In this work, we report the magnetocaloric effect (MCE) in two systems of non-interactive particles, the first corresponds to the Landau problem case and the second, the case of an electron in a quantum dot subjected to a parabolic confinement potential. In the first scenario, we realize that the effect is totally different from what happens when the degeneration of a single electron confined in a magnetic field is not taken into account. In particular, when the degeneracy of the system is negligible, the magnetocaloric effect cools the system, while in the other case, when the degeneracy is strong, the system heats up. For the second case, we study the competition between the characteristic frequency of the potential trap and the cyclotron frequency to find the optimal region that maximizes the ΔT of the magnetocaloric effect, and due to the strong degeneration of this problem, the results are in coherence with those obtained for the Landau problem. Finally, we consider the case of a transition from a normal MCE to an inverse one and back to normal as a function of temperature. This is due to the competition between the diamagnetic and paramagnetic response when the electron spin in the formulation is included.

The synthesis these past few years of novel organic molecules that spontaneously self-assemble into a large variety of molecular architecture, particularly generating organogels, has opened up new horizons for preparing functional materials. Here, we present an original preparation path of such materials through the making of hybrid gels of these molecules together with covalent polymers. Three types of systems are described: i) intermingled gels where a polymer gel and an organogel pervade one another; ii) encapsulation of self-assembled filaments in polymer fibrils, which provides a system with unusual magnetic properties; iii) the reverse situation where self-assembled nanotubes sheathe polymer fibrils. Here two covalent polymers are considered: a neutral polymer, namely stereoregular polystyrene (isotactic or syndiotactic), and a semi-conducting polymer, P3BT. In the latter case, semi-conducting nanowires are obtained.

This paper presents a complete control strategy of the active return-to-center (RTC) control for electric power steering (EPS) systems. We first establish the mathematical model of the EPS system and analyze the source and influence of the self-aligning torque (SAT). Second, based on the feedback signals of steering column torque and steering wheel angle, we give the trigger conditions of a state switch between the steering assist state and the RTC state. In order to avoid the sudden change of the output torque for the driving motor when the state switches frequently between the steering assist state and the RTC state, we design an undisturbed state switching logic algorithm. This state switching logic algorithm ensures that the output value of the RTC controller is set to an initial value and increases in given steps up to a maximum value after entering the RTC state, and the output value of the RTC controller will reduce in given steps down to zero when exiting the RTC state. This therefore ensures smooth switch control between the two states and improves the driver’s steering feeling. Third, we design the RTC controller, which depends upon the feedback signals of the steering wheel angle and the angular velocity. In addition, the controller increases the auxiliary control function of the RTC torque based on vehicle speed. The experimental results show that the active RTC control method does not affect the basic assist characteristics, which effectively reduces the residual angle of the steering wheel at low vehicle speed and improves the RTC performance of the vehicle.

Aquifer Thermal Energy Storage (ATES) systems are garnering attention as high-efficiency air conditioning technologies that contribute to the realization of a carbon-neutral society. This study focuses on an ATES system constructed in Japan, characterized by its complex geological conditions and thin aquifer layers. Through performance analysis using four years of operational data, we achieved a Coefficient of Performance (COP) of 5 on the building load side. This paper confirms the equivalence of the dimensionless production temperature and heat recovery rate, observing nearly identical changes in the dimensionless production temperature over four cycles (eight seasons). Furthermore, we propose an operational method for the following fiscal year that maintains the balance between accumulated storage quantity and pumping volume, based on predictions from this plant's operational pattern.

Introduction: Cervical cancer is the fourth most common cancer in women globally and the most common cancer in Tanzania, resulting in about 9772 new cases and 6695 deaths each year. Research has shown an association between low levels of risk perception and knowledge of the prevention, risks, signs, etiology, and treatment of cervical cancer and low screening uptake, as contributing to high rates of cervical cancer-related mortality. However, there is scant literature on the perspectives of a wider group of stakeholders (e.g., policymakers, healthcare providers (HCPs), and women at risk), especially those living in rural and semi-rural settings. Methods: We adapted Risso-Gill and colleagues’ framework for a Health Systems Appraisal (HSA), to identify HCPs' perspective of the extent to which health system requirements for effective cervical cancer screening, prevention, and control are in place in Tanzania. We adapted interview topic guides for cervical cancer screening using the HSA framework approach. Study participants (69 in total) were interviewed between 2014-2018 –participants included key stakeholders, HCPs, and women at risk for cervical cancer. The data was analyzed using reflexive thematic analysis methodology. Results: Seven themes emerged from our analysis of semi-structured interviews and focus groups: 1) knowledge of cervical cancer risk and the role of screening and preventive care/services (e.g., prevention, risks, signs, etiology, and treatment), 2) training and knowledge of HCPs, 3) knowledge of cervical cancer screening among women at risk, 4) beliefs about cervical cancer screening, 5) role of traditional medicine, 6) risk factors, and 7) symptoms and signs. Conclusion: It is evident that there is a low level of knowledge of cervical cancer risk and the role of screening and preventive services among stakeholders, HCPs, and women living in rural and semi-rural locations in Tanzania. There is a critical need to implement more initiatives and programs to increase the uptake of screening and related services and allow women to make more informed decisions on their health.

Hail causes damage to property, including roofs, automobiles, and crops, with an average annual loss of $850 million. In residential structures, tile roofing systems are common in the southern U.S. due to their resistance to hail impact and long service life. Additionally, the commercial buildings and some portions of the residential structures have low-sloped or flat roof systems. Commercial low-slope roof systems are equally prone to hail-strike damages as steep residential roof systems. The objectives of this paper are to present a literature review, inspection protocol, and case studies on a comparative assessment of hail threshold for built-up roof (BUR) and tile roof (TR) systems. The published papers determining the hail impact assessment of different roofing systems from 1969 through 2023 were studied and analyzed. This study develops a comparative hail damage assessment table between BUR and TR systems, and the hail threshold for various built-up roof composition systems. In addition, the different failure modes and their causes, the characteristics of hail impacts, and the variables influencing the impact resistance of these roofing systems were examined using field studies. To better understand the effects, it is recommended that an intelligent model be developed to predict the hail resistance threshold of various configurations of BUR and TR systems with critical variables.

Soil-borne oomycetes include devastating plant pathogens that cause substantial losses in the agricultural sector. To better manage this important group of pathogens, it is critical to understand how they respond to common agricultural practices, such as tillage and crop rotation. Here, a long-term field experiment was established using a split-plot design with tillage as the main plot factor (conventional tillage [CT] vs. no till [NT], 2 levels) and rotation as the subplot factor (monocultures of soybean, corn, or wheat, and corn-soybean-wheat rotation, 4 levels). Post-harvest soil oomycete communities were characterized over three consecutive years (2016-2018) by metabarcoding the Internal Transcribed Spacer 1 (ITS1) region. The community contained 292 Amplicon Sequence Variants (ASVs) and was dominated by Globisporangium spp. (85.1% in abundance, 203 ASV) and Pythium spp. (10.4%, 51 ASV). NT decreased diversity and community compositional structure heterogeneity, while crop rotation only affected the community structure under CT. The interaction effects of tillage and rotation on most oomycetes species accentuated the complexity of managing these pathogens. Soil and crop health represented by soybean seedling vitality was lowest in soils under CT cultivating soybean or corn, while grain yield of the three crops responded differently to tillage and crop rotation regimes.

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential-flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/ approximate the fault and uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Despite the successes earned in cataloguing and finding the role of the most of molecular components in living matter, the “biochemical and molecular” perspective, popular in biology, medicine and genetics, is unable to give account for crucial topics as the faculty of living systems to “feel”, to “perceive” what a given stimulus implies (means, indeed) for their survival. Condensed matter physics too, if bounded to a local, short-range, and perturbative approach, fails dramatically. This is also due to the role commonly assigned to water – actually the main constituent of living matter – deemed for long time to be merely chemical (as “solvent” or a reactant/product). Nonetheless, today many evidences show how living matter can be right conceived as a super-structured coherent water-based matrix, suggesting that the characterization of bio(electro)chemical and physical processes undertaken at molecular level in living matter, would let us unable to answer a question like this: what allows an amoeba, moreover without any neurons, to “know” to get closer to a nutrient or escape away from a toxin? I propose that to pursue such a fundamental inquiry it’s necessary an essentially relational approach, that is: to consider the living being at its grounding as the outcome of a physical history of relationships where symmetry-breakings, dissipation and coherence yield the emergence of the living state of matter, conceivable only as a time-dependent open process, and not as a portraited “body”. The effective tools to build up such an approach may be retrieved in far-from-equilibrium thermodynamics (TD), symmetry-breakings and gauge-fields theory, science of complexity, within the framework of a Quantum Field Theory. Indeed, within a field-view of matter, and of water especially, as it has been developed by a Quantum Electrodynamic (QED) description of condensed matter, it’s possible to give account for a physical basis too such an epistemologically elusive, though crucial, feature of living systems (i.e.: perception and meaning). The emerging landscape allows some important meditations about adaptation, evolution, ecodynamics, and about different conceptions of complexity and “information” in living realm. Furthermore, some neuroscientific themes like consciousness, qualia and their links to artificial intelligence could be supplied with due insights.

Sensitive and precise nucleic acid detection is critical for clinical diagnostics and biotechnological advancements. Diagnostic in infectious disease field is very unique from diagnosing any other disease, that is time is of the essence; in outbreaks people die even with each passing hour in some cases, if the correct diagnosis wasn't make; for example Zika in particularly is a very challenging virus to diagnose, because it's in very few numbers of copies in the infected person, so it need high sensitive diagnostic approach to spot it, In particular, the advanced tools SHERLOCKv2 and DETECTR, give almost an immediate detection of attomolar amounts of pathogenic nucleic acids with specificity similar to that of PCR but with slight technical settings and that will guide the correct intervention for the patient. SHERLOCKv2 and DETECTR technologies are game changers for our ability to identify infectious disease and rapid detection of tumor DNA or cancer-related viruses with ultra-sensitive tests that don’t require a lot of complicated processing to go through. In this paper, we will review cutting-edge infectious disease diagnosis by CRISPR-Cas systems.

The current 2019-2021 marine pharmacology literature review provides a continuation of previous reviews covering the period 1998 to 2018. Preclinical marine pharmacology research during 2019-2021 was published by researchers in 42 countries and contributed novel mechanism of action pharmacology for 171 structurally characterized marine compounds. The peer-reviewed marine natural products pharmacology literature reported antibacterial, antifungal, antiprotozoal, antituberculosis,and antiviral mechanism of action studies for 49 compounds, 87 compounds with antidiabetic and anti-inflammatory activities as well as affecting the immune and nervous system, while another group of 51 compounds demonstrated novel miscellaneous mechanisms of action, which upon further investigation, may contribute to several pharmacological classes. Thus, in 2019-2021, a very active preclinical marine natural product pharmacology pipeline provided novel mechanism of actions as well as new lead chemistry for the clinical marine pharmaceutical pipeline targeting the therapy of several disease categories.

: Multimodal Large Language Models (MLLMs) harness comprehensive knowledge spanning text, images, and audio to adeptly tackle complex problems, including zero-shot in-context learning scenarios. This study explores the ability of MLLMs in visually solving the Traveling Salesman Problem (TSP) and Multiple Traveling Salesman Problem (mTSP) using images that portray point distributions on a two-dimensional plane. We introduce a novel approach employing multiple specialized agents within the MLLM framework, each dedicated to optimizing solutions for these combinatorial challenges. Our experimental investigation includes rigorous evaluations across zero-shot settings and introduces innovative multi-agent zero-shot in-context scenarios. The re-sults demonstrated that both multi-agent models—Multi-Agent 1, which includes the Initializer, Critic, and Scorer agents, and Multi-Agent 2, which comprises only the Initializer and Critic agents—significantly improved solution quality for TSP and mTSP problems. Multi-Agent 1 ex-celled in environments requiring detailed route refinement and evaluation, providing a robust framework for sophisticated optimizations. In contrast, Multi-Agent 2, focusing on iterative re-finements by the Initializer and Critic, proved effective for rapid decision-making scenarios. These experiments yield promising outcomes, showcasing the robust visual reasoning capabilities of MLLMs in addressing diverse combinatorial problems. The findings underscore the potential of MLLMs as powerful tools in computational optimization, offering insights that could inspire further advancements in this promising field.

Investigations into the properties of generalized effective temperature are conducted across arbitrary dimensions. Maxwell-Boltzmann distribution is displayed for one, two, and three dimensions, with effective temperatures expressed for each dimension. The energy density of blackbody radiation is examined as a function of dimensionality. Effective temperatures for non-uniform temperature distributions in one, two, three, and higher dimensions are presented, with generalizations extended to arbitrary dimensions. Furthermore, the application of generalized effective temperature is explored not only for linearly non-uniform temperature distributions but also for scenarios involving the volume fraction of two distinct temperature distributions. Finally, the effective temperature is determined for a cryogenic system supplied with both liquid nitrogen and liquid helium.

This paper investigates the performance of dual hop UAV-assisted communication channels, employing a decode and forward relay architecture. The system utilizes Terahertz (THz) communication in the primary hop and Visible Light Communication (VLC) in the secondary hop. We conduct a thorough analysis by deriving closed-form expressions for end to end bit error rate (BER). Furthermore, we employ a Monte Carlo approach to generate best fitting curves and validate our numerical expressions with simulation results. Evaluation via BER and outage probability demonstrates the proposed system’s effectiveness. The outcomes of this research offer valuable insights into the feasibility and limitations of UAV-assisted THz- VLC systems.

This paper presents an approach to improve the robustness of tip-tilt controllers for fast-steering mirror (FSM)-based laser beam steering (LBS) systems in the presence of dynamic disturbances such as external shocks. To this end, we propose the addition of a disturbance observer (DOB)-based anti-shock controller in parallel to the original linear servo control loop to improve its control performance in the presence of external shocks. To increase the tip-tilt control performance against external shocks, the DOB-based control method, which is an improved control method for eliminating nonperiodic disturbances, is implemented in the original tip-tilt control system. Results indicate that the control error of the DOB-based anti-shock controller decreased, resulting in an efficient improvement in its disturbance-rejection performance.

Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

Optical current sensors have been developed and improved in recent decades, and their use has been widespread in power systems, including smart grids. Like any measuring instrument or system, the result of its measurements is associated with a measurement uncertainty, which quantifies its precision. This article presents a review of the main uncertainty contributions that affect the quality and performance of optical current sensors.

The emerging industry of offshore wind turbines mounted on floating bases has garnered significant attention from both academia and industry. The desire to understand the complex physics of these floating structures has led to the development of numerical and physical modelling techniques. While physical testing has traditionally been employed, there is a growing focus on cost-effective and accurate high-fidelity numerical modelling as a potential alternative or supplement. However, commonly used numerical engineering tools in the offshore industry are considered mid- to low-fidelity and may lack the desired precision for floating offshore wind turbines (FOWTs). Given the complexity of these simulation codes, it is crucial to validate their accuracy. To address this, the International Energy Agency (IEA) Wind Technology Collaboration Programme initiated various research endeavors, including the Offshore Code Comparison Collaboration (OC3), Offshore Code Comparison Collaboration Continuation (OC4), Offshore Code Comparison Collaboration Continuation with Correlation (OC5), and the recent Offshore Code Comparison Collaboration Continued with Correlation and Uncertainty (OC6) projects. This study offers a comprehensive survey of the simulation tools available for FOWTs which were part of OC projects, focusing particularly on horizontal axis wind turbines (HAWTs) and highlighting their capabilities and fundamental theories.

Interactions of different factors in differently tilled faba bean agrosystem is still insufficiently studied and evaluated. For these reasons we studied the results of long-term field experiment, which was carried out in the Research Station of Vytautas Magnus University, Agriculture Academy (Lithuania). The aim of this study is comprehensively evaluating the effect of the deep and shallow ploughing, deep cultivation-chiseling, shallow cultivation-disking, and no-tillage systems for faba bean cultivation on the much-interacted factors, relations between them and strength of impact by the integrated evaluation method using complex evaluation index (CEI). Calculations showed that the influence of the no-tillage system was the greatest on the faba bean agrosystem than other system used. Shallow cultivation was more efficient than deep and shallow ploughing and deep cultivation systems. In the deep ploughing system, there were calculated more minimal, i.e. close to 1 as the CEI value, which showed DP ineffectiveness.

This study delves into the complex dynamics of Underwater Wireless Optical Communication (UWOC) in varied Harbor water conditions. Focusing on four distinct types of Harbor water, the research employs the Monte Carlo method to simulate photon transmission and analyze critical UWOC characteristics. Key parameters such as the transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on communication efficacy. The study first explores the relationship between laser power variation, attenuation length, and divergence angle, revealing how different water qualities influence received power and time delay spread. Notably, the received normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality. In addition, under the condition of small received aperture and FOV, the influence on normalized power loss and time spread is more obvious in Harbor water. The findings offer novel insights into the challenges of UWOC in Harbor waters, particularly in terms of light scattering and absorption properties, and contribute to the optimization of communication systems in such complex underwater environments. Furthermore, the analysis of time pulse spread in turbid water quality is of great significance for the analysis and improvement of underwater wireless optical timing, ranging and positioning.

This paper addresses a mode-dependent state-feedback H∞ control for descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with H∞ performance ? of the closed-loop system is proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator saturated control input is expressed as a linear combination of given state-feedback control input and a virtual control input that remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the LMI conditions for the existence of the mode-dependent state-feedback H∞ control and the ellipsoidal shape of invariant set are successfully derived. Two numerical examples demonstrate the effectiveness of the proposed approach in optimizing H∞ performance $\gamma$ and identifying the largest invariant set.

Here, we critically evaluate the communication and integrative mechanisms in ctenophores or comb jellies, the descendants of the earliest animal lineage. Ctenophores convergently evolved their neuro-muscular systems compared to other animals. Besides the canonical neural organization with distinct synapses, recent 3D electron microscopy reconstruction revealed unique syncytial-type connectivity in some neuronal populations, providing additional support for the independent origins of neurons in ctenophores. Furthermore, it was proposed that non-synaptic transmission is the distinct characteristic of ctenophores, in contrast to other metazoans and Cajal’s neuronal doctrine, highlighting Golgi’s reticular theory. However, the astonishing diversity and higher information capacity of classical synapses and volume transmission indicate that chemical signaling is the hallmark of neural and other integrative systems regardless of their origins.

In today's world, wherein more than 80% of world trade is carried out by maritime routes, the safety and security of the seas where this trade takes place is of vast importance for nations and the in-ternational community. For this reason, ensuring the sustainable safety and security of the seas has become an integral part of the mission of all maritime-related entities. The utilization of big data extracted from the seas and maritime activities into meaningful information with artificial intelli-gence applications and developing applications that can be used in maritime surveillance will be of great importance for augmenting maritime safety and security. In this article, data sources which can be used by a maritime surveillance system based on big data and artificial intelligence tech-nologies and which can be established around sensitive sea areas and critical coastal facilities, are identified and a model proposal using this maritime big data is put forward.

This work introduces an approach to investigate the existence of zeros for holomorphic complex functions, defined on open and simply connected subdomains of C, in a systematic and direct way. This is achieved by means of the theory of dynamical systems (Poincar\'e index) and the perception that a zero of a specific complex function is an equilibtium for the associated dynamical system. The logical basis of proof is to find the mathematical expression for the Poincaré index of the vector field associated to the function under study, assuming the existence of a zero, and investigating the consequences. Depending on the value of the index, an inconsistency may occur, establishing a contradiction.

This study focuses on the aerodynamic optimization of the re-entry capsule (REC) Orion Crew Exploration Vehicle (CEV) module, evaluating its performance using computational fluid dynamics (CFD). Three different models, namely Baseline, 2-Duct, and 4-Duct, are investigated under a wide range of flow speeds, from supersonic (Ma=1.6) to hypersonic (Ma=6) regimes. Thermofluids aspects, drag coefficient, surface heat flux, and the effectiveness of the modified geometry are assessed using aerodynamic and temperature-based models. The results show that the introduction of ducts in the 2-Duct and 4-Duct models leads to an average decrease in the drag coefficient of approximately 2.1% and 2.9%, respectively, compared to the baseline model, across all Mach numbers. The maximum reduction in drag coefficient is 3.55%, observed at Ma=1.4 using the 4-Duct capsule. However, it is found that the addition of extra ducts to the model increases the maximum heat transfer rate. Specifically, the heat flux is increased by up to 69% for the 4-Duct model at Ma=6, compared to the baseline model. On average, the heat flux is increased by approximately 28.6% for all conditions. The findings indicate that the introduction of ducts in the REC model improves its aerothermodynamic performance by reducing drag but also increases the heat transfer rate on the surface.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

: There has been an ever-increasing demand for energy from the past decades. Renewable energy technologies play a major role in satisfying the energy demand as well as in a decreased CO2 emissions. Solar and Wind energies are the major upcoming technologies in renewable energies. Decentralized power production is a system where the energy production and consumption are very close to each other. Microgrids can be decentralized or grid connected and they, with renewable energy sources, encounter a problem of storage as the power production from solar and wind is intermittent. This paper discusses the comparison between using batteries and pumped storage hydropower (PSH) as an energy storage system and the integration of wind and solar PV energy sources. HOMER software simulations are used to obtain optimized renewable energy integration in microgrid and to understand its economic analysis. Two scenarios are run with the model where one considers battery and the other considers PSH to obtain the economic and technical best results of these microgrids. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results show that microgrid with storage of PSH is economical with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh in comparison with batteries solution which has an NPC of 95.2M€ and an LCOE of 0.786 €/kWh. The role of storage is understood by differentiating the data into different seasons using Python for data analysis. Furthermore, sensitivity analysis is made by varying the capital cost multiplier of Solar PV and Wind Turbine to obtain optimal solutions.

Current artificial intelligence (AI) technology is still considered weak AI. However, the progression towards strong AI unavoidably involves grappling with concerns surrounding consciousness, the mind, and creativity. This paper focuses on analyzing consciousness, creativity, and the generation of emotions to explore the possibility of machine-based consciousness, creativity, and emotion. We believe that the emergence of consciousness relies on the complex interplay at different layers within a complex system, rather than wholly relying on the underlying substrates of the system. Thus, it is plausible to consider the existence of machine-based consciousness, creativity, and emotion. Furthermore, swarm intelligence should be recognized as a distinct form of intelligence. Human individuals serve as the foundational substrate enabling the realization of swarm intelligence, with individual intelligence reflecting the projection of swarm intelligence at the individual level. The advanced state of human civilization is primarily attributable to the effect of swarm intelligence. Building upon basic emotions to adapt to environmental conditions, advanced human emotions are a product of the evolutionary process by swarm intelligence. Emotions serve as a pivotal impetus for the formation and development of societies grounded in swarm intelligence. In pursuit of super-AI that surpasses human civilization in the future, nurturing swarm intelligence represents an inevitable trajectory.

In recent years, antimicrobial resistance in many human pathogens has become a serious health concern. Since infections with resistant pathogens cannot be treated with traditional antimicro-bial drugs, new strategies are necessary to fight bacterial infections. Hybrid nano-systems may provide a solution to this problem, by combining multiple mechanisms for killing bacteria to synergistically increase the effectiveness of the antimicrobial treatment. In this review, we high-light recent advances in the development of hybrid nano-systems for the treatment of bacterial infections. We discuss the use of hybrid nano-systems for combinational therapy, focusing on various triggering mechanisms for drug release and the development of biomimetic nanomateri-als. We also examine inherently antimicrobial nano-systems and their uses in preventing infec-tions due to wounds and medical implants. This review summarizes recent advances and pro-vides insight into the future development of antimicrobial treatments using hybrid nanomateri-als.

Abstract: The planning of innovative changes in socio-economic systems should focus on the effective utilization of the existing potential for innovative development. This research proposes a method for strategic planning of innovative changes, which enables the determination of development goals, approaches to problem-solving, and a system of indicators to optimize plans for innovative change. The stages of organizing innovative activities, aligned with the mechanism of project development and implementation, are identified. The technique is improved through a system of analytical indicators that assess the state of innovative activity and the scientific and technical level of innovations. The establishment of admissible sets of elementary processes and their combinations leading to innovative changes is also discussed. The planning algorithm is adapted to the type of innovative behavior and the state of the innovation potential within the socio-economic system. The potential for innovation in socio-economic systems is exemplified by the case of Ukraine, with the determination of the strategy for its innovative development in relation to the potentially optimal state.

A bijective analysis is confirming, the expansion of universal space has never been directly observed, and this assumption is unproven thus far. The measurement of the gravitational redshift, which was confirmed using the Mossbauer experiment, proves only the gravitational redshift and nothing more because there is no causality between the gravitational redshift and hypothetical expansion. Thus, even if the universal space is assumed to expand, the gravitational redshift cannot be assumed to be proof of the expansion. In addition, the Doppler effect was never observed in an expanding space; thus, the claim that the cosmological redshift is partially caused by the Doppler effect, which is caused by the expansion of space, is an unproven assumption. Furthermore, the discovery of cosmic microwave background (CMB) radiation simply proves that the radiation is emitted by the entire universal space, but does not prove the existence of a recombination period. In evidence-based cosmology, every element in the model has a corresponding element in physical reality that is observed and measured. The evidence-based cosmology model is related to the real universe by a bijective function of set theory. Supermassive black holes in the centre of galaxies are rejuvenating systems of the universe. They rotate their local superfluid space which is the physical origin of galactic rotation curves.

Intense thermoacoustic oscillations may lead to severe deterioration due to the induced intolerable damage to combustors. A better understanding of unstable behaviors is important to prevent or suppress these oscillations. Active thermoacoustic coupling in practical combustors is caused primarily by two approaches: inherent turbulent fluctuations and the flame response to acoustic waves. Turbulent fluctuations are generally characterized by random noise. This paper experimentally expands on previous analytic studies regarding the influence of colored disturbances on the thermoacoustic response near the supercritical bifurcation point. Therein, a laboratory-scale Rijke-type thermoacoustic system is established, and both supercritical and subcritical bifurcations are observed. Then, Ornstein–Uhlenbeck (OU)-type external colored noise is introduced near the supercritical bifurcation point, and the effects of the corresponding correlation time τc and noise intensity D are studied. The experimental results show that these variables of the colored noise significantly influence the dynamics of thermoacoustic oscillations in terms of the most probable amplitude, autocorrelation, and correlation time. A resonance-like behavior is observed as the noise intensity or the autocorrelation time of the colored noise is continuously varied, which means that the coherent resonance occurs in the thermoacoustic system. Finally, when the system is configured closer to the stability boundary, the extent of the coherence motion is intensified in the stochastic system response. Meanwhile, the Signal to Noise Ratios (SNRs) of the colored-noise-induced response are found to become more distinguished, the optimal colored noise intensity decreases, and the optimal autocorrelation time increases. This provides valuable guidance to predict the onset of thermoacoustic instabilities.

Considering the current needs of industry 4.0, especially in the condition of systems linked to artificial intelligence, this multidisciplinary concept work and difficult to execute, proposes the main idea of recognizing patterns using mathematical concepts of simple access and logical resolution of great potential. The objective of this work is to demonstrate a process for pattern recognition by applying the Fibonacci concept to vibration signals of hybrid dynamical systems. For this to be possible, it was necessary to use the concepts of preprocessing in a vector for pattern recognition, together with the concepts of the golden rule of Fibonacci, finally, all applied in the theorem of Jeffcott Rotors. The general result, from a technical point of view, demonstrates positivity and relationship of patterns, because, considering the aspect of the location between the values of the experimental and numerical result, both signs have similarities.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

In the last decade, the genetic engineering world has been shaken up by a relatively new genetic editing tool based on RNA-guided Nucleases (RGNs): the CRISPR/Cas9 system. Since the first report in 1987 and its characterization in 2007 as a bacterial defense mechanism, the interest and research on this system have grown exponentially. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system that makes it possible to genetically modify almost any organism. There are diverse approaches regarding the refinement of these modifications, such as constructing more accurate nucleases, understanding the cellular context and facing the epigenetic conditions, or re-designing guide RNAs (gRNAs). Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.

We propose that, at equilibrium, statistically equal temperatures as mechanical torques are exerted on each kind of gas phase molecules as rates of translational action [@t=ʃmvds/dt=ʃmr2ωdϕ/dt=mv2, J]. These torques result from the impulsive density of resonant quantum fields with molecules, configuring the trajectories of gas molecules while balancing molecular pressure (p=NkT) against the density of field energy (J/m3). Gibbs energy fields contain no resonant quanta at zero Kelvin, with this measure of chemical potential diminishing in magnitude as translational action of the vapor molecules and quantum field energy increases with temperature. In illustration, we show how impulsive torques from quantum fields drive the reversible thermodynamics of Carnot’s heat-work engine cycle, sustain the decreasing atmospheric temperature gradient of increasing molecular entropy with altitude, support the translational action and field energy of vortical wind flow in anticyclones, frictionally warming the Earth’s surface while recycling greenhouse infrared absorption of surface radiation, generating electrical power from air flow in wind farms and destructive power in tropical cyclones. These cases all distinguish symmetrically between a causal field of impulsive quanta (Σhν) that energizes the action of matter and the resultant vis viva of molecular mechanics (mv2). The quanta of these different fields display mean wavelengths from 10-4 m to 1012 m, with mechanical advantages many orders of magnitude greater than the corresponding translational actions, though with mean quantum frequencies (v) similar to those of radial Brownian movement for independent particles (ω). These energy fields are also thermodynamically reversible reservoirs for heat, optimizing work processes on Earth and delaying the achievement of maximum entropy production from short wave solar radiation in conversion to outgoing long wave radiation to space.

Illegal removal of road barricades without the notice of road emergency officials and road users has resulted in fatalities, injuries, and property damages. It is only after an incident has occurred or someone noticed the removal and alerted the authorities for the barricade to be placed back at its intended location. There is a need for traditional barricades to be equipped with mechanisms that would alert officials on illegal road barricade removal and warn road users of a road/lane(s) closure to prevent fatalities, injuries, and property damages to the traveling public. This research has used the Global Positioning System (GPS) module to implement the detection of barricade displacement and an alerting system for emergency officials and road users. The barricade displacement was estimated from the haversine distance formula, corrected for errors, and interfaced with the displacement threshold value for the road users within a geofenced area to be alerted. The geofenced area radius was estimated to be 1.04 miles from the barricade location using AASHTO, NSC, and TransGuide ITS manuals. The non-parametric bootstrapping method estimated the GPS position error to 10.5 feet and corrected the measured distances. Data from a clear sunny day shows the best response to barricade movements compared to a cloudy day where movements can’t be explained easily. The proposed monitoring and warning systems would warn road users and alert emergency officials of the danger when the physical barricades have been removed illegally hence saving lives and reducing property damages.

The concentration of data from multiple links to a single output is an essential task performed by High-Energy Physics (HEP) Data Acquisition Systems (DAQs). At high and varying data rates combined with the large width of the concentrator’s output interface, this task is non-trivial. This paper presents a concentrator based on the Beneš network, which provides efficient concentration without using a high-frequency clock internally. It warrants that empty data are eliminated and does not disturb the data time-ordering if the data rates significantly differ between inputs. Additionally, it is well suited to FPGA implementation. It is based on simple data-routing primitives and may be fully pipelined.

Application of the Ramsey graph theory to the analysis of physical systems is reported. Physical interactions may be very generally classified as attractive and repulsive. This classification creates the premises for the application of the Ramsey theory to the analysis of physical systems built of electrical charges, electric and magnetic dipoles. The notions of mathematical logic, such as transitivity and intransitivity relations, become crucial for understanding of the behavior of physical systems. The Ramsey theory explains why nature prefers cubic lattices over hexagonal ones for systems built of electric or magnetic dipoles. The Ramsey approach may be applied to the analysis of mechanical systems when actual and virtual paths between the states in the configurational space are considered. Irreversible mechanical and thermodynamic processes are seen within the reported approach as directed graphs. Chains of irreversible processes appear as transitive tournaments. These tournaments are acyclic; the transitive tournaments necessarily contain the Hamiltonian path. The set of states in the phase space of the physical system, between which irreversible processes are possible, is considered. The Hamiltonian path of the tournament emerging from the graph uniting these states is a relativistic invariant. Applications of the Ramsey theory to the general relativity become possible when the discrete changes in the metric tensor are assumed. Reconsideration of the concept of “simultaneity” within the Ramsey approach is reported.

The advancement in network security threats led to the development of new Intrusion Detection Systems(IDS) that rely on deep learning algorithms known as deep IDS. Along with other systems based on deep learning, deep IDS suffer from adversarial examples: malicious inputs aiming to change the prediction of a machine learning/deep learning model. Protecting deep learning against adversarial examples remains an open challenge. In this paper, we propose “NIDS-Defend” a framework to enhance the robustness of Network IDS against adversarial attacks. Our framework is composed of two layers: a statistical test and a classifier that together detect adversarial examples in real-time. The detection process consists of two steps: (1) flagging flows that contain adversarial examples with a statistical test, and (2) extracting individual adversarial examples in the previously flagged flows with a classifier. Our approach is evaluated on binary IDS with the NSL-KDD dataset. To generate adversarial examples, the crafting methods used are (1) Boundary attack and (2) HopSkipJumpAttack. We investigate the vulnerabilities of a Network IDS against adversarial examples, then apply our defense. The statistical test can confidently distinguish adversarial flows with more than 95% accuracy, and the classifier detects individual adversarial examples with more than 80% accuracy. We also show that our framework detects adversarial examples crafted by an adversary aware of the defense and confirm the effectiveness of our solution against adversarial attacks.

Simulation-based education is a part of Intelligent Transportation Systems (ITS) education. The paper discusses the experimental success of using Virtual Reality (VR) technology in simulation-based ITS education to improve the quality of education while increasing immersion and motivation. The study documents the application of VR technology to a microscopic simulation model and the methodology to capture and evaluate student experiences. The study discusses findings and ways to improve the planned VR technology implementation in the ITS laboratory.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

This paper presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes too long for systems engineers and architects to develop a new system from scratch, particularly new space-based systems derived from the existing space systems architectures. A pattern language is a holistic view of reusable logical model artifacts; many are interdisciplinary and introductory, if at all. The results are mostly a combination of the application-specific logical solution, which further results in the best possible overall solution. The main benefit of the pattern language is reducing the time and validation required to generate a new space-based system architecture; this approach will develop top-level requirements in the initial phase of the system development. The rationale of the methodology proposed by the paper is as follows, collect, and decompose published literature and other open-source information available on space system architectures and system models; develop SysML models for systems, subsystems, products, assembly, subassembly level, and mission-specific requirements using CAMEO SysML software. Arrange these patterns to develop a functional ontology and construct a logical architecture pattern library. This approach created, updated, and managed SysML pattern language, which evaluated the expedited new model construction. Again, our objective is to develop a logical pattern language using public domain information and evaluate patterns by constructing a new space mission concept—for example, planetary surface habitat.

Despite their enormous potential the use of Indoor Localization Systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of Test and Evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed - a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based Light Detection and Ranging ILS, an Ultra Wideband ILS, and a camera-based ILS for the application of Automated Guided Vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework.

Self-healing is the capability of materials to repair themselves after damage has occurred, usually by interaction between molecules or chains. Physical and chemical processes are applied for the preparation of self-healing systems. There are different approaches for these systems such as heterogeneous systems, shape memory effects, hydrogen bonding or covalent-bond interaction, diffusion and flow dynamics. Self-healing mechanisms can occur in particular by heat and light exposure or by reconnection without direct effect. The applications of these systems display an increasing trend in both R&D and industry sectors. Moreover, self-healing systems and their energy storage applications are currently getting great importance. This review aims to provide general information on recent developments in self-healing materials and their energy applications in view of the critical importance of self-healing systems for lithium-ion batteries (LIBs). In the first part of the review, an introduction about self-healing mechanisms and design strategies of self-healing materials is given. Then, selected important healing materials in the literature for the anodes of LIBs are mentioned in the second part. The results and future perspectives are stated in the conclusion section.

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

“Money has always been something of an embarrassment to economic theory. Everyone agrees that it is important; indeed, much of macroeconomic policy discussion makes no sense without reference to money. Yet, for the most part theory fails to provide a good account for it.” (Banerjee and Maskin, 1996, p. 955)The debate about whether or not a growth imperative exists in debt based, interest bearing mone- tary systems has not yet been settled. It is the goal of this paper to introduce a new perspective in this discussion.For that purpose an SFC computational model is constructed which simulates a post Keynesian Endogenous Money system without including economic parameters such as production, wages, consumption and savings. A case is made that isolating the monetary system allows for better analysis of the inherent properties of such a system.Loan demands, which are assumed to happen, are the driving force of the model. Simulations can be run in two modes, each based on a different assumption. Either the growth rate of the money stock is assumed to be constant or the loan rate, expressed as a percentage of the money stock, is assumed to be constant.Simulations with varying parameters are run in order to determine the conditions under which the model converges to stability, which is defined as converging to a bounded debt rate.The analysis shows that stability of the model is dependent on net bank profit ratios, expressed relative to their debt assets, remaining below the growth rate of the money stock. Based on these findings it is argued that the question about the existence of a growth imperative in debt based, interest bearing monetary systems needs to be reframed. The question becomes whether a steady state economy can realistically support such a system without destabilising it. In order to answer this question real world behavior of economic actors must be included in the model.It is concluded that there are indications that it might not be feasible for a steady state economy to support a stable debt based, interest bearing monetary system without strong interventions. However, more research is necessary for a definite answer. Real world observable data should be analysed through the lens of the presented model to bring more clarity.

Without impacting the dental sciences, breakthroughs in technology and applications could not be accomplished. In the advancement of technology and information technology, dentistry and dental materials have been fully active, so much so that they have revolutionized dental techniques. Material & methods; We want to produce the first series of articles in this review on the use of digital techniques and software, such as Smile Concept Digital. The goal is to gather all the findings on the use of this program and to highlight the fields of use. The analysis included forty-nine articles, the latter discussing the use of Digital Smile Design and the area of use. The research aims to classify the dental fields are using "digitization." Change is constant in this field and will be increasing Interest in dentistry by recommending the speed and reliability of outcomes for care planning. Conclusion: As seen in the study, the digital workflow facilitates recovery that is reliable both from an aesthetic and functional point of view. The current area of use of Digital Smile Design techniques in the different branches of medicine and dentistry as well as knowledge have emerged from this research

The exact dynamics of emergence remains one of the most prominent outstanding questions for the field of complexity science. I first discuss various perspectives on emergence in various contexts, then offer a different perspective on understanding emergence in a graph-theoretic representation. From the discussion, an observer’s choice in state space seems to have an effect for that observer to detect emergent behavior. To test these ideas, I analyze the dynamics of all possible spatial state spaces near the critical temperature in an Ising model. As a result, state space topologies that appear more deterministic flip more bits than topologies that appear more random, which is contrary to our intuitions about randomness. In addition, the size of different state spaces constrain a system’s ability to explore various states within the same time frame. These results are important to understanding emergent phenomena in biological systems, which are layered with various state spaces and observational perspectives.

The primary distribution systems are comprised of power lines delivering power to utility feeders from substations. The inspection and maintenance of damaged and broken power system insulators are of paramount importance for continuous power supply and public safety. hence, to identify any faults and defects in advance a periodic inspection of power line insulators and other components be ensured beforehand. To automate the process and reduce operational cost and risk Unmanned Aerial Vehicles (UAVs) are being extensively utilized. As they present a safer and efficient way to examine the power system insulators and their components without closing the power distribution system ensuring continuous supply to the end-users. To achieve these objectives in this work a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. Deep Laplacian pyramids based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low light images a low light image enhancement technique is used for the robust exposure correction of the training images. Using computer vision-based object detection techniques to identify faults and classify them according to classes they belong. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. To improve the faults detection several flight path strategies are proposed for efficient inspection. Such strategies overcome the shuttering effect of insulators along with providing a less complex, time, and energy-efficient approach for capturing video stream of the power system components. Performance of different object detection models is presented for selecting the suitable one for fine-tuning on the specific faulty insulator dataset. Our proposed approach gives a less complex and more efficient flight strategy along with better results. For defect detection, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust faults recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat Pakistan.

In this paper, we address the problem of stabilization of switched linear systems. The idea is to look for a state feedback control law using evolutionary algorithms (EA) in order to assure the stability of the switched linear systems under control constraints. In some cases when states are not available and only outputs are measurable, the previous method is applied to design an output feedback controller which stabilizes the system. Both stabilizing controllers are developed using deferential evolution and genetic algorithm. Two numerical examples illustrate our proposed theory and point out the effectiveness of our proposed approaches.

The effects of an external strain tensor on the electrical resistivity of a manganite granular system are investigated using a simple approach describing the induced deformation in terms of the tilt angle between adjacent grains. The results obtained assuming the resistivity of each grain as given by a metallic part, coming from the inner grain, and a surface related tunnel contribution, allow to estimate appreciable resistivity variations even in the case of small deformation angles.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.

The imperfection of theoretical and methodological approaches to regulate the jump process transition when combining differentiated energy resources is a pressing issue. The goal of this paper is to develop a theory and a method to regulate the integration-balancing processes of combining diversified resources. The concept of combining integration and balancing models has been substantiated by methods of transforming multidimensional space and approximating generalized functions that represent jump-like processes. Theoretical and operational-regulatory models of economic sustainability have been developed, substantiating new concepts, patterns, properties, dependencies and indicators of the dynamics of the processes of combination; the optimality conditions for the number of approximations of generalized functions, interpreting the effects of control functions of combining resources, are determined. New methods for solving problems have been developed: the organization of the energy technology complex of facilities for enhanced resource diversification and the Center for Sustainability, improving the quality of managing dynamic processes in terms of combining and diversifying resources.

The aim of this survey is to review some recent developements in devising efficient preconditioners for sequences of linear systems A x = b. Such a problem arise in many scientific applications, such as discretization of transient PDEs, solution of eigenvalue problems, (Inexact) Newton method applied to nonlinear systems, rational Krylov methods for computing a function of a matrix. Full purpose preconditioners such as the Incomplete Cholesky (IC) factorization or approximate inverses are aimed at clustering eigenvalues of the preconditioned matrices around one. In this paper we will analyze a number of techniques of updating a given IC preconditioner (which we denote as P0 in the sequel) by a low-rank matrix with the aim of further improving this clustering. The most popular low-rank strategies are aimed at removing the smallest eigenvalues (deflation) or at shifting them towards the middle of the spectrum. The low-rank correction is based on a (small) number of linearly independent vectors whose choice is crucial for the effectiveness of the approach. In many cases these vectors are approximations of eigenvectors corresponding to the smallest eigenvalues of the preconditioned matrix P0 A. We will also review some techniques to efficiently approximate these vectors when incorporated within a sequence of linear systems all possibly having constant (or slightly changing) coefficient matrices. Numerical results concerning sequences arising from discretization of linear/nonlinear PDEs and iterative solution of eigenvalue problems show that the performance of a given iterative solver can be very much enhanced by the use of low-rank updates.

The purpose of the work is an underwater positioning safety study that used the GNSS-like underwater navigation systems. In the process of research, we used the methods of software modeling of underwater spoofing processes. The spoofing problem consists of three stages: design of spoofers, design of spoofing detection systems, and design of anti-spoofing systems. This article discusses some methods of spoofing detection. We briefly describe the known methods of underwater positioning systems. Unlike GNSS, currently only LNSS (Local Navigation Satellite System) can be considered in this case. Spoofing detection systems with one hydrophone are of great practical importance, as they allow for use of standard hydroacoustic equipment. However, detection of spoofing is not possible in static mode, which is with underwater vehicle at rest. In case of two hydrophones the detection of spoofing in static mode is possible. We discuss the navigation based on the use of an acoustically passive receiver. The receiver “listens” to the buoys and solves the problem of finding its own position using the coordinates of the buoys (such systems are called GNSS-like Underwater Positioning Systems or GNSS-like UPS). Depending on the scale of system service area, GNSS-like UPS-es are divided into global, regional, zonal and local systems. In this article, we take into account only the local class of GNSS-like UPS. The acoustic signal generator transmits a simulation of several buoy signals. If the level of the simulated signal exceeds the signal strength of actual buoys, the UPS receiver will “lock onto” the fake signal and then calculate a false position basing on it. The development of further research should be focused on the creation of hardware and software systems for conducting physical experiments at depths up to 400 m.

The Physical Random Access Channel (PRACH) plays an important role in LTE and LTE-A systems. It is through the PRACH channel that the user equipment (UE), based on eNodeB's timing estimates, aligns its uplink transmissions to the eNodeB's uplink and gain access to the network. One of the initial operations executed by the PRACH receiver at eNodeB side is the translation of the PRACH signal back to base band, $i.e.$, center the PRACH signal around DC. This operation is a necessary step for preamble detection and can be carried out through a time-domain frequency shift operation. Therefore, in this paper we present the hardware architecture and implementation details of a configurable and optimized FPGA-based time-domain frequency shifter. It is a hardware-efficient and accurate architecture for converting the relevant received PRACH signal into base band before further signal processing. The architecture is manly based on a customized Numerically Controlled Oscillator (NCO), which is used for generating complex exponentials employing only adders, a Look-Up Table (LUT) and plain logic resources. The main advantage of the proposed hardware architecture is that it completely eliminates the need for storing a large number of long complex exponential sequences by employing a single LUT and exploiting quarter wave symmetry of the basis waveform. Our simulation results show that the proposed customized NCO architecture provides high Spurious Free Dynamic Range (SFDR) signals using a minimal amount of FPGA resources. Moreover, the proposed architecture exhibits spur-suppression ranging from 62.13 to 153.58 dB without using Taylor Series correction.

One of the notable temporal properties of dynamical systems is that a set of initial states leads the solutions to reach desired states avoiding a predetermined unsafe set.This property, that we call safe reachability has been studied in literature for autonomous systems using Barrier functionand Barrier densities [1]. In this paper, we generalize a sufficient condition for safe reachability of autonomous systemto switched systems under arbitrary switching signals. The condition relies upon the existence of a common Barrier density function for each subsystem. We apply the condition using the sum of squares method together with Putinar Positivstellensatz.

Bioelectrochemical systems (BES) encompass a group of biobased technologies capable of directly converting organic matter into electricity. In these systems, which are derived from conventional electrochemical systems, the ion exchange membrane represents a key element because of its influence on the economic feasibility and on the performance of BES. This study examines the impact of long-term operation of a BES on the mechanical, chemical and electrochemical properties of five different kind of cation exchange membranes (Nafion-117, CMI-7001, Zirfon UTP 500, FKE and FKB) through several techniques: (i) scanning electron microscopy (SEM) and atomic force microscopy (AFM) to assess the changes on the membranes surface, (ii) thermogravimetric analysis (TGA) to evaluate the structural stability of the membranes, and (iii) ion exchange capacity (IEC) to monitor any change in their electrochemical properties. Results confirmed that there is not an ideal membrane for BES. While Nafion and CMI-7000 exhibited the strongest chemical structure, they also underwent the highest fouling as revealed by a fast increase in surface roughness.

We review some new approaches to the description of the evolution of states of many-particle quantum systems by means of the correlation operators. Using the denition of marginal correlation operators within the framework of dynamics of correlations governed by the von Neumann hierarchy, we establish that a sequence of such operators is governed by the nonlinear quantum BBGKY hierarchy. The constructed nonperturbative solution of the Cauchy problem to this hierarchy of nonlinear evolution equations describes the processes of the creation and the propagation of correlations in many-particle quantum systems. Moreover, we consider the problem of the rigorous description of collective behavior of many-particle quantum systems by means of a one-particle (marginal) correlation operator that is a solution of the generalized quantum kinetic equation with initial correlations, in particular, correlations characterizing the condensed states of systems.

The UK food industry faces significant challenges to remain sustainable. With major challenges such as Brexit on the horizon, companies can no longer rely on a low labour cost workforce to maintain low production costs and achieve economic sustainability. Smart Systems (SS) is being seen as an approach towards achieving significant improvements in both economic and environmental sustainability. However, there is little evidence to indicate whether UK food companies are prepared for the implementation of such systems. The purpose of this research is to explore the applicability of Smart Systems in UK food manufacturing companies and, to identify the key priority areas and improvement levers for the implementation of such systems. A triangulated primary research approach is adopted and includes a questionnaire, follow up interviews and visits to thirty-two food manufacturing companies in the UK. The questionnaire and interviews are guided by the development of a unique measuring instrument created by the authors that is focusses upon SS technologies and systems. This paper makes an original contribution in that it is one of few academic studies to explore the implementation of SS in the industry and, provides a new perspective on the key drivers and inhibitors around its implementation. Findings suggest that the current turbulence in the industry could be bringing food companies closer to the adoption of such systems, hence it is a good time to define and develop the optimum SS implementation strategy.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

The paper is devoted to the investigation of regional observability with constraints on the state of semilinear parabolic systems. The purpose is to reconstruct the initial state between two prescribed functions only on an internal subregion $\omega$ of the system evolution domain $\Omega$. The proofs use two approaches, the subdifferential and HUM approach. Finally, a numerical example is provided to verify the effectiveness of our theory results.

Consider a uniquely ergodic $C^*$-dynamical system ba\-sed on a unital $*$-endomorphism $\Phi$ of a $C^*$-algebra. We prove the uniform convergence of Cesaro averages $\frac1{n}\sum_{k=0}^{n-1}\lambda^{-n}\Phi(a)$ for all values $\lambda$ in the unit circle which are not eigenvalues corresponding to "measurable non continuous" eigenfunctions. This result generalises the analogous one in commutative ergodic theory presented in [19], which turns out to be a combination of the Wiener-Wintner Theorem (cf. [22]) and the uniformly convergent ergodic theorem of Krylov and Bogolioubov (cf. [15]).

Japanese encephalitis is a zoonotic disease caused by Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot resist various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and recently, it has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes participating in the progress of JEV infection using the systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib which can target these genes may have potential effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using the JEV-infected mice model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides a new method for the development of antiviral agents.

In this paper, an offshore airborne wind energy (AWE) farm consisting of three non-reversing pumping mode AWE systems is modelled and simulated. The AWE systems employ permanent magnet synchronous generators (PMSG). A direct interconnection technique is developed and implemented for AWE systems. This method is a new approach invented for interconnecting offshore wind turbines with the least number of required offshore-based power electronic converters. The direct interconnection technique can be beneficial in improving the economy and reliability of marine airborne wind energy systems. The performance and interactions of the directly interconnected generators inside the energy farm internal power grid are investigated. The results of the study conducted in this paper, show the directly interconnected AWE systems can exhibit a poor load balance and significant reactive power exchange which must be addressed. Power control strategies for controlling the active and reactive power of the AWE farm are designed, implemented, and promising results are discussed in this paper.

For a dynamical system, it is known that the existence of a Lyapunov density implies almost global stability of an equilibrium. It is then natural to ask whether the existence of a common Lyapunov density for a nonlinear switched system implies almost global stability, in the same way as a common Lyapunov function implies global stability for nonlinear switched systems. In this work, the answer to this question is shown to be affirmative as long as switchings satisfy a dwell-time constraint with an arbitrarily small dwell time. As a straightforward extension of this result, we employ multiple Lyapunov densities in analogy with the role of multiple Lyapunov functions for the global stability of switched systems. This gives rise to a minimum dwell time estimate to ensure almost global stability of nonlinear switched systems, when a common Lyapunov density does not exist. The results obtained for continuous-time switched systems are based on some sufficient conditions for the almost global stability of discrete-time non-autonomous systems. These conditions are obtained using the duality between Frobenius-Perron operator and Koopman operator.

We review recent progress in high-speed orbital angular momentum (OAM) multiplexed free-space optical communication systems. The outdoor atmospheric turbulence is emulated by an indoor turbulence emulator, which is based on split-step beam propagation method. Adaptive optics, channel coding, Huffman coding combined with LDPC coding, and spatial offset are used for turbulence mitigation; while OAM multiplexing and wavelength-division multiplexing (WDM) are applied to boost aggregate capacity.

The consistently positive Australian economic environment and stable population increase have led to a higher demand for new houses in recent years. Prefabrication is a promising method to help alleviate the issues related to housing shortage and affordability due to reduced material wastage, construction delays due to weather conditions, unexpected costs, shortage in labour and onsite risks. With the advancements in automation and manufacturing methods such as Design for Manufacturing and Assembly (DfMA), the quality and precision of prefabricated materials is tightly controlled, and the fabrication and assembly period are reduced. However, the full potential of prefabricated construction is yet to be realised in part due to most of developments being focused on its superstructure. A review of the current available options suitable for houses is necessary to understand the present state of the residential footing industry, which will help evaluate the necessary innovations for the growth of the Australian construction industry considering the local reactive soil conditions. This paper presents a summary of existing footing systems and potential prefabricated footing solutions for low-rise residential structures with one storey to two storeys. This paper also reviews the benefits and challenges of designing, manufacturing, transporting, handling and installing of prefabricated footings on site, which have great influence on the acceptance of these innovative footing systems.

In this paper, the current technologies of the regenerative shock absorber systems have been categorized and evaluated. Three drive modes of the regenerative shock absorber systems, namely the direct drive mode, the indirect drive mode and hybrid drive mode are reviewed for their readiness to be implemented. The damping performances of the three different modes are listed and compared. Electrical circuit and control algorithms have also been evaluated to maximize the power output and to deliver the premium ride comfort and handling performance. Different types of parameterized road excitations have been applied to vehicle suspension systems to investigate the performance of the regenerative shock absorbers including that of the nonlinear regenerative shock absorber. The research gaps for comparison of the different drive modes and the nonlinearity analysis of the regenerative shock absorbers are identified and, the corresponding research questions have been proposed for future work.

In this paper we study the class of mixed-index time fractional differential equations in which different components of the problem have different time fractional derivatives on the left hand side. We prove a theorem on the solution of the linear system of equations, which collapses to the well-known Mittag-Leffler solution in the case the indices are the same, and also generalises the solution of the so-called linear sequential class of time fractional problems. We also investigate the asymptotic stability properties of this class of problems using Laplace transforms and show how Laplace transforms can be used to write solutions as linear combinations of generalisedMittag-Leffler functions in some cases. Finally we illustrate our results with some numerical simulations.

This paper investigates existing mathematical morphology based techniques applied for performing malaria parasites detection and identification in both Giemsa and Leishman stained blood smears images. Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis; this kind of visual inspection is subjective, errorprone and time consuming. In order to cope with such issues, computer-aided methods have been increasingly evolved for abnormal erythrocyte and/or parasites detection, segmentation and semi/fully automated classification. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection.

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

This paper presents a methodology for determining the sensitivity of the main air flow directions in ventilation subnetworks to changes of aerodynamic resistance and of air density in mine workings. Formulae for determination of the sensitivity of the main subnetwork air flows by establishing the degree of dependency of the air volume stream in a given working on the variations in resistance or air density of other workings of the network have been developed. They have been implemented in the VentGraph mine ventilation network simulator. This software, widely used in Polish collieries provides an extended possibility to predict the process of ventilation, air distribution and, in the case of underground fire, also the spread of combustion gasses. The new method facilitates assessment by mine ventilation services of the stability of ventilation systems in exploitation areas and determine of the sensitivity of the main subnetwork air flow directions to changes of aerodynamic resistance and air density. Recently in some Polish collieries new longwalls are developed in seams located deeper then the bottom of the intake shaft. Such solution is called “exploitation below the level of access” or “sublevel”. The new approach may be applied to such developments to assess the potential of changes of direction and air flow rates. In addition, interpretation of the developed sensitivity indicator is presented. While analyzing air distributions for sublevel exploitation, application of current numerical models for calculations of the distribution results in tangible benefits, such as the evaluation of the safety or risk levels for such exploitation. Application of the VentGraph computer program, and particularly the module POŻAR (fire) with the newly developed options, enables an additional approach to the sensitivity indicator in evaluating air flow safety levels for the risks present during exploitation below the level of the intake shaft. The analyses performed and examples presented enabled useful conclusions in mining practice to be drawn.

Studies on the structure of economic systems are, most frequently, carried out by the methods of informational statistics. These methods, often accompanied by a wide range of indicators (Shannon entropy, Balassa coefficient, Herfindahl specialty index, Gini coefficient, Theil index etc.) around which a wide literature has been created over time, have a major disadvantage. Such weakness is related to the imposition of the system condition, therefore the need to know all the components of the system (as absolute values or as weights). This restriction is difficult to accomplish in some situations, and in others, this knowledge may be irrelevant, especially when there is an interest in structural changes only in some of the components of the economic system (either we refer to the typology of economic activities - NACE or of territorial units – NUTS). This article presents a procedure for characterizing the structure of a system and for comparing its evolution over time, in the case of incomplete information, thus eliminating the restriction existent in the classical methods. The proposed methodological alternative uses a parametric distribution, with subunit values for the variable. The application refers to Gross Domestic Product values for five of the 28 European Union countries, with annual values of over 1,000 billion Euros (Germany, Spain, France, Italy and United Kingdom) for the years 2003 and 2015. A form of the Wald sequential test is applied to measure changes in the structure of this group of countries, between the years compared. The results of this application validate the proposed method.

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to various parameters, e.g. efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach. Thereby the Cyber-Physical Systems play an important role as sources of information to achieve context sensitivity. In this paper it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of Cyber-Physical System integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution is propos encompassing run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes. This paper proposes a holistic solution to achieve context sensitivity for Flexible Manufacturing Systems, whereby the knowledge created by applying the context sensitivity approach can be used for (self-) optimization of manufacturing processes.

Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully-supervised, requiring extensive precise manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—Segment Anything Model (SAM) into the field of medical image segmentation. Fine-tuning the foundation model is an effective approach to tackle sample scarcity. However, current SAM fine-tuning techniques still rely on precise annotations. To overcome this limitation, we propose WSPoly-SAM, a novel weakly-supervised approach for colonoscopy polyp segmentation. WSPoly-SAM utilizes weak annotations to guide SAM in generating segmentation masks, which are then treated as pseudo-labels to guide the fine-tuning of SAM, thereby reducing the dependence on precise annotations data. To improve the reliability and accuracy of pseudo-labels, we have designed a series of enhancement strategies to improve the quality of pseudo-labels and mitigate the negative impact of low-quality pseudo-labels. Experimental results on five medical image datasets demonstrate that WSPoly-SAM outperforms current fully-supervised mainstream polyp segmentation networks on the Kvasir-SEG, ColonDB, CVC-300, and ETIS datasets. Furthermore, by using different amounts of training data in weakly-supervised and fully-supervised experiments, it is found that weakly-supervised fine-tuning can save 70% to 73% of annotation time costs compared to fully-supervised fine-tuning. This study provides a new perspective on the combination of weakly-supervised learning and SAM models, significantly reducing annotation time and offering insights for further development in the field of colonoscopy polyp segmentation.

To facilitate the promotion and application of the whole process engineering consulting (WPEC) model, the transmission mechanism of influencing factors in WPEC's promotion and application is investigated. It identifies and categorizes the influencing factors within the framework of "external environment-project factors-stakeholders", systematically organizing and summarizing ten primary categories and thirty-eight secondary influencing factors of WPEC's promotion and ap-plication. Utilizing the complex network analysis method, a network of influencing factors in the promotion and application of WPEC is constructed. The network is then converted into three binary networks based on weight, which are subjected to structural analysis and motif analysis. On this basis, the transmission mechanism of influencing factors in WPEC's promotion and applica-tion and its causations are explored.

With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing CCHP system efficiency by identifying optimal design parameters and assisting decision-makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize net power and exergy efficiency while minimizing CO2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved higher net power output, satisfactory exergy efficiency, and lower CO2 emissions compared to similar studies. The study shows that maximum net power and exergy efficiency, with reduced CO2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO2 emissions and improved exergy values.

This study highlights the importance of quantifying groundwater resources for the Monahans and Kermit dune fields in the northern Chihuahua Desert, West Texas USA as potential contributors to the regional Pecos Valley Aquifer (PVA). Sedimentary architecture models of these dune fields show perched aquifers with water tables 1-10m below the surface and southwestward groundwater flow sub-parallel to a Pleistocene/Pliocene aquitard. Sinkhole-like features into the top of the aquitard are infilled with the thickest occurrences of aeolian sand. Groundwater recharge occurs from the enhanced capture of precipitation on mostly bare active dunes where infiltration rates >250mm/hr. The deuterium and oxygen isotopic ratios for groundwater from the Kermit and Monahans dune fields show pronounced evaporative isotopic depletion and less isotopic variability than corresponding rainfall, particularly for deuterium values. Groundwater from a shallow central well in the Kermit dune field yielded contemporary 14C activity (Fm =1.02), indicating contributions from recent precipitation. In contrast, more evolved 14C values at the western margin (FM=0.84) and at 30m below the surface (FM=0.76), similar to proximal Fm values from the PVA (0.89-0.82), may indicate dissolution of older (> 100 ka) DIC from buried playa-lake sediments and carbonate-soils with groundwater flux to the southwest, and less direct atmospheric influence. The perched aquifers of the Monahans and Kermit dune fields contain water volumes > 0.1 km3 and may contribute up to 9% of the total annual recharge to the PVA.

This paper adopts a systems firm-centered perspective on the environmental assessment of recyclable glass and PET soft drink containers. We employ LCA and discrete-event simulation modelling for the environmental assessment of the two soft drinks packaging alternatives in operational terms, over the entire supply chain, over a period of three years. The assessment is based on real data collected from a large soft-drinks producer and its suppliers. The research and practice contribution of the paper is twofold: first, it introduces a methodological framework for environmental assessment of companies’ packaging environmental impact under different product and operations strategies, and secondly, it provides a holistic environmental assessment of the two packaging materials (PET and glass) taking into account specific operational issues, such as product mix and recycling and reuse options, and activity interdependences and stochasticity. The results of the simulation experiments confirm at system level, for glass, the importance for sustainability, to increase the number of reuse cycles and the percentage of used bottles collected for refilling, whereas for PET, to increase the percentage of recycled PET in new bottles.

Soil tillage intensity influences the distribution of nutrients, and soil’s physical and me-chanical properties, as well as gas flows. The impact of reduced tillage on these indices in spring barley cultivation is still insufficient and requires more analysis on a global scale. This study was carried out at Vytautas Magnus University, Agriculture Academy (Lithu-ania) in 2022–2023. The aim of the investigation was to determine the effect of the tillage systems on the soil temperature, moisture content, CO2 respiration and concentration in spring barley cultivation limited by the semi-humid subarctic climate. Based on a long-term tillage experiment, five tillage systems were tested: deep and shallow mold-board ploughing, deep cultivation-chiselling, shallow cultivation-chiselling, and no-tillage. Shallow ploughing technology was found to be better at conserving soil moisture and maintaining a higher temperature. Deep cultivation had a lower moisture content and lower soil temperature. Shallow cultivation fields in most cases increased CO2 emissions and CO2 concentration. The results show that in direct sowing fields, most cases had a positive effect on crop density. Direct sowing fields resulted in significantly lower grain yields of spring barley in the years studied.

Agroforestry systems (AFS) are beneficial ecosystems that increase cocoa productivity by providing shade and controlling pests. Despite deforestation, there is a transition towards more sustainable practices that combine diverse species with cocoa, generating economic and environmental benefits. This study evaluates AFS with cocoa in the Amazon region. Of the 72 farms evaluated, species diversity was observed, highlighting Cordia alliadora and Calycophyllum spruceanum with an im-portance value index (IVI) of 18.58% and 17.66%, respectively. Fruit trees such as Citrus limetta, Cocos nucifera, Mangifera indica, and Persea americana are common. The designed AFSs proved more profitable, generating revenue from the fourth year, while the conventional ones took longer. Indicators such as Net Present Value (NPV) and Internal Rate of Return (IRR) supported the effi-ciency of the designed AFS. Six optimal species were identified with specific distances and planting systems, providing up to 45.83% total shade in flat topography and up to 2405 annual shade hours on a 7° slope. These results highlight the economic and agronomic viability of AFSs designed for regional cocoa production.

The solar heat pump system based on photovoltaic photothermal (PV/T) modules is a new technology that integrates photovoltaics and heat reutilization. It can improve the efficiency of photovoltaic conversion and recovery the remaining heat generated during the photoelectric conversion process, thereby improving the comprehensive efficiency of the whole system. Simulink software is a visual simulation tool, which has the advantages of strong applicability, clear simulation structure and calculation process. The design of its graphical programming greatly simplifies the user modeling process and allows for direct viewing of results, therefore improving the simulation efficiency. At present, there is little research on simulation of solar heat pump systems with PV/T modules based on Simulink software. In this paper, the mathematical models of each component contained in the PV/T solar heat pump system were established based on the system working principle, energy conservation equations and some empirical formulas, and then the operation of the system was simulated by the Simulink software. Due to the operation characteristic of PV/T modules is the key influencing factor for the whole system performance, the model and simulation process for PV/T modules were introduced in detail. The results showed that the surface temperature of PV/T modules under different operating conditions was significantly lower than that of ordinary photovoltaic panels, and the temperature change amplitude was lower. As results, the power generation and photovoltaic conversion efficiency were significantly improved.

Over the last ten years, the integrated pond aquaculture (IPA) has been developing rapidly as a new aquaculture mode, and mechanical power oxygenator is a common oxygenation device in this aquaculture mode. However, with the gradual expansion of the application range of this aquaculture mode, the problems of low efficiency, poor oxygenation uniformity, and low applicability of mechanical power oxygenators, which can produce water reflux and suction pollution accumulation, have gradually emerged. Therefore, we designed a new type of push-water aerator device (NPWAD) applicable to the integrated recirculating water aquaculture mode of ponds. Mainly, the specific parameters of the key components were clarified through the selection calculation and structural design, and the experiments were carried out in the self-constructed pond integrated aquaculture system at the experimental base. The experiments were carried out on the water in the tank for grass carp culture, and the oxygenation and water pushing effects of the type of push-water aerator device under 15 groups of different operating parameters were carried out by using multiple types of high-precision water quality monitoring sensors, with the rotational speed of the device, the operating time (0-30 min) and the height of the water (0.5-1.5 m) as the independent variables, and the oxygenation amount R, the oxygenation power efficiency E_s and the surface layer of the water as the evaluation indexes, average flow velocity V_f were used as its evaluation indexes of oxygenation and water pushing. The results show that the optimal parameters of the NPWAD are impeller water lifting component speed n_1=1000r/min, small-hole aeration component speed n_2=2280r/min. At this time, the average oxygen increase of this device is R=1.96mg/L, the average oxygenation power efficiency is E_s=3.8 kg·(h·kW)-1, the DO uniformity of the water M_1=95.3%, and the average flow velocity V_f=190.8mm/s, which can effectively promote the water directional flow and achieve the water circulation and layer exchange, and it is in line with the preferred swimming speed of grass carp at various growth stages. The new type of push-water aerator device is able to enhance the DO concentration of the water faster, but also to achieve the purpose of promoting the directional flow of the water and renewing the water in the tank, as well as in a short period of time to improve the uniformity of the DO of the water faster, for the improvement of the pond aquaculture production efficiency has practical significance.

The behaviour and bifurcations of solutions to three-dimensional (three-phase) quadratic polynomial dynamical systems (DSs) are considered. The integrability in elementary functions is proved for a class of autonomous polynomial DSs. The occurrence of bifurcations of the type twisted fold is discovered on the basis and within the frames of the elements of the developed DS qualitative theory. The discriminant criterion applied originally to two-phase quadratic polynomial DSs is extended to three-phase DSs investigated in terms of their coefficient matrices. Specific classes of D- and S-vectors are introduced and complete description of the symmetry relations inherent to the DS coefficient matrices is performed using the discriminant criterion.

Low-complexity ultrasound systems are increasingly desired for wearable point-of-care ultra-sound and massive-channel ultrasound for 3-D matrix imaging. However, the system complexity is closely associated with the imaging capabilities, remaining as a challenge. To resolve this limitation, this study revisits the general structures of analog and digital beamformers and in-troduces a hybrid approach to implement efficient ultrasound systems. The suggested hybrid beamforming takes two stages, where the first analog stage partially beamforms M-channel RF signals to N sum-out data (M-to-N beamforming), and the second digital stage beamforms N partial sums to single final beamformed data (N-to-1 beamforming). Our approach was systematically designed and implemented, which was demonstrated with a customized 64-channel 1-D phased array using a tissue mimicking phantom. The demonstrated results indicate that the analog-digital hybrid beamforming can be applied to any kind of arrays for sophisticated 3-D imaging and tiny wearable ultrasound applications.

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission founded PHOTONGATE project. This concept will allow to quantify multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a bio-chemical technology (molecular gates) which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which combined with porous substrates will increase the sensitivity, and a photonic technology based on Local Surface Plasmonic Resonance (LSPR) structures serves as transducer for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm²). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, fully Europe Union Made, avoiding optical fibers and expensive optical components.

Agricultural activities profoundly affect groundwater levels in Saudi Arabia. The Al Kharj and Wadi Sahba areas, central Saudi Arabia, produce groundwater mainly from the Jurassic Arab/Jubaila aquifer and the overlying Cretaceous Biyadh aquifer. A geographic information system analysis and linear regression analyses show that from 1978 to 2016, significant agricultural overexploitation of the area’s fossil groundwater was associated with groundwater level drops of ~104 m in Al Kharj and ~48 m in Wadi Sahba. The estimated total volume of groundwater withdrawal across both aquifers during this period of wheat and alfalfa irrigation was ~27 billion m3. Landsat image analysis shows that agricultural land use decreased by ~66% over the same period due to overexploitation. The 2016 phase-out of alfalfa farming was associated with increases in groundwater levels: ~26 m in the Arab/Jubaila aquifer and ~3 m in the Biyadh aquifer. This recovery was likely due to lateral flow from surrounding fossil water within the aquifer given the absence of significant surface recharge. Model-based evaluation of surface recharge of the aquifer reveals very low potential for surface infiltration, with almost no recharge into the deep aquifer. Assuming that recovery continues at the present rate, the average groundwater level for the Arab/Jubaila aquifer is expected to reach 98 m below ground level in 2025 and 74 m below ground level in 2030.

With the gradual improvement of coal mining efficiency, the disturbance of groundwater system caused by the high-intensity mining mode also increases, which is a more severe challenge to the prevention of mine safety and the protection of water resources in mining areas. How to accurately describe the dynamic changes of the groundwater system under mining and quantitatively predict mine water inflow is a major problem in the current research. Based on the full analysis of the response characteristics of groundwater system to the extraction disturbance, this paper forms a kind of method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process, and uses the MODFLOW non-structural division grid to accurately characterize the formation structure, and finally make accurate water inflow prediction. Taking the Caojiatan Coal Mine in Shaanxi Province, China as an example, a numerical model of unstructured mine water inrush was established for the first time, and the changes of water inflow source and water inflow intensity were quantitatively evaluated. Compared with the traditional water inflow prediction method, it is found that the prediction accuracy is improved by 12%~17% by detailing the response characteristics of complex groundwater system under high-strength coal seam mining conditions. The method is of great significance and promotion value for the comprehensive understanding of the disturbance characteristics of human underground engineering activities, such as coal mining, on the groundwater system, and for accurately predicting water inflow.

The rapid advancements in gene therapy have opened up new possibilities for the treatment of genetic disorders, including Duchenne muscular dystrophy, Thalassemia, cystic fibrosis, Hemophilia, and Familial hypercholesterolemia. The utilization of the clustered regularly interspaced short palindromic repeats (CRISPR) - CRISPR-associated protein (Cas) system has revolutionized the field of gene therapy by enabling precise targeting of genes. In recent years, CRISPR/Cas9 has demonstrated remarkable efficacy in treating cancer and genetic diseases. However, the susceptibility of nucleic acid drugs to degradation by nucleic acid endonucleases, necessitates the development of functional vectors capable of protecting the nucleic acids from enzymatic degradation, while ensuring safety and effectiveness. This review aims to explore the biomedical potential of non-viral vector-explore CRISPR/Cas9 systems for the treatment of genetic diseases. Furthermore, it provides a comprehensive overview of recent research advances in viral and non-viral vector-based gene therapy for genetic disorders, including insights from preclinical and clinical studies. Additionally, the review analyzes the current limitations of these delivery systems and proposes avenues for the development of novel nano-delivery platforms.

Alginate films (2% w·v-1) were prepared with varying concentrations (5-20% w/w) of citric acid and aqueous grape seed extract (GSE) filtrate (11.66 ± 1.32 g GAE/L) by the solvent-evaporation method. Crosslinking alginate via ester bonds (FTIR analysis) with citric acid up to 10% (w/w) led to a 33% increase in tensile strength, a 34% reduction in water vapor transmission rate (WVTR), and had no impact on elongation at break. Crosslinking alginate with citric acid in presence of GSE increased the tensile strength by 17%, decreased WVTR by 21%, and significantly improved DPPH scavenging activity. Moreover, after incubation for 24 h at 37°C, the film-forming solutions exhibited increased antimicrobial activity, resulting in 0.5 and 2.5-log reductions for Escherichia coli and Staphylococcus aureus, respectively, compared to the values obtained without the addition of GSE. The stronger inhibitory effect observed against Gram-positive bacteria can be attributed to the unique composition and structure of their cell walls, which create a barrier that restricts the penetration of polyphenols into the cells. pH adjustment of the GSE film-forming solution from 2.0 to 10.0 shifted the UV/VIS absorption spectra, resulting in a colour change from yellow to red. The findings of this study have showcased the potential of combining GSE and citric acid to enhance the functionality and bioactivity of alginate films for applications in smart food packaging.

Emerging organic contaminants (EOCs) of anthropogenic origins are ubiquitous in environmental compartments, including aquatic systems. Thus, EOCs have attracted considerable research and public attention due to their potential human and ecological health risks. However, compared to other aquatic environments such as wastewater systems, comprehensive reviews focussing on the occurrence and human health risks of EOCs in drinking water systems are still lacking. Therefore, to address this knowledge gap, the current review posits that drinking water systems harbour a cocktail of toxic EOCs, which pose public health risks via multiple exposure routes. In the present review, global evidence is examined to track EOCs along the source-pathway-receptor-impact-mitigation (SPRIM) continuum. Evidence shows that, various groups of EOCs, including pharmaceuticals and personal care products, solvents, plasticizers, endocrine disrupting compounds, gasoline additives, per- and polyfluoroalkyl substances (PFAS), food colourants, artificial sweeteners, and musks and fragrances, have been detected in drinking water systems. The anthropogenic sources of EOCs detected in drinking water systems, including wastewater systems and industrial emissions, are summarized. Further, the behaviour and fate of EOCs in the drinking water systems, including removal processes are discussed. Once in drinking water systems, human exposure to EOCs may occur via ingestion of contaminated drinking water and cooked foods, and possibly dermal contact and inhalation. The high-risk environments, and risk factors and behaviours predisposing humans to EOC exposure are discussed. Evidence on the human health risks of the various EOCs and a critique of the data are presented. Notably, besides inferential data, quantitative epidemiological evidence directly relating the occurrence of EOCs in drinking water systems to specific adverse human health outcomes is still scarce. Lastly, future research directions, including the need for quantitative public health risk assessment, and the application of emerging detection tools are discussed.

The deployment of Artificial Intelligence (AI) systems in decentralized environments is on the rise, yet representation of shared conceptualization in such scenarios remains a challenging issue. The absence of a shared understanding can lead to suboptimal performance of AI systems and hinders the ability to comprehend the knowledge and beliefs of agents in the domain. This paper proposes a formal model for modeling conceptualization in AI systems that integrates ontology, epistemology, and epistemic logic. The model aims to address the gap in representing shared conceptualization in decentralized environments and enhance the performance of AI systems operating in such environments. The proposed model is a hybrid structure that blends extensional and intensional structures and leverages logic-based languages for modeling purposes. A case study in the healthcare sector is presented to illustrate the application of the proposed model. The study contributes to the existing literature by providing a formal model for representing shared conceptualization in decentralized environments, which can be utilized to optimize the performance of AI systems in these environments.

Digital transformation of agriculture can support economic growth and food and nutri-tion security in Africa. This study provides an overview of the status of digital agriculture in five west African countries, analyzing their efforts in developing the enabling environment and inno-vations while formulating recommendations based on the identified gaps for the effective trans-formation of the sector. Information was retrieved through a literature search from various sources, including web pages and databases of national agricultural and digital transformation institutions and start-ups of the five target countries (Benin, Burkina Faso, Côte d'Ivoire, Ghana, Nigeria) and regional/international institutions. There have been increasing agri-digital initia-tives in the five countries, which were grouped into seven categories based on their objectives. Steady progress was also observed in mobile internet adoption, despite the differences in deploy-ing crucial infrastructure to promote digital agriculture. The mobile connectivity index (MCI) in all five countries is below 60. Nonetheless, Ghana and Cote d'Ivoire demonstrated more efforts in internet and electricity access, especially in rural areas. Benin and Nigeria have developed sep-arate documents depicting the roadmap for Digital Agriculture, while the other countries are working to create one or have it embedded in their national development plans. Similarities and specificities exist among countries for laws and processes protecting Agri-digital innovators. To be competitive and self-reliant in the global e-economy, these countries must reposition them-selves to accelerate changes in digital agriculture through effective governance and synergy of actions in different sectors and across nations.

In spite of large-scale investigations of homogeneous single-site metallocene catalysts and systems based on them, the problem of controlling their activity, chemo- and stereoselectivity with the aim of developing efficient methods for the preparation of practically useful products of alkenes transformation, such as dimers, oligomers, and polymers, still remains topical. Here we studied catalytic activity of differently structured zirconocenes (10 examples of cyclopentadienyl and indenyl complexes) and co-catalysts activating the system, namely HAlBui2, MMAO-12 or (Ph3C)[B(C6F5)4] at low activator/Zr ratios in 1-hexene oligomerization reaction. The influence of catalyst structure and system composition on alkene conversion, type of products formed and reaction stereoselectivity was investigated. The composition of hydride intermediates forming in the systems L2ZrCl2 (L2= ansa-Me2CCp2)-HAlBui2-activator was studied by NMR and the participation of biszirconium hydride complex as the precursor of catalytically active sites of the alkene dimerization reaction was shown.