit: the Bacillus sp. strains DLGB2 and DKB26 and the Pantoea sp. strains DKB63, DKB70, DKB68, DKB64, and DKB65. In the tests, these strains of Bacillus sp. exhibited a common trait—the ability to produce siderophores, while those of Pantoea sp. were notable for phosphate mobilization and ACCD activity. [ABSTRACT FROM AUTHOR]

The objective of the present study is to determine the differences between the mineral content of various organs of Helix vladika and H. secernenda, the two most abundant edible snail species in Montenegro. The bioaccumulation of 12 examined elements (zinc, manganese, copper, aluminum, cadmium, lead, nickel, iron, chromium, lithium, selenium and mercury) was determined in the hepatopancreas, albumen gland, digestive tract, reproductive system, mantle, foot and shell from three sampling sites (Biogradska Gora, Nikšić and Malesija). The examined populations of H. vladika and H. secernenda showed a significant difference in their lithium and selenium contents. The levels of the most examined metals (Zn, Mn, Cu, Al, Cd, Pb, Se and Hg) varied significantly among organs. The digestive tract and hepatopancreas tend to bioaccumulate selenium and cadmium. The general mean concentration of cadmium in the examined snail tissues exceeded the maximum allowable level at the Biogradska Gora and Malesija sites. Therefore, the use of the Montenegrin edible snails collected from the wilderness for human consumption seems to be limited by their higher bioaccumulation capacity for toxic elements such as cadmium. [ABSTRACT FROM AUTHOR]