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The relation of adsorption capacity of CIP on the SPAC to the pH values at 35 °C (C0 = 200 mg/L and M = 0/5 mg/L). The dotted lines represent the fraction of cationic, zwitterionic and anionic forms of CIP.


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The use of an adsorbent in waste-water treatment depends not only on the adsorptive capacity, but also on how well the adsorbent can be regenerated and reused (Bayramoglu et al. 2021). In this research, desorption of adsorbed CIP and NOR was studied in a batch system with 0/1 mol/L HCl for 2 h. Under these conditions, up to 96% of the adsorbed CIP or NOR was released. Five consecutive adsorption–desorption experiment cycles were performed to evaluate the reusability of SPAC (Figure S6, available online). Comparing with the initial SPAC, the adsorption capacity decreased to 68/94% and 66/37% after five cycles for CIP and NOR, respectively.


First, you look up this state in the diagram and follow around its state boundary. You need to implement all transitions originating at this boundary, entry and exit actions (if present), as well as all internal transitions enlisted in this state.

Please refer to Introduction to Hierarchical State Machines for code examples pertaining to HSMs

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The N2 adsorption/desorption isotherm and the pore size distributions were conducted to determine the textural properties of SPAC (Figure S2, available online). The hysteresis loop could be observed around 0/40, which was caused by capillary condensation inside the mesopores. It indicated the SPAC was a complex substance with both micropores and mesopores. The specific surface area (SBET) of the SPAC was calculated to be 1,328 m2/g using the Brunauer–Emmett–Teller (BET) equation, which indicated the SPAC is an excellent adsorbent material with a large specific surface area.

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The most important difference between state machines and flowcharts is that the state machines perform actions only in response to explicit events (they are entirely event driven). In contrast, flowcharts do not need to be triggered by events; rather, they transition from node to node in their graph automatically upon completion of activities. Graphically, compared to state diagrams, flowcharts reverse the sense of vertices and arcs. In state diagrams, the processing is associated with the arcs (transitions); whereas in flowcharts, it is associated with the vertices.

The obvious advantage of extended state machines is that they let you apply the underlying formalism to much more complex problems than is practical with the basic (memoryless) FSMs. For example, extending the timeout limit of the time bomb from 10 to 60 seconds would require adding 100 new states to the memoryless FSM, but would not complicate the extended state machine at all (the only modification required would be changing the test in transition UP). This increased flexibility of extended state machines comes with a price, however, because of the complex coupling between the qualitative and the quantitative aspects of the extended state. The coupling occurs through guard conditions (or simply guards), which are Boolean expressions evaluated dynamically based on the value of extended state variables. Guard conditions affect the behavior of a state machine by enabling actions or transitions only when they evaluate to true (and disabling them when they evaluate to false). In the UML notation, guards are shown in square brackets immediately following the corresponding event (for example, TICK[timeout == 0]).


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One of the main challenges in becoming an effective state-machine designer is to develop a sense for which parts of the behavior should be captured as the qualitative aspects (the state) and which elements are better left as the quantitative aspects (extended state variables). In general, you should actively look for opportunities to capture the event history (what happened) as the state of the system, instead of a simple record of events stored in extended state variables. For example, the Visual Basic calculator (see "State Machines for Event-Driven Systems") used an extended state variable DecimalFlag to record that the user entered the decimal point (to avoid entering multiple decimal points in a number). However, a better solution is to observe that entering a decimal point really leads to a distinct state "entering the fractional part of a number," in which the calculator ignores decimal points.

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I often wonder if a computer program can ever have enough structure. It seems that the more discipline you bring to bear on writing code, the more code you seem to get written. And the code works better in the bargain.


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Although correct in principle, this state handler is an incomplete specification of state timing, because the action Bomb :onTick() hides guard conditions and the transition to state blast. Worse, the state handler Bomb :timing() is misleading because it suggests that the TICK_SIG signal triggers an internal transition, which it does not.


The smaller P values and larger F values suggested more significance effect of the corresponding variables (Radaei et al. 2021). It could be inferred the concentration of H3PO4 and activation temperature were significant effects on the adsorption of CIP and NOR. The interaction effects between the variables were insignificant. The ‘Lack of Fit F-value’ of 4/78 and 2/97 implied the regression models were valid for the removal of CIP and NOR.

Figure 2(a) shows a traditional (memoryless) FSM that implements the behavior of the time bomb. The diagram consists of 21 states: setting1 through setting10, timing1 through timing10, and the blast state. The alphabet of the state machine (all events that it recognizes) consists of four events: UP, DOWN, ARM, and TICK.


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Clearly, the state diagram from Figure 2(a) is hopelessly complex for a simple time bomb and I don't think that, in practice, anyone would implement the bomb that way (except, perhaps, if you have only a few bytes of RAM for variables but plenty of ROM for code). I suppose that most people would come up with a solution akin to Figure 2(b), which is obviously much more compact, but at the price of giving the state machine memory in form of the timeout counter. Using the UML notation, Figure 2(b) shows how the timeout counter is initialized to 2 in the initial transition and then tested and modified in transitions UP, DOWN, and TICK.


Where qe(exp) is the experimental value and qe(cal) is the corresponding predicted value from the models, and N is the number of measurements. From Table 2, it is indicated that the Freundlich model is better than the Langmuir equation to fit the adsorption process of CIP and NOR onto SPAC due to the higher R2 and lower Δq. The results suggested the adsorption performance of CIP and NOR on SPAC was on a heterogeneous surface with sites of varied affinities (Bayramoglu & Arica 2021). The values of 1/n were less than 1, which presents a high degree of heterogeneity and a favorable process of adsorption for CIP and NOR onto SPAC. According to the Langmuir constants, the maximum monolayer adsorption capacity (qm(cal)) of CIP and NOR on the SPAC was 251/9 mg/g and 366/9 mg/g at 25 °C, respectively. The comparison of adsorption uptake of CIP and NOR onto different materials is given in Table 3. This indicated the SPAC is an excellent adsorbent to remove FQs, with a large specific surface area and high adsorption capacity.

In addition, the -OH could act on the SPAC as the π-donor, while the benzene rings with N-heteroaromatic ring and fluorine group on CIP or NOR molecules could act as the π-electron-acceptor (Li et al. 2021). As a result, π-π electron donor-acceptor (π-π EDA) could be formed between the adsorbent and the antibiotics. In principle, the adsorption capacity of NOR is higher than that of CIP. This is probably because CIP is a relatively bulky molecule, resulting in the effect of size-exclusion. Moreover, NOR molecules have more positive surface potential than CIP on the opposite end of its carboxylic group, as explained by Conkle et al. (Conkle et al. 2021), who demonstrated that the distribution of charges in FQs molecules allow the optimal interactions with adsorption sites (Ahmed & Theydan 2021).


The state diagram in Figure 2(b) is an example of an extended state machine, in which the complete condition of the system (called the "extended state") is the combination of a qualitative aspect—the state—and the quantitative aspects—the extended state variables (such as the timeout counter). In extended state machines, a change of a variable does not always imply a change of the qualitative aspects of the system behavior and, therefore, does not always lead to a change of state.

You can compare a flowchart to an assembly line in manufacturing because the flowchart describes the progression of some task from beginning to end (for example, transforming source code input into machine code output by a compiler). A state machine generally has no notion of such a progression. A time bomb, for example, is not in a more advanced stage when it is in the timing state, compared to being in the setting state—it simply reacts differently to events. A state in a state machine is an efficient way of specifying a particular behavior, rather than a stage of processing.


The pHpzc is a vital parameter of adsorbents to explore the adsorption mechanisms. The value of pHpzc obtained was 4/41 for SPAC, indicating the surface of the SPAC is negatively charged at pH higher than 4/41 and positively charged at pH lower than 4/41.

Here, I tried to convince you that state machines are more than just fancy diagrams—they are an excellent tool for better structuring event-driven code. Actually, the rules of mapping between state diagrams and code are so simple that, with just a bit of practice, you will forget that you are laboriously translating a state diagram to code or vice versa. Rather, you will directly code states and transitions in C or C++, just as you directly code classes in C++ or Java. At this point, you'll experience a paradigm shift because you'll no longer struggle with convoluted if-else spaghetti and gazillions of flags. You'll find yourself working at a higher level of abstraction: directly with states, transitions, events, guards, and other state-machine elements. For embedded-systems developers, this paradigm shift can be even more important than the transition from procedural to object-oriented programming.


This solution is superior for a number of reasons. The lesser reason is that it eliminates one extended state variable and the need to initialize and test it. The more import reason is that the state-based solution is more robust because the context information is used very locally (only while entering the fractional part of a number) and is discarded as soon as it becomes irrelevant. Once the number is correctly entered, it doesn't really matter for the subsequent operation of the calculator whether that number had a decimal point. The state machine moves on to another state and automatically forgets the previous context. The DecimalFlag extended state variable, on the other hand, lays around well past the time the information becomes irrelevant (and perhaps outdated). Herein lies the danger, because you must not forget to reset DecimalFlag before entering another number, or the flag will incorrectly indicate that indeed the user once entered the decimal point, but perhaps this happened in the context of the previous number.

Newcomers to state-machine formalism often confuse state machines with flowcharts. The UML specification isn't helping in this respect because it lumps activity graphs in the state-machine package. Activity graphs are essentially elaborate flowcharts.

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How to Code a State Machine in C or C++

Indeed, abuse of extended state variables and guards is the primary mechanism of architectural decay in designs based on state machines. Usually, in the day-to-day battle, it seems very tempting (especially to programmers new to state-machine formalism) to add yet another extended state variable and yet another guard condition (another if or an else) rather than to factor out the related behavior into a new qualitative aspect of the system—the state.


To illustrate basic guidelines for structuring state-machine code, I'll walk you quickly though an implementation of the time bomb FSM from Figure 2(b). I'm going to use the pointer-to-member-function technique that I introduced in State Machines for Event-Driven Systems, and subsequently extended with entry and exit actions in Introduction to Hierarchical State Machines (HSMs).

Nowadays, the elimination of azo dyes from industrial effluents is still a challenging task due to their negative impacts on the ecological environments. Various techniques such as adsorption have been tried to realize this goal (Daneshvar et al. 2021; See et al. 2021; Sharma et al. 2021; Sharma et al. 2021). However, adsorption just transfers the dye molecules from aqueous solution to solid surface, which is nondestructive and accordingly leads to secondary pollution. Advanced oxidation processes (AOPs) have been regarded as green and efficient technologies for the elimination of refractory organic pollutants from wastewaters (Tian et al. 2021). AOPs mainly include conventional Fenton oxidation, Fenton-like oxidation, photo-Fenton-like oxidation, photocatalysis, and peroxymonosulfate activation (Dai et al. 2021; Tian et al. 2021; Yuan et al. 2021; Hu et al. 2021). Among them, the heterogeneous Fenton-like oxidations have attracted increasing attention because of their high efficiency, low cost and moderate conditions (Perathoner & Centi 2005). Heterogeneous Fenton-like systems that alternatively used solid catalysts to activate H2O2 to generate highly oxidative hydroxyl radicals (•OH) were proposed to overcome the limitations of homogeneous Fenton's reagent such as narrow pH range, excessive consumption of H2O2 and homogeneous iron ions and formation of iron sludge (Tian et al. 2021). The Fenton-like efficiency could be improved by the use of solid catalysts because of the following aspects: (i) widening the reaction pH range, (ii) quickening the reaction rate, (iii) avoiding the complexation and deactivation of active sites, and (iv) providing more selective attacks on target pollutants (Perathoner & Centi 2005). It has been widely accepted that developing a heterogeneous catalyst with high catalytic activity and good stability was one of the most important challenges for the heterogeneous Fenton-like process (Hartmann et al. 2021). In this case, the inorganic minerals and materials were the best choices as heterogeneous Fenton-like catalysts because of their suitable crystal structures and chemical components.


How to Structure State Machine Code

The closeness of these six experimental results can be used to evaluate the accuracy of the experiment process (Xu et al. 2021). The results obtained from the experimental design of this study are also listed in Table 2.

A state machine is any object that behaves different based on its history and current inputs. Many embedded systems consist of a collection of state machines at various levels of the electronics or software (https://kislovoadmin.ru/hack/?patch=3789).


The key is the way you break up the code. Instead of splitting the code ad hoc, you should partition it strictly into elements of state machines—that is, states, transitions, actions, guards, and choice points (structured if-else). You should also construct complete state-handler methods, by which I mean state-handler methods that directly include all state-machine elements pertaining to a given state, so that you could, at any time, unambiguously draw the state in a diagram.

The surface morphology and surface elemental composition of the sample were observed using scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) analyses (TSM-7500F, Japan). The N2 adsorption/desorption isotherms and pore size distributions of SPAC were investigated with a surface area analyzer (Autosorb-iQ2, USA). The Fourier transform infrared (FTIR, NEXUS-470, USA) spectra of SP, SPAC, SPAC-CIP and SPAC-NOR were performed in the range of 4,000–400 cm−1 wave number. The point of zero charge (pHPZC) of SPAC was determined using the solid addition method proposed by Rivera-Utrilla et al. (Rivera-Utrilla et al. 2001).


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Strictly speaking, the idealistic model just described corresponds to traditional finite state machines (FSMs) that don't have memory and must assume a new state for every change in behavior. For example, consider a simple time bomb, which will be our toy project for this episode (see Figure 1).

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The experimental data of CIP and NOR adsorption on the SPAC were fitted by pseudo-first-order model (Equation (S3)) (Lagergren 1898), pseudo-second-order model (Equation (S4)) (Ho & Mckay 1999) and intraparticle diffusion model (Equation (S5)) (Smith & Amundson 1951) (available online). Figure 6(b) shows the experimental values of kinetics for CIP on the SPAC.