This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle.

Despite ample warning and significant lead time, global society has not acted sufficiently to combat climate change, and global carbon dioxide emissions are now about 60% higher today than in 1990 [1]. This paper uses marine fisheries as an analog model to compare with the many similar management issues faced with global climate change. Both marine fisheries and global climate change are complex systems that have similar properties that make management very difficult. A simple fishery prototype model is used to educate citizens about similar issues of dynamic complexity found in climate change models. We highlight how our slow reaction time to global climate change can be thought of as a mismatch between our cognitive abilities and the nature of global collective problems. Understanding the history and system structure of marine fisheries can provide insight into managing global climate change. We suggest ways to improve communication between science and citizens for updating mental models and changing climate policy.

Quantifying different stocking rates effects in three study areas placed at Mediterranean forest (Cuenca, Spain) was conducted by applying a multiparametric soil quality index, SQI, developed in undisturbed forest soils (> 40 years). The main objective is to advance in the development and application of multiparametric indexes which allow for soil condition assessment. For it, is analyse the effectiveness of a multiparametric soil quality index (SQI) as an indicator livestock impacts on the soil in mediterranean forests. Control areas without stocking rates were compared in forest stands with different age (Thicket forest stand, < 30 years; High polewood forest stand, 30-60 years; Old growth forest stand, > 60 years); with various intensity grazing areas (permanent livestock passage: inactive sheepfold for 2-3 years, and active sheepfold; intermittent livestock passage: bare soil, pine stand, scrubland). The multiparametric soil quality index (SQI) applied sensitive to changes in forest ecosystems depending of different stocking rates. However, for even more preci-sion to assess cattle alteration, multiparameter index recalibrated, creating a new one; Soil Status Index by Livestock (SSIL). Correlation between quality ranges obtained with both indexes in dif-ferent study areas suggest that SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

Human serum albumin (HSA) is multifunctional protein, known to be a natural carrier for a number of endogenous and exogenous compounds, including drugs. Formulating anticancer drugs with HSA is a clinically validated approach to improve the pharmacological properties and provide targeted delivery (passive and active) of cytotoxic payload to the tumor (such as in Abraxane). A number of receptors facilitating HSA uptake from systemic circulation have been characterized. What is interesting, such uptake may differ depending on the nature and quantity of ligands bound to HSA. Based on this, one might like to modify the HSA in a way that its distribution is more favorable for certain therapeutic purposes, thus ensuring more efficient delivery of the therapeutic cargo to disease site. E.g. Levofloxacin (LV), broad spectrum antibiotic drug, could benefit from extended systemic exposure, and stronger interaction with plasma proteins could be useful for this purpose. We have engrafted monomeric or polymeric CDs on the surface of HSA molecules to strengthen the LV adsorption (CD-LV dissociation constant is three orders of magnitude lower than that for HSA-LF). We have found that (HSA-HPolS)conj+LV exhibited the highest activity against E. coli, whereas (HSA-HPCD)conj+LV was the most effective against B. subtilis; and both HSA conjugates were more potent than LV alone or LV with HSA. Further fine-tuning of HSA could yield an improvement in biodistribution, and thus a more favorable risk/benefit ratio.

The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.

This paper proposes a reconceptualization of the hierarchical understanding of the human organism, challenging the conventional idea of the supremacy of the nervous system. We argue that symbiotic coexistence and functional interdependence among all systems provide the human body with remarkable resistance to the universal tendency of entropy. We first examine the basis of cellular life, referring to ATP and DNA, essential substances that resist the entropic tendency through complex energy mechanisms. Next, we analyze the collaboration between the body's systems, highlighting the critical role of erythrocytes, which constitute 83.9% of the total cells (24.9 trillion), in the context of energy maintenance and homeostasis. We challenge the conventional view that the nervous system, composed of neurons and glial cells, which together make up 0.6% of the total cells (0.17 trillion), occupies the apex of a biological hierarchy, arguing that the nervous system is merely a component of a broader, interdependent mechanism. Finally, we provide a detailed description of human cellular composition, emphasizing the importance of each cell type in the functional context, thus contributing to the totality of the organism. With this, we present a vigorous appeal for the realization of critical reassessments of fundamental premises in human biology.

Installations of decentralised Renewable Energy Systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for decentralised renewable generation and hybrid storage system to create a Renewable Energy Community (REC), developed in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage data from a rural island case study location. Cost remuneration is managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective Genetic Algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years. Emissions output from grid only use is reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.

contaminated sites located in each school district. Previous studies showed that various environmental factors, such as exposure to toxic substances negatively impacts human health depending on their concentration and occurrence. Therefore, the study aims to use Geographical Information System (GIS) technology and secondary data on IEP numbers of each school district, chemical occurrence in contaminated sites, and demographic data to conduct a correlational analysis. The secondary data obtained from school districts and contaminated sites from Iowa Department of Natural Resources Facility Explorer were populated in ArcMap 10.5 (a GIS software) for generating maps and data to conduct statistical analysis. The contaminants were categorized into metals, polyaromatic hydrocarbons (PAH), and solvents with weighing factors as 1, 0.5, and 0.25. A total of 1 Superfund site and 39 CERCLA sites were identified as contaminated sites for this study. The majority of contaminants were heavy metals such as lead, arsenic, mercury, and cadmium. The mean toxic score of all contaminated sites combined was 13.4 (sd 14.4). The correlational analysis between the IEP numbers from each school districts and toxic scores from the contaminated sites was positive indicting that increased toxic score increased the number of students enrolled in the IEP program (F=23.7, p<0.0001). The correlational analysis between toxic score and demographics indicated some interesting results. Children under the age of 10 were living in areas with high toxic score and the showed mild correlation (p<0.00052). Similarly, higher number of black population resided in areas with high toxic score (p=0.0032) than the white population (p<0.0001). The result also indicated that children enrolled in IEP were predominantly among the black population (p<0.0001). The correlational analysis between household income and poverty percentage of people residing near the contaminated area indicated that people had low average household income (p=0.0002) and high poverty percentage (p=0.0203) residing in areas with higher toxic score. Regarding the educational status, less number of people with post graduate degree (p<0.0001) resided in areas with high toxic score and more number of people with no degree (p<0.0001) resided in areas with high toxic score. Finally, the study showed the increasing trend of eligible children enrolling in free and reduced lunch programs with increasing toxic scores (p=0.0012) and IEP levels (p=0.0416). This study emphasize on developing multiple exposure to correlate environmental factors contributing to the negative health outcome on people.

This paper proposes the utilization of large language models as recommendations systems for museums. Since the aforementioned models lack the notion of context, they can’t work with temporal information that is often present in recommendations for cultural environments (e.g. special exhibitions or events). In this respect, the current work aims at enhancing the capabilities of large language models through a fine-tuning process that incorporates contextual information and user instructions. The resulting models are expected to be capable of providing personalized recommendations, aligned with user preferences and desires. More specifically, Generative Pre-trained Transformer 4, a knowledge-based large language model is fine-tuned and turned into a context-ware recommendation system, adapting its suggestions based on user input and specific contextual factors such as location, time of visit, and other relevant parameters. The effectiveness of the proposed approach is evaluated through certain user studies, which ensure an improved user experience and engagement within the museum environment.

Weeds can have both positive and negative effects on agricultural environments. However, despite the growing interest in the ecology of weed communities in agricultural areas, few studies have been carried out in the northern region of the Andes of Colombia, where urban and agricultural expansion have gen-erated highly disturbed scenarios. The aim of this study is to analyze the diversity of vegetation and weed seed banks in three agricultural production systems and a forest ecosystem in the northern Andes of Colombia. Hill numbers were used to compare diversity, Beta diversity to assess changes in composition, and range-abundance-dominance curves at different sites. Likewise, indicator species were analyzed to find species associations to each system. The results revealed differences in the composition of weeds between the forest ecosystem and the agricultural production systems, with higher equitability in the forest ecosystem and higher dominance in agricultural systems. A higher differentiation was observed in the dominant species of each agricultural system, highlighting those considered pests due to their life history traits, giving them greater success against various anthropogenic selection pressures. These findings highlight the impact of anthropogenic disturbances on the ecological dynamics of weed com-munities in different ecosystems, which should be considered when planning integrated weed manage-ment techniques.

In this paper, we introduce as a pedagogical strategy an internal division-free, straightforward, and symmetrically progressing algorithm in manually computing matrix inverse and solving systems of linear equations by revisiting the application of elementary row operations in the Gauss-Jordan reduction method and connecting it to the determinant method. The proposed cross-multiplication-flip (CMF) algorithm employs cross-multiplication similar to the butterfly movement in computing determinants as a strategic application of elementary row operations to efficiently reduce the rows and then applies flipping of rows and entries to put an upper triangular matrix into lower triangular form to continue the reduction process.

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fractional variables in homogeneous epidemic SI(R)S models, with standard incidence, absence of R-susceptibility and compartment independent birth and death rates, turns out to be isomorphic to (a marginally extended version of) Hethcote's classic endemic model, first presented in 1973. The paper studies a 10-parameter master model with constant and I -linear vaccination rates, vertical transmission and a vaccination rate for susceptible newborns. As recently shown by the author, all demographic parameters are redundant. After adjusting time scale, the remaining 5-parameter model admits a 3-dimensional abelian scaling symmetry. By normalization we end up with Hethcote's extended 2-parameter model. Thus, in view of symmetry concepts, reproving theorems on endemic bifurcation and stability in such models becomes needless.

One of the stated goals of the Common Agricultural Policy reforms has been to provide a fairer distribution of payments across and within Member States, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of Direct Payments. The structural factors that underlie this inequity, notably the preponderance of specific types of farming systems, are investigated in this research. A logit model was developed using Agricultural Census data at the Commune level, using the evolution of the percentage of farmers receiving Direct Payments in Portugal as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to DP between 2009 and 2019. In traditional Mediterranean farming systems, however, access to DP has been restricted to a smaller proportion of farmers. However, there appears to have been some redistribution in the previous two CAP programming cycles, from bigger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other types of production, notably vineyards.

For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

Apicomplexan infections, such as giardiasis and cryptosporidiosis, negatively impact a considerable proportion of human and commercial livestock populations. Despite this, the molecular mecha-nisms of disease, particularly the effect on the body beyond the gastrointestinal tract, are still poorly understood. To highlight host-parasite-microbiome biochemical interactions, we utilised integrated metabolomics-16S rRNA genomics and metabolomics-proteomics approaches in a C57BL/6J mouse model of giardiasis and compared these to Cryptosporidium and uropathogenic Escherichia coli (UPEC) infections. Comprehensive samples (faeces, blood, liver, and luminal contents from duo-denum, jejunum, ileum, caecum and colon) were collected 10 days post infection and subjected to proteome and metabolome analysis by liquid and gas chromatographic mass-spectrometry, re-spectively. Microbial populations in faeces and luminal washes were examined using 16S rRNA metagenomics. Proteome-metabolome analyses indicated that 12 and 16 key pathways were sig-nificantly altered in the gut and liver, respectively, during giardiasis with respect to other infections. Energy pathways including glycolysis and supporting pathways of glyoxylate and dicarboxylate metabolism, and redox pathway of glutathione metabolism, were upregulated in small intestinal luminal contents and the liver during giardiasis. Metabolomics-16S rRNA genetics integration in-dicated that populations of three bacterial families –Autopobiaceae (Up), Desulfovibrionaceae (Up) and Akkermanasiaceae (Down) – were most significantly affected across the gut during giardiasis, causing upregulated glycolysis and short-chained fatty acid (SCFA) metabolism. In particular, the perturbed Akkermanasiaceae population seemed to cause oxidative stress responses along the gut-liver axis. Overall, the systems biology approach applied in this study highlighted that the effects of host-parasite-microbiome biochemical interactions extended beyond the gut ecosystem to the gut-liver axis. These findings form the first steps in a comprehensive comparison to ascertain the major molecular and biochemical contributors of host-parasite interactions and contribute towards the development of biomarker discovery and precision health solutions for apicomplexan infections.

Generation of unique identifiers extracted from the physical characteristics of the underlying hardware ensures the protection of electronic devices against counterfeiting and provides security to the data they store and process. This work describes the design of an efficient Physical Unclonable Function (PUF) based on the differences in the frequency of Ring Oscillators (ROs) with identical layout due to variations in technological processes involved in the manufacture of the integrated circuit. The logic resources available in the Xilinx Series-7 programmable devices are exploited in the design to make it more compact and achieve an optimal bit-per-area rate. On the other hand, the design parameters can also be adjusted to provide a high bit-per-time rate for a particular target device. The PUF has been encapsulated as a configurable Intellectual Property (IP) module, providing it with an AXI4-Lite interface to ease its incorporation into embedded systems in combination with soft- or hard-core implementations of general-purpose processors. The capability of the proposed RO-PUF to generate implementation-dependent identifiers has been extensively tested, using a series of metrics to evaluate its reliability and robustness for different configuration options. Finally, in order to demonstrate its utility to improve system security, the identifiers provided by RO-PUFs implemented on different devices have been used in a Helper Data Algorithm (HDA) to obfuscate and retrieve a secret key.

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

Individual differences in the responsiveness of the brain to transcranial electrical stimulation (tES) is increasingly demonstrated in large variability in the tES effects. Anatomically detailed computational brain models have been developed to address this variability; however, static brain models are not ‘realistic’ in accounting for the dynamic state of the brain. Therefore, human-in-the-loop optimization is proposed in this perspective article based on an extensive systems analysis of the tES neurovascular effects. First, modal analysis was conducted using a physiologically detailed neurovascular model that found stable modes in the 0 Hz to 0.05 Hz range for the pathway for vessel response through the smooth muscle cells, measured with functional near-infrared spectroscopy (fNIRS). tES effects in the 0 Hz to 0.05 Hz range can also be measured with functional magnetic resonance imaging (fMRI)-tDCS data with a maximum TR=10sec. Therefore, we investigated an open-source fMRI-tDCS dataset that used a TR=3.36sec. We found that both the anodal tDCS condition and sham tDCS condition had similar Finite Impulse Response at the region of interest underlying the anode and a remote location, which indicated a global hemodynamic effect of sham tDCS beyond the intended transient sensations. Here, transient sensations can have arousal effects on the hemodynamics so we conducted a healthy case series for black box modeling of fNIRS-pupillometry of short-duration tDCS effects. The block exogeneity test rejected the claim that tDCS is not a 1-step Granger-cause of the fNIRS total hemoglobin changes (HbT) and pupil dilation changes (p<0.05). Also, grey-box modeling using fNIRS of the tDCS effects in chronic stroke showed HbT response to be significantly different (paired-sample t-test, p<0.05) between the ipsilesional and the contralesional hemisphere for primary motor cortex tDCS and cerebellar tDCS which was subserved by the smooth muscle cells. Here, our perspective is that various physiological pathways subserving tES effects can lead to state-trait variability that can be challenging for clinical translation. Therefore, we conducted a case study on human-in-the-loop optimization using our reduced dimension model and a stochastic, derivative-free Covariance Matrix Adaptation Evolution Strategy. Future studies need to investigate human-in-the-loop optimization of tES for reducing inter-subject and intra-subject variability in tES effects.

A quantitative description of the second law of thermodynamics in small scale systems and over short time scales comes from various fluctuation theorems. The applicability of the transient fluctuation theorem in particular to small scale systems perturbed from an initial equilibrium steady-state distribution has been demonstrated both theoretically and experimentally in several works over the past few decades. In addition, some experimental works in the past have also made successful attempts to demonstrate the applicability of the fluctuation theorem to small scale systems evolving from a certain nonequilibrium steady-state distribution over relatively long time scales. To this end, this paper seeks to demonstrate the transient fluctuation theorem for a Brownian particle confined within a power-law trapping potential by following the trajectory of the particle that itself is translating linearly along one dimension with constant acceleration in a viscous fluid. Considered herein is an idealized version of this model, in that it is assumed that the force of the trapping potential is only felt by the translating Brownian particle confined within the trap, and that this Brownian particle moves relative to the fluid molecules that are held stationary. The results presented herein show that the transient fluctuation theorem applies not only to equilibrium steady-state distributions but also to nonequilibrium steady-state distributions of an ideal colloidal system in an accelerated frame of reference in the asymptotic (long-time) limit.

The Metabolic Food Waste [MFW (kg of food)], first time developed in 2016 as a new indicator by Serafini and Toti, indicates the amount of food consumed over the nutritional requirements and the impact of this overconsumption on the environment. It is necessary to identify the causes and to develop potential methods to prevent and reduce MFW, at the same time with increasing consumer awareness about unsustainable diets and changing diet habits toward more environmentally conscious consumption patterns. By analysing and corroborating external data available for food waste, nutritional requirements, environmental impact of food waste and consumer behaviour, we identified as primary causes for MFW the lack of nutritional education and little understanding of the nutritional requirements amongst all categories of consumers, no access to appropriate food resources or reduced availability of fresh produced food. We conclude that for the quantification of the negative impact of MFW on both the environment and human health, we need a decisive action to raise consumer awareness for healthy and sustainable diets, together with an uniform worldwide distribution of the nutritious food.

This paper directly addresses a long-standing issue that affects the development of many complex distributed software systems: how to establish quickly, cheaply, and reliably whether they can deliver their intended performance before expending significant time, effort and money on detailed design and implementation. We describe ∆QSD, a novel metrics-based and quality-centric paradigm that uses formalised outcome diagrams to explore the performance consequences of design decisions, as a performance blueprint of the system. The distinctive feature of outcome diagrams is that they capture the essential observational properties of the system, independent of the details of system structure and behaviour. The ∆QSD paradigm derives bounds on performance expressed as probability distributions encompassing all possible executions of the system. The ∆QSD paradigm is both effective and generic: it allows values from various sources to be combined in a rigorous way, so that approximate results can be obtained quickly and subsequently refined. ∆QSD has been successfully used by Predictable Network Solutions for consultancy on large-scale applications in a number of industries, including telecommunications, avionics, and space and defence, resulting in cumulative savings worth billions of US dollars. The paper outlines the ∆QSD paradigm, describes its formal underpinnings, and illustrates its use via a topical real-world example taken from the blockchain/cryptocurrency domain. ∆QSD has enabled challenging throughput targets to be met for a globally distributed blockchain operating on the public internet.

In a previous paper, the authors dealt with the current showstoppers for morphing systems and with the reasons that have inhibited their commercial applicability. In this work, the authors ex-press a critical vision of the current status of the proposed architectures and the needs that should be accomplished to make them viable for installation onboard of commercial aircraft. The distinc-tion is essential because military and civil issues and necessities are very different, and both the solutions and difficulties to be overcome are widely diverse. Yet, still remaining in the civil seg-ment, there can be other differences, depending on the size of the aircraft, from large jets to com-muters or general aviation, in turn classifiable in tourism, acrobatic, ultralight and so on, each with their own peculiarities. Therefore, the paper wants to try to trace a common technology de-nominator, if possible, and envisage a future perspective of actual applications.

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

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

Objectives: This study aimed to analyze the awareness of local residents regarding healthcare projects and to suggest some ideas for the revision of local ones. Methods: To delve into the opinions of local residents, the author of this study created a questionnaire composed of eight questions on the general characteristics of the respondents, eight questions on satisfaction with health centers, 16 questions on the awareness of healthcare projects, and 22 questions on local healthcare. The survey was conducted for 409 residents who visited public centers in Gimcheon from 15 March to 14 April 2021. Results: Data analysis revealed the following: The proportion of local residents who use health centers was 39.1%, and those users visit health centers, on average, 3.92 times a year. Among healthcare projects, the project known by the highest proportion of people was vaccinations (84.5%), which was also the project that was most used (38.1%). Among healthcare projects needed in the with-COVID-19 age, respondents awarded the highest score to vaccinations (4.15 points on a five-point Likert scale) and the second highest score to infectious disease management (4.12). Conclusions: For healthcare projects, central and local governments should focus on activating vaccinations, solving the problem of medical accessibility through untact remote treatment and establishing national infectious disease-specializing hospitals and local infectious disease management based on such national hospitals.

Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework proposal. The paper also contributes the Cybonto ontology, formally documenting 108 constructs and thousands of cognitive-related paths based on 20 time-tested psychology theories. Finally, the paper applied 20 network centrality algorithms in analyzing the 108 constructs. The identified top 10 constructs call for extensions of current digital cognitive architectures in preparation for the DT future.

In order to support a massive number of resource-constrained Internet-of-Things (IoT) devices and machine-type devices, it is crucial to design future beyond 5G/6G wireless networks in an energy-efficient manner while incorporating suitable network coverage expansion methodologies. To this end, this invited paper proposes a novel two-hop hybrid active-and-passive relaying scheme to facilitate simultaneous wireless information and power transfer (SWIPT) considering both the time-switching (TS) and power-splitting (PS) receiver architectures, while dynamically modelling the involved dual-hop time-period (TP) metric. An optimization problem is formulated to jointly optimize the throughput, harvested energy, and transmit power of a SWIPT-enabled system with the proposed hybrid scheme. In this regard, we provide two distinct ways to obtain suitable solutions based on the Lagrange dual technique and Dinkelbach method assisted convex programming, respectively, where both the approaches yield an appreciable solution within polynomial computational-time. The experimental results are obtained by directly solving the primal problem using a non-linear optimizer. Our numerical results in terms of weighted utility function show the superior performance of proposed hybrid scheme over passive repeater-only and active relay-only schemes, while also depicting their individual performance benefits over the corresponding benchmark SWIPT systems with the fixed-TP.

Volcanism powered by tidal forces inside celestial bodies can provide enough energy to keep important solvents for living systems in the liquid phase. Moreover, tidal forces and their environmental consequences may strongly influence habitability of planets and other celestial bodies and may result in special forms of live and living conditions. A prerequisite to calculate such tidal interactions and consequences is depending on simulations for tidal accelerations in a multi-body system. Unfortunately, from measurements in many extrasolar planetary systems only few physical and orbital parameters are well enough known for investigated celestial bodies. For calculating tidal acceleration vectors under missing most orbital parameter exactly, a simulation method is developed that is only based on a few basic parameters, easily measurable even in extrasolar planetary systems. Such a method as being presented here, allows finding a relation between the tidal acceleration vectors and potential heating inside celestial objects. Using values and results of our model approach to our solar system as a “gold standard” for feasibility allowed us to classify this heating in relation to different forms of volcanism. This “gold standard” approach gave us a classification measure for the relevance of tidal heating in other extrasolar systems with a reduced availability of exact physical parameters. We would help to estimate conditions for the identification of potential candidates for further sophisticated investigations by more complex established methods like viscoelastic multi-body theories. As a first example, we applied the procedures developed here to the extrasolar planetary system TRAPPIST-1 as an example to check our working hypothesis.

This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

Abstract: This review presents current literature on different nanocomposites coatings and surface finishing for different substrates (such as textiles, concrete, plastics, stones, metals and so on). In particular, this study is focused on smart materials, drug delivery systems, and industrial, antifouling and nano/ultrafiltration membrane coatings. Each of these nanostructured coatings shows interesting properties for different fields of application. In this review, particular attention is paid to the synthesis and the consequent physico-chemical characteristics of each coating and, therefore, to the different parameters that influence the substrate deposition process. Several techniques used in the characterization of these surface finishing coatings are also described. In this review, the sol-gel and polymerization method for preparing stimuli-responsive coatings as smart sensor materials is described; functional polymers and nanoparticles sensitive to pH, temperature, phase, light and biomolecules are also treated. Finally, nanomaterials based on phosphorus, borates, hydroxy carbonates and silicones are used and described as flame retardant coatings; organic/inorganic hybrid sol-gel coatings for industrial applications are illustrated, together with functional nanofiller (carbon nanotubes, metallic oxides, etc.) and polymers employed for nano/ultrafiltration membrane and antifouling coatings. In the last decades, several research institutes and industries have collaborated for the advancement of nanotechnology by optimizing conversion processes of conventional materials into coatings with new functionalities for intelligent, innovative, eco-sustainable and advanced applications.

In open, sustainable policymaking, we are expecting to develop the valuable results that will bring us closer to a fairer and more balanced society. One way to involve the public in these processes is to engage them in online e-participation projects. Despite the hopes, empirical analyses show that many e-participation initiatives have failed to deliver expected benefits. Revealing what actually works in examined projects and what requires improvement would allow for better policy planning in the future. In this article, I made an attempt to identify and assess the cognitive processes enabling emergence of collective intelligence (CI) in a singular e-participation project. For this reason, I worked out and tested an evaluation technique, combining the MILCS framework for group cognition and the results of empirical research on CI. A case study method based on semi-structured interviews was selected to evaluate a sample participatory budgeting initiative, Civic Budget of the City of Kraków. Results reveal that most cognitive processes are working satisfactorily, but the real problem is using collective memory, which works only to a very limited extent. A guideline for future policymakers should be to develop a shared memory system, to which all community members should have access.

This paper is our attempt on the basis of physical theory to bring more clarification on the question ``What is life?'' formulated in the well-known book of Schr\"odinger in 1944. According to Schr\"odinger, the main distinguishing feature of biosystem's functioning is the ability to preserve its order structure or, in the mathematical terms, to prevent increasing of entropy. Since any biosystem is fundamentally open, it is natural to use open system's theory. However, Schr\"odinger's analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schr\"odinger should also appeal to the ambiguous notion of negative entropy. We suggest to apply the quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of the classical entropy. We consider a complex biosystem $S$ composed of many subsystems, say proteins, or cells, or neural networks in the brain, i.e., $S=(S_i).$ We study the following problem: if the composed system $S$ can preserve the ``global order'' in the situation of increase of local disorder and if $S$ can preserve its entropy while some of $S_i$ increase their entropies We show that within quantum information theory the answer is positive. The significant role plays entanglement of the subsystems states. In the absence of entanglement, increasing of local disorder generates disorder increasing in the compound system $S$ (as in the classical regime).

An exploding demand for processing capabilities related to the emergence of the IoT, AI and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path, but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations; four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the SC framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.

Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory interactions, which study at the global scale has been challenged by the lack of data integration. Here, we update the manually-curated C. glutamicum transcriptional regulatory network, now including protein-protein interactions having a direct effect on gene transcription. The network model with regulations supported by any experimental evidence increased by 557 interactions regarding the previous (2018) version. 73 interactions supported by directed experiments were also included in a second model. We included 545 sRNA-mediated regulations in a third model with a total of 5164 interactions. We deposited the three network models in Abasy Atlas v2.4. We study the C. glutamicum regulatory structure by comparing it against the networks for more than 40 species, finding it to contrast in several global structural properties. We analyze the system-level components of the networks, finding that the inclusion of the sRNAs regulations changes their proportions, transferring part of the basal machinery to the modular class and increasing the number of modules while decreasing their size. Finally, we use strong networks of three model organisms to provide insights in future directions of the C. glutamicum network characterization.

Space vehicles may be propelled by solar sails exploiting the radiation pressure coming from the sun and applied on their surfaces. This work deals with the adoption of Shape-Memory Alloy (SMA) elements in the sail deployment mechanism activated by the Joule Effect, i.e. using the same SMA elements as a resistance within suitable designed electrical circuits. Mathematical models were analyzed for the thermal analysis by implementing algorithms for the evaluation of the temperature trend depending on the design parameters. Several solar sail prototypes were built up and tested with different number, size and arrangement of the SMA elements, as well as the type of the selected electrical circuit. The main parameters have been discussed in the tested configurations and advantages discussed as well.

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

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

Reductions in water availability and increasing rainfall variability are generating a narrative of growing competition for water in the Mediterranean basin. In this article, we explore the distribution and importance of water resources in the Muga River Basin (Catalonia, Spain) based on key stakeholders’ perceptions. We performed a sociocultural evaluation of the main water ecosystem services in the region through stakeholder interviews and participatory mapping. The basin was generally perceived as a hotspot of ecosystem services, but we detected varying opinions and considerable differences in the perceptions of importance and spatial distribution of water ecosystem services. These discrepancies were linked to the varying levels of stakeholders’ dependence on water. Our findings are important for contributing to correct water planning and management in the river basin, which is a complex water social system marked by conflicts between different stakeholder groups vying for the same resource. This complex situation requires bottom-up strategies to create transparent, participatory decision-making models.

Contemporary climate change is exposing plant populations to novel combinations of temperatures, drought stress, [CO₂] and other abiotic and biotic conditions. These changes are rapidly disrupting the evolutionary dynamics of plants. Despite the multifactorial nature of climate change, most studies typically manipulate only one climatic factor. In this opinion piece, we seek to explore how climate change factors interact with each other and with biotic pressures to alter evolutionary processes. We first explore the ramifications of climate change for key life history stages (germination, growth and reproduction). We then examine how mating system variation influences population persistence under rapid environmental change and propose that mixed mating could be advantageous in future climates. Furthermore, we discuss how spatial and temporal mismatches between plants and their mutualists and antagonists could promote or constrain adaptive responses to climate change. For example, plant-virus interactions vary from highly pathogenic to mildly facilitative, and are partly mediated by temperature, moisture availability and [CO₂]. Will host plants exposed to novel, stressful abiotic conditions be more susceptible to viral pathogens? Finally, we propose novel experimental approaches that could illuminate how plants will cope with unprecedented global change, such as resurrection studies combined with experimental evolution, genomics or epigenetics.

The microorganisms play crucial roles in the cycle of matter and damage the organic substances, sources of electrons, source of carbon, source of energy for their biosynthesis. While dying, the microorganisms contribute on their turn to enrich soil in different composed of the carbon. In this work, we studied the impact of the aerobic mesophiles microorganisms on the black sigatoka of banana in the old secondary forest. The objective was to count and to identify the microbial diversity of the forest ecosystems, as well as to study their impact on the development of the black sigatoka of banana around the forest reserve of Masako. The assessment of the microbial populations has been done in an experimental field according to a device in blocks of Fischer by the method of successive dilutions of coloration of Gram and by the discharge of ascospores. The results showed that the rain season was lower in total microbial biomass (700, 7 colonies) than the subdry season (840,3 colonies). The Bacillus genera have been more represented more than the Coccus genera. The impact of black sigatoka of banana was raised at the cultivar Libanga Likale (40%) and low at Yangambi 5 Km (14%). The distribution of microorganisms in depth in the sub-dry season to the level of surface with vegetation was considerable either 3819, 3. 103 UFC( UNIT FORMAT COLONY) by gram of soil between 0 and 5 cm against 2754,5.103 UFC by gram of soil between 15 and 20 cm. This suggests that the raised number of microorganisms could have positive impact on soil fertility by decreasing the illnesses in this ecosystem.

Reconfigurable computing is becoming ubiquitous in the form of consumer-based Internet of Things (IoT) devices. Reconfigurable computing architectures have found their place in safety-critical infrastructures such as the automotive industry. As the target architecture evolves, it also needs to be updated remotely on the target platform. This process is susceptible to remote hijacking, where the attacker can maliciously update the reconfigurable hardware target with tainted hardware configuration. This paper proposes an architecture of establishing Root of Trust at the hardware level using cryptographic co-processors and Trusted Platform Modules (TPMs) and enable over the air updates. The proposed framework implements secure boot protocol on Xilinx based FPGAs. The project demonstrates the configuration of the bitstream, boot process integration with TPM and secure over-the-air updates for the hardware reconfiguration.

The umbrella concept for the current efforts to digitize construction is known as Construction 4.0. One of its key concepts is cyber-physical systems. The construction industry is not only creating increasingly valuable digital assets (in addition to physical ones) but also the buildings and built infrastructures are increasingly monitored and controlled using digital technology. Both make construction a vulnerable target of cyber-attacks. While the damage to digital assets, such as designs and cost calculations, may result in economic damage, attacks on digitally-controlled physical assets may damage the well-being of occupants and, in worst-case scenarios, even damage (or death) to the users. The problem is amplified by the emerging cyber-physical nature of the systems, where the human checks may be left out. We propose that construction learns from the work done in the context of critical infrastructures (CI). First, a lot of CI is construction-related, and the process of designing and building it must be secured accordingly. Second, while most assets may not be critical in the CI sense, they are critical to the operations of a business and the lives of citizens. In the end, we recommend some steps so that well-established processes of critical infrastructure protection trickle down to make Construction 4.0 and the built environment more cyber-secure. With that in mind, we describe the possible inclusion of Construction 4.0 considerations into existing critical infrastructure protection (CIP) frameworks with minimum frictions. We also propose some suggestions regarding possible future courses of action to improve the increasingly vulnerable cyber-security environment of the built environment across all life cycle phases - design, construction, operation, maintenance, and end of life.

Supplier selection/replacement strategies and optimized purchasing policies play a key role in efficient supply chain management in today’s dynamic market. Here we study supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. To assemble the product, we need to provide multi-type components, but assembly will be interrupted if any single component is missing, and incoming units will get hoarded until the missing component arrives. The assembly process can be interrupted by various sources of uncertainty, including delays in component deliveries. There is consequently a non-negligible risk that the assembly process may get stopped any moment. This brings inventory-related costs, which should be minimized. Here we consider discrete lead-time distributions to mimic industry-world reality. We present a model that takes into account not only optimal assignment of component order release dates but also replacement of a critical supplier. For a given unit, we model several alternative suppliers with alternative pricing and lead-time uncertainties, and we evaluate the impact on the total assembly system. For a more general case where several suppliers may be replaced, we propose a genetic algorithm.

The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, the energy storage technology can also provide security of supply, thereby constituting a cost-efficient alternative to expensive conventional reinforcements. In this context, the current paper presents a methodology for the economic quantification of the security contribution of energy storage. This methodology makes use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction as compared to the power capability of the energy storage asset. In this context, a case study is presented in which the security contribution of energy storage is analyzed as a function of its power capability, energy capacity and efficiency as well as of characteristics of load patterns.

A community food environment plays an essential role in explaining the healthy life-style patterns of its community members. However, there is a lack of compelling quantitative approaches to evaluate these environments. This study introduces and validates a new tool named the Facility List Coder (FLC), whose purpose is to assess food environments based on data sources and classification algorithms. Using the case of Mataró (Spain), we randomly selected 301 grids areas (100 m2) where we conducted street audits in order to physically identify all the facilities by name, address and type. Then, audit-identified facilities were matched with those automatically-identified and were classified using the FLC in order to determine its quality. Our results suggest that automatically-identified and audit-identified food environments have a high level of agreement. The ICC estimates and their respective 95% confidence intervals for the overall sample, yield the result “excellent” (ICC ≥ 0.9) for the level of reliability of the FLC.

Despite their obvious differences, biological systems at different scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are usually obscured by the parcelled terminology employed by various scientific sub-disciplines. To explore these commonalities, this papers a comparative study of diverse applications of the maximum entropy principle, ranging from amino acids up to societies. By presenting these studies under a common language, this paper establishes a unified view over seemingly highly heterogeneous biological scenarios.

Traditional phase change composites usually suffer poor mechanical property and easy collapsing in the phase changing process. Herein, a highly flexible phase change composite is fabricated using thermoplastic elastomer as the basic gel and the expanded graphite/paraffin as the filler. This new phase change composite shows a tensile strength of 2.1 MPa and a breaking elongation of 220%. It has a melting enthalpy of 145.4 J•g^-1 and a thermal conductivity of 2.2 W•m^-1•K^-1 with 70% of expanded graphite/paraffin. The thermoplastic elastomer based phase change composite exhibits great reversible property after 200 heating/cooling cycles. This flexible phase change composite demonstrates good photo-thermal energy charging/discharging property and shows great potential to be applied in the solar thermal energy systems.

Electric vehicles, being able to reduce pollutant and greenhouse gas emissions and shift the economy away from oil products, can play a major role in the transition towards low-carbon energy systems. However, the related increase in electricity demand inevitably affects the strategic planning of the overall energy system as well as the definition of the optimal power generation mix. With this respect, the impact of electric vehicles may vary significantly depending on the composition of both total primary energy supply and electricity generation. In this study, Italy and Germany are compared to highlight how a similarity in their renewable shares not necessarily leads to a CO₂ emissions reduction. Different energy scenarios are simulated with the help of EnergyPLAN software assuming a progressive increase in renewable energy sources capacity and electric vehicles penetration. Results show that, for the German case, the additional electricity required leads to a reduction in CO₂ emissions only if renewable capacity increases significantly, whereas the Italian energy system benefits from transport electrification even at low renewable capacity. Smart charging strategies are also found to foster renewable integration; however, power curtailments are still significant at high renewable capacity in the absence of large-scale energy storage systems.

This paper discusses a MEMS switch that can be fabricated using low temperature (<100^oC) deposition and patterning techniques suitable for the back-end-of-the-line integration with CMOS. The resulting cross-bar switches can be used for electrostatic discharge protection, FPGA implementation, chip security assessment and lock-down, and circuit block power management. We discuss platinum and iron switch with turn-on voltages of ~ 1.8 V. In the case of the iron switches, we also show that they can be magnetized to have “memory” and stay on when turned on. Platinum switch cycling of up to 1000 times did not show any changes in their turn-on voltage and their contact resistance was unchanged. The 10-100 nm switch airgaps were formed using low temperature sputtered sacrificial polysilicon and XeF₂ etching. XeF₂ does not attack any of the metals used in CMOS enabling fabrication of cross-bar switches with any of these metals. Once activated, it takes ∿ 6 to mechanically turn on the switch that can be decreased to ~1 ns by optimizing the device structure. Interestingly, the nm-scale gaps can be used as spark gap as a fast plasma switch to discharge first followed by the activation of the MEMS switch.

Considering the need to reduce greenhouse gas emissions, onshore wind energy is certain to play a major role in future energy systems. This topic has received significant attention from the research community, producing many estimations of Europe's onshore wind potential for capacity and generation. Despite this focus, previous estimates have relied on distribution assumptions and simulation schemes that summarily under predict both the amount of available future wind capacity as well as its performance. Foremost in this regard is the common use of contemporary, or at least near-future, turbine designs which are not fitting for a far-future context. To fulfill this role, an improved, transparent, and fully reproducible work flow is presented for determining European onshore wind potential. Within a scenario of turbine cost and design in 2050, 13.5 TWof capacity is found to be available, allowing for 34.4 PWh of generation. By sorting the explicitly-placed potential generation locations by their expected generation cost, national relations between turbine cost and performance versus a desired capacity are exposed. In this way, it is shown that all countries possess some potential for onshore wind energy generation below 4 €_ct kWh^-1. and, furthermore, that it is unlikely for these costs to exceed 6 €_ct kWh^-1.

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.

Bike sharing systems are a key element of a smart city as they have the potential for reducing pollutant emissions and traffic congestion thus substantially improving citizens’ quality of life. In these systems, bicycles are made available for shared use to individuals on a very short-term basis. They are rented in a station and returned in any other station with free docks. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty. The occupancy level of the stations can be constantly monitored through IoT-based services. The goal of this work is to analyze occupancy level data acquired from real systems to discover situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type, called Occupancy Monitoring Pattern (OMPs), is proposed to characterize situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, OMPs can be filtered and evaluated by considering also the spatial and temporal correlation of the acquired measurements. The results achieved on real Smart City data highlight the potential of these techniques in supporting domain experts in maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving the user experience, such as suggesting alternative stations in the neighborhood.

The language we use to describe the past can have a strong influence on the audience’s interpretation of our story. In our experiment, we explore, using 3 different conditions, how the framing and language of an accident report can affect the audience’s proposed solutions to manage the problems found. We find that the approach used to create an accident report can have a powerful influence on the audience’s decision making. Whether we are describing an accident in a similar manner to a crime, using a systems approach or we are accepting of multiple stories which are not linear or coherent, the methods we use to capture and communicate the story have a profound impact on the actions decided upon by the reader.

Robotic decision-support systems must facilitate a robots interactions with their environment, this demands adaptability. Adaptability relates to awareness of the environment and `self-awareness', human behaviour exemplifies the concept of awareness to arrive at an optimal choice of action or decision based on reasoning and inference with learned preferences. A similar conceptual approach is required to implement awareness in autonomous robotic systems which must adapt to the current dynamic environment (the context of use). By incorporating `self-awareness' with knowledge of a Robot's preferences (in decision making) the decision maker interface should adapt to the current context of use. This paper proposes a novel approach to enable an autonomous robotics which implements path planning combining adaptation with knowledge reasoning techniques and hedge algebra to enable an autonomous robot to realise optimal coverage path planning under dynamic uncertainty. The results for a cleaning robot show that using our proposed approach demonstrated the capability to avoid both static and dynamic obstacles while achieving optimal path planning with increased efficiency. The proposed approach achieves the multiple decision-making objectives (path planning) with a high-coverage and low repetition rates. Compared to other current approaches, the proposed approach has demonstrated improved performance over the conventional robot control algorithms.

The switched inertance hydraulic system (SIHS) is a novel high-bandwidth and energy-efficient digital device which can adjust or control flow and pressure by a means that does not rely on throttling the flow and dissipation of power. An SIHS can provide an efficient step-up or step-down of pressure or flow rate by using a digital control signal. In this article, analytical models of an SIHS in a four-port high-speed switching valve configuration are proposed, and the system dynamics and performance are investigated theoretically and experimentally. The flow responses, system characteristics and power consumption can be predicted effectively and accurately by using the proposed models, which were validated by comparing with experiments and with numerical simulation. The four-port configuration is compared with the three-port configuration, and it is concluded that the former one is less efficient for valves of the same size, but provides a bi-direction control capability. As bi-direction control is a common requirement, this constitutes an important contribution to the development of efficient digital hydraulics.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

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.

The Great Reset has been presented by the World Economic Forum (WEF) in response to the COVID-19 pandemic in 2022 as a model through which a “stakeholder economy” would achieve “resilient, equitable and sustainable” economic, social and environmental goals. A more equitable distribution of resources, better working conditions, and ecological transitions are included in a social, economic and environmental reform agenda. This article raises the question of whether the Great Reset project should be interpreted as a “realistic utopia” and what is its reform potential. In an attempt to answer the question of whether the Great Reset as a realist utopia is consistent with the current state of science and philosophy, the article re-examines the idea of utopia in the light of recent philosophical and scientific approaches, such as critical realism in philosophy, so-cial systems theory in sociology and complexity theory in science. A comparative conceptual analysis is carried out and discussed introducing the idea of a realist utopia in Rawls' theory of justice as fairness. In the final discussion, some doubts are raised about the logical coherence, rigor of scientific theorizing, policy prescriptions, and predictive potential of the Great Reset emerging paradigm. It is concluded that utopian projects of radical reform can become realistic when they offer explanations of the deep structural changes that underlie social and economic evolution, drawing from these explanations the policy implications and prescription that can support struc-tural changes, rather than from assumed long-term effects of exogenous shocks or “black swan” events such as the COVID-19 pandemic.

This paper investigates the strategic dimensions influencing the adoption of environmental inno-vations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. The study aims to understand the fundamental strategic dimensions defining eco-innovation and ex-amine how SMEs manage the complexities of incorporating these practices. Employing a mixed methods approach with documentary analysis and expert interviews; the study identifies key factors influencing EI adoption through causal maps and the Viable System Model. Findings high-light the significance of external and internal factors such as competitive pressure, economic con-straints, technological capabilities, political support, and social influence. This integrative approach emphasizes the importance of adaptability and responsiveness, presenting eco-innovation as a source of sustainable competitive advantage for SMEs.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

This study examines geothermal energy extraction potential from the basement rock within the Denver-Julesburg (DJ) Basin, focusing on flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-Shaped, V-Shaped, Inclined V-Shaped, and Pipe-in-Pipe configurations against Enhanced Geothermal Systems (EGS) setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. Results suggest that while closed-loop systems offer moderate temperature increases, EGS configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings like the DJ Basin. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

Rural electrification is a crucial step for the socio-economic development of isolated communities. Decentralized power generation, typically more favorable for renewable energies, requires an accurate analysis of the different electrification options, whose convenience depends on multiple factors. The application of Geographical Information System (GIS) to energy planning allows the assessment at a local level, considering spatial resources variability and demand distribution. This work introduces IntiGIS-local, a GIS-based model implemented in ArcGIS environment, designed to calculate the Levelized Energy Cost (LEC) for different electrification options. The model allows the comparison between three power generation alternatives: solar system, diesel generator set and solar-diesel hybrid system. Configurations are adjustable through input variables, with a special focus on the confrontation between individual systems and microgrids. The objective is to provide an adequate groundwork for developing a decision-making tool to assess diverse rural electrification options in future studies. The model IntiGIS-local is tested in the case study of Guasasa community (Cuba).

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

Academics in healthcare grapple with the challenge of efficiently managing time while addressing diverse roles and responsibilities, leading to heightened levels of anxiety and stress within the academic community. The primary objective of this study is to offer a sustainable and systemic solution to the prevalent time-management challenges in healthcare academia, through the development of a predictive model. Employing systemic analysis software and adopting a systemic management approach, we crafted a model to address academic time-management concerns. The dynamics of time allocation for academicians are governed by three fundamental pillars: teaching, research, and administrative tasks. Significantly prioritizing administrative tasks depletes valuable academic time, making time management within academia excessively costly. This occurs as highly skilled individuals find themselves diverted towards administrative duties at the expense of their academic roles diminishing resilience levels. The proposed Timebooster Academic Systemic Model (TASM) advocates for the cultivation of soft skills in academia, en-compassing prioritization, seeking assistance, and addressing tendencies of perfectionism and procrastination. Additionally, TASM recommends the implementation of full automation, delegation of administrative responsibilities, and the mitigation of both overt and covert time-consuming elements. Furthermore, TASM reports on the pivotal role of the academic resilient manager-leaders, who actively contribute to their own satisfaction and academic growth by enhancing collaboration and teamwork. Time-management challenges emerge as a critical determinant of academic well-being and resilience. Systemic models, exemplified by TASM, offer a valuable framework for investigating prioritization concerns, identifying administrative impediments in academic processes, and managing low-value time-consuming elements. The TASM model, along with the systemic philosophy underpinning its design, presents an avenue for advancing the exploration of time-management phenomena within the context of healthcare academia.

Omega-3 polyunsaturated fatty acids (ɷ-3 PUFAs) offer diverse health benefits, such as supporting cardiovascular health, improving cognitive function, promoting joint and musculoskeletal health, and contributing to healthy aging. Despite their advantages, challenges like oxidation susceptibility, low bioavailability, and potential adverse effects at high doses persist. Nanoparticle encapsulation emerges as a promising avenue to address these limitations while preserving stability, enhanced bioavailability, and controlled release. This comprehensive review explores the therapeutic roles of omega-3 fatty acids, critically appraising their shortcomings and delving into modern encapsulation strategies. Furthermore, it explores the potential advantages of Metal-organic framework nanoparticles (MOF NPs) compared to other commonly utilized nanoparticles in improving the therapeutic effectiveness of omega-3 fatty acids within drug delivery systems (DDS). Additionally, it outlines future research directions to fully exploit the therapeutic benefits of these encapsulated omega-3 formulations for cardiovascular disease treatment.

This paper introduces a rigorous and comprehensive approach for the assessment of marine shafting systems through the utilization of an advanced Pi-Theorem based scaling methodology. Integrating journal bearing similarity assessment and shaft-line scaling methodology with advanced dimensional analysis, the study aims to provide a methodology foundation for systematic replication and analysis of marine shafting systems through scaled models. The proposed scaling methodology ensures geometric and mechanical similarity in terms of shaft-line deflection, considering key scaling parameters such as shaft length, diameter, weight, loads, rotational speed, material properties, bearing locations, and offsets. The advanced dimensional analysis computes specific non-dimensional ratios to guarantee a close resemblance between a real-size system and a scaled lab model, enabling meaningful experimentation. The methodology is analytically derived and validated with numerical simulations for a case study, conducting comparative analysis, evaluating discrepancies, and utilizing the integrated framework for experimentation.

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs´ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with σ-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with standard Proportional Integer controller (PIC) and direct APBC, show the proposed CAPBC effectiveness and robustness under normal and changing conditions

DNA damage causes the mutations that are the principal source of genetic variation. DNA damage detection and repair mechanisms therefore play a determining role in generating the genetic diversity on which natural selection acts. Speciation, it is commonly assumed, occurs at a rate set by the level of standing allelic diversity in a population. The process of speciation is driven by a combination of two evolutionary forces: genetic drift and ecological selection. Genetic drift takes place under conditions of relaxed selection, and results in a balance between rates of mutation and rates of genetic substitution. These two processes, drift and selection, are necessarily mediated by a variety of mechanisms guaranteeing genome stability in any given species. One of the outstanding questions in evolutionary biology concerns the origin of the widely varying phylogenetic distribution of biodiversity across the tree of life, and how the forces of drift and selection contribute to shaping that distribution. The following examines some of the molecular mechanisms underlying genome stability and the adaptive radiations that are associated with biodiversity and the widely varying species richness and evenness in the different eukaryotic lineages.

This study presents a systematic review of the impact of Industry 4.0, digitalization, and information systems on the procurement function with a focus on the Portuguese reality. Despite global trends emphasizing digital transformation and innovation, the procurement practices in Portugal seem to lag. The adoption of cutting-edge technologies and the comprehensive integration of digital solutions within the procurement and supply chain management sector are yet to materialize fully in the Portuguese SME context. The benefits and obstacles of these technologies in transitioning procurement into a more strategic role are analyzed. By examining existing literature, critical factors for organizations to consider when implementing these technologies and the potential benefits are identified. The results indicate that digitalization has resulted in a data-driven approach to procurement, with the aid of advanced analytics and artificial intelligence for decision-making. Industry 4.0 has facilitated smarter factories, warehouses, and logistics operations with a focus on automation, connectivity, and real-time data. Procurement can more efficiently manage suppliers, track contracts, identify cost-saving opportunities, and increase sustainability with the aid of information systems. The study also highlights potential challenges for organizations. Overall, this study provides insightful findings on the role of digitalization in transforming procurement and the challenges and opportunities that organizations may face.

Unmanned agricultural tractor (UAT) represents the advanced stage of autonomous navigation and serves as a core technology in agricultural production. It reduces the operator’s workload, and improves operational accuracy and efficiency. This article reviews three aspects of key autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages, and shortages of these key technologies on UATs are clarified by analyzing technical principles and the current research status. We conducted summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of key autonomous navigation technologies on UATs, it can be seen that fruitful research progresses have been achieved. The review also summarizes the common problems in current technologies. The application research on sharing and integrating multi-source data for autonomous navigation are relatively weak. There is an urgent need of high-precision, and high-stability sensing equipment. The universality of path planning methods, and the efficiency and precision of path tracking need to be improved. It is necessary to develop high reliability electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprised of fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed wing aircraft systems. Here the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity techniquesto recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models will enable us to avoid common control design methods, such as gain scheduling, where stability issues can be extremely challenging.

Agriculture is the main driver of depletion resources worldwide, and its duty is to ensure food security within a rapidly increasing demographic and urbanization, so it is important to transition to sustainable production systems. Vertical crops (VCs) can reduce the pressure on conventional agriculture because they save water and nutrients and increase crop yield. Therefore, this study aimed to validate a proposed predictive model (PM) to simulate water and nutrient uptake in vertical crops under greenhouse conditions. Based on the Penman-Monteith equation, PM estimates transpiration, while nutrient uptake was estimated using the Carmassi-Sonneveld submodel. PM was experimentally evaluated for vertically grown lettuce under Mediterranean greenhouse conditions, during spring 2023. The irrigation technique was a closed-loop fertigation circuit. The experimental consisted of testing two densities (50 and 80 plants·m-2), where each unit of the experiment unit was divided into three heights (low, medium, and upper). It performed ANOVA with a value of p &lt; 0.05 and R2 to assess PM performance. The results suggest a high degree of PM, since R2 ranged from 0.7 to 0.9 for the uptake of water and nutrients. Both densities had a yield between 17-20 times higher than conventional lettuce production and significant savings in water, between 85-88%. In this sense, PM has great potential to intelligently manage VC fertigation, saving water and nutrients, which represents an advance towards reaching SDG 6 and SDG 12, within the 2030 Agenda.

Organic horticulture is a holistic management system which follows good production practices and should be considered as the cornerstone of mitigating climate change and to produce healthier fruits and vegetables. This agroecosystem practice not only benefits the green economy but promotes and enhances soil biological activity, biodiversity, and other biological cycles in the sphere. The last decade has observed a rise in the production and consumption of organically certified agricultural products due to their higher nutritional value, lowered risk of exposure to harmful chemicals and improved organoleptic properties. The aim of this review was to demonstrate the considerable impact of organic horticulture on mitigating climate change and simultaneously satisfy consumer needs. The outcome of this review demonstrates that there are still numerous research studies required to evaluate how different farming systems and pedoclimatic conditions can contribute to more efficient horticultural practices.

: The article presents a brief review of cooling systems of various temperature levels (from 0.1 K to 230 K) for radio astronomical receivers of photonics and electronics. The features of various levels of cooling and the requirements for the design of the cooling system are considered in detail, as well as approaches to designing interfaces for cooled receivers: vacuum, cryogenic, electrical, mechanical, optical, and others required for the effective operation of the receiver,. The presented approaches to design are illustrated by a series of joint developments of the authors carried out over the past 45 years, including those produced last year.

New possibilities of Gramian computation by using canonical transformations into diagonal, controllable and observable canonical forms are shown. With the help of such a technique the Gramian matrices can be represented in the form of products of Hadamard matrices of multipliers and matrices of the transformed right-hand side of Lyapunov equations. It is shown that the multiplier matrices are invariant under various canonical transformations of linear continuous systems. The modal Lyapunov equations for continuous SISO LTI systems in diagonal form are obtained and their new solutions based on Hadamard decomposition are proposed. New algorithms for element-by-element computation of Gramian matrices for stable continuous MIMO LTI systems are developed. For continuous SISO LTI systems given by equations of state in controllable and observable canonical forms, new algorithms for the computation of controllability Gramians and their traces in the form of Hadamard products in the form of Xiao matrices are developed. The application of transformations to the canonical forms of controllability and observability allowed to simplify the formulas of spectral decompositions in the form of Xiao matrices. In the paper new spectral decompositions in the form of Hadamard’s products for solutions of Sylvester algebraic and differential equations of MIMO LTI systems, including spectral decompositions of finite and infinite cross-Gramians of continuous MIMO LTI systems. Recommendations on the use of the obtained results are given.

Understanding the genetic architecture of a disease is crucial for development of valid diagnostic and therapeutic interventions. The analysis of genomic variations associated with pathological conditions is the starting point for uncovering disease-causing pathways (candidate processes). However, the complexity of intergenic and genetic-environmental interactions hinders the identification of pathogenic values of genomic changes. Furthermore, heredity, epigenetics and somatic mosaicism make the interpretation of genomic data even more sophisticated. To succeed, a variety of bioinformatic techniques are applied. Here, reviewing own and literature data, knowledge-based prioritization of genomic variations is described. Theoretical basis of the knowledge-based prioritization is given with a special regard to gene ontology, heuristics, hermeneutics (genomic hermeneutics) and analytics. Practical and methodological issues of prioritization using ontology- or pathway-based systems analysis are considered in the light of optimistic and realistic scenarios of cumulative phenotypic effects of the variome (the whole set of genomic variations in an individual or specific set of genomic variations for a phenotypic outcome). In the present communication, copy number variants (CNVs) in children with neurodevelopmental diseases are used as a practical foundation for the prioritization, inasmuch as these genome variations are systematically overlooked in the so-called NGS era. Nonetheless, it is highly likely that the prioritization is applicable to almost all types of genomic variations (e.g. chromosome abnormalities, gene mutations, functional synonymous variants etc.). The present methodology seems to be a valuable addition to current biomedical science widening the opportunities for medical genomics and genetics.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

This article aims to investigate the potential of artificial intelligence (AI) in revolutionizing the education sector and further advancing the vision of Education 6.0. This study explores how AI can foster personalized learning experiences and adaptive pedagogy. By analyzing recent developments and real-world applications of AI in education, this article offers insights into how AI can support educators in tailoring instruction to individual students' needs, promoting engagement, and optimizing learning outcomes. The article also discusses ethical considerations and challenges associated with the integration of AI in education, emphasizing the importance of responsible implementation and human supervision.

Ensuring fire safety is an important condition for the successful operation of industrial enterprises. It is necessary to detect the threat of fire before ignition. An indicator of danger is the appearance of gas, which occurs as a result of unauthorized heating of equipment. Gas sensors make it possible to detect the appearance of gases characteristic of the decomposition of materials at the stage of smouldering and pyrolysis, which cause a change in the composition of the atmosphere. This paper presents a mathematical method for the rational placement of gas analyzers for premises of potentially hazardous industries. It is the rational placement of gas sensors, which ensures safety, taking into account economic feasibility that determines the effectiveness of their use. For the first time, the analysis of gas accumulation during the movement of the flow from the source to the ceil-ing, then propagation to the location of the sensors, is used for the first time to select the placement of gas sensors. Depending on the permissible values of the gas volume, the placement and number of gas sensors are selected. The calculation of the conditions for the rational placement of gas sen-sors for the most common indicators, such as carbon monoxide and hydrogen at different sizes and heights of rooms, is carried out. The method of calculating the location of gas analyzers makes it possible to diagnose and predict fire hazard at technological facilities on the basis of gas control, and compare the obtained values with the maximum permissible values. This method provides cal-culation of the coordinates of the gas source from the coordinates of the triggered sensors. Knowing the coordinates of all sensors, the coordinates of the sensors that gave the alarm, and the formula for determining the volume of the gas cloud under these sensors, it is possible to assess the localization of the source of dangerous gas in the protected room. This increases the efficiency of the fire safety system. The method makes it possible to control the layout of sensors depending on the volume of protected premises, types of production, the creation of new sensors, changes in the composition of hazardous gas in different technological conditions.

This review delves into the significance of chemical communication in mammals, with an emphasis on the role of the vomeronasal system in sensing pheromone-mediated interactions. The vomeronasal system shapes a variety of social and sexual behaviors, including reproduction, hierarchy formation, maternal bonding, and species recognition. Intriguingly, this system presents a vast degree of evolutionary variability, more so than variations observed in the olfactory system. This adaptive diversity underscores the challenges in studying the vomeronasal system, emphasizing the need for detailed research into its neuroanatomy and functional morphology. This review traverses an array of topics central to our understanding of chemical communication in mammals. We begin with the foundational role and basic concepts of chemocommunication. This is followed by an in-depth exploration of the olfactory systems. Subsequently, the neuroanatomical underpinnings of the vomeronasal system are juxtaposed against those of the olfactory system. Our focus then shifts to an exhaustive study of the sensory element of the vomeronasal system, the vomeronasal organ, and the integrating center of this information, the accessory olfactory bulb. The discussion extends to secondary projections of both the olfactory and vomeronasal systems in the light of the dual olfactory hypothesis. The review culminates with a detailed look at four specific models of vomeronasal system organization belonging to four evolutionarily distinct mammalian families: rodents, marsupials, herpestids, and bovids. These have been meticulously analyzed by the authors, aiming to spotlight the significant morphofunctional differences stemming from the adaptive changes imprinted on the system.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

Resilient urban water infrastructure (UWI) is essential to maintaining public health and safety in urban areas and preventing consistent disruptions. However, UWI is vulnerable to a wide range of shocks and stresses due to the complex nature and interdependency of its components. The primary objective of this study is to evaluate the advances in resilience assessment of UWI comprising water supply, stormwater, and wastewater systems. This assessment involves examining bibliometric analysis, developed frameworks to understand resilience concepts for infrastructure and society, strategies for improving resilience, and resilience indicators. The study findings indicate that resilience assessment has primarily been conducted in developed countries, highlighting the macroeconomic importance of UWI. Three major areas were identified for analysing resilience in UWI: system design, development of resilience concepts, and implementation of green infrastructure. It was also found that while resilience is commonly defined based on technical approaches, a more thorough understanding of resilience can be obtained through holistic approach. While strategies such as system upgrade, decentralisation, digitalisation, and nature-based solutions can enhance resilience in UWI, they may be insufficient to achieve all resilience indicators. To address the issue of proper comparison of different resilience options, comprehensive and qualified indicators and metrics should be extensively examined in future.

Type 1 Diabetes mellitus (T1DM) occurs when insulin is not produced in the Pancreas due to the autoimmune destruction of the beta cells. The patients may be diagnosed after 80-90% of the beta cells are destroyed. Insulin remains the essential medicine in T1DM management, under continuous monitoring of glucose levels. T1DM management consists of a complex and precise set of self-care measures for the entire life; during this continuous process, the patients could become overwhelmed, angry, anxious, frustrated, and/or discouraged. Therefore, to be widely used and provide general benefits, new technologies for DM management must provide clinical advantages without compromising safety. The compliance of T1DM patients is also essential, thus improving their quality of life (QoL). Moreover, the accessibility of the new system for patients through healthcare coverage is critical; they must be able to buy the new devices and accessories and pay for their maintenance. In this context, our work aims to describe the most recent technologies regarding continuous glucose monitors (CGMs) and continuous subcutaneous insulin infusion (CSII) systems, correlated with psychosocial impact and capacity to improve T1DM patients' life quality.

The global trade and transportation sectors heavily rely on the maritime industry. Still, its dependence on fossil energy sources poses significant environmental challenges and leads to unstable fuel prices that affect the cost of goods transported by sea. This paper aims to evaluate the viability of seaports as energy-intensive entities and explore the feasibility of implementing a Nuclear-Renewable Hybrid Energy System (NRHES). The study presents a case study of the Tanjung Priok Port in Indonesia, focusing on estimating energy consumption, emissions, and the potential impact of carbon taxation on seaport operations. By quantifying these factors, the research provides insights into the energy requirements, environmental effects, and potential costs associated with seaport carbon taxation. A comprehensive analysis of the technical and economic feasibility of implementing an NRHES in the seaport case study is conducted, determining the optimal sizing and composition of components, considering the proportion of nuclear and renewable energy sources. Additionally, the economic analysis considers energy costs, net present cost, cash flow, return on investment, and internal rate of return. The findings aim to inform decision-makers about the benefits and challenges of adopting an NRHES, contributing to a cleaner and more sustainable future for the maritime industry.

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry and science related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, rational design of combination therapies and the global response to the Covid19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human focused, systems-based approach to drug discovery and research.

The influence of neutrosophy in the previous period is constantly growing in many areas of science and technology. Moreover, various applications of the neutrosophic approach have become more common in recent years. Our goal in this research is to utilize the neutrosophy to improve the performance of the Dai-Liao conjugate gradient (CG) method. Specifically, in this research, we propose and investigate a new neutrosophic logic system to calculate the key parameter t involved in the Dai–Liao CG iterations. Theoretical analysis and numerical experience indicate that the efficiency and robustness of the new rule for determining t. Combining the neutrosophy and the Dai-Liao conjugate gradient method, we propose and explore a new Dai-Liao CG iterations for solving large-scale unconstrained optimization models. The global convergence is established under common assumptions and the backtracking line search. Finally, by conducting numerical experiments, computational evidence demonstrates that the new fuzzy neutrosophic Dai-Liao conjugate gradient method is computationally effective and robust.

The study of the human psyche has elucidated a bipartite structure of logic reflecting the quantum--classical nature of the world. Accordingly, we posited an approach toward studying the brain by means of the quantum--classical dynamics of a mixed Weyl symbol. The mixed Weyl symbol can be used to describe brain processes at the microscopic level and, when averaged over an appropriate ensemble, can provide a link to the results of measurements made at the meso and macro scale. Within this approach, quantum variables (such as, for example, nuclear and electron spins, dipole momenta of particles or molecules, tunneling degrees of freedom, and so on) can be represented by spinors, whereas the electromagnetic fields and phonon modes can be treated either classically or semi-classically in phase space by also considering quantum zero-point fluctuations. Quantum zero-point effects can be incorporated into numerical simulations by controlling the temperature of each field mode via coupling to a dedicated Nos\'e-Hoover chain thermostat. The temperature of each thermostat was chosen in order to reproduce quantum statistics in the canonical ensemble. In this first paper, we introduce a general quantum--classical Hamiltonian model that can be tailored to study physical processes at the interface between the quantum and the classical world in the brain. While the approach is discussed in detail, numerical calculations are not reported in the present paper, but they are planned for future work. Our theory of brain dynamics subsumes some compatible aspects of three well-known quantum approaches to brain dynamics, namely the electromagnetic field theory approach, the orchestrated objective reduction theory, and the dissipative quantum model of the brain. All three models are reviewed.

This work presents a nonlinear model predictive control scheme that challenges overcoming the obstacles holding back over decades to develop affordable autonomous control and monitoring systems applied in the large-scale industry. Among the numerous proposals in the literature, most do not consider the significant fluctuation of kinetic parameters in the reduced mathematical model ADM2, widely used for control and monitoring purposes. The prevalent cause, on a basis, is the lack of information caused by some dynamics and parameters that cannot be measured in real-time by reliable sensors. In addition, to make matters worse, those systems inherently act with nonlinear nature and have a high sensitiveness to uncontrollable inputs and perturbations. Therefore, to prevent these drawbacks, this work proposes a new methodology that reconstructs the lack of information from the non-measurable dynamics, concentration of bacterias, and the kinetic parameters related to reaction rates. Simulations results demonstrate the effectiveness of the methodology compared with traditional industrial control schemes.

The application of transformations of the state equations of continuous linear and bilinear systems to the canonical form of controllability allows one to simplify the computation of Gramians of these systems. In the paper we developed the method and obtain algorithms for computation of the controllability and observability Gramians of continuous linear and bilinear stationary systems with many inputs and one output, based on the method of spectral expansion of the Gramians and the iterative method for computing the bilinear systems Gramians. An important feature of the concept is the idea of separability of the Gramians expansion: sep333arate computation of the scalar and matrix parts of the spectral Gramian expansion reduces the sub-Gramian matrices computation to calculation of numerical sequences of their elements. For the continuous linear systems with one output the method and the algorithm of the spectral decomposition of the controllability Gramian are developed in the form of Xiao matrices. Analytical expressions for the diagonal elements of the Gramian matrices are obtained, making use of which the rest elements can be calculated. For continuous linear systems with many outputs the spectral decompositions of the Gramians in the form of generalized Xiao matrices are obtained, which allows to significantly reduce the number of calculations. The obtained results are generalized for continuous bilinear systems with one output. Iterative spectral algorithms for computation of elements of Gramians of these systems are proposed. Examples are given that illustrate theoretical results.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.