Perpustakaan Digital

Yeasts are truly fascinating microorganisms. Due to their diverse and dynamic activities, they have been used to produce many interesting products, such as beer, wine, bread, biofuels, and biopharmaceuticals. Saccharomyces cerevisiae (bakers’ yeast) is likely the most human-exploited yeast species. Saccharomyces is a popular choice for industrial applications, and its use in beer production dates back to at least the 6th millennium BC. Bakers’ yeast represents a cornerstone of modern biotechnology, enabling the development of efficient production processes for antibiotics, biopharmaceuticals, technical enzymes, and ethanol and biofuels. Today, more diverse yeast species are being explored for industrial applications, e.g., the Saccharomyces cerevisiae, S. cerevisiae bayanus, Pichia pastoris (Komagataella phaffii), Saccharomycopsis fermentans, Wickerhamomyces anomalus, and Candida species. This instalment — “Yeast Biotechnology 6.0” — continues the “Yeast Biotechnology” Special Issue series of the MDPI journal Fermentation. This issue highlights the current prominent research directions in the fields of yeasts as a cell factory, yeast nanobiotechnology, wine yeasts and wine fermentation, yeasts and food fermentation, and biocontainment for yeast biotechnology.

Microcantilevers are typically rectangular-shaped bars, approximately 100–200 μm long, 20–40 μm wide, and 0.5–1 μm thick, and are made of silicon or silicon nitride. Their mechanical response is often described as a very soft spring. The static deformation of a cantilever allows for the detection of the smallest forces with unprecedented sensitivity, whereas the resonance frequency of its dynamic response can be used to measure extremely small masses or fluid properties. Cantilever-based sensors have received considerable interest in the last few decades, as they offer an unparalleled opportunity for the development of highly sensitive biophysical and chemical sensors, employed in a very wide spectrum of applications. These sensors have been widely utilized in electronics, automotive and aerospace systems, biophysics, environmental monitoring, and medical diagnosis sectors, among others. Their working principle is often based on the interaction between a micrometric cantilever and it

This Special Issue examines the intersection of NLP, semantic networks, and sentiment analysis, exploring how these technologies can contribute to more intelligent systems. The contributions gathered address, among other topics, the detection of irony and sarcasm, the generation of semantic explanations, the automatic construction of semantic graphs, the expansion of linguistic resources for underrepresented languages, and the use of large language models. Ultimately, this Special Issue aims not only to reflect the state of the art in the areas above but also to stimulate critical reflection on the methodological challenges, ethical implications, and interdisciplinary opportunities that arise at the intersection of computational linguistics and artificial intelligence.

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics. Related Books Trends and Challenges in Robotic Applications Engineering Advances in Industrial Robotics and Intelligent Systems Engineering Multi-Agent Systems Engineering Advances in Robotics and Autonomous Systems Engineering Modern Trends in Multi-Agent Systems Computer Science & Mathematics The recommendations have been generated using an AI system. About: About MDPI Books Advisory Board Legal Information: MDPI Privacy Policy Terms and Conditi

The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Specia

The study of polymers has been central to numerous scientific and technological advancements over the past several decades. The primary aim of this reprint is to present, in a concise format, recent developments in polymer theory and its applications, as contributed by experts actively engaged in the study of polymer phenomena across condensed matter physics, chemistry, biology, and engineering. The contributions emphasize theoretical and simulation-based approaches to polymers in complex environments, whether as individual chains or in multi-polymer systems such as melts. The applications span a wide range of materials, including block copolymers, polymer brushes, thermoplastic resins, carbon fiber-reinforced resins, gels, rubbers, cross-linked polyethylene, polyelectrolytes, and proteins.

The aim of the following reprint is to immerse the reader in the latest approach of Chemical Sensors for Volatile Organic Compound Detection. As VOCs are ubiquitous in multiple specific settings, such as confined spaces and chemical production processes, it is essential to achieve superior detection of VOCs, particularly in terms of lower detection limits of sub-ppm and shorter response time. The detection of volatile organic compounds (VOCs) is in high demand in various fields, such as environmental pollution monitoring, early disease screening, and food freshness assessment. A variety of methods, including spectroscopic analysis, mass spectrometry, chromatographic analysis, electrochemical gas sensors, infrared gas sensors, and semiconductor gas sensors, have been extensively used for VOC detection. In particular, semiconductor sensors have been extensively investigated to detect VOCs due to their high sensitivity, fast response time, and cost-effectiveness. Under the unremitting efforts of researchers, research on semiconductor gas sensors has made a major breakthrough. Articles included in this reprint focus on gas sensors with selective enhancement, lower power consumption, and fast response. The characterization and evaluation of sensing performance, and the completion of gas-sensitive mechanistic discussions of experimental phenomena are also included. This reprint serves as a detailed entry point for readers interested in exploring VOC detection.

The present reprint contains a total of 10 articles accepted and published in the Special Issue “Applied and Computational Mathematics for Digital Environments, 2nd Edition”. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: 1. Building mathematical, structural, and information models of intelligent computer systems for monitoring and managing the parameters of digital environments;2. Software and mathematical technologies in the implementation of the intelligent monitoring and computer control of digital environments’ parameters; 3. The application of mathematical models, Internet of Things technologies, machine learning, and artificial intelligence for big data analysis of digital environments; 4. Mathematical models and algorithms for identifying stable patterns between the parameters of digital environments and their complex and separate influence; 5. Mathematical modeling, machine learning, and their implementation within the concept of “smart” digital environments. I hope that this reprint will be useful to those who are interested in different aspects of applied and computational mathematics for digital environments in solving actual practical problems in all spheres of human life and activity.

This reprint is a printed edition of the Special Issue on Maritime Autonomous Vessels that was published in the Journal of Marine Science and Engineering. It contains an editorial and 17 peer-reviewed research studies in the field of Maritime Autonomous Surface Ships (MASS), Unmanned Surface Vessels (USVs), Autonomous Underwater Vehicles (AUVs), and underwater gliders, to name a few. The main goal of this reprint is to address key challenges, thereby promoting research on marine autonomous ships. There are many topics on autonomous vessels involved in this reprint, for instance, automatic control, manoeuvrability, collision avoidance, ship target identification, motion planning, and buckling analysis. Related Books Maritime Autonomous Surface Ships Engineering Autonomous Marine Vehicle Operations Computer Science & Mathematics ... Autonomous Marine Vehicle Operations Computer Science & Mathematics ... Safe Maneuvering, Efficient Navigation and Intelligent Management for Ships Comput

The aim of this special issue is to help readers, students, engineers, researchers in academia and in industry, and decision makers in the fields of autonomous vehicles and transportation to identify some of the most active and promising research directions in the last three years to improve their navigation and control in cities and other challenging environments using new technologies and solutions. This special issue, therefore, covers some of the latest advances in sensor fusion for vehicle navigation, safe and efficient path planning algorithms from advanced graph-based methods to machine learning and advanced topics such as the collaborative navigation and control of connected and increasingly autonomous vehicles. The advances in the control and power technologies for electric vehicles are also covered, as well as the human–machine interaction factors in vehicle navigation and control for different levels of vehicle autonomy.

This reprint discussed Coding and compression; Content storing and management; Electronic media; Internet technology; Image and video signals; Multimedia processing; Quality of experience (QoE); Quality of service (QoS); Signal processing; Streaming services; Subjective and objective metrics; User experience (UX). Related Books Digital Signal, Image and Video Processing for Emerging Multimedia Technology Engineering Image and Signal Processing Engineering Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods Engineering Image and Video Processing and Recognition Based on Artificial Intelligence (Volume II) Engineering Image and Video Processing and Recognition Based on Artificial Intelligence Engineering The recommendations have been generated using an AI system. About: About MDPI Books Advisory Board Legal Information: MDPI Privacy Policy Terms and Conditions Terms of Use Accessibility Sales: Order and Delivery Indexing and Distrib

This Reprint showcases the latest breakthroughs in the design and optimization of heat exchanger networks and thermal energy systems. It emphasizes innovative strategies aimed at enhancing energy efficiency in industrial processes through modern modeling techniques, thermodynamic evaluations, and retrofitting methods. The contributions include advancements in digital tools for process integration, analyses related to exergy–pinch, and strategies for recovering heat from electrified and decarbonized systems. Several case studies illustrate real-world applications, such as the incorporation of heat pumps, effective waste-heat utilization, and hybrid systems that support the transition to sustainable energy. It provides essential insights for engineers, researchers, and decision-makers focused on minimizing carbon emissions and operational costs while promoting sustainability in thermal energy systems.

At present, data is constantly generated across various industries, including the internet. New technologies have emerged to trace data origins and assess their potential for collection, quantification, decoding, and analysis. Big data, signals, and images are of particular importance due to the wealth of domain-specific information they hold. These data play a critical role in addressing issues like national security, cybersecurity, marketing, medical informatics, and fraud detection. Deep learning techniques have gained immense popularity. They empower the analysis and understanding of vast amounts of unsupervised data, making them invaluable for processing when raw data lack categorization and labels. These applications extend to working with medical images and signal processing for wellness devices, remote monitoring, and neural devices. In industrial settings, data can be used for early warning systems in assembly lines, while massive datasets can be derived from electronic heal

In the field of magnetic sensing, a wide variety of different magnetometer and gradiometer sensor types, as well as the corresponding read-out concepts, are available. Well-established sensor concepts such as Hall sensors and magnetoresistive sensors based on giant magnetoresistances (and many more) have been researched for decades. The development of these types of sensors has reached maturity in many aspects (e.g., performance metrics, reliability, and physical understanding), and these types of sensors are established in a large variety of industrial applications. Magnetic sensors based on the magnetoelectric effect are a relatively new type of magnetic sensor. The potential of magnetoelectric sensors has not yet been fully investigated. Especially in biomedical applications, magnetoelectric sensors show several advantages compared to other concepts for their ability, for example, to operate in magnetically unshielded environments and the absence of required cooling or heating sys

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such a

As the global climate continues to change, there is an increasing need to develop climate-resilient food crops to feed the growing population. As such, research in plant stress biology is expanding, and systems biology approaches involving plant physiology, biochemistry, ‘omics’ and ‘multi-omics’ methodologies are shedding light on the complex mechanisms underlying plant responses to abiotic stresses. All this knowledge could contribute to crop improvement programs to increase global food security under changing environments. Therefore, this Reprint consists of 13 publications that expand our understanding of plant responses to a range of abiotic stresses using ‘omics’ and ‘multi-omics’ methods. Such studies are critical as agricultural research is focused on developing climate-resilient food crops.

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc. Related Books Research on Fuzzy Logic and Mathematics wit

This Reprint collects peer-reviewed articles from the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications. The Reprint focuses on recent advances in cognitive radio networks, which aim to address spectrum scarcity and improve spectrum utilization through dynamic spectrum access and intelligent decision making. The contributions reflect several important research trends in the field, including the integration of machine learning and artificial intelligence for spectrum sensing, spectrum access, and spectrum management, as well as the interaction of cognitive radio networks with programmable networking architectures. The accepted papers cover topics such as spectrum sensing and sharing, spectrum learning and prediction, machine learning techniques for cognitive radio networks, energy harvesting cognitive networks, and signal processing methods based on high-order cumulants. The studies also explore practical applications and system implementations related to cooperative spectrum sensing, interference detection, adaptive spectrum allocation, communication reliability, and efficient spectrum utilization. Overall, this Reprint provides a concise overview of recent developments and challenges in cognitive radio networks and offers useful insights for researchers and practitioners working on spectrum-efficient wireless communication technologies.

The aim of this Special Issue is to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. The main goals are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability. Research areas in this Special Issue include digital twin technology, multimodal data recognition, sensor data ingestion and real-time processing, multi-objective path-planning, conditional generative adversarial network, generating job recommendations, comprehensive risk assessment, large language models, self-supervised key-point learning, trustworthy article ranking, engine optimization model, and bioinspired generative design. These powerful and intelligent algorithms use data for control, decision making, and parameter optimization, driving industrial systems to unprecedented levels of efficiency, reliability, and adaptability. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of data-driven intelligent modeling and optimization algorithms for industrial processes, providing readers with valuable ideological inspiration in the field.

This reprint collects the papers published on the special issue “Kinematics and Robot Design V, KaRD2022”, which is the fifth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2022, after the peer-review process, accepted 13 papers. The accepted papers cover some theoretical and many design/applicative aspects. Related Books Kinematics and Robot Design VI, KaRD2023 Computer Science & Mathematics ... Kinematics and Robot Design IV, KaRD2021 Engineering Kinematics and Robot Design I, KaRD2018 B

The book is reprint version of the Special Issue entitled Research on the Regulatory Mechanisms of Algae Reproduction under Abiotic Stress Conditions, highlighting novel findings that significantly contribute to the development of our understanding of how abiotic stress-inducible reproduction is regulated by physiological responses including the life cycle trade-off. Related Books Exploring Abiotic Stress in Plants Biology & Life Sciences Molecular Mechanisms in Plant Stress Tolerance Biology & Life Sciences Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants Biology & Life Sciences ... Plant Physiology under Abiotic Stresses Biology & Life Sciences Abiotic Stresses in Plants: From Molecules to Environment Biology & Life Sciences The recommendations have been generated using an AI system. About: About MDPI Books Advisory Board Legal Information: MDPI Privacy Policy Terms and Conditions Terms of Use Accessibility Sales: Order and Delivery Indexi

The following Reprint of the Special Issue “Flexible and Wearable Sensors: Design, Fabrication Methods, and Applications” collates scientific contributions highlighting the expanding role of flexible and wearable technologies in sensing, monitoring, and human–machine interaction. The included studies demonstrate how advances in material science, structural design, and fabrication methods have enabled the development of sensors that are flexible, stretchable, biocompatible, and conformable, thereby transforming applications across healthcare, rehabilitation, sports, and interactive systems. Contributions cover experimental investigations of novel material systems, innovative structural designs, and advanced fabrication techniques. Emphasis is placed on diverse transduction mechanisms—piezoresistive, capacitive, piezoelectric, iontronic, triboelectric, and optical fiber sensing—in addition to practical applications in cardiac monitoring, motion detection, self-powered energy harvesting, tactile sensing, and wearable musical expression. The Reprint also showcases interdisciplinary approaches that combine wearable sensors with emerging technologies such as artificial intelligence, knowledge graph-based design, time-reversal algorithms, and wireless body area networks, while addressing challenges related to long-term stability, biocompatibility, and scalable manufacturing.

Mathematical modeling is an active area of applied mathematics. At its beginning, engineers were the main practitioners of this area of mathematics, developing mathematical models to solve engineering problems in natural sciences.However, analysis methods and models in social sciences are similar to those of nature sciences, including engineering, with the only difference being that instead of using principles of the nature, one uses principles or theories from experts of such social sciences.Models based on ordinary or partial differential equations describe a wide variety of phenomena such as sound, heat, diffusion, electrostatics, electrodynamics, fluid dynamics, elasticity, or quantum mechanics, for example. Further, stochastic models have recently received increasing attention. Obviously, some of these types of complex problems also require a deep analysis of the tools utilized to solve these situations.In this collection, we will attempt to integrate models, methods, and also a

This Special Issue sheds light on the remarkable resilience of plants as they confront a wide array of abiotic stresses, such as drought, extreme temperatures, high salinity, waterlogging, and nutrient imbalances, among other challenges. By delving into the molecular intricacies, this collection offers a comprehensive journey from nucleic acids to proteins, from cellular responses to tissue-level adaptations, and from individual plants to the broader ecosystem. Through this exploration, we can unveil fascinating, yet unexplored, natural laws and interplays. The research presented in this Special Issue not only enhances our understanding of how plants respond to environmental changes but also offers valuable insights into mitigating the impacts of climate change on agriculture and ecosystems. The collection encompasses a diverse range of research articles and comprehensive reviews, providing a deep dive into the molecular essence of plant responses to various environmental cues. Finally, the special issue serves as an invaluable resource for researchers, scientists, and practitioners seeking to expand their knowledge of plant resilience and adaptability in the face of current environmental challenges.