This ethnographic study concentrates on the co-creation of experiential value between the tourist and tour guide in a single historic tourism site; Huntingdon Castle, Ireland. Built upon the principles of service dominant logic, the research explores how storytelling acts as an engagement platform and value enhancing strategic resource. In doing so, it impels the value co-creation journey and shapes the tourist's experience. Observation is coupled with qualitative interviews to capture the dual perspective of both guides and tourists. Findings exhibit the co-creation process through the performance of stories; how and when people derive pleasure (value); the influencing aspects of the environment or place; and guide/tourist perspectives on how they feel and think during the experience. The research contributes by taking a practical operational view of how co-creation occurs. It goes beyond the guide's perspective and exhibits the importance of co-creation of lived experience in the story enhanced tourism experience framework.
Enzymes are biological catalysts capable of speeding up biochemical reactions, providing a novel and eco-friendly alternative to chemical catalysts. Enzymes are widely employed in industrial food processing particularly the beverage industry. The main enzymes used in the beverages industries are hydrolytic and they are categorised into carbohydrases, proteases, and lipases. Due to increased awareness regarding sustainability and the extensive range of advantages enzymes provides to the beverage industry, such as alcoholic fermentation and enhanced organoleptic properties, more focus is directed toward immobilized and microbial-derived enzymes. These enzymes not only make processing more efficient but also reduce costs and waste generation. In this chapter, an introduction to the main processes involved in the beverage industry and the rationale for utilising enzymes in the industry are provided. In addition, the current enzymes used in both alcoholic and non-alcoholic beverages, the sustainability of enzymes, and recommended future work will be discussed.
Long chain polymers and aggregates are responsible for juice turbidity. Various hydrolysing enzymes have been tested to break down these chains and aggregates to improve juice clarity. The key enzymes for juice clarification are pectinases, cellulases, hemicellulases, amylases, and laccases. There are several other enzymes which are used by industry for juice clarification, such as tannase, naringinase, and xylanase. These enzymes are mainly found in higher plants and microbes. Enzymes of microbial origin are preferred to the enzymes of plants origin as they are cheap, efficient, easy to extract. Scaling-up of production of enzymes of microbial origin is considerably less challenging. Although these enzymes are more efficient in clearing up juices than the mechanical and chemical procedures, further optimisations of the factors affecting the enzymes’ performances and structural modifications of the enzymes are warranted.
Background The aetiology of ANCA-associated vasculitis (AAV) and triggers of relapse are poorly understood. Vitamin D (vitD) is an important immunomodulator, potentially responsible for the observed latitudinal differences between granulomatous and non-granulomatous AAV phenotypes. A narrow ultraviolet B spectrum induces vitD synthesis (vitD-UVB) via the skin. We hypothesised that prolonged periods of low ambient UVB (and by extension vitD deficiency) are associated with the granulomatous form of the disease and an increased risk of AAV relapse. Methods Patients with AAV recruited to the Irish Rare Kidney Disease (RKD) ( n = 439) and UKIVAS ( n = 1961) registries were studied. Exposure variables comprised latitude and measures of ambient vitD-UVB, including cumulative weighted UVB dose (CW-D-UVB), a well-validated vitD proxy. An n -of-1 study design was used to examine the relapse risk using only the RKD dataset. Multi-level models and logistic regression were used to examine the effect of predictors on AAV relapse risk, phenotype and serotype. Results Residential latitude was positively correlated (OR 1.41, 95% CI 1.14–1.74, p = 0.002) and average vitD-UVB negatively correlated (0.82, 0.70–0.99, p = 0.04) with relapse risk, with a stronger effect when restricting to winter measurements (0.71, 0.57–0.89, p = 0.002). However, these associations were not restricted to granulomatous phenotypes. We observed no clear relationship between latitude, vitD-UVB or CW-D-UVB and AAV phenotype or serotype. Conclusion Our findings suggest that low winter ambient UVB and prolonged vitD status contribute to AAV relapse risk across all phenotypes. However, the development of a granulomatous phenotype does not appear to be directly vitD-mediated. Further research is needed to determine whether sufficient vitD status would reduce relapse propensity in AAV.
The development of current and next generation high performance electronic devices has led to smaller components in more densely packed spaces. The increasing power levels have resulted in ever-increasing heat flux densities which necessitates the evolution of new liquid-based heat exchange technologies. Pulsating flow in single-phase cooling systems is viewed as a potential solution to the problems involving high heat flux densities. A review of published literature indicates a lack of time-resolved and space-resolved links between the hydrodynamic pulsating characteristics and associated heat transfer perturbations. The scope of this work involves the development of a validated three-dimensional conjugate heat transfer computational model to investigate hydrodynamically and thermally fully developed pulsating flows in a heated rectangular minichannel. Simulations were performed for a sinusoidal waveform with a fixed pulsation amplitude for varying pulsation frequencies in the range of 0.02 Hz to 25 Hz, corresponding to Womersley numbers in the range of 0.5 ≤Wo≤ 18.33. Low pulsation frequencies exhibited the well known parabolic profile for the fluctuating hydrodynamic and thermal parameters, i.e., velocity, wall shear, and wall temperature. As a result, the axial pressure gradient, velocity, and wall shear stress profiles were in phase and similar results were obtained for the oscillating wall and bulk fluid temperatures. For the inertia dominated high frequency flows, an increase in axial pressure gradient leads to a phase lag of π/2 when compared with the velocity and wall shear profiles. The shorter time period pulsations exhibit unique attributes in the form of flow reversal effects at local near wall regions. High near wall thermal gradients were observed as a result of stronger viscous effects due to a narrowing thermal boundary layer; consequently the transverse diffusion of heat was ineffective. A phase lag and a subsequent drop in the peak magnitudes existed between the oscillating bulk and wall temperatures for high frequency flows. Fluctuations in near wall heat flux profiles showed a dependency on the imposed pulsation frequencies. For the chosen pulsation profile and frequencies, the overall time averaged thermal performance indicates that pulsating flow performs worse than steady flow for a flow rate amplitude of 1. The highest thermal performance was achieved for Wo=5.1 while maintaining a low friction factor.
Introduction This paper introduces OS-WALK-EU, a new open-source walkability assessment tool developed specifically for urban neighbourhoods and using open-source spatial data. A free and open-source tool, OS-WALK-EU is accessible to the general public. It uses open data available worldwide and free online services to compute accessibility, while at the same time allowing users to integrate local datasets if available. Based on a review of existing measurement concepts, the paper adopts dimensions of walkability that were tested in European city environments and explains their conceptualization for software development. We invite the research community to collaboratively test, adopt and use the tool as part of the increasing need to monitor walkability as part of health-promoting urban development. Methods Tool development is based on spatial analysis methods to compute indicators for five dimensions of walkability: residential density, weighted proximities to amenities, pedestrian radius of activity, share of green and blue infrastructure, and slope. Sample uses in the cities of Dublin, Düsseldorf and Lisbon test the validity of input data and results, including scenarios for target groups like older people. Results Overall, application of the tool in Dublin, Düsseldorf and Lisbon shows conclusive results that conform to local knowledge. Shortcomings can be attributed to deficiencies in open source input data. Local administrative data, if available, is suitable to improve results. Conclusions OS-WALK-EU is the first software tool that allows free and open walkability assessments with pedestrian routing capacities for ‘proximity to facilities’ calculations. Large scale implementation for 33 German city regions in an online application shows the value of comparative assessments of walkable neighbourhoods between urban and suburban neighbourhoods. Such assessments are important to monitor progress in a mobility transition towards improved walkability and public health.
In this paper, a new isolated hybrid system is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing a small remote area with a peak load of 420 kW. The major configuration of this hybrid system is Photovoltaic (PV) modules, Biomass gasifier (BG), Electrolyzer units, Hydrogen Tank units (HT), and Fuel Cell (FC) system. A recent optimization algorithm, namely Mayfly Optimization Algorithm (MOA) is utilized to ensure that all load demand is met at the lowest energy cost (EC) and minimize the greenhouse gas (GHG) emissions of the proposed system. The MOA is selected as it collects the main merits of swarm intelligence and evolutionary algorithms; hence it has good convergence characteristics. To ensure the superiority of the selected MOA, the obtained results are compared with other well-known optimization algorithms, namely Sooty Tern Optimization Algorithm (STOA), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA). The results reveal that the suggested MOA achieves the best system design, achieving a stable convergence characteristic after 44 iterations. MOA yielded the best EC with 0.2106533 $/kWh, the net present cost (NPC) with 6,170,134 $, the loss of power supply probability (LPSP) with 0.05993%, and GHG with 792.534 t/y.
The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. To overcome the TEG system behavior and shortcomings, A novel control technology for the TEG system is proposed, which utilizes the improved generalized regression neural network and fitness dependent optimization (GRNNFDO) to track the GMPP under dynamic operating conditions. Conventional TEG system control techniques are not likely to trace true GMPP. Our novel GRNNFDO can trace the true GMPP for NUTD and under varying temperature conditions. In this article, some major contributions in the area of the TEG systems are investigated by solving the issues such as NUTD global maxima tracking, low efficiency of TEG module due to mismatch, and oscillations around optimum point. The results of GRNNFDO are compared with the Cuckoo-search algorithm (CSA), and grasshopper optimization (GHO) algorithm and particle swarm optimization (PSO) algorithm. Results of GRNNFDO are verified with experiments and authenticated with MATLAB/SIMULINK. The proposed GRNNFDO control technique generates up to 7% more energy than PSO and 60% fast-tracking than metaheuristic algorithms.
Nowadays, load serving entities require more active participation from consumers. In this context, demand response programs and home energy management systems play a crucial role in achieving multiple goals such as peak clipping. However, the adoption of demand response initiatives typically has a negative impact on the monetary expenditures of the users. This way, a demand response program should be as effective as possible to make the different goals more easily achievable without compromising the financial requirements of the users. This paper develops a home energy management system that incorporates three novel effective demand response strategies. The effectiveness of the adopted demand response strategies is checked through extensive simulations in a benchmark prosumer environment. To this end, a novel scenario-based approach is developed in order to manage uncertainties. The introduced strategies are compared with other well-known demand response mechanisms. To that end, a novel comparative index, which serves to evaluate the compromise between demand response achievements and energy bills, is introduced. Results obtained demonstrate that the developed strategies are more effective than other approaches. In fact, through the use of the proposed mechanisms, different indicators can be improved until ∼70%, while the electricity bill is only scarcely increased (∼0.11€). Other relevant aspects like the influence of the storage capacity and computational performance of the introduced optimization framework are also analysed.
Most of the loads used in our day-to-day life are non-linear in nature. To investigate the performance of various non-linear loads, a laboratory setup on different load combinations such as light, fan and drive system in this work. The unbalance in supply voltages, supply current and frequency, reduction in power factor and total harmonic distortion for current and voltages are monitored through this lab setup and also the results obtained from these systems are discussed in this paper. To check the nature of voltage, current, reactive power and power factor in real-time systems, a turrent punch machine incorporated with several single-phase AC servo motors is considered. The variations in the parameters are recorded using the fluke analyzer. Finally, it is observed that current harmonics at the source side are dominant in both the laboratory setup and the real-time system. Next, shunt active power filters are applied to mitigate the current harmonics. The simulation for the system with compensations is conducted in the MATLAB platform and the hardware implementation validates the same.
This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.
The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
The actual energetic situation has several challenges such as pollution, the rarefaction of fossil fuel and the dangers of nuclear. Renewable sources are proposed as a solution and suggested, such as a cost-effectiveness system. The paper deals with the problem of feeding a domestic load with electricity which should respect the ecologies factors, so this work is a design problem of the hybrid renewable energy systems; PV/biomass, PV/diesel/battery, PV/wind/diesel/battery, and wind/diesel/battery to choose the best one of them which feed the load with the lowest cost. The study’s goal is to design a microgrid system by the minimization of the total investment cost with respect to the required technical factor, the minimum allowed renewable energy fraction, and the minimum allowed availability factor. The methodology flowed utilizes frameworks based on a recent algorithm called Movable damped wave algorithm (MDVP). The proposed optimization algorithm is compared with other algorithms to prove its efficacy which are; the artificial electric field algorithm (AEFA), harris hawks optimization (HHO), and the grey wolf optimizer (GWO). The project case study is investigated in Al-Majmaah, Saudi Arabia. The contribution of this work is implementing a recent algorithm that proves its efficacy and finding the best microgrid configuration following many investigations and comparisons. The results confirm that the MDVP is better compared to the other algorithms, its computational time is fast, and its convergence is good; otherwise, the PV/biomass is considered the best configuration in the area of study with a size of 237.698 m² from PV panel and 954.097 t/year of biomass, which obtained the best Net Present Cost (NPC) of $299504, and a cost of energy (LCOE) assumed as $0.228/kW. A sensitivity analysis is applied to prove the effect of size variation on project factors. The simple observation, by the way, is that any change in the PV size affects the output factors.
This paper discusses some examples where human performance and/or human error prediction was achieved by using a modified version of the Rasch model (1980), where the probability of a specified outcome is modelled as a logistic function of the difference between the person capacity and item difficulty. The model needs to be modified to take into account an outcome that may not be dichotomous and to take into account the interaction between two macro factors: (a) Task complexity: that summarises all factors contributing to physical and mental workload requirements for execution of a given operative task & (b) Human capability: that considered the skills, training and experience of the people facing the tasks, representing a synthesis of their physical and cognitive abilities to verify whether or not they are matching the task requirements. Task complexity can be evaluated as a mathematical construct considering the compound effects of Mental Workload Demands and Physical Workload Demands associated to an operator task. Similarly, operator capability can be estimated on the basis of the operators’ set of cognitive capabilities and physical conditions. The examples chosen for the application of the model were quite different: one is a set of assembly workstation in large computer manufacturing company and the other a set of workstations in the automotive sector. This paper presents and discusses the modelling hypothesis, the interim field data collection, results and possible future direction of the studies.
A new avenue of fractional calculus applications has emerged that investigates the design of fractional gradient based novel iterative methods for analyzing fractals and nonlinear dynamics in solving engineering and applied sciences problems. The most discussed algorithm in this regard is fractional least mean square (FLMS) algorithm. This study presents an auxiliary model based normalized variable initial value FLMS (AM-NVIV-FLMS) algorithm for input nonlinear output error (INOE) system identification. First, NVIV-FLMS is presented to automatically tune the learning rate parameter of VIV-FLMS and then the AM-NVIV-FLMS is introduced by incorporating the auxiliary model idea that replaces the unknown values of the information vector with the output of auxiliary model. The proposed AM-NVIV-FLMS scheme is accurate, convergent, robust and reliable for INOE system identification. Simulation results validate the significance and efficacy of the proposed scheme.
Consistent with the notion that strategies are made up of an amalgam of strategies and, as a result, must be implemented with this in mind, the two chapters in this section explain the role of a financial strategy in markedly different ways. The first chapter in this section emphasises the importance of managing and transforming capital effectively while the second chapter defines how organisations operationalise their strategic priorities using strategic management accounting principles and analysis techniques. Both chapters clarify how the contemporary organisation can achieve high levels of strategic alignment by implementing an effective financial strategy.
It would be easy to assume that non-market strategiesNon-market strategies are only necessary for organisations operating primarily in the non-market environment. However, this would be wrong. All organisations are impacted by the non-market environment to some extent. The non-market and market environments are subject to continual institutional change too. Much of this change depends on individual stakeholders’ objectives. These may be of a personal nature or something that the stakeholder wants to achieve on behalf of an institution. Therefore, as the two chapters in this section each distinctly conclude, organisations with integrated non-market and market strategies are likely to be higher performing than those without integrated strategies.
Since first identified in 1879, plasma, the fourth state of matter, has been developed and utilised in many fields. Nonthermal atmospheric plasma, also known as cold plasma, can be applied to liquids, where plasma reactive species such as reactive Oxygen and Nitrogen species and their effects can be retained and mediated through plasma-activated liquids (PAL). In the medical field, PAL is considered promising for wound treatment, sterilisation and cancer therapy due to its rich and relatively long-lived reactive species components. This study sought to identify any potential antagonistic effect between antioxidative intracellularly accumulated platinum nanoparticles (PtNPs) and PAL. We found that PAL can significantly reduce the viability of glioblastoma U-251MG cells. This did not involve measurable ROS influx but instead lead to lipid damage on the plasma membrane of cells exposed to PAL. Although the intracellular antioxidative PtNPs showed no protective effect against PAL, this study contributes to further understanding of principle cell killing routes of PAL and discovery of potential PAL-related therapy and methods to inhibit side effects.
Market demand for “clean and green” food products is increasing, and so there is growing opportunity for the seaweed aquaculture industry to take a position as a key food producer in this area. In this study, in order to investigate the impact of dry fractionation on seaweed protein qualities, dried and milled seaweed powder from three seaweed species was sieved into 6 fractions (F1 to F6) of different particle size from >710 µm to <50 µm. True protein, total protein and amino acid profiles were analyzed to evaluate the protein content and quality of three brown seaweed species commercially harvested in Ireland; Alaria esculenta, Laminaria digitata and Saccharina latissima. In general, A. esculenta had the highest protein content, followed by S. latissima and then L. digitata (4.15 ± 0.12 g/100 g, 2.28 ± 0.1 g/100 g and 1.73 ± 0.01 g/100 g, respectively). Fractionation had a significant impact (p < .01) on protein content, essential amino acid content (p < .05) and non-essential amino acid content (p < .01) across six fractions of seaweed powder within species. F6 (<50 was the fraction that contained the highest protein and amino acid content in both A. esculenta and S. latissima. F1 (>710 µm) contained the highest protein and amino acid content in L. digitata. Glutamic acid was the most prevalent amino acid in A. esculenta and L. digitata (55.34 mg/g and 23.78 mg/g), while aspartic acid was the most prevalent in S. latissima (19.41 mg/g). This information is valuable to both researchers and seaweed producers who can use particle size separation as a simple method to create value-added products using their green biomass for applications across multiple markets.
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