The combination of different wood modification technologies to obtain improved performance is increasingly receiving attention in research. In this study, Scots Pine (Pinus sylvestris) sapwood was impregnated with furfuryl alcohol (FFA) in pure aqueous 20, 40 and 60% solution strength without adding any catalyst. In a second step, the FFA was polymerized while simultaneously performing thermal modification in a closed system at 130, 150 or 180 °C. After leaching and ageing tests, the 9 different combinations were tested for use class 4 applications according to CEN/TS 15083-2 (2005) decay lab test. It was noted that even the minimum intensity combination of 20% FFA at 130 °C resulted in maximum durability class (DC) 1 performance. On the contrary, DC 4 was assigned to thermally modified control samples, even at the maximum intensity of thermal modification. Similarly, for FFA modifications, previous research has suggested that an uptake of 35% solution strength is required to obtain an adequate durability performance for use class 4 applications. High levels of resistance against termites were also noted by corresponding termite lab tests. Moisture studies showed the combined treatments resulted in improved stability and reduced moisture uptakes. Thus, the results obtained by this study revealed synergistic performance effects which originate from the combined thermo-chemical modification approach and which were higher than simple accumulation of the individual performance of purely thermally or chemically modified wood. Thus, the presented findings have provided positive implications for industrial applications of thermo-chemical modification techniques and offers an array of new research opportunities.

Snakebite envenomations (SBE) are a significant global public health threat due to their morbidi-ty and mortality. It is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported in Brazil only in 2020, posing a high burden for this population. We describe the data structure of the Snakebite data on Notifiable Disease Information System (SINAN) made available by the Brazilian Minis-try of Health from 2007 to 2020. In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work is related to clinical and demographic information of SBE cases. Also, data on out-comes, laboratory results, and treatment is available. The database is available and freely acces-sible; however, preprocessing, adjustments, and standardization are necessary due to incom-pleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for as-sessing different aspects and the national burden of envenoming.

This paper presents a comparison between a kite model with a constant-length tether and a model based on a system identification algorithm. The concept of system identification is applied to predict the uncertainties related to the variation of the wind speed and the shape deformation of the tethered membrane wing during flight. A pole-placement controller is used to ensure that the kite follows the planned flight path. Thus, we can determine the required locations of the closed loop poles, and then enforce them by changing the controller's gains in real-time. The capability of the system identification algorithm to recognize sudden changes in the dynamic model, and the ability of the controller to stabilize the system in the presence of such changes are confirmed. Furthermore, the system identification algorithm is applied to determine the parameters of a kite with variable-length tether used in a flight test of the 20 kW kite power system of TU Delft. Experimental data of this test were compared with the system identification results in real-time and significant changes were observed in the parameters of the dynamic model which heavily affect the resulting response.

Rotorcrafts operate HUMS for fault diagnosis, but the capabilities of HUMS analysts are important due to the complex mechanical configuration of rotorcraft and the difficulty of analysis. Accordingly, an advanced algorithm is needed to identify faults, and flight data can be used to develop the algorithm. However, flight data contains abnormal or missing data due to problems such as sensor defects, which can be a major problem in algorithm development. Therefore, in this paper, we study a gap filling method using MLP to obtain normal data when there are problems such as abnormal or missing data in rotorcraft flight data. Existing methods typically fill the gap through interpolation or curve fitting using the before and after values of missing data. However, in this study, normal values were estimated using the values of other parameters at the moment of missing or abnormal data occurred. As a result of evaluating the performance of the trained MLP using normal data, it was confirmed that the output of the model well estimates the changing trend of the target. And when abnormal data was applied to the trained model, it was confirmed that point outliers were eliminated and appropriate values were found in the case of missing data.

Good Boussinesq equations will be considered in this work. First we apply three combined compact schemes to approximate spatial derivatives of good Boussinesq equations. Then three fully discrete schemes are developed based on symplectic scheme in time direction, which are sympletic-structure preserving.Meanwhile, the convergence and conservation of the fully discrete schemes are analyzed. Finally, we present numerical experiments to confirm our theoretical analysis. Both our analysis and numerical test indicate that the fully discrete schemes are efficient in solving the spatial derivative mixed equation.

This article analyses entropy changes triggered by specific events in deterministic and indeterministic systems. Article considers a simple model consisting of water in a cuvette, an ice cube in the device above the cuvette and a random number generator (RNG) that controls the probability of dropping the ice into water. Article introduces the entropic potential Z(T, A) of an event A occurred in a system R at the moment Т₀, which describes the influence of the event A to the entropy of the system R in the future (for the moments T>Т₀). The entropic potential of an event Z(T,A) can be calculated as the difference between the mathematical expectations of entropy of the system R for the moment T (T>Т₀) made immediately before and immediately after the event A as Z(T, A) = Ŝ_T(Т₀+dT) - Ŝ_T(Т₀-dT). Article also presents examples of calculations of the entropic potentials of events in indeterministic systems with different probabilities of events. Since real-life systems are mostly indeterministic, the entropic potentials of events in real-life usually have non-zero values. The entropic potentials of the events "useful" for the system are negative, and entropic potentials of the events "harmful" for the system are positive.

The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids are well-established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge and this results in high costs. To enable a democratization of the technology by reducing the costs this study provides a novel modular plug-and-play open source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink as well as experimental validation. The results show transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems.

We define the function $Col: \mathbb{N} \to \mathbb{N}$ as the Collatz function, given by $3n + 1$ if $n$ is odd and $\displaystyle\frac{n}{2}$ if $n$ is even \cite{Lagarias1990}. The conjecture postulates that for any positive integer, at some point, its iteration will reach 1, or equivalently, every orbit will fall into the periodic cycle $\{4, 2, 1\}$. Two conditions would invalidate the conjecture: the existence of a divergent orbit or the presence of another cycle. In 2019 Terence Tao \cite{tao} proved that almost all orbits converge to the trivial cycle of $\{1,4,2\}$, in this work we will prove the non-existence of divergent orbits. The central idea is to decompose the dynamic system $Col$ into a binary dynamic system, generated by different compositions of two functions. This differs from a typical dynamic system, which is generated by the iteration of a single function. We consider the system generated by the functions $\theta, \psi: \mathbb{R} \to \mathbb{R}$, defined as $\displaystyle\theta(x) = \frac{x}{2}$ and $\displaystyle\psi(x) = \frac{3x + 1}{2}$, denoted as $\langle \theta, \psi \rangle$. We examine sequences of functions in $\langle \theta, \psi \rangle$ of the form $S_k(x) = s_k \circ S_{k-1}(x)$ with $s_k \in \{\theta, \psi\}$. We define a function that assigns to each element of these sequences an integer, called the minimum value positive integer or simply the minimum value. This corresponds to the smallest positive integer $n$ such that $S_k(n)$ is an integer. As we will prove later, the minimum value is monotonically increasing, meaning that increasing the terms of the sequence will never result in a value lower than the previous one. Based on this behavior, we distinguish two types of sequences: stable ones, where the minimum value is constant from a certain point, and unstable ones, where the minimum value is divergent. The main result for unstable sequences is that when the slope of the sequence is divergent, the sequence is unstable. From this result, we can prove that there are no divergent orbits for the function $Col$.

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain the company's operational capacity at the required level. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology is a systematic literature review using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2024) and studies published in English. This resulted in the selection of 201 of the most relevant papers in the area of investigation. Finally, selected articles in this review were categorized into seven groups: a) air transportation, b) railway transportation, c) land transportation (road), d) in-house logistics, e) water and intermodal transportation, f) supply chain operation, and g) other applications. One of the main advantages of the study is that the results are obtained from different scientific sources/databases thanks to using the multi-search tool. Moreover, a bibliometric analysis of the selected papers was performed. The results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives. In addition, based on the review results, a framework for DT in transportation system maintenance was developed. The study ends with conclusions and future research directions.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there's a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input-multiple-output (SIMO) fuzzy logic control system, the study aimed to improve displacement control, augmenting the traditional Proportional-Integral (PI) tuning approach. Implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. The study also included testing of daily-living (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and a more accurate emulation of real-world physiological dynamics.

Background: In this investigation, we explored the effects of pharmacological cholinergic stimulation on cardiac function and renal inflammation following acute myocardial infarction (AMI) in spontaneously hypertensive rats (SHRs). Methods: Adult male SHRs were randomized into three experimental groups: sham-operated; AMI+Veh (infarcted, treated with vehicle); and AMI+PY (infarcted, treated with the cholinesterase inhibitor pyridostigmine bromide (PY) - 40 mg/kg, once daily for seven days). Rats were euthanized 7 or 30 days post-surgery. Clinical parameters were assessed on the day before euthanasia. Subsequent to euthanasia, blood samples were collected and renal tissues were harvested for histological and gene expression analyses aimed to evaluate inflammation and injury. Results: Seven days post-surgery, the AMI+PY group demonstrated improvements in left ventricular diastolic function and autonomic regulation, and a reduction in renal macrophage infiltration compared to the AMI+Veh group. Furthermore, there was a notable downregulation of pro-inflammatory gene expression and an upregulation of anti-inflammatory gene expression. Analysis 30 days post-surgery showed that PY treatment had a sustained positive effect on renal gene expression, correlated with a decrease in biomarkers indicative of subclinical kidney injury. Conclusion: Short-term cholinergic stimulation with PY provides both cardiac and renal protection by mitigating the inflammatory response after AMI.

Intending to analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the Levelized Cost of Energy (LCOE) alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO₂ prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, this study has shown that lower financing costs (e.g., EU taxonomy support) considerably reduce the relevance of nuclear cost uncertainties on its investments. Furthermore, we demonstrate that the economic feasibility of national nuclear power investments can vary considerably depending on the cross-border electricity trade assumptions. Additionally, we found that lowering the cost of small modular reactors has more impact on their economic feasibility than increasing their generation flexibility. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.

Climate projections show that southern Indonesia such as West Nusa Tenggara is projected to experience a lower precipitation and higher temperatures. To date, research on climate change impact on Indonesian rice production yield is limited. As climate change is projected to decrease rainfall and to increase temperatures, this paper offers a qualitative analysis using system archetypes to understand the impacts of climate change on rice production. Two system archetypes are identified including Limits to Growth and Success to Successful. Both archetypes explain that rice production is hampered by high minimum temperature as photosynthesis output is decreased by increasing respiration. This paper shows that using a simple tool, system archetypes, we can describe the impacts of climate change on rice production. The outputs of this study such as a causal loop diagram and system archetypes can be a basis to develop a simulation model in understanding the impacts of climate change on main crops.

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

To achieve low-cost and on-demand launches of micro satellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous-oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar-turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of low fuel regression rate; i.e., it achieved a higher fuel-gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up and its total impulse was increased to over 50 kNs for reaching an altitude of 15 km. In addition to the engine, other components were also improved, such as through incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device and data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. The presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments.

The endocannabinoid system (ECS) is a complex neuromodulatory network in the human body that have enormous impact in developing the central nervous system, as well as modulates various physiological processes. The ECS is made up of anandamide, 2-arachidonoylglycerol (2-AG), which are responsible for the synthesis and degradation of endocannabinoids, and enzymes such as FAAH and MAGL ensures the precise control over endocannabinoid levels. The ECS relies on CB1 receptors found in the central nervous system (CNS), and CB2 receptors found in the peripheral nervous system and immune cells. Dysregulation of the ECS has been associated with several health conditions, prompting investigations into its association with phytocannabinoids to harness their therapeutic potential. The ECS is intricately linked with the nervous system, and it is known to modulate neuronal activities in the CNS, influencing pain and mood modulation. The ECS is also associated with neuropathies-peripheral nerve damage leading to symptoms such as weakness, numbness and pain as evidenced in diabetic neuropathy, Guillain-Barré Syndrome, carpal tunnel syndrome, peripheral neuropathy, and postherpetic neuralgia. The focus of this review is on the interplay between 2-arachidonoylglycerol and anandamide amidohydrolase in neuropathy, elucidating their roles in pain regulation, inflammation, and specific neuropathies. The knowledge generated is expected to have broad implications, providing comprehensive insights into neuropathic conditions and how to improve on them. Ultimately, this understanding may lead to more effective treatments and improved quality of life for individuals affected by these conditions.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

Natural products are considered an essential source of chemical compounds with relevant biological activities. Important antibiotics, anti-virals, and anticancer agents within healthcare facilities have been isolated from them. In recent decades, the study of alkaloids and the modulation of the immune response has aroused interest. The survey of SAR alkaloids with immunomodulatory effects is of great importance since structural modifications impact the effectiveness of the immunomodulatory activity, thus promoting the rational design of more effective and specific drugs. This review exhibits the main alkaloids with an immunomodulatory effect and their relationship with SAR studies, particularly in activating leukocytes and secretion of molecules with critical inflammatory activities such as cytokines, chemokines, and nitric oxide. These studies optimize the properties of alkaloids and allow a deeper understanding of the mechanisms of action at a molecular level, which is fundamental for the innovation of immunological therapies.

This study comprehensively explores Serbia's Disaster (Risk) Management System, aiming to critically evaluate its inherent strengths and weaknesses. Using descriptive-analytical methods and qualitative interpretation, SWOT analysis was applied to collect data from scientific papers, books, and institutional reports. This study delves deeply into the nuances of Serbia's Disaster Management Framework, revealing a nuanced landscape of strengths, weaknesses, and strategic improvement opportunities. Identified strengths underscore a robust foundation characterized by proficient professionals, state-of-the-art technology, and cohesive teamwork, all pivotal for effective emergency preparedness and response. Conversely, weaknesses underscore pressing imperatives for enhancements in regulatory enforcement, resource allocation, and infrastructure upgrades, presently hampering the framework's efficacy and response readiness. This research contributes to the ongoing discourse on disaster risk management and serves as a catalyst for further scholarly inquiry and policy formulation. By mapping out the structure of Serbia’s Disaster (Risk) Management Framework and integrating diverse data sources, the study enriches theoretical models and lays the groundwork for practical policy interventions. Its findings, including precise recommendations for policy reform and strategic improvements, provide valuable insights for policymakers, emergency management experts, and stakeholders seeking to enhance disaster preparedness and response capabilities on both national and global scales.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

White adipose tissue (WAT) and gut are involved as inducers of neuroinflammation when they detect injury and trigger inflammation. The autonomous nervous system innervates both tissues, although the roles of the sympathetic, parasympathetic and enteric nervous systems have not been fully elucidated. Here, we revisited the participation of both types of inflamed peripheral tissue in neuroinflammation. We first analyzed how inflamed peripheral WAT plays a key role in neuroinflammation once metainflammation is installed. Second, we described the impact of insulin resistance (IR) on hypothalamic dysfunction, T2DM and neurodegenerative diseases. Finally, we analyzed the gut-brain axis, examining cell interactions, soluble factors, the sensing of microbes and the role of the intestinal microbiota and mucosal barriers in neuroinflammation. Since intestinal mucosa and secondary lymphoid organs are densely innervated by the sympathetic, parasympathetic, and enteric nervous systems, we analyzed bidirectional crosstalk between different tissues, neurons, and immune systems in healthy and pathological circumstances to restore normal tissues functions, and consequently homeostasis.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

The review scrutinizes the currently performed research on the new methods for enhancing bituminous binder performance through radiation and radical grafting of polymer modifers of bitumen. It investigates innovative methods, including using waste polymers as modifiers, and applying radiation for polymer grafting, to overcome challenges like high costs, low aging resistance and storage stability issues, of which separation of phases polymer/bitumen is the most significant obstacle. These advanced modification techniques promise sustainability through the decrease of the carbon footprint of transportation systems by improving the properties and durability of binders. Additionally, the review discusses the parameters and mechanistic aspects from a scientific perspective, shedding light on the underlying processes that contribute to the improved performance of modified bituminous binders.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

The intestinal microbiota, comprised of bacteria, archaea, and phages, inhabits the gastrointestinal tract of the organism. Male reproductive sterility is currently a prominent topic in medical research. Increasing research suggests that gut microbiota dysbiosis can result in various reproductive health problems. This article specifically investigates the impact of gut microbiota dysbiosis on male reproductive infertility development. Gut microbiota imbalances can disrupt the immune system and immune cell metabolism, affecting testicular growth and sperm production. This dysfunction can compromise the levels of hormones produced and secreted by the endocrine glands, affecting male reproductive health. Furthermore, imbalance of the gut microbiota can disrupt the gut-brain-reproductive axis, resulting in male reproductive infertility. This article will explore how the imbalance of the gut microbiota impacts male reproductive infertility through immune regulation, endocrine regulation, and interactions of the gut-brain-reproductive axis, concluding with recommendations for prevention and treatment.

It is not plausible to predict disasters; however, still the least that may be done is to get ready for that. In disaster circumstances, nowadays, the existing internet of things (IoT) technology is rather developed and capable of being highly beneficial. For many countries, the early warning system for natural catastrophes is a crucial challenge, especially for high seismic hazard countries. Primary function of an early warning system is to assist individuals and authorities to save lives and reduce secondary damage once a disaster occurs. Even though such systems have been defined for the earthquake on the basis of IoT, the least attention has been focused on the post decisions for substructure monitoring systems which can control the gas and electric lifelines given the critical time. Combination of Fuzzy system with IoT can improve the decision making process after the disasters occur. This program minimizes costs and destruction in cities. Tehran is a high seismic hazard area which contains an expanded network of the gas transmission system. Hence, a major seismic event may cause serious loss directly and indirectly owing to the damage to the gas and electric transmission network. The proposed early warning system contains a grid of strong ground motion stations. Each station contains five sensors to collect accurate data and send the data to the cloud server, then the server executes relevant scenarios.

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical fodder legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real-time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.

A flat-plate unglazed solar water heater (SWH) with polymer thermal absorber was developed and tested. Polymer thermal absorber as a viable alternative to metal thermal absorber that is found in most SWH systems. The performance of this polymer SWH system is measured based on inlet and outlet water temperature, water flowrate, ambient air temperature and solar irradiance. The polymer thermal absorbers are hollow Polyvinyl Chloride (PVC) pipes with 20mm external diameter and 3 mm thick, painted in black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward-outward opposing-flow (RSioof). The collector pipes are placed in a 1 m2 square enclosure with bottom insulation and reflective surface for better radiation capture. Water circulation is a closed loop with a 16-liter uninsulated storage tank, driven by a pump and controlled by valves to maintain a mass flow rate of 0.00031 to 0.00034 kg/s. The test was conducted under partially clouded sky from 9 am to 5 pm, with solar irradiance between 105 and 1003 W/m2 and 27-36oC ambient air temperature. The SHW produced outlet hot water at 65oC by mid-day and maintained the storage temperature at 63oC until the end of test duration. Photo-thermal energy conversion was recorded with 23% maximum value. Results indicate that a flat plate solar water heater with polymer thermal absorber in RSioof design can be an effective alternative to the metal thermal absorber solar water heater and can be further optimized with glazing and tube sizing.

This study aims to automate the optimization of a Full-Range speaker in an SUV's audio system according to the equal loudness contours principle. The input signal, and frequency responses of the amplifier and speaker were transferred to Matlab. Using ideal filter parameters, ten parametric equalizer models were created, and the speaker output was obtained using the convolution technique. The same filter settings were applied to the vehicle multimedia system, and experimental results were obtained. The simulation and experimental results were compared, showing high similarity with a Pearson correlation coefficient of 0.9295 and an RMSE of 2.29. Results were compared with the ideal contour. The Pearson correlation coefficients for simulation and experimental results were 0.6341 and 0.6715, with RMSE values of 4.88 and 2.57, showing low similarity. Consequently, each parametric equalizer filter's parameters were optimized using a genetic algorithm. The optimized parameters were applied to the vehicle multimedia system, and results were compared with the ideal contour. The Pearson correlation coefficients for simulation and experimental results were 0.965 and 0.9661, with RMSE values of 2.12 and 1.48. These results indicate that optimization aligns the speaker output closer to the ideal contour, enhancing in-vehicle audio system performance and increasing user satisfaction.

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers.

The integration of magnetic observatories of many countries into the Intermagnet network has provided researchers with new opportunities to investigate many geophysical processes. The joint processing of measurement evidence from magnetic observatories located in various geolocations makes it possible to better understand the dynamics of the interaction of solar plasma fluxes with the Earth's magnetosphere. For the correct interpretation of the dynamics of physical processes, the accuracy of the time reference of measurement results is of great importance. The measurement results of local observatories were transformed to a unified ecliptic coordinate system oriented towards the Sun in order to extract small variations in the magnetic field occurring before the magnetic storm commencement. Before transforming the coordinates, a constant component equal to the average daily value of the magnetic field components was removed from the results of minute-by-minute measurements. Owing to the averaging evidence from 84 magnetic observatories of the Intermagnet network, variations in the magnetic field of cosmic origin, preceding the magnetic storm commencement were detected. The correlation analysis of these variations recorded by different observatories revealed the presence of time shifts between the data series of different observatories. Only 62% of the observatories had a relative data shift within the range of ± 1 minute. Before the joint data processing in order to efficient use of data recorded with a time shift a method for correcting time shifts has proposed.

The concept of Digital Twins has been in the field for a long time, constantly challenging the specification, modeling, design, implementation, and exploitation of complex cyber-physical systems. Despite the various foundations, standards, and platforms in Systems Engineering, there are ongoing challenges with Verification and Validation methodology. This study aims to establish a generic framework that addresses the various aspects of Digital Twinning. The multifaceted nature of the problem requires raising the abstraction level in both the Real (Actual) and Virtual domains, effective dissemination of information resources, and a design inspired by verification and validation. The proposed framework combines the Quintuple Helix model with the Problem and Operational Domains of a Real (Actual) Twin, the Solution and Implementation Domains of a Virtual Twin, and the Execution Domain as the bridge that links them. Verification and Validation dimensions follow the Meta Object Facility abstraction layers (Instance, Model, Meta-model, and Meta-meta-model) mapping over five Helices. Embedding the complexity reduction mechanisms in the proposed framework builds a suite for extendible and verifiable digital twinning in simulation and real-time scenarios.

In the development of power systems it is indicated very often, that transformation of power systems should be carried out in accordance with the idea of energy democracy. This will develop energy communities, that are trying to meet energy needs by using local renewable generation sources. This may result with a temporary low load on the MV lines connecting the community grid and the power system. Such state may cause incorrect operation of power protection systems. This can cause an extended protection operation time, due to decision algorithms inactivity at low values of measurement currents. Therefore, the detailed MV lines overcurrent digital protection model and a dynamic model of the power network were developed. The simulation results are showing that the settings of the parameters activating the protection decision algorithms affect their operation time in dynamic conditions. The conclusion is that the development of the power protection automatics must be carried out in the same time (preferably in advance) with the change of the power system operation model. This is very important for future power systems with high penetration energy communities and renewable generation sources.

Since the launch of Protein Data Bank (PDB) in 1971, Cartesian coordinate system (CCS) has been the default approach to specify atomic positions in biomolecular experimental structures with X, Y and Z. In 2020, a local spherical coordinate system (LSCS) approach was proposed as an alternative to CCS, i.e., ρ, θ and φ. Recently, the continued application of deep learning technology in protein structure prediction (PSP) saw a leap forward in the accuracy of PSP, as evidenced by AlphaFold of Google’s DeepMind. However, there still is room for the improvement of the performances of PSP algorithms to date. Given that geometrically, CCS and LSCS are like the two sides of a coin, this short article puts forward a hypothesis that the time is now ripe to end the half-a-century burial of ρ, θ and φ in PDB, and use them as LSCS features for the design of novel PSP algorithms in future.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.

Under the action of cement hydration heat, construction environment, thermal insulation measures, and pipe cooling system, the mass concrete pile cap produces a complex temperature field inside the structure, which makes it difficult to control the internal surface temperature difference, the maximum adiabatic temperature rise and the surface temperature of the structure. In this paper, the mass concrete pile cap of a super large bridge (the length, width, and height are 26.40m，20.90m, and 5.00m, respectively, and the central pier pile cap is constructed with concrete with a strength of C40) is taken as the research support project. The controlling factors affecting the temperature field of the mass concrete pile cap are found by comparing the temperature monitoring of the pile cap with the calculated value of finite element software simulation analysis. Through the finite element software Midas Civil and Midas FEA, the control factors affecting the temperature field of the main pier cap (concrete mold temperature, concrete surface convection coefficient, ambient temperature, and pipe cooling system parameters, etc.) are numerically analyzed, and the influence law and influence degree of the control factors are found out.

Overweight/obesity and cardiovascular diseases are associated with high rates of mobility and mortality. We investigated inflammatory lesions and ionic transporters in the left ventricle of male rats chronically fed a high-fat diet (HL). They developed moderate overweight with early expansion of visceral adiposity. The inflammatory markers interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-), Na+-transporting ATPases, sarco-endoplasmic reticulum Ca2+-ATPase (SERCA2a), and the abundance of Angiotensin II receptors were studied together with lipid and glycemic profiles, and the left ventricle echocardiographic parameters fractional shortening (FS) and ejection fraction (EF). HL rats doubled IL-6 and TNF-, which returned to normal levels when signaling coupled to Angiotensin II type 2 receptors (AT2R) was supraregulated by Ang-(3–4) administration. The decreased glucose tolerance was accompanied by significantly reduced plasma triglycerides due to intense hepatic steatosis (Crisóstomo et al. Metabolism Open. 2022, 14, 100176). AT2R (and AT1R) abundance did not change. (Na++K+)ATPase and ouabain-resistant Na+-ATPase were downregulated and upregulated, respectively, normalized with Ang-(3–4). SERCA2a lost its most critical regulatory property, inhibition by excess substrate. The ultrasound showed no changes in FS and EF. We conclude that very moderate overweight can cause silent initial molecular tissue damage in heart, which can evolve to cause systolic dysfunction.

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

Ecosystemic services are those that provide material or immaterial benefits to the inhabitants of a landscape from its water, food, medicines and raw materials. They are classified into four types: provisioning, regulating, supporting and cultural. The latter can be defined as those non-material benefits that humans obtain through ecosystems. They include educational value, cultural diversity, sources of inspiration, spirituality and religious values, aesthetic value, social relations, rootedness or belonging, cultural heritage, recreational and ecotourism services, and scientific knowledge, among others. Characterising such diverse factors requires a systematisation in study facets for their management. We propose three: memory, image and socio-system, as differentiated spheres of approach to the urban landscape. The memory facet would include the ecosystemic services of belonging, cultural heritage and, in certain themes, cultural diversity, sources of inspiration and religious values. The image facet includes the aesthetic value and perceptual aspects of ecotourism. In the socio-system we would study the educational value, scientific knowledge and recreational or spirituality services, reflected in the endowments; but also social relations, rootedness and another vision of ecotourism.

This study introduces a cascade nonlinear proportional integral retarded (CNPIR) controller for the first time in the literature, aiming to achieve real-time precise control of a double-tank liquid level system. It begins by outlining the characteristics of de-lay-based systems and detailing the dynamic behavior of the liquid-level system. The study evaluates the system's performance by developing and analyzing the mathe-matical models of both the feedforward proportional-integral (FF-PI) controller and the CNPIR controller. Two different control techniques were employed, with real-time tests conducted to assess the system's axis movement performance. The FF-PI controller was used as the initial control technique, while the CNPIR controller was introduced to enhance system performance during trajectory tracking. Experimental results demon-strated that the CNPIR method significantly improved trajectory tracking response, reduced overshoot, and achieved better steady-state error under external disturbances compared to the FF-PI controller. Thus, the experimental study, highlights the effec-tiveness of the proposed control design and its superiority over some existing classical methods.

Initial evaluations of the System of Rice Intensification in India and elsewhere have mainly fo-cused on its impact on yield and income and have usually covered just one or two seasons. Re-searchers at the ICAR-Indian Institute of Rice Research conducted a more comprehensive evalua-tion of SRI methods over six years (six wet and six dry seasons), comparing these methods with three other rice crop management systems: modified, partially mechanized SRI (MSRI); direct-seeded rice (DSR); and normal transplanting with inundated fields (NTP). SRI grain yield was found to be about 50% higher than with NTP (6.35 t ha-1 vs 4.27 t ha-1), while the MSRI yield was essentially the same (6.34 t ha-1) and 16% higher than with DSR (5.45 t ha-1). Compared to NTP, SRI methods significantly enhanced soil microbial populations over time, bacteria by 12%, fungi by 8%, and actinomycetes by 20%. Indicators of biological activity in the rhizosphere were also higher, dehydrogenase by 8.5% and FDA enzymes by 16%. Glucosidase activity, an indicator of soil organic matter, was 78% higher. Relative to normal transplanting methods, SRI reduced GHG emissions by 21%, while DSR reduced them by 23% and MSRI by 13%, which indicated pos-itive effects of the alternate and improved methods over normal transplanting. Economic analy-sis showed that both gross and net economic returns to be higher with SRI than with MSRI and the other management systems evaluated. While the six-year study documented many ad-vantages of SRI crop management, the MSRI version evaluated is a promising adaptation that provide similar benefits but with lower labor requirements.

The present study compared the contralateral suppression and the amplitude of distortion product otoacoutsic emissions (DPOAEs) between smokers and non-smokers to determine the influence of smoking. Thirty smokers and thirty non-smokers within the age range of 18-40 years with a normal hearing sensitivity were considered for the study. For both the groups, DPOAEs were measured and the efferent auditory system functioning was measured by presenting the white noise of 50 dB HL to the contralateral side, while recording the DPOAEs. There was no significant effect of age on the amplitude of DPOAEs in both the groups. However, there were significant differences in the amplitude of DPOAEs between smokers and non-smokers. The amount of suppression and DPOAE amplitude were reduced in smokers when compared to non-smokers. The study found no significant correlation between the amount of smoking and amount of suppression between smokers and non-smokers. However, there were significant correlations between the amount of smoking and DPOAE at low and mid frequencies between smokers and non-smokers. Therefore, the present study highlights the increased damage to the efferent auditory system risk and the smoking ill-effects on the efferent auditory system.

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems, and lays the foundation for guiding component repair and maintenance.

To identify the exchange coefficients of a nonlinear polynomial tricompartmental general system of α+β , we follow the procedure; firstly, the recommended solution is to introduce an adequate time t*>0 in a determinable manner. That is, after injecting quantity a into the main compartment, wait a moment for the exchange in the polynomial α+β order nonlinear general system to settle, and then compare this compartment to compartment 2 at t*. Secondly, applying the Taylor formula will linearize the system and identify the exchange coefficients. Finally, we will prove that the linearization method is stable.

The separation of rare earth metals (REM) from a neodymium magnet has been widely studied in the last year. We have shown that the waste of computer hard disk contains 25.41 % neodymium, 64.09 % iron, and <<1 % boron. To further isolate rare-earth metals, the magnet was acidically dissolved in open and closed systems. In both methods of dissolution was used concentrated nitric acid. The difference between these methods are the conditions of dissolution of magnet. The magnet was dissolved in a microwave sample preparation system at different temperatures and pressures in a closed system. In the open system, the acid dissolution of the magnet conducted at room temperature. 0.2 g of the neodymium magnet sample was taken under two conditions, and the dissolution process in the closed system lasted 1 hour, and in the open system-30-40 minutes. The open system is a non-laborious, simple and cheap method of dissolving the magnet by comparing both systems. Therefore, an open sample preparation system is used for further work. To remove the iron in the magnet, oxalic acid was used and precipitated as oxalates under both conditions. According to the result of the ICP-MS method, it is shown that the neodymium and iron contents in the precipitate are 24.66 % and 0.06 %, respectively. This shows that the iron has almost completely passed to the filtrate. Thus, it is possible to remove the iron from the sample.

Blade Health Monitoring (BHM) is often necessary in power plants and in aviation to prevent excessive blade vibration and cracks. This article proposes a network of blade tip timing sensors operated in a distributed BHM system. A number of cooperating agents is implemented in smart conditioning units which can autonomously operate in an adverse environment and communicate with other nodes via a serial interface. The project uses special versions of reduced instruction set chips that are able to operate near the hot section of the engine. Due to the limited number of types of microprocessors available in the extended temperature range grading, it was necessary to fully utilize the limited hardware resources and implement preemptive multitasking. For this purpose, a custom operating system and communication protocol were designed. The protocol hosts the middle layer which hides the implementation of the distributed system. The presented architecture ensures the sufficient computational capacity in individual nodes of the network operated in adverse conditions. It is scalable and resistant to transmission errors.

Current fertilizer recommendations often neglect nutrient cycling across crop rotations. This study aimed to assess the decay rate and nutrient (N, P, K) release patterns of sorghum, black oat, and corn residues and their effects on corn yield in an integrated crop-livestock system. The experiment used factorial treatments based on two sward heights (high and low) and two nitrogen fertilization levels (N-Pasture at 200 kg N ha-1 and N-Corn at 0 kg N ha-1. Litter bags were collected at various intervals from each crop to measure nutrient release patterns and decomposition rates. The results showed that pasture height and nitrogen fertilization significantly influenced decomposition and nutrient release, affecting the subsequent grain crop phase. Potassium was released rapidly and in high amounts. Nitrogen fertilization during the pasture phase prevented nitrogen and phosphorus immobilization in black oat residue and reduced immobilization in corn residue.These findings highlight the importance of accounting for nutrient cycling and decomposition rates in fertilization strategies to enhance the sustainability of integrated crop-livestock systems.

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

The present paper deals with the retrofitting of unreinforced masonry (URM) buildings, subjected to in-plane shear and out of-plane loading when struck by an earthquake. After an introductive comparison between some of the latest punctual and continuous active retrofitting methods, the authors focused on the two most effective active continuous techniques, the CAM system and the Φ system, which also improve the box-type behavior of buildings. These two retrofitting systems allow us to increase both the static and dynamic load-bearing capacity of masonry buildings. Nevertheless, information on how they actually modify the stress field in static conditions is lacking and sometimes questionable, in the literature. Therefore, we performed a static analysis in the plane of Mohr/Coulomb, with the dual intent to clarify which of the two is preferable under static conditions and whether the models currently used to design the retrofitting systems are fully adequate.

The United Arab Emirates (UAE) is significantly dependent on desalinated water and groundwater resource, which is expensive and highly energy intensive. Despite the scarce water resource, only 54% of the recycled water was reused in 2015. In this study, an “Oasis” complex comprised of Sustainable Farming Compartments (SFCs) was proposed for reusing treated wastewater to decrease the ambient temperature of the SFC via an evaporative cooling system. A prototype SFC with half the original scale (width = 1.8 m, depth = 1.5 m, front height = 1.2 m back height = 0.9 m) was designed, built, and tested in an environmentally controlled laboratory and field site to evaluate the feasibility and effectiveness of the SFC under the climatic conditions in Abu Dhabi. Based on the experimental results, the temperature drops obtained from the SFC in the laboratory and field site were 5 ̊C at initial relative humidity of 60% and 7- 15 ̊C at initial relative humidity of 50%, respectively. An energy simulation using dynamic numerical simulations was performed in comparison to the results of the experiment. The energy-based dynamic simulation shows good agreement with the experimental results. The total power consumption of the SFC system was approximately three and a half times lower than that of an electrical air conditioner.

Energy management, especially in communities, is attracting growing interest, but is often marred by confusion between the terms "Energy Information System" (EIS) and "Energy Man-agement System" (EMS). This ambiguity hinders understanding of their use and role in improv-ing energy performance. Our objective is to clarify these terms and their contribution to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, role, as well as correlated elements such as or-ganizational structure, responsibilities, planning and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study reveals differences in the roles of the EIS and the SME, with a complementarity of 40% between the two systems, depend-ing on the positioning of the EIS in relation to the elements of the SME process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarifica-tion contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner.

The effectiveness of Fuzzy Inference Systems (FISs) in manipulating uncertainty and nonlinearity makes them a subject of significant interest for decision-making in embedded systems. Accordingly, optimizing FIS hardware improves its performance, efficiency, and capabilities, leading to a better user experience, increased productivity, and cost savings. To be compatible with the limited power budget in most embedded systems, this paper presents a framework to realize ultra-low power FIS hardware. It supports optimizations for both conventional arithmetic as well as MSDF-computing to be highly consistent with MSDF-based sensors. In MSDF-computing FIS all the processes of fuzzification, inference, and defuzzification are done on serially coming data bits. To demonstrate the efficiency of the proposed framework, we utilized Matlab, Chisel3, and Vivado to implement it from high-level descriptions of FIS to hardware synthesis. We also developed a Scala library in Chisel3 to establish a connection between these tools, bridging the gap, and facilitating design space exploration at the arithmetic level. Furthermore, we realized an FIS for the navigation of autonomous mobile robots in unknown environments. Synthesis results show the superiority of the output of our suggested design framework in terms of resource usage as well as power and energy consumption compared to the Matlab HDL code generator output.

For a system that turns out to be inactive in time t, the past entropy is considered as an uncertainty measure for the past lifetime distribution. In this study, we consider a coherent system that includes n components and has the property that all the components of the system have failed at time t. To assess the predictability of the coherent system’s lifetime, we use the system’s signature to determine the Rényi entropy of its past lifetime. We study several analytical results, including expressions, bounds, and order properties for this measure.

FPGA-based data acquisition and processing systems play an important role in modern high-speed, multichannel measurement systems, especially in High-Energy and Plasma Physics. Such FPGA-based systems require an extended control and diagnostics part corresponding to the complexity of the controlled system. Managing the complex structure of registers while keeping the tight coupling between hardware and software is a tedious and potentially error-prone process. Various existing solutions aimed at helping that task do not perfectly match all specific requirements of that application area. The paper presents a new solution based on the XML system description, facilitating the automated generation of the control system’s HDL code and software components and enabling easy integration with the control software. The emphasis is put on reusability, ease of maintenance in case of system modification, easy detection of mistakes, and the possibility of use in modern FPGAs. The presented system has been successfully used in data acquisition and preprocessing projects in High-Energy Physics experiments. It enables easy creation and modification of the control system definition and convenient access to the control and diagnostic blocks. The presented system is an open-source solution and may be adopted by the user for particular needs.

The advancement of high-temperature structural materials is crucial for the efficiency and performance of modern gas turbines, which are widely used in aviation and power generation. This paper explores the development and optimization of materials capable of withstanding extreme temperatures, mechanical stresses, and corrosive environments within turbine engines. The focus is on nickel-based superalloys, ceramic matrix composites (CMCs), and emerging materials such as high entropy alloys (HEAs) and refractory metals. Nickel-based superalloys are the current standard due to their excellent high-temperature strength and creep resistance, while CMCs offer potential for weight reduction and improved thermal efficiency. High entropy alloys, characterized by their unique multi-component systems, present promising properties including high strength and oxidation resistance at elevated temperatures. Additionally, advances in coating technologies and additive manufacturing are enhancing the durability and performance of these materials. The paper also discusses the challenges associated with material development, including the trade-off between strength and ductility, manufacturing complexities, and the need for long-term performance data. Future research directions are identified, focusing on the integration of computational materials science and advanced characterization techniques to accelerate the discovery and implementation of next-generation high-temperature materials. The successful development of these materials is essential for improving the efficiency and reducing the environmental impact of gas turbines.

This review summarizes the use of CRISPR system in yeasts, identifying advantages and disadvantages of its applications. 39 articles were evaluated including 12 articles that discussed the advantages of new CRISPR systems that improved the initial system, and another 27 were evaluated, among these: three were applications in Cryptococcus neoformans, four in candida sp., three in Schizosaccharomyces pombe, nine in Saccharomyces cerevisiae, four in Yarrowia lipolytica, and four in industrially important yeasts such as Pichia pastoris and Saccharomyces pastorianus. It was concluded that the CRISPR system is one of the most versatile genetic editing systems available nowadays. It can be applied in different organisms for several effects including gene knock-outs, performing point mutations, gene expression, or even applying multiple edition operations in several genes. However, we recognize that numerous studies lack a control group of the mutated strains, which leaves many questions unanswered. For instance, the extent and precision of this techniques, it also represents a risk to biosecurity standards. Therefore, this review shows the compilation of CRISPR system information, which could be used to generate different alternatives in the industry and clinical fields.

Legionnaires’ disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. In this study, we demonstrated the constant presence of Legionella in water samples from the water system of a large university hospital in Messina (Sicily, Italy) consisting of 11 separate pavilions during a period of 15 years (2004-2018). In total, 1346 hot water samples were collected between January 2004 and December 2018. During this period, to recover Legionella spp. from water samples the standard procedures reported by the Italian Guidelines emanated in 2000 were adopted; from May 2015 to 2018 Italian Guidelines revised in 2015 (ISS, 2015), were used. The most water samples (72%) were positive to L. pneumophila serogroups 2-14 whereas L. pneumophila serogroup 1 accounted for the 18% and Legionella spp. for the 15%. Most of the positive samples were found in the buildings where are situated critical wards as ICU, Neurosurgery, Surgeries, Pneumology and Neonatal Intensive Unit Care. We highlighted the importance of a continuous monitoring of hospital water samples to prevent the potential risk of nosocomial legionellosis.

Energy consumption modelling is of critical importance for improving energy efficiency in the drive towards sustainable manufacturing. Modern manufacturing processes and machine tools are very complex as they involve interplay of several advanced technologies. Therefore, energy modelling of modern machine tools is a complex multi-disciplinary task. This paper proposes a systematic and practical framework to choose the appropriate modelling approach for a given industrial problem. The framework was validated by applying it to diverse industrial scenarios.

Unusual outbreak of Covid 19 has put the whole new challenges around the globe. In a short span of time, the world has been changed, lifestyles, social events, cultural, political events everything has been put on hold as number of countries has enforced partial or complete lockdown that resulted positive in context of controlling spread and contamination of COVID-19. Such activities have also put some effects on environment in both positive and negative way. In this study only positive impacts of COVID-19 on Environment have been highlighted that played role in regeneration of natural systems i.e., reduced air emissions, cleaner air, beaches &water bodies, reduced noise levels, biodiversity rehabilitation etc.

. Cucurbiturils are a family of macrocyclic oligomers capable of forming host-guest complexes with various molecules. Due to noncovalent binding to drug molecules and low toxicity, cucurbiturils has been extensively investigated as a potential carrier for drug delivery. However, the immune system interactions with different drug carriers, including cucurbiturils, are still under investigation. In this study, we focused on cucurbiturils immunosafety and immunomodulation properties in vivo. We measured blood counts, lymphocyte subpopulations in blood, spleen, and bone marrow, and assessed in vivo toxicity to spleen and bone marrow cells after intraperitoneal administration to mice. We also evaluated the effect of cucurbiturils on cellular and humoral immune responses. Cucurbiturils in high concentrations effect on the immune system in vivo. The action of various cucurbiturils differs in different homologues, which, apparently, is associated with different interactions in the internal environment of the body.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

Full-order state-space models represent the starting point for the development of advanced control methods for wind turbine systems (WTSs). Regarding existing control-oriented WTS models, two research gaps must be noted: (i) There exists no full-order WTS model in form of one overall ordinary differential equation that considers all dynamical effects which significantly influence the electrical power output; (ii) all existing reduced-order WTS models are subject to rather arbitrary simplifications and are not validated against a full-order model. Therefore, in this paper, two full-order nonlinear state-space models (of 11th and 9th-order in the (a, b, c)- and (d, q)-reference frame, resp.) for variable-speed variable-pitch permanent magnet synchronous generator WTSs are derived. The full-order models cover all relevant dynamical effects with significant impact on the system’s power output, including the switching behavior of the power electronic devices. Based on the full-order models, by a step-by-step model reduction procedure, two reduced-order WTS models are deduced: A non-switching (averaging) 7th-order WTS model and a non-switching 3rd-order WTS model. Comparative simulation results reveal that all models capture the dominant system dynamics properly. The full-order models allow for a detailed analysis covering the high frequency oscillations in the instantaneous power output due to the switching in the power converters. The reduced-order models provide a time-averaged instantaneous power output (which still correctly reflects the energy produced by the WTS) and come with a drastically reduced complexity making those models appropriate for large-scale power grid controller design.

Coronaviruses are highly transmissible and pathogenic viruses for humans and animals. The vast quantity of information collected about SARS-CoV-2 during the pandemic helped to unveil details on the mechanisms behind the infection, which are still largely elusive. Recent research demonstrated that different class I/II human leukocyte antigen (HLA) alleles might define an individual susceptibility to SARS-CoV-2 spreading, contributing to the differences in the distribution of the infection through different populations; additional studies suggested that the homolog of the HLA in cats, the Feline Leukocyte Antigen (FLA), plays a pivotal role in the transmission of viruses. With these premises, this study aimed to exploit a bioinformatic approach for the prediction of the transmissibility potential of two distinct Feline Coronaviruses (FCoVs) in domestic cats (Feline enteric Coronavirus, FeCV and Feline Infectious Peritonitis Virus, FIPV) using SARS-CoV-2 as the reference model. We performed an epitope mapping of nonapeptides deriving from SARS-CoV-2, FeCV and FIPV glycoproteins and predicted their affinities for different alleles included in the three main loci in class I FLAs (E, H and K). The predicted complexes with the most promising affinities were then subjected to molecular docking and molecular dynamics simulations to provide insights into the stability and binding energies in the cleft. Results showed the FLA proteins encoded by alleles in FLA-I H (H*00501 and H*00401) and E (E*01001 and E*07001) loci are largely responsive to several epitopes deriving from replicase and spike proteins of the analyzed coronaviruses. The analysis of the most affine epitope sequences resulting from the prediction can stimulate the development of anti-FCoV immunomodulatory strategies based on peptide drugs.

The stability of the gut barrier is very important for keeping the gut healthy and stopping dangerous substances from getting into the bloodstream. Several diseases, including cancer, have been linked to the gut barrier being broken. Live bacteria called probiotics that are good for the host have been shown to have the ability to improve the gut barrier and lower the risk of cancer in the gastrointestinal system. This review looks at how probiotics affect the health of the gut barrier and what that might mean for preventing cancer. Researchers have found that probiotics strengthen the connections between intestine cells, encourage the production of protective mucins, and change the make-up and balance of the gut bacteria. These processes help keep the gut barrier strong and stop harmful chemicals from getting into the bloodstream. Probiotics may help lower the chance of cancer by reducing inflammation throughout the body and protecting the GI tract from harmful substances. But more study is needed to fully understand the benefits of different probiotic strains, the best amounts to take, and how they affect the health of the gut barrier. Understanding how probiotics, the health of the gut barrier, and the risk of cancer all work together can help us come up with new ways to avoid and treat cancer.

The accurate of model attitude angel plays an important role on the aerodynamic test results in the wind tunnel test. The original method applies the spherical coordinate system transformation to obtain attitude angel calculation. The model attitude angel is obtained by coordinate transformation and spherical surface mapping applying the nominal attitude angel (the balance attitude angel in the wind tunnel coordinate system) indicated by the mechanism. First, the coordinate transformation of this method is not only complex but also difficult to establish the transformed relationship between the space coordinate systems especially after many steps of coordinate transformation, moreover it cannot realize the iterative calculation of the interference relationship between attitude angels; Second, during the calculate process to solve the problem the arc is approximately used to replace the straight line, the angel for the tangent value, and the inverse trigonometric function is applied. Therefore, in the calculation of attitude angel, the process is complex and inaccurate, which can be solved approximately when calculating small attack angel. However, with the advancing development of modern aerodynamic unsteady research, the aircraft tends to develop high or super large attack angel and unsteady research field. According to engineering practice and vector theory, the concept of vector angel coordinate system is proposed for the first time, and the vector angel coordinate system of attitude angel is established. With the iterative correction calculation and avoiding the problem of approximate and inverse trigonometric function solution, the model attitude calculation process is carried out in detail, which validates that the calculation accuracy and accuracy of model attitude angels are improved. Based on engineering and theoretical methods, a vector angel coordinate system is established for the first time, which gives the transformation and angel definition relations between different flight attitude coordinate systems, that can accurately calculate the attitude angel of the corresponding coordinate system and determine its direction, especially in the channel coupling calculation, the calculation of the attitude angel between the coordinate systems is only related to the angel, and has nothing to do with the change order s of the coordinate system, which simplifies the calculation process.

A spinning gyroscope precesses about the vertical due to a torque acting upon the wheel. The torque is generated by the shift of moment of force by gravity and it points to the vertical instead of the tangential direction of precession. This intuition offers an alternative and straightforward view of precession dynamics in comparison with the literature. It also presumes a dynamic balance of momentum between circular motions of the wheel spin and precession. Accordingly, the gyroscopic dynamics is then applied to the study of galactic motion of the solar system in space and the Galactic mass is calculated with the inclusion of gyroscopic effect of the solar planets. Results indicate that the gyroscopic effect of Mercury orbiting around the Sun can increase the calculated Galactic mass by 23% in comparison with the result obtained by the classic approach.

Chronic high glucose (HG) exposure increases methylglyoxal (MG)-derived AGEs and is involved in the onset of pathological conditions, such as diabetes, atherosclerosis and chronic‐degenerative diseases. Under physiologic condition the harmful effects of MG are contrasted by glyoxalase system that is involved in the detoxification of Reactive Carbonyl Species (RCS) and maintain the homeostasis of the redox environment of the cell. Polyphenols are the most abundant antioxidants in the diet and present various health benefits. The study aimed at investigating the role of polyphenols extracted from an apple high in polyphenols (Calville White Winter), on glyco-oxidative stress induced by chronic HG-exposure. Intestinal Caco-2 cells were treated in physiological glucose condition (25mM) as a control and in HG condition (50mM) with or without apple extract for one week. Our data demonstrated that HG-treatment triggers glyco-oxidation stress with a significantly increase in ROS, lipid peroxidation, AGEs and Glyoxalase I (GlxI) activity with a significant decrease in total antioxidant intracellular defense. Treatment with polyphenols under HG condition restores to the control levels GlxI activity, decreases Glyoxalase II (GlxII) in relation to the control and induces a drop of glyco-oxidative damage. This paper seeks to highlight the roles of polyphenols in glyco-oxidative stress.

Recent discoveries have shown that enteric glial cells play an important role in different neurodegenerative disorders such as Parkinson's disease (PD), which is characterized by motor dysfunctions caused by progressive loss of dopaminergic neurons in the substance nigra pars compacta and non-motor symptoms including gastrointestinal dysfunction. In this study we observed the modulatory effects of the flavonoid rutin on enteric glia in a PD animal model. Adult male Wistar rats were submitted to stereotaxic injection with 6-hydroxydopamine or saline, treated or not with rutin (10 mg/Kg) for 14 days. Intestinal segments (ileum and colon) were collected to analyze tissue reactivity and immunohistochemistry for neurons (HUc/d) and enteric glial cells (S100b) in the myoenteric plexuses. Behavioral tests demonstrated that treatment with rutin im-proved the motor capacity of parkinsonian animals, and also improved intestinal transit without interfering with the cell population; rutin treatment modulated reactivity of the ileal musculature by muscarinic activation, reducing relaxation through the signaling pathway of nitric oxide donors, and increasing the longitudinal contractility of the colon musculature in parkinsonian animals. Rutin demonstrated modulatory activities on the enteric nervous system, bringing relevant answers about the effect of the flavonoid in this system and po-tential application for PD adjuvant treatment.

This paper investigates the search for an exact analytic solution to a temporal first-order differential equation that represents the number of customers in a non-stationary or time-varying

One of the interactions that has been studied in recent decades under the ecohydrological approach is infiltration, which is difficult to determine because the existing methods for estimation depends on certain variables that are difficult to establish or identify, and in other cases, the implementation of instrumentation is expensive. Given this, saturated hydraulic conductivity has been the descriptive variable of the infiltration process by recent authors, being understood as the conduction of water that occurs through the soil and its composition through the vegetation cover. The present design generated an easily accessible portable instrumentation model for the monitoring and collection of infiltration data in situ for basins and ecosystems with different land uses, in an automated manner with the objective of determining the vegetation patterns necessary to sustain the infiltration function. The INDI-INECOL tension infiltrometer [1] was chosen as the reference instrument. It evaluates the saturated hydraulic conductivity based on a column of water that descends through the system and it is exchanged in the soil by the air between the pores. To automate data collection, a measurement system has been proposed through a SCADA system, which is defined as a control, action, decision, storage and data recording system. This system collects field data from a sensor VL053L0X connected to a master station, in our case an Arduino board UNO, connected through the serial port and by a graphical user interface programmed on Python, to display, storage and enrichment of the sensor data and finally to be exported in files suitable for Microsoft Excel.

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002, 2003 and 2004 data sets from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002, 2003 and 2004 were 34,914.732 kWh/m2, 33,898.316 kWh/m2 and 34,338.324 kWh/m2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

The present paper deals with research of maintenance strategy and maintenance process improvement procedure to increase the efficiency and quality of the production system. It includes the design of maintenance performance indicators relevant to today's manufacturing systems to track improvements in the sustainable development of the manufacturing system. Describes the basic principles of maintenance strategy with linkages to the manufacturing system and selecting the right strategy for the organization. The paper emphasizes maintenance management auditing and quality implementation in maintenance. Further, there is the process of changing the maintenance strategy described. This process includes reassessing the criticality of machines and equipment and their design units, then resource and capacity planning and input for maintenance management. It is described the impact of changing the maintenance management organization and maintenance capacity in the light of the change of strategy and the evaluation of maintenance choice alternatives, together with an analysis of the optimization procedure. The paper concludes by presenting the achieved impact of maintenance strategy change on the production efficiency and production quality of the selected enterprise.

Since the advent of the Internet, there has been a significant shift from wired to wireless communication between devices. The volume of data transmitted has surged, primarily driven by the exponential growth of network services, Internet of Things (IoT) devices, and online users. This trend became especially pronounced with the onset of the COVID-19 pandemic, as the Internet's role and significance expanded dramatically. People began to spend more time online, engaging in activities like e-learning, remote work, and online shopping. Concurrently, concerns about the security of wireless communication channels have escalated. As more data is stored and transmitted over the Internet, cybercriminals are actively seeking unauthorized access to it to further their malicious objectives. Through a comprehensive literature review in the realm of wireless network security and privacy, it has become evident that several factors render these networks susceptible to vulnerabilities. The aim of this paper is to shed light on the major and common security and privacy challenges faced by existing wireless networks. Additionally, it seeks to demonstrate the array of techniques available to mitigate these issues. Ultimately, this paper will culminate in an in-depth analysis of cybersecurity measures to address the issues in wireless networks.

The ever-evolving landscape of technology continually drives the demand for more robust and secure systems. Intrusion Detection and Prevention Systems (IPS) play a pivotal role in safeguarding digital infrastructures. IPS harnesses a blend of cutting-edge technologies, specifically Network-Based, Wireless-Based, and Host-Based Intrusion Detection Systems (IDS), to fortify network security. This multifaceted approach enhances the system's capacity to scrutinize incoming data and network traffic, effectively reducing the risk of intrusion. In the realm of malicious activity detection, IPS employs a diverse array of techniques and mechanisms. Our proposed IPS integrates both anomaly-based and signature-based analysis approaches. In signature-based analysis, intrusions are identified by matching data collected from various activities with pre-defined signatures, employing rule-based methods to detect anomalies effectively. In the anomaly-based analysis approach, the system juxtaposes current activities with a baseline of normal behaviour to flag any deviations. This is achieved through distance-based methods, ensuring a well-rounded approach to threat detection. Our comprehensive prevention system encompasses a suite of security measures, including anti-virus software, Deep Packet Inspection, and Quarantine. These components work in synergy to detect and thwart malicious activities effectively. Even when concealed or hidden, the IPS remains a reliable sentinel, ensuring the integrity of your digital ecosystem. This review delves into the intricate web of terminologies and concepts surrounding Intrusion Detection and Prevention Systems, shedding light on the evolving technology landscape and the critical role IPS plays in bolstering cybersecurity.

This study aims to develop a performance evaluation system that can facilitate performance evaluation at region, hospital, and department levels to enable better cost management for sustainable development. A multi-level system of performance evaluation informs a hierarchical assessment of cost management from regions to hospitals to departments using diagnosis-related group (DRGs). Various metrics are developed employing the variances between targets and actuals where targets are determined from two perspectives: benchmarking using external regional prices and change management using internal data. Targets for the latter are statistically based and specifically incorporate variability. The model is applied to two hospitals, twenty departments, nine DRGs and 1071 inpatients. The analyses indicate that the approach can provide a practical evaluation tool that allows for particular characteristics at multiple levels. The system provides macro-micro and external-internal perspectives in performance, enabling high-level variances to be decomposed thereby identifying sources of performance variability and financial impact.

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

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions.

The proliferation of Unmanned Aerial Vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within UAV networks’ highly dynamic and adaptable environments. Decentralized reputation systems emerge as a promising solution to enhance the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. It highlights the crucial role of reputation systems in strengthening trust, security, and privacy throughout these networks and discusses various strategies to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

Italy has been defined as the "logistics platform" of the Mediterranean Sea. The Italian port system, with 11,6 million TEUs handled and 61,4 million passengers by 2022 (Assoporti data Jan‐Dec 2022) [1] is the key to guarantee this condition through adequate levels of reliability, safety and sustainability. This contribution addresses port logistics and shipping, focusing on primary issues related to the energy sector with a specific focus on what can be observed in the Italian context. Specifically, decarbonization of the maritime sector and related infrastructural problems (e.g. Cold Ironing or alternative fuels, where the uncertainty about resources availability and related cost do not allow for an easy strategic planning by both the shipowner and the port authority), as well as policies such the Emission Trading System (ETS) will be analyzed. All these issues, if addressed with a critical review, could represent the driving force of the national port sector for its growth and its competitiveness at a global scale.

Rice wheat cropping system prevailed in more than fifty percent areas of rice belt of central and upper Punjab. In this area yield of wheat is curtailed every year due to late harvesting of rice crop. In addition to this, lack of machinery for direct sowing of wheat into combine harvested rice is a major limitation for timely wheat sowing due to presence of heavy straw load dropped by the combine harvester. A field experiment was planned and executed during two consecutive years 2017-18 and 2018-19 at Agronomic Research Station Farooqabad to evaluate the various stubble management techniques for the sowing of wheat in a rice-wheat cropping system. The Experiment consist of four treatments Happy seeder (Zero tillage), conventional sowing without burning of rice stubble, Rotavator two times, and Conventional sowing with the burning of rice stubble. The trail was arranged as a Randomized Complete Block Design with four replications having a net plot size of 8 m x 20 m. The Treatments have significant effects on Plant height, Number of productive tillers, 1000- grain weight, and grain yield. Maximum plant height (94.74 and 94.25 cm), numbers of productive tillers/m2 (324.00 and 322.25), 1000-grain weight (36.35 and 36.20 g), grain yield (3780 and 3613 kg/ha), and benefit-cost ratio (2.54 and 2.43) were recorded from the treatment happy seeder (Zero tillage) during both years respectively. It is concluded that the sowing of wheat with a happy seeder (Zero tillage) is an appropriate technique in the rice-wheat cropping system by protecting the environment and soil health from the burning of rice stubbles.

Differential equation models to understand interaction between plant and nutrient solution are presented. The root cells selectively emit H+ ions with active transport consuming ATPs to establish electrical gradient along the cell membrane. It establishes electrical field with Nernst potential to make positively charged ions outside the cell membrane flow into the root cell. Anion influx is also modulated by H+ ion concentration because plant root cell absorbs negatively charged particles with symport. If an anion collides with H+ cell to make net charge as neutral, at symport channel, it can flow through. In this paper, mathematical models for cation and anion absorption are introduced. Cation absorption model was induced from Ohm's law combined with Goldman's equation. Anion absorption model is similar to chemical reaction rate model. Both models have physiological terms influenced by gene expression pattern, species or phenotypes. Cation model also includes terms for ion's kinetic and electrical properties, growth of plant and interaction between the root and the surroundings. Simulation for 20 different sets of coefficients showed that the physiology-related coefficient has important role on nutrition absorption tendencies of plants.

We have presented a controllable and human-readable polynomial neural network (CR-PNN) that is the first human-readable neural network. One can imagine its influence on system identification. Subsequently, we developed a relation spectrum in a medical application, which is likely to stand alongside the Fourier spectrum. However, the system analysis methodology is incomplete in contrast to signal processing methodology. Here, we presented the system filters for the first time. In this paper, we used the simulation system to verify the availability of the system analysis methodology. The system analysis methodology showed great properties in system identification and filter. The contribution of this paper is the system analysis methodology: transform method (CR-PNN), relation spectrum, and system filter design.

Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence time, duration and level of damage can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

Obstructive sleep apnea (OSA) is a common sleep disorder that is associated with a wide variety of health conditions, including cardiovascular, cerebrovascular, metabolic, neoplastic, and neurocognitive manifestations. OSA as a chronic condition is mainly characterized by repeated upper airway obstructions during sleep that cause episodes of intermittent hypoxia (IH), resulting in tissue hypoxia-reoxygenation cycles. Decreased arterial oxygen pressure (PaO₂) and hemoglobin saturation (SatO₂) stimulate reflex responses to overcome the obstruction. The prevalence of OSA is significant worldwide, and an underrated problem when focusing on women during pregnancy. The physiological changes associated with pregnancy, especially during its latest stages, are related with a higher prevalence of OSA events in pregnant mothers, and associated with an increased risk of hypertension, pre-eclampsia and diabetes, among other deleterious consequences. Furthermore, OSA during pregnancy can interfere with normal fetal development and is associated with growth retardation, preterm birth, or low term weight. Carotid body overstimulation and hypoxia-reoxygenation episodes contribute to cardiovascular disease and oxidative stress, which can harm both mother and fetus and have long-lasting effects that can reach into adulthood. Because IH is the hallmark of OSA, this review examines the literature available about the impact of gestational intermittent hypoxia (GIH) on the respiratory system at maternal, fetal, and offspring levels. Offering the latest scientific data about OSA during pregnancy, we may help to tackle this condition with lifestyle changes and therapeutic approaches, that could influence the mothers, but also impact adult health problems, mostly unknown, inherited from these hypoxic episodes in the uterus.

Risk-based redispatch optimization is proposed as a methodology to support the Transmission System Operator (TSO) with preventive remedial actions obtained by extending the security constrained unit commitment/economic dispatch with constraints resulting from the risk assessed for the power system. Although being heuristic, the methodology is based on comprehensive dynamic security assessment as time-domain simulations are used, allowing to express the degree of all types of instabilities, e.g., caused by contingencies, in monetary terms. Therefore, the risk is assessed as the expected value of the cost incurred by the TSO. Such approach forms a new pathway to including risk in planning procedures already used by TSOs. Results obtained for the IEEE39 dynamic power system, with costs assigned to load shedding and generator tripping due to single transmission lines short-circuits, are shown as a reference case.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

The last decade was characterized by the reduction of hospital beds throughout Europe. When facing Covid pandemic, this has been an issue of major importance as hospitals were seriously overloaded with an unexpected growth in demand. The dichotomy formed by the scarcity of beds and the need for acute care was handled by the Bed Management (BM) function. This study explores how BM was able to help the solidness of the healthcare system, managing hospital beds at best and recruiting others in different set-tings as intermediate care, in a large Local Health Authority (LHA) in central Italy. Ad-ministrative data show how the provision of appropriate care was achieved recruiting approximately 500 beds belonging to private healthcare facilities affiliated with the re-gional healthcare system and exercising at best the BM function. The ability of the system to absorb the extra demand caused by Covid was made possible using intermediate care beds, which allowed to stretch the logistic boundaries of the hospitals, and by the promptness of Bed Management in converting beds in Covid beds and reconverting them and timely managing internal patient logistic, thus creating space according to the healthcare demand.

In this paper, we consider particles together with the surrounding space as an indivisible system, and then investigate the maximum symmetry of the system at the quantum level. We introduce a new quantity to describe the evolution of this system before and after measurement. The effective mass of gravity is estimated. We also show that a free quantum particle as the source of energy and momentum do not produce gravity.

When a microgrid is grid-tied to a distribution system, it can provide surplus power generation to the distribution system, if any abnormality or interruption occurs in the distribution system, the microgrid can operate in standalone mode to isolate the impact of the abnormality or interruption. However, if the microgrid can not collect enough information from the distribution system, it may cause the failure of generation transferring of distribution feeders, or even further influence the stability of the distribution system. In this paper, a strategy for the resilient control of a microgrid is proposed. It can solve the above-mentioned problem, reduce the duration of the outage of loads. This strategy is experimented in the microgrid in the Institute of Nuclear Energy Research (INER), the reliability is also analyzed to evaluate the unavailability of the microgrid in INER, and it is verified that the proposed strategy can reduce the duration of the outage of loads, and hence the reliability of a microgrid can be upgraded.

Present paper is dealing with the adaptive static balancing of robot or other mechatronic arms that are moving in vertical plane and whose static loads are variable, by using counterweights and springs. Some simple passive and approximate solutions are proposed and an example is shown. The active and exact solutions by using adaptive real time control in the case of unknown variation of static loads are simulated on VIPRO platform developed at Institute of Solid Mechanics of Romanian Academy.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. The innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.

A nuclear reactor leak is undesirable and unpredictable occurrence in the plant of nuclear energy generation. For this reason, an accurate equipment is needed to inspect and supervise reactors. There are two kind of reactors for research and industry reactors, both of them are needed to inspect. Concern to inspection, regularly check is needed to ensure reactor conditions are always safe to operate. For that reason, an application of advanced technology for detecting leaks is priority. In this research, we are using underwater robot to be implemented. Using the robot is able to detect periodically, continuously with accurate reports to the operator or reactor supervisor. Furthermore, Underwater Robot namely Robaru, with 4 propellers equipped at the robot, Robaru can move on 3 axes x, y and z and rotate. The use of cameras, as well as temperature measurements to obtain data when conducting inspections of the reactor. The survey method used is direct observation to detect leaks. The robot is at a certain distance by following a predetermined connection or path. The robot has successfully performed a good check with an accurate level of positioning accuracy.

The purpose of this research is to present a thorough evaluation of the energy management system, which consists of hybrid energy storage systems, and its control algorithms, which may be used in electric vehicles. This paper outlines the characteristics of electric vehicles, research methods, analysis of the hybrid energy storage system architecture, converter topology, and energy management techniques. The strength and co-occurrence of keywords over the past ten years are shown in this study using a systematic research framework for performing a literature review and using keyword analysis techniques. The study reveals a pattern of recently and frequently used terms in works of literature. Consequently, their suitability, benefits, and drawbacks are assessed. In this study, the hybrid energy storage system and converter circuit architecture are evaluated and rated. Non-isolate DC-DC converter converter connected to SC is a suitable configuration for the hybrid converter because it is simple to build, reliable, and has minimal loss/weight/cost, which improves vehicle performance. In terms of the application of control strategies, it is shown that deterministic and fuzzy rule-based control techniques are successfully assessed using real-scale vehicle experiments and selected for manufacturing. On the other hand, real-time optimization-based energy management techniques have been effectively shown in lab-scale tests and may be used in a future real-scale vehicle.

1) Background: Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges are acknowledged; however, a comprehensive understanding of the status and the remedies remained unexplored. 2) Objective: This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. 3) Methods: A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history-taking, and attitudes toward adopting digital solutions for managing patient histories; 4) Results: 92.67% of the physicians face workloads, 88.46% struggle in medical history-taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. An AI-based Smart Health Gantt Chart (SHGC) system has been introduced for their instant feedback. 93.27% of physicians expressed their willingness to use such a system. 5) Conclusions: The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

The innate immune system is the first line of defense against pathogens. It’s composition includes barriers, mucus and other substances as well as phagocytic and other cells. The purpose of the paper is to analyze in general grounds the immune system and the body immunity to cancer. Simple ideas and the qualitative theory of differential equations are used along with general principles such as the minimization of the pathogen load and economy of resources. In the simplest linear model, the annihilation rate of pathogens in any tissue should be greater than the pathogen’s average rate of growth. When nonlinearities are added, a reference value for the number of pathogens is set, and a stability condition emerges, which relates strength of regular threats, barrier height and annihilation rate. On the other hand, in cancer immunity, the linear model leads to an expression for the lifetime risk, which accounts for both the effects of carcinogens (endogenous or external) and the immune response. The stability condition allows a comparison of immunity in different tissues. The way the tissue responds to an infection shows a correlation with the way it responds to cancer. These statements are formulated at the qualitative level, but may and deserve to be quantitatively checked.

This article addresses a challenge modeling novices face in model conceptualization: the recognition of behavior modes. Despite behavior modes being at the heart of system dynamics modeling, there is no unifying taxonomy for them, making them harder to learn for beginners. The article proposes criteria a taxonomy should satisfy, critically reviews previous taxonomies in the literature, and then introduces a new consistent taxonomy based on slope and curvature with mode names that refer only to visual cues and free of references to prior mathematical or domain-specific knowledge. Evidence from an exploratory experiment suggests that novices actually have difficulties classifying curves when using previous taxonomies. Links from these elementary modes to analogous terms in other taxonomies in diverse disciplines allow this taxonomy to be related to other ones. The article concludes by mentioning relevant limitations and future steps.

In nonlinear self-consistent system, Lorenz system (Lorenz equations) is a classic case with chaos solutions which are sensitively dependent on the initial conditions. As it is difficult to get the analytical solution, the numerical methods and qualitative analytical methods are widely used in many studies. In these papers, Runge-Kutta method is the one most often used to solve these differential equations. However, this method is still a method based on explicit time scheme, which would be the main reason for the chaotic solutions to Lorenz system. In this work, numerical experiments based on implicit time scheme and explicit scheme are setup for comparison, the results show that: in implicit time scheme, the numerical solutions (counterexamples) are without chaos; for an original volume, the volume shrinks exponentially fast to 0 in common.

Migraine is a primary headache disorder characterized by unilateral throbbing, pulsing headache, which lasts for hours to days, and the pain can interfere with daily activities. It exhibits various symptoms, such as nausea, vomiting, sensitivity to light, sound, and odors and physical activity consistently contributes to worsening pain. Despite the intensive research, little is still known about the pathomechanism of migraine. It is widely accepted that migraine involves activation and sensitization of the trigeminovascular system. It leads to the release of several pro-inflammatory neuropeptides and neurotransmitters and causes a cascade of inflammatory tissue responses including vasodilation, plasma extravasation secondary to capillary leakage, edema, and mast cell degranulation. Convincing evidence obtained in rodent models suggests that neurogenic inflammation is assumed to contribute to the development of a migraine attack. Chemical stimulation of the dura mater triggers activation and sensitization of the trigeminal system and causes numerous molecular and behavioral changes; therefore, this is a relevant animal model of acute migraine. This review article discusses the emerging evidence supporting the involvement of neurogenic inflammation and neuropeptides in the pathophysiology of migraine, presenting the most recent advances in preclinical research and the novel therapeutic approaches to the disease.

In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the prediction differential model (PDM) using the global approximation capability of genetic algorithm (GA) and local quick interior-point algorithm scheme (IPAS), i.e., MWNN-GAIPAS. The famous PDM is known as a variant of functional differential system that works as an opposite of the historical delay differential models. A fitness function is optimized using the mean square error by applying the GA-IPAS for solving the PDM. Three PDM examples have been presented numerically to check the authenticity of the MWNN-GAIPAS. For the perfection of the designed MWNN-GAIPAS, the comparability of the obtained results and exact results is performed. Moreover, the neuron analysis is performed by taking the 3, 10 and 20 number of neurons. The statistical observations have been performed to authenticate the reliability of the MWNN-GAIPAS for solving the PDM.