90s Computer Game Fish

[Othon Sdcd]

Thisis a mineral oil submerged computer. Mineral oil is a liquid that looks and behaves like water but does not conduct electricity, hence why it is safe to run electronics inside it. The mineral oil also aids to control the temperature of the entire system, by soaking up and removing heat away from hot components like the CPU.

The previous build was done many years ago when I just moved out into my own apartment. Today, I am wiser, having learned many new things, and made many new friends with laser cutters! The frame of the old computer was made with polycarbonate sheets cut by hand, the new frame is made with laser cut ABS sheets.

The new computer also required less soldering done to the PSU. I decided to minimize the soldering since the new bigger tank can fit the original cables that are attached to the PSU. The only solder was done to safely ground a few points on the PSU.

The old computer was built using a 5 gallon acrylic fish tank. Unknown to me, a small crack developed in the back of the tank, and oil was leaking out at a very very slow rate, but enough to require some cleaning of the desk once in a while.

The crack probably happened because of some poor planning of the motherboard mounting plate. While cleaning the oil, I attempted to move the tank, and it caused a much more catastrophic crack where all 5 gallons of oil broke out like a burst dam.

The old radiators are not worth salvaging, the screws that was provided were sheet metal screws and they were almost impossible to remove. The new computer has a brand new radiator, just a single one with 3x 120mm fans, and a filter.

All the PVC coated wires and the vinyl tubing on the old computer became super stiff. I suspect this also contributed to the bubble tube leaking. Having learnt this, the new computer uses silicone tubing everywhere as opposed to vinyl.

I have (for a few years) used the game site big fish games. And all of a sudden about 2 months ago I can no longer install the games onto my pc I receive a message that is asking for a name and password. the message is coming from my computer not from the big fish game site. I have attached a screenshot so that you can see exactly what I keep getting.

I do not have any passwords or anything set up on my pc. I have run your malware software ( it found 2 items from game vance) I removed them successfully. And my anti virus software (Avira) didn't find anything. So I don't know why this is happening.

The site is secure.

The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Zebrafish has been in the forefront of developmental biology and genetics, but only recently has interest in their behavior increased. Zebrafish are small and prolific, which lends this species to high throughput screening applications. A typical feature of zebrafish is its propensity to aggregate in groups, a behavior known as shoaling. Thus, zebrafish has been proposed as a possible model organism appropriate for the analysis of the genetics of vertebrate social behavior. However, shoaling behavior is not well characterized in zebrafish. Here, using a recently developed software application, we first investigate how zebrafish respond to conspecific and heterospecific fish species that differ in coloration and/or shoaling tendencies. We found that zebrafish shoaled with their own species but not with two heterospecific species, one of which was a shoaling the other a non-shoaling species. In addition, we have started the analysis of visual stimuli that zebrafish may utilize to determine whether to shoal with a fish or not. We systematically modified the color, the location, the pattern, and the body shape of computer animated zebrafish images and presented them to experimental zebrafish. The subjects responded differentially to some of these stimuli showing preference for yellow and avoidance of elongated zebrafish images. Our results suggest that computerized stimulus presentation and automated behavioral quantification of zebrafish responses are feasible, which in turn implies that high throughput forward genetic mutation or drug screening will be possible in the analysis of social behavior with this model organism.

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Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.

The trend toward increased automation in agriculture is significant1 and the aquaculture sector is not exception. The development of new methods for machine vision and digital cameras enables the automation of several asks under the non-trivial conditions of fish cultivation2. The main idea of the automatized aquaculture concept is implementation of precision fish farming3 (The automation of aquaculture production improves the control, monitoring and documentation of the biological process of fish growth. Based on automatically extracted knowledge, the farmers can increase the profits by controlling diseases and monitor fish welfare and fish growth. Early disease detection and the prediction of outbreaks can safeguar the livestock of fish4. One of the critical components of automation is individual fish identification5 (Yusup et al., 2020).

The standard approach to the identification of individual fish of the same specie is tagging6. There are several disadvantages and limitations of this invasive approach: the high mortality after injection in specific cases7,8 (Bolland et all., 2009; McMahon et al., 1996), stress caused the fish by the application of the invasive approach, need for the fish to be caught for identification, time-consuming nature of the method9 (Whitfield et al., 2004), and limited fish size. The possibility of the non-invasive identification of individuals becomes an alternative for fish tagging10 and address all the previously listed disadvantages.

Today, photo- or video-based identification systems are used in agriculture11 and wildlife for ensuring population welfare and estimating the size of the population12,13. All systems use the visible pattern of the animal for the identification of the species or individuals of the same species. The identification principle is based on human biometric identification as documentefor cow iris-based identification14. Similar issues of aging15 are therefore recoginized in animal identification.

In the aquaculture sector, machine vision is usually used to identify fish species16. Several studies17,18,19,20,21,22,23, exist on the non-invasive individual identification of fish using images but only few of them use machine vision. The majority of the studies use human experts for identification based on the fish images20,22. These studies proved that fish appearance based individual identification is feasible for the carp (Ciprinus carpio) (15 fish, 95.76% accuracy)20 and catshark (Scyliorhinus canicular) (25 fish, 99.6% accuracy)22 in the short-term. A long-term study of wild populations of cutthroat trout21, in which datasets from 1997 and 1999 were compared, showed that two adult fish can be identified using the skin spots after two years. The stability of the pattern was also proved by Stien18. They manually labeled the dots on salmon and performed the identification of 25 fish for ten months. The identification accuracy was 85%.

A computer-assisted approach for the identification of 30 individuals was used for armored catfish17. Computer-assited means that the 20 most similar images were ranked based on the SIFT (scale-invariant feature transform). The humans used this ranked list for identification. The identification accuracy was 99% for the images taken on two days.

The only fully automated approach has been described by Al-Jubouri19, who performed the identification of five zebrafish (Danio rerio) with 99% accuracy. The histogram of the hue-saturation-value color space of the part of zebrafish stripes was coupled with the KNN (K-nearest neighbour) classifier. All images were collected for one day.

To the best of our knowledge, there is no study using fully automated computer vision for individual fish identification working with a high number of fish or tested for a long-term period. The abovementioned studies showed the feasibility of appearance-based identification but for a limited number of fish or species with low value for the aquaculture industry.

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