This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

Our concern was to obtain a biocomposite material with improved properties of the constituent materials (poly(lactic- co-glycolic acid) (PLGA) and zinc–boron (Zn–B) complex) in accordance with the novelties in the field of delivery systems for therapeutic agents which lately redefine the importance of biopolymer nanocomposites with PLGA (biodegradable composites). The advantages of such a biomaterial target the health system, being easy to obtain, through a cost-effective method. The water/oil/water double emulsion method also allows the adjustment of the synthesis parameters, to maximize the degree of Zn–B complex encapsulation. The morphological aspects of the samples (size, shape, porosity) were established by scanning electron microscopy (SEM). Particle size distribution (by volume and by number) was determined by direct light scattering (DLS). For all the synthesized materials, the observed morphology was typical for PLGA, spherical one. The particle size distribution showed that depending on the synthesis conditions, the particles can be obtained with diameters between 10–450 nm range and the value of the zeta potential (ZP) shows that the particles have electronegative surface charge, which offers a favorable perspective on the phenomena of aggregation, flocculation, dispersion. It was observed, applying the design of experiments (DoE), that the size of the particles increased with increasing amounts of PLGA and polyvinyl alcohol (PVA) in the formulation, while ZP increased with higher PLGA and smaller PVA. The encapsulation efficiency was determined by ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS).

In this paper, we investigate the cumulative historical entropy of the lifetime of an n-component coherent system under the assumption that all system components have failed at time t. Using the system signature, we can calculate the cumulative historical entropy of the system’s lifespan, which is an important aspect in determining a system’s predictability. This research produces a number of significant results, including expressions, bounds, and ordering aspects for this measure. In addition, we offer a method for finding a preferred system based on cumulative Kullback-Leibler discriminating information that is closely related to the parallel system. These findings provide important insights into the predictability and dependability of coherent systems, emphasizing the importance of this field.

In complex systems constrained by multiple factors, it is of great significance to accurately identify the key influencing factors for mastering the evolution and development law of the system and obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; the method based on decision trial and Evaluation laboratory (DEMATEL) involves a wide range of subjects and is greatly influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. The model uses the FCI algorithm with prior knowledge to learn the global causal network among multiple factors of the complex system. The causal effect among variables in the causal network is calculated by using heuristic causal inference method. Specifically, the causal path contribution degree of cause variable to target variable is calculated to replace the causal effect of each cause variable to target variable. The key influencing factors in the system are screened out according to the contribution degree of causal pathways. Based on the dataset generated in the production process of a semiconductor manufacturing system, we carried out simulation experiments, identified several factors that have a key impact on product quality, and proved the feasibility and effectiveness of the proposed model.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid bat-tery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is of particular importance because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system perfor-mance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, and cost-effective energy storage op-tion. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. In this work, battery-supercapacitor storage system (HBSS) is established by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that ef-fectively controls the different power sources. The algorithm's purpose is to regulate the charge and discharge cycles of the HBSS, ensuring that the state of charge (SOC) of both batteries and supercapacitors remains within the desired range. The proposed management algorithm is de-signed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. By maintaining the SOC of these energy storage components within the specified limits, the proposed method ensures their longevity and max-imizes their performance. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

Encapsulated nanofibers have emerged as a promising approach for the treatment of acne, owing to their ability to provide controlled release, targeted delivery, increased efficacy, and improved stability. Electrospinning is a well-established method for producing encapsulated nanofibers and has been shown to be effective for encapsulating various active ingredients. However, there are still several challenges that need to be addressed in the development of encapsulated nanofibers for acne treatment. One major challenge is the need for comprehensive in vitro and in vivo studies to evaluate the safety and efficacy of these treatments. The cost and scalability of production also need to be considered to make these treatments accessible and affordable for patients. In addition, the long-term stability of encapsulated active ingredients is another challenge in the development of encapsulated nanofibers for acne treatment. Regulatory frameworks need to be developed to ensure the safety and efficacy of these treatments. Future research may focus on developing multifunctional nanofibers that combine active ingredients with other properties, such as antimicrobial, anti-inflammatory, and wound-healing properties, to provide a comprehensive approach to acne treatment. Moreover, the development of nanofiber-based skincare products may have a significant impact on the cosmetic industry. Overall, while there are still challenges to overcome, the potential benefits of encapsulated nanofibers for acne treatment make them an exciting and promising area of research for the future. In particular, the integration of smart drug delivery systems and responsive materials may enable the development of more personalized and effective treatments for acne. The development of new materials and encapsulation techniques, as well as the exploration of combination therapies that target multiple aspects of acne pathogenesis, are also future perspectives for encapsulated nanofibers in acne treatment.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

This paper is concerned with variational methods for open quantum systems with Markovian dynamics governed by Hudson-Parthasarathy quantum stochastic differential equations. These QSDEs are driven by quantum Wiener processes of the external bosonic fields and are specified by the system Hamiltonian and system-field coupling operators. We consider the system response to perturbations in these operators and introduce a transverse Hamiltonian which encodes the propagation of the perturbations through the unitary system-field evolution. This approach provides an infinitesimal perturbation analysis tool which can be used for the development of optimality conditions in quantum control and filtering problems. Such settings employ, as performance criteria, quadratic (or more complicated) cost functionals of the system and field variables to be minimised over the energy and coupling parameters of system interconnections. We demonstrate an application of the transverse Hamiltonian variational approach to a mean square optimal coherent quantum filtering problem for a measurement-free field-mediated cascade connection of a quantum system with a quantum observer.

Diffusion in recent decades of Cannabis sativa L. varieties with low concentrations of tetrahydrocannabinol (THC) is leading to a specialization in the whole sector, requiring innovative techniques for input optimization according to the variety and the growing environment. The continuous agricultural evolution aims at increasing the sustainability of cultivation systems, pushing toward precision technologies application for inputs management. Cannabis monitoring can benefit from Unmanned Aerial Systems applications combined with image thresholding techniques for reliable and effective near-real-time plant detection and numbering. The work compares and evaluates the potential of two threshold segmentation techniques for Cannabis plant detection and counting in two experimental fields in Italy on a multitemporal scale, bringing such techniques in competition with machine learning for object detection. The Otsu segmentation technique demonstrated more reliable performances at the early stage of cultivation with an accuracy of 0.95. The Canopy Height Model technique showed increasing performances during the growing season. Future works will compare thresholding segmentation techniques with machine learning (ML) approaches and their potential as a supporting tool for ML image annotation.

Dental-implant-supported reconstructions provide comfort and improvements in prosthetic function, adaptation, and stability over conventional treatment options. The objective of this study was to evaluate the effect of multiple cleaning solutions on dentures and their influence on the deterioration and loss of retention of overdenture attachments in a 12-month clinical-use simulation. In this way, four different brands of retentive caps made of Teflon (OT Equator, Locator®, Kerator®, and Locator R-Tx®) were immersed in five different cleaning solutions (Kukident®, Benfix®, Corega®, and Protefix®), and tap water was used as the control group, in a simulation that lasted 12 months. Data were analyzed using two-way ANOVA and a Tukey HSD. Furthermore, a Levene Test and Shapiro–Wilk tests were performed to assess the validation of the ANOVA assumptions. The statistical analysis was performed using R version 4.2.2 software with the significance level set to p < 0.05. There were significant statistical differences between the different manufacturers on the retention forces of the attachment’s retentive caps. For the cleaning solution groups, different statistical results between Kukident® and Benfix® were observed. There were no significant statistical differences between Corega®, Protefix®, and tap water despite the fact that the retention forces decreased in all of them.

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Maritime transport has become very important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport, with the aim of decarbonizing its impact on the environment. In order to improve the economy and people's lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from GPS, and security through AIS. This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo, as well as maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented.

Since the process of waste recycling generates dust and flammable gas during fragmentation, there is always a risk of fire resulting therefrom, and fire does, in fact, frequently occur. However, research on disaster management at recycling facilities deals only with the problem of processing systems from a technical point of view, but it does not suggest concrete alternatives from a management point of view. Therefore, in this study, we analyzed the influence of the disaster response network of a waste recycling center at the organizational level based on the concept of the cognitive accuracy of a network when considering administrative aspects. Through this analysis, we confirmed that factors affecting the influence of the network exist, such that the entire network and the networks of different levels of position are different. We suggest that this can be improved by deploying members who perform formal tasks at the center of the network so that everyone can agree political approach.

Human gut microbiome is associated with various diseases, including autism spectrum disorders (ASD). Variations of the taxonomical composition in the gut microbiome of children with ASD have been observed repeatedly. However, features and parameters of the CRISPR-Cas systems in the gut microbiome of children with ASD have not been investigated yet. Here we demonstrate such an analysis in comparison with the healthy microbiome. For the identification of CRISPR-Cas systems, we used a combination of the publicly available tools suited for completed genomes with subsequent filtrations. In all considered datasets, the microbiomes of children with ASD contained fewer arrays per Gb of assembly, than the control group, but the arrays included more spacers on average. These patterns were observed systematically in our datasets, although their statistical significance hardly matched the thresholds. CRISPR arrays from the microbiomes of children with ASD differed from the control group neither in the fractions of spacers with protospacers from known genomes, nor in the sets of known bacteriophages providing protospacers. The majority of bacterial protospacers of the gut microbiome systems for both children with ASD and the healthy ones was located in the prophage islands.

Let $a, b$ and $n > 1$ be three positive integers such that $a$ and $\sum_{j=0}^{n-1} b^j$ are relatively prime. In this paper, we prove that the toric ideal $I$ associated to the submonoid of $\mathbb{N}$ generated by $\{\sum_{j=0}^{n-1} b^j\} \cup \{\sum_{j=0}^{n-1} b^j + a\, \sum_{j=0}^{i-2} b^j \mid i = 2, \ldots, n\}$ is determinantal. Moreover, we prove that for $n > 3$, the ideal $I$ has a unique minimal system of generators if and only if $a < b-1$.

Innovative practices in irrigation systems can bring improvements in terms of economic efficiency and in the same time can reduce environmental impact. Investment in high tech technologies frequently involves additional costs, but an efficient water management can increase the lifetime of the equipment. The main objective of this article is to reduce the energy consumption by one thousand cubic meters pumped and automatically to increase the economic efficiency of the pumping groups. This paper develops a new operating algorithm that ensures the operation of the pumping group at safe operating intervals and in the same time identifies the equivalent pump operating points for the entire flow range and pumping height of the pumping group. This methodology is based on the principles of an Expert System to perform the optimization process of the energy consumption in pumping groups. The resulting methodology avoids the combinatorial explosion of the solutions to be analyzed and determines the point of maximum efficiency without violation of any of the system constraints under any operating condition. The proposed methodology is tested on an irrigation system that includes a pumping group with 5 pumps, showing its effectiveness in obtaining the optimal solution with a relatively low computational burden.

This article has been developed to assess the economic feasibility of a roof-top photovoltaic installation of industrial self-consumption. Numerical models that enable an interested person to obtain the main expected parameters will be generated. To do this, a calculation methodology will be generated through which the reader, knowing the location of the facility and dimensions of the roof, will be able to calculate the maximum installable power, the main parameters related to production, the cost of the installation, and the LCOE of the plant. The use of actual costs will be facilitated in case they are known, but it will remain possible to apply the costs of the major equipment (modules, inverter, and structure) considered throughout the article. This developed calculation methodology will also allow a quick comparison of the forecasts of production, CAPEX, and LCOE of plants designed with different inclinations and different types of modules. Consequently, it will be especially useful for decision-making before developing the plant's basic engineering. Moreover, the calculations used for modeling the LCOE will be analyzed in depth. This analysis will allow evaluating how the different technical variables affect the profitability of a photovoltaic installation, such as the selected tilt, the location, the module's technology, or the available area.

Obstacle-avoidance robots have become an essential field of study in recent years. This paper analyzes two cases that extend reactive systems focused on obstacle detection and its avoidance. The scenarios explored get data from their environments through sensors and generate information for the models based on artificial intelligence to obtain a reactive decision. The main contribution is focused on the discussion of aspects that allow comparing both approaches, such as the heuristic approach implemented, requirements, restrictions, response time, and performance. The first case presents a mobile robot that applies fuzzy logic to achieve soft turning basing its decision on depth image information. The second case introduces a mobile robot based on multi-layer perceptron and ultrasonic sensors to decide how to move in an uncontrolled environment. The analysis of both options offers perspectives to choose between reactive obstacle-avoidance systems based on ultrasonic or Kinect sensors, models that infer optimal decisions applying fuzzy logic or artificial neural networks, with key elements and methods to design mobile robots with wheels. Therefore, we show how AI or Fuzzy Logic techniques allow us to design mobile robots that learn from their “ experience ” by making them safe and adjustable for new tasks, unlike traditional robots that use large programs to perform a specific task.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

The work focused on the development of high-temperature electrical insulation coatings for film photovoltaics. The idea was into replacing the electroconductive metal dispersed phase in siloxane high-temperature coating to ceramic particles with phonon thermal conductivity. The slurry of industrial composition based on polysiloxane lacquer and thermally conductive paste containing zinc oxide was centrifuged to obtain a thin, optically transparent coating with the destruction temperature of over 600 °C. Topology, electrical properties, and thermal conductivity of the resulting film were investigated. The mathematical model of thermal processes in films in the course of heating was figured out. Quantitatively the relation of thermal conductivities of a control sample and a sample with a heat-conducting filler was established. The effectiveness of using this technology is shown.

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

Nanoparticle (NP) transport is increasingly relevant to subsurface engineering applications such as aquifer characterization, fracture electromagnetic imaging and environmental remediation. An efficient field-scale simulation framework is critical for predicting NP performance and designing subsurface applications. In this work, for the first time, a streamline-based model is presented to simulate NP transport in field-scale subsurface systems. It considers a series of behaviors exhibited by engineered nanoparticles (NPs), including time-triggered encapsulation, retention, formation damage effects and variable nanofluid viscosity. The key methods employed by the algorithm are streamline-based simulation (SLS) and an operator-splitting (OS) technique for modeling NP transport. SLS has proven to be efficient for solving transport in large and heterogeneous systems, where the pressure and velocity fields are firstly solved on underlying grids using finite-difference (FD) methods. After tracing streamlines, one-dimensional (1D) NP transport is solved independently along each streamline. The adoption of OS enhances flexibility for the entire solution procedure by allowing different numerical schemes to solve different governing equations efficiently and accurately. For the NP transport model, an explicit FD scheme is used to solve the advection term, an implicit FD scheme is used for the diffusion term and an adaptive numerical integration is used to solve the retention terms. The model is implemented in an in-house streamline-based code, which is verified against analytical solutions, a commercial FD reservoir simulator (ECLIPSE) and an academic FD colloid transport code (MNMs). For a 1D homogeneous case, the effluent breakthrough curves (BTC) produced by the in-house simulator are in good agreement with the analytical solution and MNMs, respectively. For a two-dimensional (2D) heterogeneous case, the BTC and concentration pattern of the in-house simulator all match well with the solution produced by commercial simulator. Simulations on a synthetic three-dimensional (3D) nanocapsule application engineering design case, are performed to investigate the effect of fluid and NP properties on the displacement pattern of an existing subsurface fluid.

The construction of molecular robotic-like objects that imitate living things is an important challenge for current chemists. Such molecular devices are expected to perform their duties robustly to carry out mechanical motion, process information, and make independent decisions. Dissipative self-organization plays an essential role in meeting these purposes. To produce a micro-robot that can perform the above tasks autonomously as a single entity, a function generator is required. Although many elegant review articles featuring chemical devices that mimic biological mechanical functions have been published recently, the dissipative structure, which is the minimum requirement, has not been sufficiently discussed. This article aims to show clearly that dissipative self-organization is a phenomenon involving autonomy, robustness, mechanical functions, and energy transformation. Moreover, the author details the recent experimental results of an autonomous light-driven molecular device that achieves all of these features. In addition, a chemical model of cell-amplification is also discussed to focus on the generation of hierarchical movement by dissipative self-organization. By reviewing this research, it may be perceived that mainstream approaches to synthetic chemistry have not always been appropriate. In summary, the author proposes that the integration of catalytic functions is a key issue for the creation of autonomous microarchitecture.

Chemistry has been viewed as one of the most fruitful near-term applications to science of quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Much less effort has been placed on how one finishes these calculations by using the results from the quantum computer (on the active region of the molecule) and embeds them back into the remainder of the molecule in order to determine the properties of the entire molecule. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with classical algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.

In recent years, farmers and policymakers have faced ample challenges and have struggled to support the sustainability of the agricultural sector. Sustainable agriculture encompasses multiple concepts, and its performance produces extensive debate about data requirements, appropriate indicators, evaluation methods, and tools. Under the European Union (EU) financed project FLINT (Farm Level Indicators for New Topics in policy evaluation), detailed data have been collected at the farm level to provide broader coverage of sustainability indicators on a wide range of relevant topics to facilitate the assessment of sustainability performance. The approach has been applied in a pilot network of representative farms at the EU level, considering the heterogeneity of the EU farming sector to provide data infrastructure with up to date information for sustainability indicators. This study aims to assess sustainability performance at the farm level in Greece. Representative and dominant agricultural systems, such as permanent crops, olive trees, arable crops, and livestock (sheep) farms, comprise the Greek sample. It uses the analytical hierarchy process (AHP) methodology and attempts to gain insights into the sustainability performance of agricultural systems. The outcome of the sustainability assessment reveals knowledge and develops support for strategic farm choices in order to support both farmers and policymakers towards more sustainable development plans. The results indicate that three typical Mediterranean farming systems, like permanent crops, olive trees, and extensive livestock systems (sheep farms), are more sustainable in contrast to intensive and arable crop farms.

A good recommender system can infer customers’ preferences based on their historical purchase records, and recommend products that the customers may be interested in, saving them a lot of time and energy. For enterprises, it is difficult to recommend accurately to each customer, and the bad recommendation may be counterproductive. Customer loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. A hypothesis is proposed in this study: if companies can divide customers into different groups based on customer loyalty, the recommendation effect on certain groups is better than that on overall customers. In this study, customer loyalty is measured by four features of the RFML model. All customers are viewed as points on a four-dimensional space, which are clustered by the k-means model. Two recommendation algorithms based on complex networks are tested: recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and recommendation algorithm based on a single vertex set network and DeepWalk (SVDW). The experimental results show that customer loyalty has improved the effectiveness of the two algorithms over 14%, and the recommendation effect is the best on customer groups with a loyalty level of 4 (the highest level is 5). The recommendation algorithms with customer loyalty are better than using them alone.

In this study a new approach for planning Micro-Catchment Water Harvesting (M.C.W.H.) systems for irrigation in semi-arid regions such as the Aegean islands, is presented. M.C.W.H. is a cheap solution for constructing irrigation infrastructure with zero energy cost in regions where water is scarce. The proposed approach introduces simple linear relationships for estimating the annual volume of water V_s collected mainly from the CA (Contributing Area), stored in the root zone (Infiltration Basin, IB), according to the annual rainfall and runoff depths, after having determined the ratio of areas of micro-catchment (MC) components i.e. λ= Α_CA/Α_ΙΒ and its whole area A_MC This procedure was applied in Paros island of the Cyclades complex in the middle of the Aegean sea in east Mediterranean. Besides, income-cost analysis was performed via NPV method for almond, peach and apricot trees. The new approach was proved versatile and easy to use. Besides, the investment turned out to be advantageous two years after the MCs construction.

Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

We experimentally demonstrate, for the first time, blind nonlinearity compensation using the Density-Based Spatial-Clustering of Applications with Noise (DBSCAN) algorithm for a 40-Gb/s 16-quadrature amplitude modulated being transmitted at 50 km of standard single-mode fiber. At high launched optical powers, DBSCAN offers up to 0.83- and 8.84-dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalization, respectively.

Ocean wave energy conversion as one of the renewable clean energy sources is attracting the research interests of many people. This review introduces different types of power take-off technology of wave energy converters. The main focus is the linear direct drive power take-off devices as they have the advantages for ocean wave energy conversion. The designs and optimizations of power take-off systems of ocean wave energy converters have been studied from reviewing the recently published literature. Also, the simple hydrodynamics of wave energy converters have been reviewed for design optimization of the wave energy converters at specific wave sites. The novel mechanical designs of the power take-off systems have been compared and investigated in order to increase the energy harvesting efficiency.

Radio Frequency Identification (RFID) devices use radio waves to relay identifying information to an electronic reader using low-cost RFID Tag. RFID is expected to replace the conventional bar-code identification system due to its advantage like real-time recognition of a considerable number of objects. However, in RFID systems an attacker can get the tag that may lead to various security threats, and the limited computation power of RFID tags can cause delay. Lightweight authentication protocols proposed using cryptographic algorithms (one-way hash function, symmetric key encryption/decryption, and exclusive-OR) in order to cope with these problems. One such lightweight cryptographic protocol has been presented by Gope and Hwang using RFID systems. However, it analyzed in this article that their protocol is infeasible and vulnerable to Collision Attack, Denial-of-service (DoS), and Stolen verifier Attacks. A realistic, lightweight authentication protocol has been presented in this article to ensure protection against the mentioned attacks for IoT based RFID system. The proposed protocol has been formally analyzed using BAN logic and ProVerif as well as also analyzed informally using security requirement. The results show that the proposed protocol outperforms the existing protocols not only in security enhancements but also in terms of computation and communication complexity. Furthermore, the proposed protocol has also been analyzed for storage complexity.

A robust guaranteed cost preview repetitive controller is proposed for a class of polytopic uncertain discrete-time systems. In order to improve the tracking performance, the repetitive controller combined with preview compensator is inserted in the forward channel. By using the L-order forward difference operator, an augmented dynamic system is constructed. Then, the guaranteed cost preview repetitive control problem is transformed into the guaranteed cost control problem for the augmented dynamic system. For given performance index, the sufficient condition of asymptotic stability for the closed-loop system is derived by combining parameter-dependent Lyapunov function method with linear matrix inequality (LMI) techniques. By incorporating the controller obtained into the original system, the guaranteed-cost preview repetitive controller is derived. A numerical example is also included to show the effectiveness of the proposed method.

Glioblastoma (GBM), the most common and aggressive primary brain tumor in adults, remains one of the least treatable cancers. Current standard of care—combining surgical resection, radiation, and alkylating chemotherapy—results in a median survival of only 15 months. Despite decades of investment and research into the development of new therapies, most candidate anti-glioma compounds fail to translate into effective treatments in clinical trials. One key issue underlying this failure of therapies that work in pre-clinical models to generate meaningful improvement in human patients is the profound mismatch between drug discovery systems—cell cultures and mouse models—and the actual tumors they are supposed to imitate. Indeed, current strategies that evaluate the effects of novel treatments on GBM cells in vitro fail to account for a wide range of factors known to influence tumor growth. These include secreted factors, the brain’s unique extracellular matrix, circulatory structures, the presence of non-tumor brain cells, and nutrient sources available for tumor metabolism. While mouse models provide a more realistic testing ground for potential therapies, they still fail to account for the full complexity of tumor-microenvironment interactions, as well as the role of the immune system. Based on the limitations of current models, researchers have begun to develop and implement novel culture systems that better recapitulate the complex reality of brain tumors growing in situ. A rise in the use of patient derived cells, creative combinations of added growth factors and supplements, may provide a more effective proving ground for the development of novel therapies. This review will summarize and analyze these exciting developments in 3D culturing systems. Special attention will be paid to how they enhance the design and identification of compounds that increase the efficacy of radiotherapy, a bedrock of GBM treatment.

The apparent metal-insulator transition (MIT) in two-dimension (2D) was discovered by Kravchenko et al. [1] more than two decades ago in strongly interacting 2D electrons residing in a Si-metal-oxide-semiconductor field-effect transistor (Si-MOSFET). Its origin remains unresolved. Recently, low magnetic field reentrant insulating phases (RIPs), which dwell between the zero-field (B=0) metallic state and the integer quantum Hall (QH) states where the Landau-level filling factor υ > 1, have been observed in strongly correlated 2D GaAs hole systems with large interaction parameter rs (~20-40) and high purity. A new complex phase diagram was proposed, which includes zero field MIT, low magnetic field RIPs, integer QH states, fractional QH states, high field RIPs and insulating phases (HFIPs) with υ < 1 in which the insulating phases are explained by the formation of Wigner crystal. Furthermore, evidences of new intermediate phases were reported. All contribute to the further understandings of the puzzle. This review article serves the purpose of summarizing those recent experimental findings and theoretical endeavors, to foster future research efforts.

Carbon concrete PAN/lignin-based CF composites are a new promising material class for the building industry. The replacement of the traditional heavy and corroding steel reinforcement by carbon fiber (CF) based reinforcements offers many significant advantages: a higher protection of environmental resources because of lower CO2 consumption during cement production, a longer lifecycle and thus muss less damage in structural components and a higher degree of design freedom because lightweight solutions can be realized. However, due to cost pressure in civil engineering, completely new process chains are required to manufacture CF based reinforcement structures for concrete. The article describes the necessary process steps in order to develop CF reinforcement: (1) the production of cost-effective CF using novel carbon fiber lines, (2) the fabrication of CF rebars with different geometry profiles. It was found that PAN/lignin-based CF is currently the most promising material in order to meet the future market demands. However, significant research needs to be undertaken in order to improve the properties of lignin-based and PAN/lignin-based CF, respectively. The CF can be manufactured to CF-based rebars using different manufacturing technologies which have been developed on prototype level in this study.

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

Living systems must solve the problem of realizing their environment to act appropriately to maintain external relationships and internal order for survival and reproduction. This problem is a biological version of the philosophical enigma of how the self can escape solipsism. This study develops a comprehensive model to solve this adaptation problem. LSs must be composed of material entities that have the property of detecting their external states. The detection of the outside is conceptualized as “cognition,” a related state change that occurs at three levels of nested hierarchy: physical, chemical, and semiotic cognitions. Any entity that cognizes the surroundings is a “cognizer.” The hierarchical extension of cognition to all levels of the world provides a monistic view that only cognizers exist. Semiotic cognition is essential for the emergence and evolution of life. Living systems invented semiotic cognitions based on physical and chemical cognitions to manage the probability distribution of events that occur to them. This study proposes a theoretical model in which semiotic cognition is an adaptive process in which inverse causality operations produce particular internal states as symbols that signify hidden external states. This operation makes the living systems aware of the external world.

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits, and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in Active Distribution Systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies, and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of a more active distribution networks and that the internet of energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS) which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.

In this paper, an adaptive technology and the interconnection and damping assignment passivity-based control method are combined to solve the stabilization problem for underactuated mechanical systems with uncertainties (including matched and unmatched). Uncertainties include unknown friction coefficients and unknown terms in kinetic energy and potential energy. A novel adaptive interconnection and damping assignment passivity-based control scheme is proposed and an adaptive stabilization controller is designed to make the closed-loop system locally stable. Verification is conducted on the ball and beam system, taking into account uncertainties of friction coefficients, kinetic energy, and potential energy. The locally asymptotic stability is demonstrated using the LaSalle’s invariance principle and approximate linearization. The effectiveness of the proposed control law is verified through numerical simulations.

No-till and cereal-legume intercropping have been recognized as favorable cropping practices to increase crop yields while maintaining soil quality in arid and semiarid environment, but the bio-logical mechanisms are poorly understood. The present study was to determine the response of soil properties, enzyme activities, and microbial community diversity and composition in mono- and inter-cropping under conventional and no-tillage conditions. We initiated a field experiment in Wuwei, a typical arid area of China, in 2014. Soil was sampled in August 2022 and, yields, soil properties, enzyme activities, and the microbial community diversity and composition were de-termined in the maize and pea strips in inter- and mono-cropping systems. Results revealed that the maize and pea strips in the no-till intercropping significantly increased yields, total and or-ganic carbon stocks, decreased NO3--N, and obtained the highest total and organic P in the soil. The α- and β-diversity of archaea and eukaryotes were significantly affected by planting patterns, while α- and β-diversity of the bacterial community were significantly affected by tillage practices. Both no-tillage and intercropped maize significantly increased the abundance of archaea phylum Thaumarchaeota and bacterial phylum Nitrospirae, benefiting nitrogen fixation of intercropped pea from the atmosphere under the no-tillage cereal/legume intercropping. No-till intercropping was conducive to the accumulation of organic carbon, while decreasing the abundance of Prote-obacteria, Acidobacteria, and Verrucomicrobia. Limited soil enzyme activities (ACP, ALP, DP, NAG, BG, AG, CB) led to decreases in organic carbon turnover and utilization. Intercropping al-tered soil microbial community diversity and composition due to changes in soil properties and enzyme activities. These findings suggest that no-tilled cereal-legume intercropping is a sustaina-ble cropping practice for improving soil properties and enhancing microbial (archaea, bacterial, Eukaryota) diversity, but the long-term persistence is not conducive to rapid turnover of soil nu-trients due to limited enzyme activities.

Existing research lacks studies that explore the impact of government fiscal support on the high-quality development of regional economy and society from the perspective of livelihood-based infrastructure construction. The investigation in this paper explores the economic and social effects of government fiscal support behavior from the perspective of livelihood infrastructure construction. On the basis of the TOPSIS-Entropy Method, this paper constructs an evaluation indicator that closely conforms to the denotation and requirements of high-quality development and then carries out empirical regressions in combination with government expenditure on people's livelihood to verify the theoretical hypothesis, whose transmission mechanism and moderating effect are further empirically tested. The results reveal that increasing government expenditure on people's livelihoods can effectively improve regional high-quality development, but the marginal improvement degree varies with different periods and locations. The mechanism test proves that the regional consumption level plays a significant part in the mediating effect, accounting for 24.27% of the total effect. Urbanization is also the critical impetus for regional high-quality development and positively moderates the promotion effect of people's livelihood fiscal expenditure. Our study presents a theoretical basis and policy inspiration for improving residents' quality of life and welfare level and improving people's livelihood in the midst of high-quality economic development.

An energy model that correlates fan airflow, head, speed, and system power input is essential to detect device faults and optimize control strategies in fan systems. Since the application of variable frequency drives (VFDs) makes the motor efficiency data published by manufacturers inapplicable, the fan efficiency and drive (belt-motor-VFD) efficiency must be identified for each individual system to obtain accurate energy models. The objectives of this paper are to identify an energy model of existing VFD-motor-fan systems using available experimental data and demonstrate its applications in loose belt fault detection and virtual airflow meter development for optimal control. First, an approach is developed to identify the fan head, fan efficiency and drive efficiency curves using available fan head, speed, and system power input as well as temporarily measured airflow rate without measuring shaft power. Then the energy model is identified for an existing VFD-motor-fan system. Finally, the identified model is applied to detect the slipped belt faults and develop the virtual airflow meter. The experiment results reveal that the developed approach can effectively obtain the energy model of VFD-motor-fan systems and the model can be applied to effectively detect slipped belt faults and accurately calculate the fan airflow rate.

The past years have shown the widespread vulnerability of agro-food systems and rural diets to external perturbations such as wars, climate events, and pandemics. Experiencing numerous obstacles, Cuba constitutes an example of success in the transition to agroecological sustainability models. This article characterizes how processes of agricultural change, local development, and industrial degrowth have impacted food availability and dietary diversity among rural livelihoods in the municipality of Yaguajay, Sancti Spíritus, for the past forty years (1980s-2020s). It integrates findings from focus groups, repeated nutritional surveys, and interviews carried out between 2016 and 2022 among residents of the towns of Yaguajay and La Picadora. The goal is to identify effects and response strategies within agro-food systems of rural populations. Distinguishing between periods of abundance and shortage, our findings show two counterpoints: intensive sugar monocrop cultivation which resulted in high dietary variety; and economic crises in 1990s and during the last period of the pandemic, which have led to significant dietary adjustments. The article concludes underscoring the importance of comprehensive assessments of dietary strategies to elicit what agroecological transitions mean for local realities and of the value of food consumption and small-holder production experiences to understand the limits to sustainable transformations.

This paper presents a new information technology platform specially tailored for infrastructure asset management of urban water systems operated by water utilities of lower digital maturity level, developed in the scope of DECIdE research project. This platform aims at the integration of different data from the water utilities with several information systems and the assessment of the system performance, in terms of water losses, energy efficiency and quality of service by using developed tools (i.e., water and energy balances and key performance indicators). This platform was tested with data from five small to medium size Portuguese water utilities with different maturity levels in terms of technological and human resources. Obtained results are very promising since the platform allows to assess the systems performance periodically which constitute an important part of the infrastructure asset management for small and medium-sized water utilities

Systems genetics is key for integrating a large number of variants associated with diseases. Vitamin K (VK) is one of the scarcely studied conditions in lieu of ascertaining either the differentially expressed genes (DEGs) or variants in an individual subpopulation of diseased phenotypes associated with VK, viz. myocardial infarction, renal failure, prostate cancer, thrombosis, thrombocytopenia, coagulation related diseases to name a few. In this work, we have screened characteristic DEGs common to three VK-related diseases, viz. myocardial infarction, renal failure and prostate cancer and asked whether or not any DEGs in addition to pathogenic variants are common to these conditions. We attempt to bridge the gap in finding characteristic biomarkers and discuss the role of long noncoding RNAs (lncRNAs) in the biogenesis of VK deficiencies.

Rajshahi, blessed by the geographical advantage provided by the river Padma, is a beautiful and one of the cleanest cities in Bangladesh. It presents a spectacular image and stunning sight to its residents. But Rapid urbanization created immense pressure on the infrastructures and cityscape by rescinding most of the natural assets and scenic beauty it possessed historically. The growing pressure of population influx accompanied by unplanned urbanization threatens the survival of its waterbodies, wetlands, and greeneries, which many other contemporary cities strive to achieve with hard-hearted planning measures. Moreover, the city suffers from inadequate provision of quality outdoor recreational facilities. Yet because of the verdant resources, it still can set an example of becoming green and fluid landscape. There are scopes to connect the city people to the urban landscape and blue-green infrastructures by applying proper planning and urban design techniques. For example, it can explore the potential and scope of a water-based transportation system to create outdoor recreation space for city dwellers as well as a transport mode in the city. This SECTION discusses the prospects and potential of enhancing recreational aspects of Circular Inland Waterways. It also analyzes the benefits of a network of waterways well-connected and integrated with the existing conventional transportation system to reduce pressure on land transport and promote a sustainable system. This research was conducted in two phases each involving a distinct approach. The first one was a macroscopic approach to probe the potential of the water transport system in Rajshahi city. The required data on traffic volume, traffic growth rate, roads and existing waterbodies were collected, and GIS and Remote Sensing tools were used to find the scope of waterway transport around Rajshahi City. In the second approach, a detailed design was proposed for a potential circular waterway network in the city. Results indicate that there is a huge prospect for introducing a circular waterway transport system around Rajshahi city to create a place of fluid landscape and minimize the pressure on the existing land transportation system.

The article presents the results of tests related to failure analysis and finding ways to diagnose used semiconductor elements, among other, in power electronics converter systems on vessels and offshore facilities (drilling and production rigs, wind turbines). Diagnostic relationships were found between the temperature change in the above systems and the signals generated in form of elastic waves of acoustic emission.

System-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a High Level Architecture (HLA) simulation of 3 networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

Green Infrastructure promotes the use of natural functions and processes as potential solutions to reduce negative effects derived from anthropocentric interventions such as urbanization. In cities of Latin America, for example, the need for more nature-sound infrastructure is evident due to its degree of urbanization and degradation of ecosystems, as well as the alteration of the local water cycle. In this study, an experimental approach for implementation of a prototype is presented. The experiment took place in a highly urbanized watershed located in the Metropolitan Area of Costa Rica. Initially, understanding the characteristics of the study area at different scales was achieved by applying the Urban Water System Transition Framework to identify the existing level of development of the urban water infrastructure, and potential future stages. Subsequently, preferences related to spatial locations and technologies were identified from different local decision-makers. Those insights were adopted to identify a potential area for implementation of the prototype. The experiment consisted on an adaptation of the local sewer to act as a temporal reservoir to reduce the effects derived from rapid generation of stormwater runoff. Unexpected events, not considered initially in the design, are reported in this study as a means to identify necessary adaptations of the methodology. Our study shows from an experimental learning-experience that the relation between different actors advocating for such technologies influences the implementation and operation of non-conventional technologies. Furthermore, the perception of security associated to green spaces was found as a key driver to increase the willingness of residents to modify their urban environments. In consequence, those aspects should be carefully considered as factors of designs of engineering elements when they are related to complex socio-ecological urban systems.

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Since the late 1980s the idea of sustainable development has been gaining widespread recognition as a guiding framework for policies on development and the environment. However, the concept of sustainable development has received a number of criticisms, including its over-emphasis on meeting human needs through economic growth, as well as its failure to recognize dynamic human-environment interactions. In response to these shortfalls, the concepts of resilience and adaptive governance have emerged as alternative perspectives for pursuing sustainable development. Resilience in social-ecological systems emphasizes the capacity of coupled human-environment systems to deal with change while continuing to develop. Adaptive governance relies on diverse and nested institutional mechanisms for connecting actors across multiple scales to manage conflicts and uncertainties in ecosystem management processes. However, the ethical dimensions of resilience and adaptive governance have not received enough attention. A promising ethical perspective for guiding policies on human-environment interactions is the philosophy of deep ecology which highlights the need for recognition of the intrinsic values of all living things, as well as the nurturing of ecological and cultural diversity. We argue that an integration of the principles of deep ecology and adaptive governance provides a complementary set of ethical principles and institutional attributes that offers better prospects for pursuing sustainable development in the era of the Anthropocene. The implications of this integrative agenda include: adoption of a holistic conception of dynamic human-environment interactions; recognition of diverse knowledge systems through an anti-reductionist approach to knowledge; promotion of long term sustainability through respect for ecological and cultural diversity; and embracing decentralization and local autonomy. We further illustrate this integrative agenda using the management of protected areas as a case study.

One way to increase overall natural gas engine efficiency is to transform exhaust waste heat into useful energy by means of a bottoming cycle. Organic Rankine cycle (ORC) is a promising technology to convert medium and low grade waste heat into mechanical power and electricity. This paper presents an energy and exergy analysis of three ORC-Waste heat recovery configurations by using an intermediate thermal oil circuit: Simple ORC (SORC), ORC with Recuperator (RORC) and ORC with Double Pressure (DORC), and Cyclohexane, Toluene and Acetone have been proposed as working fluids. An energy and exergy thermodynamic model is proposed to evaluate each configuration performance, while available exhaust thermal energy variation under different engine loads was determined through an experimentally validated mathematical model. Additionally, the effect of evaportating pressure on net power output , absolute thermal efficiency increase, absolute specific fuel consumption decrease, overall energy conversion efficiency, and component exergy destruction is also investigated. Results evidence an improvement in operational performance for heat recovery through RORC with Toluene at an evaporation pressure of 3.4 MPa, achieving 146.25 kW of net power output, 11.58% of overall conversion efficiency, 28.4% of ORC thermal efficiency, and an specific fuel consumption reduction of 7.67% at a 1482 rpm engine speed, a 120.2 L/min natural gas Flow, 1.784 lambda, and 1758.77 kW mechanical engine power.

Crystallization behavior of hexadecane (C₁₆H₃₄), octadecane (C₁₈H₃₈), eicosane (C₂₀H₄₂), and docosane (C₂₂H₄₆) dispersions of similar mean droplet diameter (x_50.2 ≈ 15 µm) was investigated in quiescent systems and compared to crystallization under mechanical stress. In quiescent systems, the required supercooling decreased with increasing chain length of the alkanes to initiate crystallization. Crystallization of alkane dispersions under mechanical stress resulted in similar onset crystallization supercooling, as during quiescent crystallization. Increase of mechanical stress did not affect the onset crystallization supercooling within alkane dispersions.

Global warming, as well as europhication are predicted to promote cyanobacterial blooms, but how tropical phytoplankton communities from different trophic state systems respond to temperature variation is less known. To further explore the effect of temperature changes and nutrient addition on phytoplankton communities and to get insight in possible resistance to these effects, we tested the hypothesis that temperature variation will have a stronger effect on cyanobacteria dominance in eutrophic water than in oligo-mesotrophic. Hereto, we conducted an experiment with phytoplankton communities from two aquatic ecosystems differing in trophic state. Water samples from a eutrophic and an oligo-mesotrophic system were collected and incubated in 25 and 30ºC. Also, treatments that received additional surplus N and P were included that served as eutrophication treatments. Temperature variation itself did not promote cyanobacteria in either water from the oligo-mesotrophic or the eutrophic system. However, nutrient enrichment of water from the eutrophic system significantly boosted cyanobacteria, and biomass increased 10 times in both 25ºC and 30ºC treatments. In contrast, eutrophication of water from the oligo-mesotrophic system did not change the relative contribution of phytoplankton groups and response ratios were much lower than those for water from the eutrophic system. Although using a very simple experimental design, the results suggest that in eutrophic systems cyanobacteria dominance can be favoured by further addition of nutrients, independently of a direct temperature effect and that more pristine environments possess some resistance against eutrophication. Since global warming is assumed to intensify eutrophication symptoms indirectly, our study underscores the importance of nutrient control.

A high step-up DC-to-DC converter that integrates an isolated transformer and a switched-clamp capacitor is presented in this study. The voltage stress of the main power switch should be clamped to 1/4 V by using the turn ratio and switched-clamp capacitor of an isolated transformer to achieve a high voltage gain. In addition, a passive clamp circuit is employed reduce voltage stress on the main power switch. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, thereby improving the power converter efficiency. The converter consists of one isolated transformer, one main switch, three capacitors, and four diodes. Operating principle and steady-state analyses are also discussed. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory. The maximum efficiency of the converter is 95.1 at 60 W.

Alzheimer's disease (AD) is a stealthy and progressive neurological disorder that is a leading cause of dementia in the global elderly population, imposing a significant burden on both the elderly and society. Currently, the condition is treated with medications that alleviate symptoms. Nonetheless, these drugs may not consistently produce the desired results and can cause serious side effects. Hence, there is a vigorous pursuit of alternative options to enhance the quality of life for patients. Ginkgo biloba (GB), an herb with historical use in traditional medicine, contains bioactive compounds such as terpenoids (Ginkgolides A, B, and C), polyphenols, organic acids, and flavonoids (quercetin, kaempferol, and isorhamnetin). These compounds are associated with anti-inflammatory, antioxidant, and neuroprotective properties, making them valuable for cognitive health. A systematic search across three databases using specific keywords GB in AD and dementia yielded 1702 documents, leading to the selection of 15 clinical trials for synthesis. In eleven studies, GB extract/EGb 761® was shown to improve cognitive function, neuropsychiatric symptoms, and functional abilities in both dementia types. In four studies, however, there were no significant differences between the GB-treated and placebo groups. Significant improvements were observed in scores obtained from the Mini-Mental State Examination (MMSE), Short Cognitive Performance Test (SKT), and Neuropsychiatric Inventory (NPI). While the majority of synthesized clinical trials show Ginkgo biloba has promising potential in these conditions, more research is needed to determine optimal dosages, effective delivery methods, and appropriate pharmaceutical formulations. Furthermore, a thorough assessment of adverse effects, exploration of long-term use implications, and investigation into potential drug interactions are critical aspects that must be carefully evaluated in future studies.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Robotics and autonomous technologies enable space travel. Robotics change space colonization. We study space-based robotics. Human-centric missions need robotics. Vision, decision-making, movement, and manipulation follow. These developments influence resource utilization, extravehicular activities, planetary surface exploration, long-duration space missions, and colonization. Robots improve research, productivity, and astronaut safety but present problems. Highlighting research and knowledge gaps. Space ethics, human-robot interaction, and failure recovery are gaps. This extensive study showed how robots and autonomous systems may transform space exploration and colonization. Researchers, engineers, and mission planners may benefit. Robotics and autonomy will improve space exploration.

Bacteriological and physico-chemical analyses of fish pond water are very important in aquaculture as they give insights into likely threats to aquaculture and associated personnel. Bacteriological and physico-chemical profiles of selected fish ponds in the Ilorin West area of Kwara State, Nigeria, were investigated to evaluate the water quality of rearing enclosures. Physico-chemical analyses revealed quality parameters were within the recommended range for aquaculture. Following bacteriological analyses of static water pond culture, the TVC and TCC showed temporal variations, with concentration increasing with sampling time. However, the FCC showed fluctuation. Totally, 8 bacteria groups were isolated from both rearing enclosures. Of these, Gram-negative bacteria showed dominance. In which 5 Gram-negative (Escherichia coli, Proteus spp, Serratia spp, Enterobacter spp, and Pseudomonas spp) and 3 Gram-positive (Staphylococcus spp, Streptococcus spp, and Bacillus spp) were encountered. Estimates of bacteria occurrence in both rearing facilities respectively gave: Staphylococcus spp (20%), Streptococcus spp (12%), Proteus spp (8%) Enterobacter spp (20%), Serratia spp (16%), Bacillus spp (9%), Escherichia coli (8%), Pseudomonas spp (7%) from earthen pond water sampled. While Staphylococcus spp (18%), Streptococcus spp (16%), Proteus spp (8%), Enterobacter spp (22)%, Serratia spp (8%), Bacillus spp (15%), Escherichia coli (8%), Pseudomonas spp (6%) from concrete water sampled. Conclusively, although there is the presence of bacteria groups of public health concern, the static water exchange provides benefits of natural processing of wastes and restoration of the pond ecosystem. Notably, the presence of Escherichia coli indicates the presence of pathogenic organisms of enteric origin. The presence of these organisms has been associated with a lack of tentative pond management and effective biosecurity procedures.

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

This study investigated the speciation, transformation and availability of P during indigenous vegetable production by employing a combination of chemical and spectroscopic techniques. The study focused upon sites in two ecozones of SSA, the Dry Savanna (lna, Benin Republic) and Rainforest (Ilesha, Nigeria). Both sites were cultivated with two indigenous vegetable species; local amaranth (Amaranthus cruentus (AV)) and African eggplant (Solanum macrocarpon (SM)). The soils were treated with 5 t/ha poultry manure and urea fertilizer at the rate of 0, 20, 40, 60 and 80 kg N/ha. Soil samples were collected before planting and after harvest. Phosphorus K-edge X-ray absorption near-edge structure (XANES) spectroscopy was used to determine P speciation in these soils. Quantitative analysis showed that adsorbed and organic P were the two dominant P species in the manure amended Dry Savanna (DS) soils before planting and after harvest in soils cultivated with both AV and SM, with the addition of urea (40 kg N/ha) causing an increase in the organic P form in Dry Savanna soils cultivated with AV. Soils of the Rainforest (RF) cultivated with AV initially had large amounts of apatite P in the manure amended soils prior to planting which was transformed to adsorbed and organic P after harvest. Urea addition to the Rainforest soils shifted the dominant P species from organic P to adsorbed and apatite P, which is likely to limit P availability. Soils cultivated with SM had similar proportions of both organic and adsorbed P forms, with 40 kg N/ha addition slightly increased the proportion of adsorbed P.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

Earth Observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: Atmosphere, Water, Agriculture, Land, Disaster Risk Reduction (DRR), Cultural Heritage, Energy, Marine Safety and Security (MSS) and Big Earth Data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and Land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high resolution and near real time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems and services.

In electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel EMAT (electromagnetic acoustic transducer)-PEC (pulse eddy current) composite sensor is designed. We use amplitude of bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on amplitude of bottom echo are distinguished combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect the bottom crack as small as 0.1mm* 0.3mm. The signal fusion method can accurately correct the amplitude of defect signal and the relative error is less than ±8%.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

Rutin, a flavonoid compound found in various plants, has gained attention for its potential applications in the cosmetic industry due to its antioxidant, anti-inflammatory, and potential photoprotective properties. Our review explored the use of rutin (rutoside, 3-O-rutinoside-quercetin) in cosmetics/dermocosmetics, focusing on its physicochemical properties and stability, cutaneous permeability, and efficacy in sunscreen systems, involving in vitro tests and the current state of clinical trials. Rutin's ability to scavenge free radicals, prevent peroxidation, and reduce vascular fragility makes this flavonoid a promising ingredient for photoprotection. Studies have shown that rutin can enhance the sun protection factor (SPF) of sunscreen formulations, especially when combined with organic UV filters. The encapsulation of rutin in nanostructures has also been investigated to improve its applicability. Overall, rutin shows potential as a safe and effective ingredient in cosmetic/dermocosmetic formulations, offering protection against the harmful effects of UV radiation and oxidative stress on the skin.

Abstract: Transcription factors are crucial pre-transcriptional regulatory mechanisms that can modulate the expression of downstream genes by binding to their promoter regions. DOF (DNA binding with One Finger) proteins are a unique class of transcription factors with extensive roles in plant growth and development. Our previous research indicated a decrease in iron content in chlorotic bamboo leaves. However, to our knowledge, genes related to iron metabolism pathways in bamboo species have not yet been studied. Therefore, in the current study, we identified iron metabolism-related (IMR) genes in bamboo and determined the transcription factors that significantly influence them. Among these, DOFs were found to have widespread effects and potentially significant impacts on their expression. We identified specific DOF members in Dendrocalamus latiflorus with binding abilities through homology with Arabidopsis DOF proteins, and established connections between some of these members and IMR genes using RNA-seq data. Additionally, molecular docking confirmed the binding interactions between these DlDOFs and the DOF binding sites in the promoter regions of IMR genes. The co-expression relationship between the two gene sets was further validated using q-PCR experiments. This study paves the way for research into iron metabolism pathways in bamboo and lays the foundation for understanding the role of DOF transcription factors in D. latiflorus.

This paper is focused on typical rural settlements in the Stara Planina Mountain area in Serbia. They are distinguished by their authenticity, identity and character, and they have still not been destroyed by inappropriate development as is the case in some other mountain areas in Serbia, where the effects of urbanization, globalization and homogenization are obvious. Consequently, there is a need to prevent these effects in mountain areas where there is a threat of disbalance between development and the inherited natural and cultural values. The paper starts from several important concepts that relate contemporary approaches to cultural heritage preservation, place morphology and planning future development. The concept definitions from recent documents from related fields are used. These concepts are then applied to the area of Stara Planina Mountain and its rural settlements. The necessity for an integrative approach to planning this area with significant natural and anthropogenic potential is pointed out, including its integration into international programs of sustainable development.

This paper proposes a novel approach called Multi-strategy Improved Chaotic Whale fused with Perturbation Observation method (IWOA-PO) for Maximum Power Point Tracking (MPPT) con-trol, to address the challenge of low efficiency in photovoltaic (PV) power generation under local shadows, according to the features of whale optimization algorithm (WOA) and perturbation observation (P&O) method. In the early stage of control algorithm, IWOA is used for global search to quickly locate the position of the Maximum Power Point (MPP). In the later stage of control algorithm, P&O is used for fine-grained local search to quickly, accurately and low oscillation track the position of the global maximum power point (GMPP). In order to verify the correctness, the tracking performance of IWOA-PO is comprehensively compared with WOA-PO、WOA、PSO under four conditions including uniform illumination, static partial shade, dynamic irradiance mutation and sudden changes in temperature and irradiance. In the above four conditions, the IWOA-PO algorithm has faster convergence and less power oscillations compared to the re-maining three algorithms, indicating that the IWOA-PO algorithm is effective. Finally, a physical verification experiment process is built to verify the correctness and effectiveness of the algorithm proposed in this paper.

Leaky-wave antennas (LWAs) represent a recent class of radiating systems, first appeared in the second half of the past century. Initially treated with a certain dose of skepticism due to the apparent non-physical nature of supported wave solutions, LWAs have so far deeply evolved from the original configuration of slotted metallic lossy waveguide and adapted to a huge number of practical application fields, from microwave to millimeter waves and even up to the terahertz frequency range. Due to physics of relevant working principle, LWAs are often found in planar and low-profile configurations, being critically required in many cutting-edge applications, where device miniaturization, scaling and integration are required. Here we propose a review of some of the most recent and perspective array-fed 2-D LWAs, selecting two of most widespread categories in applications, Fabry–Perot Cavity Antennas (FPCAs) and Bull’s-Eye Antennas (BEAs), discussing and illustrating some advanced features recently proposed.

According to the ECCB, cancer represents the transition of a genomically damaged multicellular cell to a lower level of cell organization, controlled by the ancestral gene regulatory network (GRN) from the ancestral compartment of human and metazoan genomes. Cancer adopts ancient mechanisms and rules of the common ancestor, similar to those observed in parasitic amoebae. In cancer, the productive cell line generating CSCs is a non-gametogenic cancer germline. This germline proliferates through asymmetric cell division (ACD phenotype). CSCs lack proliferative capacity; they persist in hypoxic niches in a quiescent cell state or transition to an amplifying cell state that performs cyst-like polyploidization cycles, giving rise to precursors and CSCs capable of differentiation. Malignization arises from genomically dysfunctional tetraploid germline cells (DSCD phenotype) that proliferate in a precancerous stage through symmetric cell cycles. DSCD progeny undergo homotypic fusion, forming multinucleated genome repair syncytia (MGRS pathway) driving carcinogenesis. During tumorigenesis, metastasis, and recurrence, subsequent germline clones initiate a cascade of increasingly pathogenic CSCs through mechanisms such as heterotypic cell fusion, soma-to-germ transition, and polyploid giant cell structures (PGCCs). Irradiation and chemotherapies drive some of the very few surviving but dysfunctional CSCs through a PGCC repair pathway homologous to the MGRS pathway of carcinogenesis. The large number of PGCCs during metastasis and recurrence results from the perpetual germline cell damage and CSC poverty induced by oxygen levels above 6.0% O2 (germline hyperoxia).

Transdermal drug delivery systems (TDDS) are designed to administer a consistent and effective dose of medication through the patient's skin. These pharmaceutical preparations are self-contained, discrete dosage forms designed to be placed topically on intact skin to release the active component at a controlled rate by penetrating the skin barriers. This medication provides a continuous and prolonged administration of a substance at a consistent rate. TDDS, or transdermal drug delivery system, has gained significant attention as a non-invasive method of administering drugs to vulnerable patient populations, such as pediatric and geriatric patients. This approach is considered easy to administer and helps overcome bioavailability issues associated with conventional drug delivery, which can be hindered by poor absorption and metabolism. TDDS has various advantages compared to conventional methods of drug administration. It is less intrusive, more patient-friendly, and can circumvent first-pass metabolism and the corrosive acidic environment of the stomach that happens when medications are taken orally. Multiple methodologies have been devised to augment the transdermal permeability of diverse pharmaceutical substances. Recent advancements in transdermal drug delivery systems have facilitated the precise delivery of medications to the intended site of action by improving the penetration of drugs through the outermost layer of the skin, known as the stratum corneum, resulting in enhanced drug availability throughout the body. This review seeks to provide a concise overview of the several methods employed in the production of TDDS, as well as their evaluation, therapeutic uses, clinical considerations, and current advancements. These advancements have resulted in the development of intelligent, biodegradable, and highly efficient transdermal drug delivery systems.

Plasmonic nanocomposites have the ability to enhance light-matter interaction due to the 1 plasmonic effects leading to promising potential applications. In this paper, we theoretically study the 2 group index of the silver nanoparticle-gold nanoparticle-quantum dot plasmonic hybrid system in 3 the optical region of the electromagnetic spectrum using the density matrix approach. The quantum 4 dot is considered to be a three-level atomic system of a lambda configuration, interacting with strong 5 control and weak probe fields. We find that the plasmonic hybrid system exhibits at near resonance a 6 strong switching between slow and fast light, which can be controlled by the inclination angle of the 7 gold nanoparticle and the size of the metals nanoparticles, in addition to the strength and detuning 8 of the control field. Thus, the silver nanoparticle-gold nanoparticle-quantum dot could potentially be 9 used in optoelectronic devices.10

Plasmonic nanocomposites have the ability to enhance light-matter interaction due to the plasmonic effects leading to promising potential applications. In this paper, we theoretically study the group index of the silver nanoparticle-gold nanoparticle-quantum dot plasmonic hybrid system in the optical region of the electromagnetic spectrum using the density matrix approach. The quantum dot is considered to be a three-level atomic system of a lambda configuration, interacting with strong control and weak probe fields. We find that the plasmonic hybrid system exhibits at near resonance a strong switching between slow and fast light, which can be controlled by the inclination angle of the gold nanoparticle and the size of the metals nanoparticles, in addition to the strength and detuning of the control field. Thus, the silver nanoparticle-gold nanoparticle-quantum dot could potentially be used in optoelectronic devices.

Commercially available multispectral and thermal imagers are commonly deployed for environmental monitoring on small uncrewed aerial systems (sUAS, <55 lbs). Our team assessed the challenges of deploying these imagers on a Group 3 classification UAS (weight: 55-1320 lbs, maximum altitude: 18,000 ft MSL, maximum speed: 250 kts) for the purpose of land-atmosphere interaction studies. A Micasense Altum multispectral imager was deployed on two very similar mid-sized (Group 3) UAS, a Mississippi State University (MSU) TigerShark XP and the Department of Energy (DOE) ArcticShark. This paper examines the effects of flight on imaging systems mounted on UASs flying at higher altitudes, faster speeds, and for longer duration, which near future technology will make more the norm. For these platforms we found that the acquisition rate may need to be higher to achieve a minimum 75% overlap, as required by certain post-processing algorithms. Additionally pre-flight calibration panel referencing was found to be problematic for converting flight images to reflectance, due to the changing illumination conditions during the extended duration flights. We developed an automated workflow to correct the image frames via data from an onboard hemispherical solar sensor mounted on the top of the airframe, and we assessed these data against known spectral ground targets and independent sources. Finally, this manuscript may be used as a reference for collecting similar datasets in the future. The datasets described within this manuscript may be used as a starting point for future research.

Metals are indispensable in the life of all organisms, in fact, their dysregulation causes various disorders caused by an interruption of their homeostasis. Nowadays different transition metals are incorporated into pharmaceutical products as diagnostic and therapeutic agents because of their electronic structure which gives them versatility in terms of tuning molecule properties, differently from organic molecules. Hence the interest in the study of metal-drug complexes from different points of view was born and many approaches were developed for the characterization, activation, delivery and molecular mechanisms clarification. The integration of these different approaches starting from chemoproteomics passing through nanoparticle systems to different activation strategies allows the understanding of the cellular responses of metallic drugs, which can be the basis for the design of new drugs and/or modification of drugs currently in use. This review aims to briefly summarize the recent advances in this area by describing the technological platforms and their possible applications to identify protein targets for discovering the mechanisms of action of metallodrugs, and to improve their efficiency in terms of administration.

A variety of factors ranging from ethnicity and occupants’ lifestyles to local climatic characteristics of any studied location and people’s age factors can affect thermal comfort assessment globally. Due to the detrimental impact of climate change, the building industry has started to implement energy efficiency schemes while considering the thermal comfort of vulnerable population both for the society and any sphere of economy. Although, considering human-based approaches have been neglected by other scholars in thermal comfort studies. This paper reviews energy effectiveness of state-of-the-art passive systems in providing neutral adaptive thermal comfort for elderly people, through exploring passive design strategies in four distinct climates namely, Canada, India, Abu Dhabi and the South-eastern Mediterranean basin. The aim of the study is to analyze the available data provided by the ASHRAE Global Thermal Comfort Database II version record 2.1. The main objective of the study is to develop an effective methodological framework for the on-going development of adaptive thermal comfort theory. To this extend, this study presents a comprehensive review on the assessment of energy effectiveness of passive design systems. To accomplish this, the impact of climate change factor in passive design systems was investigated. The meta-analysis method was adopted to determine the input variables for the statistical analysis. Cramer’s V and Fisher’s Exact tests were used to assess occupants’ thermal sensation votes (TSVs). The findings revealed that there are discrepancies detected between the in-situ field experiments and the data recorded in the ASHRAE Global Thermal Comfort Database II. According to the Köppen-Geiger climate classification, it was found from the study that the slightly high temperature fluctuations were identified in India where the weather is dry and savanna climate. The study findings contribute to the development of adaptive thermal comfort theory by reviewing the existing methodologies globally. Furthermore, a critical review on the significance of occupants’ age differences should be conducted in the identification of neutral adaptive thermal comfort.

Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.

Diabetic retinopathy is the most common microvascular complication of diabetes mellitus and one of the leading causes of blindness globally. Due to the progressive nature of the disease, earlier detection and timely treatment can lead to substantial reductions in the incidence of irreversible vision-loss. Artificial intelligence (AI) screening systems have offered clinically acceptable and quicker results in detecting diabetic retinopathy from retinal fundus and optical coherence tomography (OCT) images. Thus, this systematic review and meta-analysis of relevant investigations was performed to document the performance of AI screening systems that were applied to fundus and OCT images of patients from diverse geographic locations including North America, Europe, Africa, Asia, and Australia. A systematic literature search on Medline, Global Health, and PubMed was performed and studies published between October 2015 and January 2020 were included. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and AI-based investigations were mandatory for studies inclusion. The abstracts, titles, and full-texts of potentially eligible studies were screened against inclusion and exclusion criteria. Twenty-one studies were included in this systematic review; 18 met inclusion criteria for the meta-analysis. The pooled sensitivity of the evaluated AI screening systems in detecting diabetic retinopathy was 0.93 (95% CI: 0.92-0.94) and the specificity was 0.88 (95% CI: 0.86-0.89). The included studies detailed training and external validation datasets, criteria for diabetic retinopathy case ascertainment, imaging modalities, DR-grading scales, and compared AI results to those of human graders (e.g., ophthalmologists, retinal specialists, trained nurses, and other healthcare providers) as a reference standard. The findings of this study showed that the majority AI screening systems demonstrated clinically acceptable levels of sensitivity and specificity for detecting referable diabetic retinopathy from retinal fundus and OCT photographs. Further improvement depends on the continual development of novel algorithms with large and gradable sets of images for training and validation. If cost-effectiveness ratios can be optimized, AI can become a financially sustainable and clinically effective intervention that can be incorporated into the healthcare systems of low-to-middle income countries (LMICs) and geographically remote locations. Combining screening technologies with treatment interventions such as anti-VEGF therapy, acellular capillary laser treatment, and vitreoretinal surgery can lead to substantial reductions in the incidence of irreversible vision-loss due to proliferative diabetic retinopathy.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.