VenlafaxineVenlafaxine, sold under the brand name Effexor among others, is an antidepressant medication of the serotonin-norepinephrine reuptake inhibitor (SNRI) class. It is used to treat major depressive disorder, generalized anxiety disorder, panic disorder, and social anxiety disorder. Studies have shown that Venlafaxine improves quality of life. It may also be used for chronic pain. It is taken by mouth. It is also available as the salt venlafaxine besylate in an extended-release formulation (Venbysi XR).
AntidepressantAntidepressants are a class of medications used to treat major depressive disorder, anxiety disorders, chronic pain, and addiction. Common side effects of antidepressants include dry mouth, weight gain, dizziness, headaches, sexual dysfunction, and emotional blunting. There is an increased risk of suicidal thinking and behavior when taken by children, adolescents, and young adults. Discontinuation syndrome, which resembles recurrent depression in the case of the SSRI class, may occur after stopping the intake of any antidepressant, the effects of which may be permanent and irreversible.
Antidepressant discontinuation syndromeAntidepressant discontinuation syndrome, also called antidepressant withdrawal syndrome, is a condition that can occur following the interruption, reduction, or discontinuation of antidepressant medication following its continuous use of at least a month. The symptoms may include flu-like symptoms, trouble sleeping, nausea, poor balance, sensory changes, akathisia, intrusive thoughts, depersonalization and derealization, mania, anxiety, and depression. The problem usually begins within three days and may last for several weeks or months.
Response surface methodologyIn statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The method was introduced by George E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process.
Sewage treatmentSewage treatment (or domestic wastewater treatment, municipal wastewater treatment) is a type of wastewater treatment which aims to remove contaminants from sewage to produce an effluent that is suitable to discharge to the surrounding environment or an intended reuse application, thereby preventing water pollution from raw sewage discharges. Sewage contains wastewater from households and businesses and possibly pre-treated industrial wastewater. There are a high number of sewage treatment processes to choose from.
Selective serotonin reuptake inhibitorSelective serotonin reuptake inhibitors (SSRIs) are a class of drugs that are typically used as antidepressants in the treatment of major depressive disorder, anxiety disorders, and other psychological conditions. SSRIs increase the extracellular level of the neurotransmitter serotonin by limiting its reabsorption (reuptake) into the presynaptic cell. They have varying degrees of selectivity for the other monoamine transporters, with pure SSRIs having strong affinity for the serotonin transporter and only weak affinity for the norepinephrine and dopamine transporters.
Serotonin–norepinephrine reuptake inhibitorSerotonin–norepinephrine reuptake inhibitors (SNRIs) are a class of antidepressant medications used to treat major depressive disorder (MDD), anxiety disorders, obsessive–compulsive disorder (OCD), social phobia, attention-deficit hyperactivity disorder (ADHD), chronic neuropathic pain, fibromyalgia syndrome (FMS), and menopausal symptoms. SNRIs are monoamine reuptake inhibitors; specifically, they inhibit the reuptake of serotonin and norepinephrine. These neurotransmitters are thought to play an important role in mood regulation.
Secondary treatmentSecondary treatment (mostly biological wastewater treatment) is the removal of biodegradable organic matter (in solution or suspension) from sewage or similar kinds of wastewater. The aim is to achieve a certain degree of effluent quality in a sewage treatment plant suitable for the intended disposal or reuse option. A "primary treatment" step often precedes secondary treatment, whereby physical phase separation is used to remove settleable solids.
Water treatmentWater treatment is any process that improves the quality of water to make it appropriate for a specific end-use. The end use may be drinking, industrial water supply, irrigation, river flow maintenance, water recreation or many other uses, including being safely returned to the environment. Water treatment removes contaminants and undesirable components, or reduces their concentration so that the water becomes fit for its desired end-use. This treatment is crucial to human health and allows humans to benefit from both drinking and irrigation use.
Optimal designIn the design of experiments, optimal designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design.
Industrial wastewater treatmentIndustrial wastewater treatment describes the processes used for treating wastewater that is produced by industries as an undesirable by-product. After treatment, the treated industrial wastewater (or effluent) may be reused or released to a sanitary sewer or to a surface water in the environment. Some industrial facilities generate wastewater that can be treated in sewage treatment plants.
Factorial experimentIn statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.
PHIn chemistry, pH (piːˈeɪtʃ ), also referred to as acidity, historically denotes "potential of hydrogen" (or "power of hydrogen"). It is a scale used to specify the acidity or basicity of an aqueous solution. Acidic solutions (solutions with higher concentrations of hydrogen () ions) are measured to have lower pH values than basic or alkaline solutions. The pH scale is logarithmic and inversely indicates the activity of hydrogen ions in the solution. where [H+] is the equilibrium molar concentration (mol/L) of H+ in the solution.
Dependent and independent variablesDependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.
Fractional factorial designIn statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.
Glossary of experimental designA glossary of terms used in experimental research. Statistics Experimental design Estimation theory Alias: When the estimate of an effect also includes the influence of one or more other effects (usually high order interactions) the effects are said to be aliased (see confounding). For example, if the estimate of effect D in a four factor experiment actually estimates (D + ABC), then the main effect D is aliased with the 3-way interaction ABC. Note: This causes no difficulty when the higher order interaction is either non-existent or insignificant.
Errors-in-variables modelsIn statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples.
Continuous or discrete variableIn mathematics and statistics, a quantitative variable may be continuous or discrete if they are typically obtained by measuring or counting, respectively. If it can take on two particular real values such that it can also take on all real values between them (even values that are arbitrarily close together), the variable is continuous in that interval. If it can take on a value such that there is a non-infinitesimal gap on each side of it containing no values that the variable can take on, then it is discrete around that value.
Groundwater pollutionGroundwater pollution (also called groundwater contamination) occurs when pollutants are released to the ground and make their way into groundwater. This type of water pollution can also occur naturally due to the presence of a minor and unwanted constituent, contaminant, or impurity in the groundwater, in which case it is more likely referred to as contamination rather than pollution.
Omitted-variable biasIn statistics, omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to those that were included. More specifically, OVB is the bias that appears in the estimates of parameters in a regression analysis, when the assumed specification is incorrect in that it omits an independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables.
