The incremental sheet forming represents a relatively recent technology that guarantees high customization, thanks to the layered manufacturing principle typical of rapid prototyping, and cost-effectiveness, because it does not require dedicated equipment. Research has initially shown that this process is effective in metal materials capable of withstanding plastic deformation but, in recent years, the interest in this technique has been increasing for the manufacture of complex polymer sheet components as an alternative to the conventional technologies, based on heating-shaping-cooling manufacturing routes. Conversely, incremental formed polymer sheets can suffer from some peculiar defects, for example twisting. To reduce the risk of this phenomenon and the occurrence of failures, a viable way is to choose toolpath strategies that make the tool/sheet contact conditions less severe, and this represents the main goal of the present research. Polycarbonate sheets were worked by incremental forming; in detail, cone frusta with a fixed wall angle were manufactured with a reference and a stair toolpath strategy, and both the forming forces and the twist angle were monitored. The analysis of the results highlighted that a stair toolpath involving an alternation of diagonal up and vertical down steps represents a useful strategy to mitigate the forming forces and, with them, the occurrence of the twisting phenomenon in incremental formed thermoplastic sheets.

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

This paper introduces a novel multi-strategy enhanced dung beetle optimization (MSDBO) algorithm that is designed to address several issues identified in the standard dung beetle optimization algorithm. Specifically, the MSDBO aims to enhance convergence speed, reduce susceptibility to local optima, and increase search accuracy. By incorporating three strategies: tent chaotic mapping for population initialization, the golden sinusoidal strategy for position updating, and the Lévy flight strategy for balancing exploration and exploitation, the standard dung beetle optimization algorithm is enhanced. The MSDBO algorithm is evaluated using twelve benchmark test functions and compared against five state-of-the-art algorithms. The results consistently show that MSDBO exhibits faster convergence speeds and more accurate solutions than the other algorithms across most of the test functions. In addition, MSDBO is also applied to optimize the parameters of a valve plate, including the close angle, cross angle, triangle groove sizes, and wrap angle. The optimization outcomes reveal that MSDBO effectively minimizes pressure ripples in the piston chamber, resulting in reduced flow rate fluctuations and noise emission compared to the initial design. This study highlights the potential of the MSDBO algorithm in tackling complex nonlinear engineering optimization problems.

. Manufacturers are increasingly adopting mixed-flow manufacturing models to meet the growing customized, diverse, and dynamic customer demand. In such an environment, material distribution scheduling optimization is vital for smooth operations and is integral to production management. However, manufacturers frequently encounter problems like disordered and delayed material distribution. Traditional scheduling methods suffer from problems like inadequate transparency, delayed decision directives, and suboptimal results, impacting performance. To this end, this study proposes a dynamic material distribution scheduling optimization model and strategy based on digital twin (DT) to address these problems. Firstly, we introduce workstation satisfaction and establish a material distribution path optimization model minimizing total distribution cost while maximizing workstation satisfaction. Subsequently, we present a cloud-edge computing-based decision framework and explain the DT-based material distribution system's components and operation. Furthermore, a dynamic material distribution scheduling optimization mechanism based on DT is designed. By incorporating a savings method and incentive, penalty strategies, improvements are made to the path node selection probabilities and the information pheromone update rules of the traditional ant colony optimization (ACO) algorithm. Finally, a numerical case study, using real data from collaborating enterprises, validates the proposed algorithm and strategy. This research offers valuable insights into logistics management and algorithm design in smart manufacturing environments.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

Fluorescent bioprobes are invaluable tools for visualizing live cells and deciphering complex biological processes by targeting intracellular biomarkers without disrupting cellular functions. In addition to protein-binding concepts, fluorescent probes utilize various mechanisms, including membrane, metabolism, and gating-oriented strategies. This study introduces a novel fluorescent mechanism distinct from existing ways. Here, we developed a B-cell selective probe, CDrB, with unique transport mechanisms. Through SLC-CRISPRa screening, we identified two transporters, SLCO1B3 and SLC25A41, by sorting out populations exhibiting higher and lower fluorescence intensities, respectively, demonstrating contrasting activities. We confirmed that SLCO1B3, with comparable expression levels in T and B cells, facilitates the transport of CDrB into cells, while SLC25A41, overexpressed in T lymphocytes, actively exports CDrB. The observation suggests that SLC25A41 plays a crucial role in discriminating between T and B lymphocytes. Furthermore, it reveals the potential for reversible localization of SLC25A41 to demonstrate its distinct activity. This study is the first report to unveil a novel strategy of SLC by exporting the probe. We anticipate that this research will open up new avenues for developing fluorescent probes.

The integration of transportation systems with land use through the Transit-Oriented Development (TOD) model can effectively promote sustainable development in cities. By increasing the intensive use of land within the supply capacity of urban public transportation, this model can comprehensively improve the efficiency of urban transportation and land use, while also addressing problems such as traffic congestion and environmental pollution. To implement this model, a "Node-Place-Function" (NPF) framework is proposed based on the coordination degree between rail transit and land use in Qingdao. A comprehensive evaluation system is constructed to quantitatively assess the TOD level and comprehensive development level of land use around rail transit stations from three dimensions. By applying the NSGA-II genetic algorithm, a land use allocation optimization model is developed to maximize rail transit station accessibility, land economic benefits, and environmental comfort. The research results guide other cities in China to implement TOD in land use planning and promote coordinated development between urban rail transit and land use.

Additive Manufacturing (AM) provides an opportunity to fundamentally redesign components previously limited by conventional manufacturing techniques. A new process for this workflow of design, manufacture by Powder Bed Fusion (PBF) and validation is presented, to which a case study of a crank for a high performance racing bicycle is applied. Topology optimisation generated conceptually ideal geometry from which a functional design was interpreted. Design for AM considerations were employed to reduce build time, material usage and post-processing labour. PBF was employed to manufacture the parts, and the build quality assessed using Computed Tomography (CT). Static and dynamic functional testing was performed and compared to a Finite Element Analysis (FEA). CT confirmed good build quality of tall, complex geometry with no significant geometrical deviation from CAD over 0.5 mm. Static testing proved performance close to current market leaders, although failure under fatigue occurred after just 2495 ± 125 cycles, the failure mechanism was consistent in both its form and location. These physical results were representative of those simulated, thus validating the FEA. This research demonstrates a complete workflow from design, manufacture, post-treatment and validation of a highly loaded PBF manufactured component, offering practitioners with a validated approach to the application of PBF.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

We present a case of serious and progressive neuropathy shortly following administration of the herpes zoster subunit (HZ/su) vaccine. The progressive neuropathy occurred a week following the vaccination and progressed until discharge 16 days post admittance. Mild patient’s symptoms persist. The development of neuropathy following HZ/su is exceedingly rare, with an attributable risk of 3 cases per million vaccines administered. A black box warning was issued for this indication, although diagnosis and treatment were not confirmed for this patient. Reporting cases like this is crucial for a comprehensive understanding of vaccine risks and to characterize the underlying etiology of these serious adverse events.

The use of privileged scaffolds as starting point for the construction of libraries of bioactive compounds is a widely used strategy in drug discovery and development. Scaffold decoration, morphing and hopping are additional techniques that allow to modify the privileged framework chosen and better explore the chemical space around it. In this study, two series of highly functionalized pyrimidine and pyridine derivatives were synthesized by scaffold morphing approach of triazine compounds obtained previously as antiviral agents. Newly synthesized azines were evaluated against lymphoma, hepatocarcinoma, and colon epithelial carcinoma cells showing in five cases acceptable to good anticancer activity associated to low cytotoxicity on healthy fibroblasts. Finally, ADME in vitro studies were conducted on the best derivatives of the two series showing good passive permeability and resistance to metabolic degradation.

The hybrid in-band on-channel (IBOC) transmission system as a digital audio radio scheme could transmit analog FM and digital audio simultaneously. In IBOC systems, broadcasters transmit signals within the allocated channel bands, so the bandwidth allocated for digital audio transmission is a big challenge. In view of the above, the use of cooperative game theory, supported by the use of the bankruptcy game and Shapley's value, is proposed as a strategy to optimize the bandwidth allocation at each node or station, according to the demand in the service, the number of stations and the radio channel conditions in the FM band. The paper proposes a scenario under saturated traffic conditions, in order to evaluate the degree of optimization that the Shapley value can perform under clearly established traffic and channel conditions. The evaluation process yielded a quite favorable result, where it is observed that the use of cooperative game theory supported by the Shapley value can be considered as an excellent alternative when performing bandwidth optimization processes for digital broadcasting over IBOC in FM and with the possibility of being implemented in other digital radio broadcasting systems.

The eutrophication caused by excessive total nitrogen(TN) and total phosphorus(TP) emissions has been widely concerned by the whole society. Studies have revealed the relationship between land use and TN and TP, but the relationship between land use compound topographic position and TP and TN was seldom studied. Therefore, Spearman correlation and redundancy analyses were used to reveal the relationship between land use compound topographic position and TN and TP based on the monthly data of 28 water quality sampling sites and the land use data of 2013 and 2016 in the lakes of Guizhou Plateau. The results show that the nutritional state of the HBA watershed is medium. The trophic level index (TLI) value and TN concentration were high during flood, while TP concentration was high in dry period. The TN concentration in the tributaries was higher than that in the reservoir area. Construction and valley were the sources of the pollution, whereas forest land and gentle slope were the sink. According to the”source-sink”effect, the optimal zoning of land use was completed, and the urban land pollution govern area should be strengthened firstly. This paper can provide decision support for water environment management and sustainable development decision-making.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Innovation is an essential key factor in the technology development history. Past research on innovation focused more on the innovation behavior of technology, but seldom described knowledge assets which also influence innovation behavior greatly. The effect of knowledge assets attribute and result on disruptive innovation is therefore regarded as the research topic in this study, where disruptive innovation is divided into outbound and inbound to combine combination-embeddednessandmajor business specificityof knowledge assets as the research model. Manufacturing enterprises in China are proceeded the questionnaire survey, and 173 valid copies are collected. The empirical analysis shows that combination-embeddedness of knowledge assets presents significantly positive effects on major business specificity and outbound innovationof an enterprise but reveals remarkably negative effects on inbound innovation. Enterprises are suggested to constantly accumulate knowledge assets with low major business specificity before disruptive innovation in order to reduce ineffective inbound innovation.

The expansion of renewable energies is progressing strongly. The influence on the power supply networks by the volatility of the infeed must be met with new concepts. In this paper we investigate the possibilities of integrating microgrids as a cooperating unit in the power supply network to support further expansion of RES power plants. In this paper a differentiation of microgrids from similar network structures is established, a classification of proposed groups is made. Then, after the description of simulation of components in a microgrid, with practical advice, an example model is shown, which aids the dimensioning of the components within a microgrid to achieve a specified goal.

In today's human society, diesel generators (DGs) are highly applied into the human energy and electricity supply system due to its technical, operational and economic advantages. This paper proposes an intelligent nonlinear $H_2/H_{\infty}$ robust controller based on the chaos particle swarm gravity search optimization algorithm (CPSOGSA), which controls the speed and excitation of a DG. In this method, firstly, establish the nonlinear mathematical model of the DG and then design the nonlinear $H_2/H_{\infty}$ robust controller based on this. The direct feedback linearization and the $H_2/H_{\infty}$ robust control theory are combined and applied. Based on the design of the integrated controller for DG speed and excitation, the system's performance requirements are transformed into a standard robust $H_2/H_{\infty}$ control problem. The parameters of the proposed solution controller are optimized by using the proposed CPSOGSA. The introduction of CPSOGSA completes the design of an intelligent nonlinear $H_2/H_{\infty}$ robust controller for DG. The simulation is implemented in MATLAB/Simulink, and the results are compared with the PID control method. The obtained results prove that the proposed method can effectively improve the dynamic accuracy of the system and the ability to suppress disturbances and improve the stability of the system.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

MRI shows in-vivo the three archetypal patterns of CNS volume loss underlying progressive ataxias, namely spinal atrophy (SA), cortical cerebellar atrophy (CCA) and olivopontocerebellar atrophy (OPCA). The MRI-based CNS atrophy pattern was reviewed in 128 progressive ataxias. A CNS atrophy pattern was identified in 91 conditions: SA in Freidreich’s ataxia, CCA in 5 acquired and 72 (24 dominant, 47 recessive,1 X-linked) inherited ataxias, OPCA in Multi-System Atrophy and 12 (9 dominant, 2 recessive,1 X-linked) inherited ataxias. The MRI-based CNS atrophy pattern may be useful for genetic assessment, identification of shared cellular targets, and repurposing therapies or enlargement of drugs indications in progressive ataxias.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

Green synthesis of silver nanoparticles (AgNPs) using raw Whey has never been reported. Silver nanoparticle formation involves a reduction reaction of silver salt solution with Whey extract. Extract and metal salt concentrations, and temperature can control this reaction. In this work, incubation period, extract, AgNO3, and NaOH concentrations were used as the independent factors using central composite design under response surface methodology. The aim was to elucidate the influence these factors have on the measured UV-Vis absorption spectra fitted with a Voigt function, and to optimize the conditions for nanoparticle formation. The fitting parameters, peak wavelength (λ0), peak area (A), and Full Width at Half Maximum (FWHM), were the responses. Silver nanoparticle formation was only possible in an alkaline environment (pH 10). The peak wavelength and the FWHM are influenced mainly by extract and AgNO3 concentrations. In contrast, the peak area is influenced by a total of 33.54 % from the interaction terms of AgNO3 and extract concentrations with NaOH. Metallic AgNPs were formed for the following parameter ranges: a) Whey (0.4 – 1.6 % v/v), b) AgNO3 (0.15 – 0.6 mM), c) NaOH (10 – 50 mM), and d) Time (20 – 60 min). Silver oxide nanoparticles were also observed.

The construction industry has a notable negative impact on the environment; thus, the promotion of the use of timber structures is an alternative to mitigate its effects. The research develops an artificial intelligence-based decision approach in the calculation of timber structures focused on the enhancement of the sustainability of roof structures. Based on the optimization carried out through genetic algorithms and the framework established in Eurocode 5, a general set of equations has been proposed for a laminated timber roof structure. The tool, which determines the most suitable roof structure for each strength class of laminated timber, allows for the determination of the dimensions of beams and purlins and their respective separations in order to minimize wood consumption. The ultimate goal is to offer multiple solutions regarding strength classes and structural designs in order to foster informed sustainable choices that promote efficient use of resources in construction.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. The simulation results verify the feasibility of the proposed algorithm and the validity of the proposed theory.

Studies on evolution have made significant progress in multiple disciplines, but evolutionary theories remain scattered and controversial. Here we deduce that, thermodynamically, many carbon-based entities (CBEs) on the Earth tend to absorb energy from widespread relatively temperate heat streams on the Earth flowing from the solar, geothermal, and other energy sources, to form higher-hierarchy CBEs (HHCBEs). This has been the driving force of evolution leading to accumulation of HHCBEs for billions of years. We further deduce three progressive mechanisms of evolution including natural selection from the driving force. We hence establish the CBE evolutionary theory (CBEET) which reinterprets the major aspects of evolution in a comprehensive and comprehensible way. The CBEET provides novel explanations for natural selection, origin of life (abiogenesis), macroevolution, sympatric speciation, and evolutionary tempos. It suggests that evolution is driven hierarchy-wise by thermodynamics and favors fitness and diversity. It elucidates that altruism, collaboration, and obeying rules with balanced freedom are important throughout the CBE evolution which includes chemical evolution, biological evolution, and animal group evolution. The CBEET refutes several erroneous views including negative entropy and survival of the fittest. It integrates with research advances in multiple disciplines and links up laws of physics, evolution in biology, and harmonious development of human society.

Lactoperoxidase (LP), which is a high-value minor whey protein, has recently drawn extensive attention from research scientists and industry due to its multi-function and potential therapeutic applications. In this study, the separation and optimization of two similar-sized proteins, LP and lactoferrin (LF) were investigated using strong cation exchange column chromatography. Optimization was started with central composite design based experiments to characterize the importance of different decision variables. The three variables used in the optimization were flow rate, length of gradient and final salt concentration in the linear elution gradient step. The obtained empiric functional model represented the effect of the significant factors on the yield as the objective function. Afterwards, the calibrated mechanistic model was employed to predict accurate optimal set of variables. The optimal operating points were found and the results were compared with validation experiments. Predictions respecting yield confirmed a very good agreement with experimental results while keeping purity, a product quality characteristic, equal or above to a predefined value.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Extraction techniques of important bioactive molecules from the major concern of Seafood byproducts are getting emphasis for better valorization. Employing green extraction technologies for efficient and quality production of these bioactive molecules is also strictly required. Hence, understanding the extraction process parameters to effectively design an applicable optimization strategy could make this real. In this review, statistical optimization strategies applied for the extraction process parameters of bioactive molecules from Seafood byproducts are focused. The type of experimental designs, and techniques applied to criticize and validate the effects of independent variables on the extraction output was addressed. Dominant parameters studied were enzyme/substrate ratio, pH, time, temperature and power of extraction instruments. The yield of bioactive compounds, chitin and chitosan, proteins and peptides, enzymes and carotenoids (Astaxanthins) were the most studied responses. Efficiency and/or economic and quality considerations and their selected optimization strategies that favour the production of potential bioactive molecules were also reviewed.

Methicillin-resistant Staphylococcus aureus (MRSA) infections, in particular biofilm-organized bacteria, remain a clinical challenge and a serious health problem. Rifabutin (RFB), an antibiotic of the rifamycins class, has shown in previous work excellent anti-staphylococcal activity. Here, we proposed to load RFB in liposomes aiming to promote the accumulation of RFB at infected sites and consequently enhancing the therapeutic potency. Two clinical isolates of MRSA, MRSA-C1 and MRSA-C2, were used to test the developed formulations, as well as the positive control, vancomycin (VCM). RFB in free and liposomal forms displayed high antibacterial activity, with minimal inhibitory concentrations (MIC) ranging from 0.009-0.013 μg/mL and MBIC50 values ranging from 0.008-0.012 μg/mL, in planktonic and biofilm bacteria, respectively. Confocal microscopy studies demonstrated the rapid internalization of unloaded and RFB-loaded liposomes in the bacterial biofilm matrix. In murine models of systemic MRSA-C1 infection, Balb/c mice were treated with RFB formulations and VCM at 20 and 40 mg/kg of body weight, respectively. Obtained data proved the superior therapeutic effectiveness of RFB formulations compared to Control and VCM groups even using a two-fold lower therapeutic dose as demonstrated by significantly reduced bacterial burden and growth index values in major organs. In addition, RFB formulations were the only formulations associated with 100% survival. Globally, this study emphasizes the potential of RFB nanoformulations as an effective and safe approach against MRSA infections.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

Purpose: To assess clinical outcomes of corneal cross-linking (CXL) intervention in a population diagnosed with progressive keratoconus. Methods: This single-center retrospective cohort study included consecutive patients who underwent standard CXL or accelerated CXL for progressive keratoconus at a major teaching hospital in south Israel between January 2015 and December 2019. Patient medical files were reviewed to extract demographic, clinical, and tomographic data from outpatient clinic reports before treatment and one year post-operatively. Results: This study included 166 patients (representing 198 eyes), out of which 98 patients (123 eyes) were ethnically Bedouin, and 68 patients (75 eyes) were ethnically Jewish. Overall, 126 patients (144 eyes) had a follow-up of at least 12 months (16.84±5.76). The mean patient age was 20.62 ± 7.1 years old. There were significant baseline differences between the two ethnical groups in best-corrected visual acuity (BCVA; p < 0.001), uncorrected visual acuity (UCVA; p < 0.001), mean keratometry (p = 0.028), and corneal thickness (p < 0.001). Significant changes in BCVA, UCVA, and pachymetry parameters within each group were found after 12 months. Negative binomial regression analysis showed a maximal keratometry below 55D (RR=1.247, p < 0.001) and a standard CXL procedure (RR=1.147, p = 0.041) are significantly related to the stability of KC after 12 months. However, the effect size of the origin of patients is negligible (RR=1.047, p = 0.47). Conclusions: In this study, the Bedouin population suffered from more progressive keratoconus when compared to the Jewish population. CXL was significantly effective in improving BCVA and UCVA in both groups after 12 months of follow-up.

Progressive supranuclear palsy (PSP) is a sporadic parkinsonism tauopathy characterised by the deposition of aggregations of abnormal, hyperphosphorylated four-repeat tau (4R-tau). A revised clinical diagnostic criterion for PSP allows early presentations for the full spectrum of clinical phenotypes to be recognised enabling doctors to make a more accurate diagnosis. The major genetic risk factor for sporadic PSP is a common variant in the gene encoding microtubule-associated protein tau (MAPT). Research into the biochemical and pathological pathways of tau is vital to improve the chances of developing an effective diagnostic biomarker to monitor tau pathogenesis. Neuroimaging biomarkers, such as tau PET ligands, are proving the most successful tool in providing a differential diagnosis between neurodegenerative disorders. There are currently no effective treatments for PSP, however tau-directed therapies in the last five years have rapidly advanced. Latest tau therapies are proposed to have disease-modifying effects by reducing toxic aggregations of tau through manipulating tau gene expression. After encouraging results from long awaited trials, additional funding is being injected into this field and with new results expected, this proves an exciting area for scientific discovery. This paper reviews advances in pathophysiology, diagnosis, biomarkers and disease-modifying therapeutic treatments for PSP.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

Objective: The aim of this study was to optimize the fermentation process of Radix Ranunculi ternate via microbial fermentation and analyze the changes in the contents of the main components, the antioxidant and hypoglycemic capacities of the extract before and after fermentation. Methods: The solid-state fermentation process was optimized using single-factor tests and the response surface method, with the yield of the alcohol extract of R. ternate as an evaluation index. Results: The best fermentation process was optimized using solid-state endophytic fungus fermentation technology as follows: strain addition ratio of Chaetomium globosum/Fusarium equiseti =1:1, fermentation for 5 d, sieve size of 40 mesh, liquid/material ratio of 0.8:1 mL·g−1, fermentation temperature of 31 °C, and inoculation amount of 7.5%. Under the optimized conditions, the contents of the water-soluble extract and total polysaccharides decreased by 12.71% and 12.95%, respectively, in the fermentation, The contents of ethanol-soluble-extract, flavonoids, saponins, polyphenols, organic acids, and total amino acids of fermented R. ternate increased by 19.77%, 57.14%, 79.67%, 14.29%, 17.63% and 3.82%, respectively, and the scavenging rate for DPPH, ABTS+, ·OH free radicals and inhibitory rate for α-amylase also increased by 19.02%, 14.17%, 7.53% and 34.54%, respectively, compared with unfermented R. ternate. Conclusions: Solid-state fermentation opens new avenues for the development and application of R. ternate as natural antioxidant and hypoglycemic food.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Preterm infants have increased body adiposity at term equivalent age and risk for adverse metabolic outcomes. The aim of the study was to define how nutrient intake may impact body composition (BC) of very low birth weight infants fed with early progressive enteral feeding and standard fortification. Eighty-six infants with

Viviparity is made possible by the placenta, a structure acquired relatively recently in the evolutionary history of eutherian mammals. Compared to oviparity, it increases the survival rate of the fetus, owing to the eutherian placenta. Questions such as “How was the placenta acquired?” and “Why is there diversity in placental morphology among mammalian species?” remain largely unsolved. Our present understanding of the molecules regulating placental development remains unclear, owing in no small part to the persistent obscurity surrounding the molecular mechanisms underlying placental acquisition. Many genes that characterize eutherian placental morphology likely evolved to be expressed at the fetal-maternal interface concurrently with those during embryogenesis. Therefore, identifying these genes, how they were acquired, and how they came to be expressed specifically at the fetal-maternal interface will shed light on some crucial molecular mechanisms underlying placental evolution. Exhaustive studies support the hypothesis that endogenous retroviruses (ERVs) could be evolutional driving forces for trophoblast cell fusion and placental structure in mammalian placentas including those of the bovine species. This review focuses on bovine ERVs (BERVs) and their expression and function in the placenta.

Abstract: The increased penetration of renewables in distribution power systems has motivated researchers to take significant interest in local energy transactions. The major goal of Local Energy Markets (LEM) is to promote the participation of small consumers in energy transactions and providing an opportunity for transactive energy systems. Such energy transactions in LEM are considered as a bi-level optimization problem in which all agents at upper and lower levels try to maximize their profits. But typical bi-level problem is very complex as it is inherently nonlinear, discontinued and strongly NP-hard. So, this article proposes the application of hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem of LEM. The proposed CE-CMAES secured the 1st rank in Testbed-2 entitled, “Bi-level optimization of end-users’ bidding strategies in local energy markets (LM)” at international competitions on Smart Grid Problems, held at GECCO 2020 and WCCI 2020. CE method is used for global exploration of search space and CMAES is used for local exploitation as its adaptive step-size mechanism prevents its premature convergence. A practical distribution system with renewable energy penetration is considered for simulation. The comparative analysis shows that the overall cost, mean fitness and Ranking Index (R.I) obtained from CE-CMAES are superior to those obtained from the state-of-the-art participated algorithms. Wilcoxon Signed Rank Statistical test also proves that CE-CMAES is statistically different from the tested algorithms.

Gradual disability worsening in Multiple Sclerosis (MS) is indicative of confirmed progression that mostly involves progression independent of relapse activity (PIRA) and is associated with neurodegeneration. Insights from pathology reveal grey matter involvement (neocortex, hippocampus, spinal cord, and deep grey matter structures) and microglia activation in early stages of MS. Cortical lesions result in thinning and atrophy and are considered accurate predictors of disability progression and cognitive decline.Neuroimaging biomarkers, such as high-resolution 7T MR images and TSPO-PET, have been examined for the detection of cortical pathology linked to cognitive deficits and thus progression, but are limited in clinical practice due to availability, time constraints, and cost.Electroencephalography (EEG) emerges as a non-invasive, cost-effective tool that reflects cortical activity in MS. Its potential in monitoring cognitive impairment is explored by focusing on nonlinear EEG analysis. The MuST PREDICT^TM project aims to extract linear and nonlinear EEG features, investigating their role in predicting disease progression and optimizing treatment. The study employs retrospective, cross-sectional, and prospective designs, utilizing EEGs from various MS forms, cognitive assessments, serum/cerebrospinal fluid biomarkers and neuroimaging. The methodology involves time-based and spectral feature extraction, employing artificial intelligence classifiers and brain criticality-based approaches.The conceptual framework of an innovative modality is herein presented, that would EEG for early MS progression detection. Standardization of the methodology could lead to the creation of a digital tool for better prognostication and treatment strategy optimization.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

The behaviour of steel frame buildings under progressive collapse conditions depends on a combination of several parameters including the interplay between different collapse resistance mechanisms mobilized in different structural components. Previous studies have shown that the extent to which these mechanisms may contribute to progressive collapse resistance depends on the ability of the beam-column connections to undergo large inelastic deformations prior to reaching their deformation capacity limits. For this reason, and due to the important role of their flexural strength and tying capacity in the development of essential collapse resistance mechanisms, the response of beam-column connections is one of the most important features of progressive collapse performance. Based on the knowledge gained through previous studies on the mechanics of the problem, the role of the connections is critically reviewed by examining the results of several experimental studies conducted during the past decade. The factors that may adversely affect progressive collapse resistance – such as failure modes of certain connection types – are evaluated, and novel approaches of limiting these factors which are currently under development are reviewed. The assessment of these parameters leads to useful conclusions of practical significance, while highlighting the aspects of the problem that need further study and understanding.

Direct and indirect effects after mine operations cease must ideally be subject to perpetual monitoring routines in order to detect possible risks or avoid adverse effects on the surrounding ecosystems at an early stage. In this contribution, mining subsidence lakes created inside the nature reserve Kirchheller Heide and Hilsfeld Forest are subjected to analysis for a long-term monitoring scheme. For this purpose, we employ high-resolution unmanned aerial system (UAS)-based multispectral and thermal mapping tools to provide a fast, non-invasive and multitemporal environmental monitoring method. Specifically, we propose to monitor vegetation evolution through multispectral analysis, biotypes identification using machine learning algorithms, and water surface extent detection, together with their thermal behavior. The aim of this contribution is to present the proposed workflow and first results to establish a baseline for future analyses and subsequent surveys for longterm multi-temporal monitoring

Pump Sizing is the process of dimensional matching an impeller and stator to provide a satisfactory performance test result and good service life during the operation of progressive cavity pumps. In this process, historical data analysis and dimensional monitoring are done manually, consuming a large amount of man-hours and requiring a deep knowledge of progressive cavity pump behavior. This work presents the use of complex networks in the construction of a prototype to recommend interference during the Pump Sizing process in a Progressive Cavity Pump. For this, data from different expert applications were used in addition to individual control Excel spreadsheets to build the database used in the prototype. From the pre-processed data, complex network techniques and the Betweenness Centrality metric were used to calculate the degree of importance of each order confirmation, as well as to calculate the dimensionality of the rotors. The model was evaluated using the mean squared error (MSE), obtaining a MSE of 0.28 for the cases where there were recommendations for order confirmations. Based on the results obtained, it was realized that there is a similarity with the dimensional defined by design engineers during the Pump Sizing process.

The influenza virus has been the primary cause of the pandemic, posing a constant threat to human society. Due to its genetic evolution and continuous outbreak, antiviral research currently focuses on exploring a novel lead agent. A comprehensive antiviral screening discovered a marine bacterium whose extract exerted excellent efficacy against influenza viruses. Parerythrobacter sp. M20A3S10, a novel strain under the family Erythrobacteraceae, could produce carotenoids exhibiting antiviral and anticancer activity by enhancing the cellular immune system. Post-treatment of M20A3S10 extract showed outstanding therapeutic indexes: against influenza virus A/PR8 (H1N1) [selectivity index (SI) = 24.0], A/Wisconsin/15/2009 (H3N2) (SI = 30.1) and B/Florida/78/2015 (SI = 38.2). Comparably, the effectiveness was demonstrated against Zika virus (ZIKV) and dengue virus type 2 (DENV2) with an SI of 22.5 and 24.1, respectively, namely broad-spectrum activity. Of note, the antiviral responses resulted from the common replication mechanism between IAV, ZIKV, and DENV2. The stimulation of apoptosis-mediated cellular immunity prevented the viral release and protected the host, suggesting that switching from necroptosis to apoptosis is a novel antiviral target. Although the specific compound affecting the antiviral activity was not identified, its promising efficacy with broad activity will contribute to developing a strategy for preventing future pandemics.

This study was designed for the first time to improve phenolic yield and antioxidant activity of ultrasonic-assisted extraction from Acer truncatum leaves (ATL) using response surface methodology, and phenolic composition in ATL extracted under the optimized condition were characterized by UPLC-QTOF-MS/MS. Solvent and extraction time were selected based on preliminary experiments, and a four-factors-three-levels central composite design was conducted to optimize solvent concentration (X₁), material-to-liquid ratio (X₂), ultrasonic temperature (X₃) and power (X₄) for an optimal total phenol yield (Y₁) and DPPH• antioxidant activity (Y₂). The results showed that the optimal combination was ethanol: water (v:v) 66.21%, material-to-liquid ratio 1:15.31 g/mL, ultrasonic temperature 60 °C, power 267.30 W, and time 30 min with three extractions, giving a maximal total phenol yield of 7593.62 mg gallic acid equivalent /100 g d.w. and a maximal DPPH• antioxidant activity of 74241.61 μmol Trolox equivalent/100 g d.w.. Furthermore, 22 phenolics were first identified in ATL extract obtained under the optimized conditions, indicating that gallates, gallotannins, quercetin, myricetin and chlorogenic acid derivatives were the main phenolic composition in ATL. What’s more, a gallotannins pathway existing in ATL from gallic acid to penta-O-galloyl-glucoside was interpreted. All these results provided practical information aiming at full utilization of phenolics in ATL, together with fundamental knowledge for further research.

This article presents a combined route and path planning strategy to guide Skid-Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity, and ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrated the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

Sweat rate and electrolyte losses have a large inter-individual variability, a personalized approach to hydration can overcome this issue to meet an individual’s needs. This study aimed to investigate the effects of personalized hydration strategy (PHS) on fluid balance and intermittent exercise performance. Twelve participants conducted 11 laboratory visits including a VO2max test and two 5-day trial arms under normothermic (NOR) or hyperthermic (HYP) environmental conditions. Each arm began with 3-day of familiarization exercise following by two random exercise trials with a either PHS or control (CON). Then, participants crossed-over to the second arm for: NOR+PHS, NOR+CON, HYP+PHS, and HYP+CON. The PHS was prescribed according to participants’ fluid and sweat sodium losses. CON drank ad-libitum of commercially-available electrolyte solution. Exercise trials consisted of two phases: 1) 45min constant workload; 2) high-intensity intermittent exercise (HIIT) until exhaustion. PHS had a significantly greater fluid intake compared to CON. HYP+CON produced the lowest sweat sodium concentration. HYP+PHS had a lower thirst perception and a longer HIIT compared to HYP+CON. Thus, PHS reinforces fluid intake and successfully optimizes hydration status, regardless of environmental conditions. PHS may be or is an important factor to preventing negative physiological consequences during high-intensity exercise in the heat.

Multiple sclerosis (MS) is a heterogeneous disease of the central nervous system that is governed by neural tissue loss and dystrophy during its progressive phase, with complex reactive pathological cellular changes. The immune-mediated mechanisms that promulgate the demyelinating lesions during the relapses of acute episodes are not characteristic of chronic lesions during progressive MS. This has limited our capacity to target the disease effectively as it evolves within the central nervous system white and gray matter, thereby leaving neurologists without effective options to manage individuals as they transition to a secondary progressive phase. The current review highlights the molecular and cellular sequelae that have been identified to cooperate and/or contribute to neurodegeneration that identifies individuals with progressive forms of MS. We emphasize the need for appropriate monitoring via known and novel molecular and imaging biomarkers that can accurately detect and predict progression for the purposes of newly designed clinical trials that may demonstrate efficacy of neuroprotection and potentially neurorepair. To achieve neurorepair, we focus on the modifications required in the reactive cellular and extracellular milieu, in order to enable endogenous cell growth as well as transplanted cells that can integrate and/or renew the degenerative MS plaque.

The contribution deals with the selection of progressive methods and technologies to improve the management of production processes in the industrial area. The first chapter of the contribution summarises the theoretical starting points for the management of production processes and their progressive concepts. Chapter 2 deals with the analysis of the current state of play in the use of methods and technologies in production management and summarise its problem areas. Chapter 3 presents the design of a system for selecting the appropriate method and technology to improve the management of the production process. The system presented is designed from data collection blocks, a benchmark for the performance of industrial enterprises' production processes, and data mining to gain knowledge of the impact of the methods and technologies used on the performance of production processes. The last block of the system allows the presentation of the obtained results, which sets out the recommendation for the choice of progressive method and technology. The system is partially verified in the last chapter of the contribution.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

Micellar Casein Concentrate (MCC) is manufactured from microfiltration (MF) of skim milk utilizing ceramic or polymeric membrane filtration. While ceramic filtration has higher efficiency, use of polymeric is cost effective and the process is familiar to several US dairy processors. The aim of the present study was to develop an optimized membrane filtration process to produce MCC using spiral wound polymeric membrane filtration (SW MF) system by systematic selection of transmembrane pressure (TMP) and level of diafiltration (DF). Using skim milk as feed material, preliminary lab-scale MF experiments were conducted using 0.5 µm polyvinylidene fluoride (PVDF) membrane. Three TMP (34.5, 62.1, and 103.4 kPa) and three levels of DF (70, 100, and 150%) along with a process without DF as control were used in the study. Effect of TMP and effectiveness of DF on flux rates, SP removal, casein to total protein (CN/TKN) ratio, casein to true protein (CN/TP) ratio, rejection of casein (rej CN) and SP (rej SP) were evaluated. At all TMP values used in the study, the overall flux (O Flux) increased with the level of DF. Highest O Flux of 30.77 liter per meter square per hour (LMH) was obtained with 34.5 kPa pressure and 150% DF. The impact of DF was more pronounced at lower pressures than at the higher pressures used in the study. With controlled DF, instantaneous flux was maintained within 80% of initial flux for the entire process run. For all the experiments, casein has a rejection of 0.97 to 1.0, while serum protein has the lowest rejection of 0.10 at 34.5 kPa pressure and 150% DF level. Use of 34.5 kPa and DF level of 150 % contributed to 81.45% SP removal, and casein to true protein ratio of 0.96. SP removal data from the lab-scale experiments were fitted into a mathematical model using DF and square of TMP as factors. The model predicts SP removal within 90-95% of actual SP removal got from the pilot plant experiments.

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

Statistical management policy and system plays significant role in development of any nation as it guides the planner to analyze the gaps, design programs and allocate budget in efficient manner. Globally, design and usage of statistical management system varies relying on country specific ability and resources availability. Government of Nepal has been managing database in individual as well as thematic manner. Policy wise, thematic database is in operation based on Statistics Act 1958 and individual database is in operation based on Birth, Death and Other Personal Related Events Registration Act 1976. Though civil registration related policies have been either updated or newly formed, sectoral statistic management related policy has not been updated since its first promulgation period (1958). With no policy supporting the consolidated statistical management system, government as well as non-government agencies has been investing millions of dollars annually to collect, compile and analyze individual related data and fulfill their respective objectives. It has forced the government services and performance to be paper-based, time-consuming and tedious thus creating dis-trust among citizen each passing day. Though, formulation of legal instrument promoted digital movement and mandated shift towards e-government modality, the process has been low. Theoretically, Technology Acceptance Model (TAM) state the need of having advanced and efficient technology to be in place in current setting to improve governance, efficiency, accountability, transparency and trust. Also, the same theory state the requirement of strong willingness and motivation from the users (government, non-government and citizen) to adopt the technology based policy and system. As the integrated statistical management policy emphasizes on technology-based system or digital system, the perspectives of Technology Acceptance Model also termed as Information System Theory aid in understanding the context, conceptualizing and addressing the needs with an establishment and implementation of digital system or technology based system. This policy paper applied the Technology Acceptance Model (TAP) to examine the underlying opportunities and challenges associated with an integrated statistical management policy. With existing gaps in existing policy, system and practice alongside an overview of opportunities and challenges, this paper recommends the need of stand-alone integrated statistical management policy to consolidate sectoral as well as civil related information through single system based on recommended policy.

Rapidly progressive glomerulonephritis, the term coined for a subtype of nephritic syndrome, is characterized by a state of acute loss of renal function, along with those components of renal damage on urinalysis, over a relatively short period (a few weeks or months). It is the aftermath of an acute, severe glomerular injury. RPGN, famously known as Crescentic glomerulonephritis (cGN) is not a single disease entity but a pattern that can occur in a variety of glomerular diseases caused by different pathologic lesions leading to the development of necrosis in the glomerular capillaries seen in both, systemic and kidney-restricted diseases. While still little is known about the exact etiologic cause behind the primary injury to the capillary wall leading to the development of the condition, many mechanisms are put forth to describe the pathogenesis leading to the progression. Immunopathologic studies have demonstrated that rapidly progressive glomerulonephritis can result from glomerular antibody deposition, can be immune complex-mediated, or from some as yet undefined mechanism that doesn’t involve glomerular antibody deposition. The condition is often severe with poor prognosis as it develops so rapidly and with extensive glomerular involvement, the damage is almost irreversible. Many elements can be associated with the occurrence and progression of the disease but it often involves a disruptive immune response one way or the other. RPGN’s morphological hallmark, the extensive extra-capillary cellular proliferation into the bowman’s space, commonly known as crescent formation which is often observed by electron microscopic view of the tissue biopsy, is considered one of the great diagnostic approaches for the detection of the disease. Also, the severity of the disease is related to the nature and type of immunologic process causing the disease and (in part) to the degree of crescent formation, that can only be observed by histopathological studies. This activity attempts to explain the etiology and pathogenesis involved in the development leading to the rapid progression of the disease.

Buckling defect will appear on the outer surface of the deformed ring during constrained ring rolling (CRR) of aluminum alloy thin wall conical ring with inner high ribs (AATWCRIHR), if the geometrical dimension of the ribs doesn't match the wall thickness. To avoid the buckling defect, a quantitative method for characterizing the degree of the buckling defect is proposed using the area of buckling profile. Then an orthogonal experimental scheme was designed taking the width of the middle rib, thickness of wall, and height of the middle rib as design variables and defining the area of buckling profile as optimization objective. Subsequently, a quadratic polynomial response surface model was established by combining the optimization algorithm with the finite element method (FEM), and the geometrical dimension of the middle ribs of the deformed AATWCRIHR is optimized. Also the optimal parameter combination to minimize the area of buckling profile is obtained and verified using FE simulation. Results show that the AATWCRIHR after optimization doesn’t generate the buckling defect during constrained ring rolling, and it is proved that the quantitative buckling defect representation method and the optimization design method based on the response surface model and the finite element simulation results are feasible for the constrained ring rolling process of the AATWCRIHR.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

The rampant misuse of drones poses a serious threat to national security and human life. Currently, the CNN method has been widely used in drone detection. However, there is a challenge that traditional CNN cannot cope with, which is small drone targets often have reduced amplitude or even lost features in infrared images. This paper proposes a Progressive Feature Fusion Network (PFFNet) that gradually increases the response amplitude of the target in the deep network. The Feature Selection Model (FSM) is designed to improve the utilisation of the output coding graph and enhance the feature representation of the target in the network. A lightweight segmentation head is also designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm can achieve low duration and high accuracy in drone target detection. On the public dataset, the IoU is improved by 2.53% and the detection time is reduced by 81.03%.

A progressive FEA fatigue degradation model for composites was developed and implemented using a UMAT user material subroutine in Abaqus. Numerical results were compared to experimental strain field data from high frequency Digital Image Correlation (DIC) of split disk fatigue testing of pressure vessel cut outs with holes. The model correctly predicted the onset and evolution of damage in the matrix as well as the onset of fiber failure. The model use progressive failure analysis based on the maximum strain failure criterion, the cycle jump method and Miner sum damage accumulation rule. A parameter study on matrix properties was needed to capture the scatter in strain fields observed experimentally by DIC. S-N curve for the matrix material had to be lowered by 0% to 60% to capture the experimental scatter. The onset of local fiber failure had to be described by local S-N curves measured by DIC having 2.5 times greater strain than that of S-N curves found from standard coupon testing.

Atomic force microscopy (AFM) has been extensively used for the nanoscale characterization of polymeric materials. The coupling of AFM with infrared spectroscope (AFM-IR) provides another advantage to the chemical analyses and thus helps to shed light upon the study of polymers. In this perspective paper, we review recent progress in the use of AFM-IR in polymer science. We describe first the principle of AFM-IR and the recent improvements to enhance its resolution. We discuss then the last progress in the use of AFM-IR as a super-resolution correlated scanned-probe IR spectroscopy for chemical characterization of polymer materials dealing with polymer composites, polymer blends, multilayers and biopolymers. To highlight the advantages of AFM-IR, we report here several results in studying crystallization of both miscible and immiscible blends as well as polymer aging. Then, we demonstrate how this novel technique can be used to determine phase separation, spherulitic structure and crystallization mechanisms at the nanoscale, which have never been achieved before. The review also discusses future trends in the use of AFM-IR in polymer materials, especially in polymer thin film investigation.

Colombia is the world's leading producer of mild washed arabica coffee and produces 12.6 million bags of green coffee, but at the same time 784,000 tons of waste biomass are dumped in open fields, of which only 5% is recovered or used. The objective of this project was to evaluate the production of platform chemicals from these coffee wastes. To achieve this, biomass characterization was carried out using proximate analysis, ultimate analysis and structural analysis. Hydrothermal valorization was carried out at a temperature range of 120-180 °C (LHW) and 180-260 °C (HTC) for one hour, the platform chemicals obtained were quantified by HPLC-IR, monitored by pH and conductivity, and the solid fraction was characterized by monitoring the functional groups in IR spectroscopy, and elemental analysis. Hydrolysis processes were obtained from 150 °C, production of platform chemicals from 180 °C and maximum concentration at 180 °C-4h, over 200 °C degradation of the products in the liquid fraction starts to take place. Homogeneous basic and acid catalysts were used to improve the yields of the reaction. The kinetics of the hydrolysis of lignocellulosic structures to sugars were also analyzed and described, and reaction orders of 1 (LHW), 3 (HTC) and their respective reaction rate equations were reported.

Combining modern thermodynamics theory branches, including finite time thermodynamics or entropy generation minimization, constructal theory and entransy theory, with metallurgical process engineering, this paper provides a new exploration on generalized thermodynamic optimization theory for iron and steel production processes. The theoretical core is to thermodynamically optimize performances of elemental packages, working procedure modules, functional subsystems, and whole process of iron and steel production processes with real finite-resource and/or finite-size constraints with various irreversibilities toward saving energy, decreasing consumption, reducing emission and increasing yield, and to achieve the comprehensive coordination among the material flow, energy flow and environment of the hierarchical process systems. A series of application cases of the theory are reviewed. It can provide a new angle of view for the iron and steel production processes from thermodynamics, and can also provide some guidelines for other process industries.

Water pollution, intensified by the release of hard-to-degrade pollutants, poses severe threats to ecosystems, human health, and economic development. The existing advanced oxidation processes often involve high operational costs and may result in secondary pollution, highlighting the necessity for innovative and more sustainable solutions. To address these challenges, our study introduces a cost-effective and eco-friendly corona discharge barrier plasma discharger for wastewater treatment. Through the central composite design/response surface methodology, a high decolorization rate of 98% of methylene blue (MB) within 10 minutes was achieved by optimizing parameters such as pH and voltage. Furthermore, the mechanisms of the generation of reactive oxygen species via this device was discussed in detail and the degradation pathways of MB were described. Moreover, this device is also very energy-efficient, with a low energy density and electrical energy per order of 0.15 watt/mL and 5.79 kWh/m³/order, respectively. In conclusion, the plasma discharger developed in this study provides a cost-effective and environmentally sustainable solution for dye wastewater treatment. This research contributes significantly to the advancement of sustainable dye wastewater management practices, offering an innovative method that meets both environmental and economic objectives.

This paper examines the most prominent “progressive” American religious groups’ (as defined by those that liberalized early on the issue of birth control, circa 1930) views of women between the first and second waves of the feminist movement (1930-1965). We find that some groups have indeed had a long and outspoken support for women’s equality. Using their modern-day names, these groups, the United Church of Christ, the Unitarian Universalist Church, and to a lesser extent, the Society of Friends, or Quakers, professed strong support for women’s issues, early, and often. However, we also find that prominent progressive groups –the Protestant Episcopal Church, the Methodist Episcopal Church and the United Presbyterian Church, were virtually silent on the issue of women’s rights – even as the second wave of the feminist movement was picking up steam – as late as 1965.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

This study, which was supported by a quantitative research paradigm with two experiment and two control groups of 4x4 quasi-experimental design, aimed to find out the impact of a sustainable progressive STEAM (SP-STEAM Model) application model on 5th grade primary school students critical thinking dispositions and mathematic achievements in Northern Cyprus. The treatment model was applied to experimental groups for 14 weeks of time. Split-plot multiple group analysis of variance (Split-plot ANOVA) statistical technique was used to calculate between and within group significances regarding exogenous variable. SPSS24 software package was used for the analysis. Pre-test and post-test results derived from experiment and control groups revealed significant effect of SP-STEAM Model on 5th grade primary school students’ critical thinking dispositions as measured with CCTDI and mathematic achievements as measured with a dedicated ex-am. The results were discussed in detail under the light of related literature and suggestions for further studies were proposed.

We describe a 90-year-old male presenting with disequilibrium, loss of balance and difficulty walking for three days prior to initial presentation. Interestingly, he denied cough, fever or dyspnea prior to arrival. Over the course of 48 hours, the patient developed acute respiratory distress syndrome (ARDS) requiring intubation, diagnosed with COVID-19 infection and was treated in the intensive care unit where he died. Since the initial cases in Wuhan China in Dec 2019, the medical and epidemiological communities have learned much about the presenting features, symptomatology, epidemiology, transmission and common physical, laboratory and radiological findings of this disease. Although common symptoms are already established, it is very important to learn and record atypical symptoms or presentations of this highly contagious disease. By doing so, we will be able to recognize earlier atypical symptoms and prevent the environmental exposure to Health care workers and future patients as well. We report that Central disequilibrium may be such as initial presenting sign and symptom of impending respiratory failure from SARS-CoV-2 virus. These atypical findings such as presyncope may precede common respiratory complications of SARS-CoV-2.

In the present work, a predictive modelling and optimization with the adaptive network based fuzzy inference system(ANFIS) modelling of mechanical properties of laser-coated NB/SiC/Ni welds was studied based on Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties was taken as the output of the ANFIS. The microstructure of welds were analysed using scanning electron microscopy with an energy dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi's design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

This article explores how digitalisation is impacting the hospitality industry and assesses the evolving role of Information Technology (IT) strategy in the digitalisation process. The research approach is qualitative and inductive, based on six in-depth interviews with senior IT professionals in the hospitality industry. Findings indicate significant differences in the role of IT strategy in guiding digitalisation in the companies studied. The depth of information provided by the interviewees supports the development and application of a model that profiles the companies regarding their degree of digitalisation and technology integration. Analysis of interview material allows the identification of key properties for successful digitalization: process agility, workforce adaptability, and technology manageability, along with a clear data culture and ensured cybersecurity. However, disparate systems and technologies, and a lack of data integrity, are key issues that leave hospitality companies with difficult choices in progressing digitalisation initiatives. The applied model and identification of key properties for successful digitalisation contribute to the development of related theory and can also be used as a reference point for senior IT professionals working in the industry.

Nodes in wireless sensor networks (WSN) are characterized particularly by their limited power and memory capabilities. Limited memory is an important parameter as it defines the size of the operating system and the processing code. As established previously, energy and memory efficiency is the most important evaluation factors of WSNs as they are directly related to data loss and network lifetime. However, based on our simulation results, memory efficiency determines the selection or abandon of nodes by the botmaster for the propagation of bots in an IoT infrastructure. Consequently, the node’s memory efficiency determined the spread of bots in the network and provides defense actors with an insight of the botmaster behavior for mitigation of the attack. Conventional botnet propagation and mitigation models did not consider the impact of node’s memory efficiency in the IoT platform. To address this gap, we build IoT-SIEF, a novel propagation model with forensic capability that will analyze command and control propagation behavior based on the perspective of the node’s memory efficiency. IoT-SIEF model used to explore the dynamics of propagation using numerical simulation with more than 50% outperform other models in mitigating the number of secondary bots. Consequently, it can serve as a basis for assisting the planning, design, and defense of such networks from the investigator's point of view.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

The present study analyzed the effect of temperature, pH, retention time, pretreatment and mixing ratio on the anerobic digestion process. The parameters during the anaerobic co digestion of cow manure and food waste were then optimized using the Taguchi experimental design method. ANOVA is carried out to find the significant parameters which influence the biogas production. Experimental tests were carried out at laboratory scale reactors kept at different temperatures (28 °C, 35 °C, and 50 °C). The specific methanogenic Performance (SMP) during anerobic digestion at higher temperatures was characterized with the analysis of acetate, propionate, butyrate, hydrogen, glucose, and formate. The improvement of biogas production with different pre-treatment i.e., ultrasonic, autoclave, and microwave techniques also analysed. The results showed that reactor that maintained at 35 °C showed the highest biogas production. While the reactor that maintained at lower temperature (28 °C) produced the lower volume of biogas. As the retention time increases, the amount of biogas produced increases. Methanogenic activities of microorganisms reduced at higher temperature conditions (65 °C). Biogas production increased by 28.1%, 20.23%, and 13.27 % when the of substrates treated with ultrasonic, autoclave, and microwave, respectively, compared to the untreated substrate. The optimized condition for the highest biogas production during anaerobic co digestion of food waste and cow manure is temperature of 35 °C, pH of 7 and mixing ratio (CM: FW=1.5:0.5). ANOVA showed that temperature is the most important input parameter affecting biogas production, followed by mixing ratio.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations having already been an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placements in CCS operations, employing a derivative-free method known as Bayesian optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian optimization in the context of optimizing well placements for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

For marine vessels, a propeller shaft system, also known as a propulsion shaft system, is an essential changer in engine torque and propeller thrust. The propeller shaft system is subjected to transverse, longitudinal, and torsional excitations during operation. These sources of excitations originate from alternating thrust generated by the propeller, operation of a propeller in a stern flow field which is non-uniform in time and space, misalignment of components such as shafts, bearings, and keyholes in the assembly, excitations in the form of rotational motion, and the whirling motion of the shafts. These dynamic excitations posed a challenging issue for the integrity and reliability of a marine vehicle. Thus, it is crucial to analyze the dynamic response of the propulsion shaft system. The dynamic analysis of the propulsion shaft system is typically conducted using numerical /analytical models. At present, Finite Element Model (FEM) as a numerical model plays a vital role in analyzing the dynamic response of the structure. The computed response and the modal parameters from the FE model are reliable only if accurate boundary conditions, such as stiffness, can be imposed on the model. However, the numerical FE model deviates from the experimental results because of uncertainties in the stiffness values of the bearings and other connectors. This paper uses the Response Surface Optimization (RSO) technique to estimate the unknown stiffness of the bearings used in the propulsion shaft system. The experimental model of the propeller shaft system is constructed from the steady-state response of the system using the step sine excitation. The natural frequencies and the amplitude of vibrations in the FE model are updated using the parametric RSO technique. The updated model shows that the difference in natural frequencies and vibration amplitude is less than 10% between the optimized and experimental models. Propeller Shaft, Vibrations, Optimization, Response Surface

This research endeavors to illuminate the path toward efficient and optimized propeller design by delving into the realm of aerodynamics. Propellers, as vital mechanical marvels, constitute the core of numerous propulsion systems, spanning from aircraft and marine vessels to wind turbines. The pursuit of enhanced propeller efficiency and performance holds paramount importance in industries where propulsion assumes a critical role, underscoring the indispensable need for a profound understanding of aerodynamics. Through meticulously crafted theoretical insights and their practical applications, this study embarks on a journey into the fascinating realm of propeller aerodynamics. While the paper primarily addresses the aerodynamic aspects of propeller design and optimization, it serves as a foundational guide for those seeking to comprehend the intricacies of propeller engineering.The research journey commences with an in-depth exploration of the fundamental principles governing propeller performance. It unveils the intricate dynamics of airflow surrounding propeller blades, shedding light on their proficiency in generating thrust, lift, and torque. The bedrock of the theoretical insights is a comprehensive analysis encompassing airfoil profiles, blade geometry, and the underlying principles of blade element theory. In navigating the ensuing pages, the study embarks on the task of demystifying the intricate methods and calculations that propel the design and optimization of propellers. Leveraging cutting-edge methodologies, the research systematically assesses the aerodynamic characteristics of propellers across various operational conditions. These invaluable insights empower the tailoring of propeller designs with precision, aligning them seamlessly with specific performance objectives. The aim is not only to impart a comprehensive understanding of propeller aerodynamics but also to provide actionable insights for engineers and researchers striving to enhance propeller efficiency. This research endeavor serves as a guiding light for both propeller enthusiasts and professionals alike. It not only enriches the broader discourse on propeller technology but also equips readers with the requisite knowledge and tools to embark on their own quests for optimized propeller design. As propulsion technologies continue their evolution, the steadfast principles elucidated herein will persist, guiding future innovations and advancements in the realm of propeller engineering. This paper serves as a profound resource, offering new researchers a comprehensive understanding of propellers and their intricate design processes.

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

World Health Organization (WHO) has announced the monkeypox (MPX) epidemic a global public health emergency due to its re-emergence, remarkable increase in the number of MPX cases worldwide, and its potential spread. This paper introduces the symptoms, complications, and features of MPX; its transmission, diagnosis and testing, vaccines and treatment; MPX and sexually transmitted diseases, especially the human immunodeficiency virus (HIV); possible natural hosts or reservoirs of the monkeypox virus (MPXV). A useful tool for MPX and surgical safety recommendations are presented. The challenges in fighting the MPX epidemic, One Health strategy, and future research are discussed.

Climate change, biodiversity loss, and other social and environmental problems pose grave risks. Progress so far has been incremental and insufficient, and as a result scientists, global policy experts, and the general public increasingly conclude that bold change is required across all sectors of society. At least two kinds of bold change are conceivable: reform of existing societal systems (e.g., financial, economic, legal, and governance systems), including their institutions, policies, rules, and priorities; and transformation, understood as the de novo development of and migration to new, improved systems. This paper is the second in a series of three that together propose a novel science-driven research and development program aimed at societal transformation. Moreover, the series advances a conceptual framework and formal mechanics by which societal transformation might be approached. Two of the underlying hypotheses are that new societal systems can be developed in a science-driven process to be fit for purpose, and system fitness can be compared across designs. Societies are viewed as superorganisms, and systems are viewed as a societal cognitive architecture. The first paper in the series provides definitions, aims, hypotheses, and a worldview. This paper discusses motivations, the role of science in societal transformation, a theory of change, and fitness metrics. The proposed R&amp;D program and theory of change are sound, viable, and affordable. The local-global-viral strategy invites the global science community to play a unique co-leadership role with local communities in the development, testing, and monitoring of new societal systems. Systems are implemented via a novel civic club model, where participation is voluntary. Clubs grow and replicate based on merit and aided by club networks, whose systems are also viewed as societal cognitive architectures. Benefits of the program and strategy are discussed.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Transcranial direct current stimulation (tDCS) over the left Inferior Frontal Gyrus (IFG) was found to improve apraxia of speech (AOS) in post-stroke aphasia, speech fluency in adults who stutter, naming and spelling in primary progressive (PPA). This paper aims to determine whether tDCS over the left IFG coupled with AOS therapy improves speech fluency in patients with PPA more than sham. Eight patients with non-fluent PPA with AOS symptoms received either active or sham tDCS, along with speech therapy for 15 weekday sessions. Speech therapy consisted of repetition of increasing syllable-length words. Evaluations took place before, immediately after, and two months post-intervention. Words were segmented into vowels and consonants and the duration of each vowel and consonant was measured. Segmental duration was significantly shorter after tDCS than sham for both consonants and vowels. tDCS gains generalized to untrained words. The effects of tDCS sustained over two months post-treatment in trained words. Taken together, these results demonstrate that the tDCS over the left IFG facilitates speech production by reducing segmental duration. The results provide preliminary evidence that tDCS can maximize efficacy of speech therapy in non-fluent PPA with AOS.

The growing attention for plug-in electric vehicles, and the associated high-performance demands, have initiated a development trend towards highly efficient and compact on-board battery chargers. These isolated ac-dc converters are most commonly realized using two conversion stages, combining a non-isolated power factor correction (PFC) rectifier with an isolated dc-dc converter. This, however, involves two loss stages and a relatively high component count, limiting the achievable efficiency and power density and resulting in high costs. In this paper a single-stage converter approach is analyzed to realize a single-phase ac-dc converter, combining all functionalities into one conversion stage and thus enabling a cost-effective efficiency and power density increase. The converter topology consists of a quasi-lossless synchronous rectifier followed by an isolated dual active bridge (DAB) dc-dc converter, putting a small filter capacitor in between. To show the performance potential of this bidirectional, isolated ac-dc converter, a comprehensive design procedure and multi-objective optimization with respect to efficiency and power density is presented, using detailed loss and volume models. The models and procedures are verified by a 3.7 kW hardware demonstrator, interfacing a 400 V dc-bus with the single-phase 230 V, 50 Hz utility grid. Measurement results indicate a state-of-the-art efficiency of 96.1% and power density of 2.2 kW/dm³, confirming the competitiveness of the investigated single-stage DAB ac-dc converter.

The multiobjective optimization problem is a key research direction in the field of optimization. Based on the multivariant optimization algorithm (MOA), this paper puts forward a multiobjective multivariant optimization algorithm (MMOA). A good point set is adopted for population initialization and to ensure the homogeneity of the initial solution set and avoid prematurity; global atoms are acquired by non-dominated sorting and an external archiving strategy; a nonlinear search radius is designed to balance global and local searches and enhance the convergence precision of the algorithm. Levy Flight and Sine chaotic mapping are introduced to redesign the update formula for local atoms, increase the convergence rate of the algorithm, and boost the optimization efficiency. Comparative experiments are conducted between MMOA and NSGA-II, MOEA/D, NSWOA, MOGWO, NSMFO on multiobjective test functions and an engineering example. The simulation results show that MMOA has superior convergence and diversity to the other five algorithms.

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.

The notion of luck clustering has gained traction in recent years due to its potential influence on performance and decision-making across a range of domains. This study concentrates on the application of luck clustering in sports, with an emphasis on its consequences for performance metrics and strategic decision-making. We employ time series analysis to investigate the presence of luck clustering in sports data, such as win-loss records, scoring, and player rankings, while considering the role of the Principle of Luck Conservation in the observed clustering patterns. Our findings provide evidence of luck clustering in sports, implying that periods of high luck tend to be followed by more high luck events, and vice versa for low luck events. These insights carry significant implications for coaches, players, and teams, who can utilize the understanding of luck clustering to develop more effective strategies, manage resources efficiently, and ultimately enhance their performance. By enriching our comprehension of luck's nature and its effects on sports outcomes, this study contributes valuable knowledge for practitioners and researchers in sports analytics and performance management.

Definition: Most multicellular animals have a nervous system that is based on the following three components: 1) Sensory cells gather information and send it to processing units; 2) the processing units use the information to decide on what action to take; and 3) effector neurons activate the appropriate muscles. Due to the importance of making the right decisions, evolution made profound advances in the processing units. I shall review present knowledge regarding the evolution of neurological tools for making decisions, here referred to as strategies or algorithms. Consciousness can be understood as a particularly sophisticated strategy. It may have evolved to allow for the use of feelings as a ‘common currency’ to evaluate behavioral options. The advanced cognitive capacity of species such as humans further improved the usefulness of consciousness, yet in biological terms it does not seem to be an optimal, fitness-enhancing strategy. A model for the gradual evolution of consciousness is presented. There is a somewhat arbitrary cutoff as to which animals have consciousness but based on current information it seems reasonable to restrict the term to amniotes.

Methotrexate (MTX) is commonly used in intrathecal chemotherapy for patients with acute lymphocytic leukemia (ALL) to prevent central nervous system (CNS) involvement. However, the use of MTX-based chemotherapy can lead to rare yet severe complications, such as MTX-induced myelopathy. Here we report the case of MTX-induced myelopathy initially misdiagnosed as Guillain-Barre syndrome, leading to a delay in diagnosis and treatment. We present a case of a 39-year-old male with a history of B-cell acute lymphoblastic leukemia (B-ALL) who experienced bilateral foot paresthesia and progressive lower extremity weakness after intrathecal methotrexate (MTX) treatment. Initially suspected as Guillain-Barre syndrome (GBS) due to similar clinical features and nerve conduction studies. The patient received intravenous immunoglobulin (IVIG) treatment, but his condition worsened. A subsequent spine MRI revealed MTX-induced myelopathy. This case highlights the diagnostic challenge posed by the similarity in clinical presentation between MTX-induced myelopathy and GBS. Differentiating between these conditions is critical for appropriate management. Prompt recognition and treatment with folate metabolism compounds may mitigate neurological sequelae.

A new layout optimization method for offshore wind farms is proposed to minimize power deficits due to wake effect. The main engine of the algorithm is a gradient-descent method, in which an analogy is established between power deficit and repulsive electrostatic potential energy. To achieve a maximum dispersion in the initial solution, throughout the optimization process, new turbines are included in the centre of the concession area, and quickly expand outward looking for areas with less potential, in turn pushing the previous ones. When the optimization process ends, to avoid local maximums, it enters into a process of suppression of the set of locations that cause the greatest potential (power deficit). Then, a potential map of the entire area is created and a greedy algorithm includes new turbines to complete the layout with the final number of turbines. To speed up the search process and the creation of the potential map, two simplifications have been validated and added: the spatial distribution of velocities has been converted into its equivalent with point symmetry; and, for turbines affected by multiple wakes, the power deficit has been calculated by using a linear aggregation of powers, instead of the usual linear (or quadratic) aggregation of speeds. Owing to this type of aggregation, the process is accelerated in a factor of 90.

Strengthening the analysis and risk assessment of the pig epidemic will help to better prevent and mitigate epidemic risks and promote high-quality development of the pig industry. Based on the systematic grasp of the situation of live pig epidemics, a risk assessment index system was constructed, and the spatial and temporal variation characteristics of the risk of pig epidemics in China were explored by the entropy method. In recent years, the overall trend in pig epidemics in time first increased and then decreased, and in space, the trend in the acceleration of the spread across the country continued to weaken. China still faces challenges, such as many types and a wide range of diseases, large total livestock breeding and weak epidemic prevention and control capacity, and a large risk of introduction of foreign animal epidemics. The spatial and temporal variation in the pig epidemic risk was obvious; there was one high-risk area, two higher-risk areas and 10 medium-risk areas in recent years, and the epidemic risk was highest in Beijing, Hainan, Liaoning, Tibet and Zhejiang. However, there were significant differences in the regional distribution of the risk of pig epidemics in different years. To further build a secure “defense system” for the high-quality development of the pig industry, it is recommended to improve the monitoring and early warning system of pig epidemic risk, perfect the pig epidemic prevention and control system, and strengthen the regional collaboration mechanism of epidemic prevention and control.

A COVID-19 disease threatens the population and the economies of the countries significantly. Till now, this pandemic has affected 215 counties or territories. Unavailability of vaccine is the primary concern for the society. To avoid the spread of this disease, social isolation must be preserved and the inter and intra-population movement must be minimized. To reduce the possibility of transmission, the categorization of regions based on susceptibility to COVID-19 infection is a must.Due to the unavailability of a large amount of paper collection for this novel COVID-19 diseases, we used current literature available on a COVID-19 susceptibility of the diabetic patient, human reproductivity, hemodialysis patient’s, pregnant women and meteorological factors and geographical location. Countries in the cold region are more susceptible to the risk of COVID-19 transmission. There was no evidence of the spread of this disease from non-respiratory bodies. Diabetic patients and pregnant women were found to be more susceptible to COVID-19 infection. Anosmia was observed in the majority of the COVID-19 infected cases in European countries. No evidence indicates COVID-19's impact on the human reproductive system explicitly. No cases of vertical transmission of this disease have been observed until now. All the studies available till now is the small scale study. Correlation with something always does not mean causation. There are certain factor like pollution level, temperature Diurnal temperature range, geographical factor, humidity, pollution level, wind speed, population density, medical healthcare facilities social and political factor plays a critical role in transmitting the SARS-COV-2 virus. Besides the adverse effects, it has taught us to shed our selfish goals and to promote the welfare of all.

Background: The current review was conducted to determine the effectiveness of atenolol on all-cause mortality rate among Asian patients with chronic progressive diseases, mainly diabetes mellitus, primary hypertension, and coronary artery disease. Methods: We searched the COCHRANE, MEDLINE, TRIP, and EMBASE databases for published articles up to 31 March 2023. Studies that compared all-cause mortality rates among Asian patients who were on atenolol or other medications were included. Results: The review included 79603 Asian patients from three cohort studies. Out of the studied patients, 36046 were atenolol users and 43557 were non-atenolol users. The review revealed that atenolol users recorded lower all-cause mortality rates compared to non-users with a significant difference (OR= 0.57, CI= 0.44-0.75, P<0.001). The pooled estimate of the all-cause mortality rate was also lower among atenolol users (7.02%) compared to metoprolol tartrate users (13.15%) with a significant difference (OR= 0.50, CI= 0.47-0.53, p < 0.0001). Although the included studies were categorized as having a low risk of bias for most of the studied domains, significant heterogeneity was recorded across these studies (I2 =88%, P=0.001). Conclusion: This review found that atenolol when compared to a control or metoprolol tartrate, has a significant effect in reducing the all-cause mortality rate among Asian patients with chronic progressive disease.

Managers are often faced with the task of improving the management of the production 17process in order to maintain the sustainability of production efficiency in a highly competitive en-18vironment. The submitted contribution deals with the design of a system that will support them in 19the selection of progressive methods and technologies in order to improve the management of pro-20duction processes. Managers often follow new trends in this issue, but it is not easy to work on the 21knowledge that proven progressive technologies can bring them higher efficiency. The presented system is based on the use of knowledge of existing solutions of manufacturers of automotive components, where companies solve similar problems in production management. The mix of methods and technologies used in the management of production processes brings companies different results in the achieved efficiency. The proposed system for supporting the selection of the production management method and technology is designed from blocks of data collection, benchmarking of the performance of production processes of industrial enterprises, and further from data mining technology to obtain knowledge about the effect on efficiency from already implemented technologies. The last blocks help to examine the differences in the implementation of the same methods and technologies and allow to present the obtained results to the manager in the form of recommendations for choosing a suitable progressive method and technology.