 + salt).digest('hex');
    const number = parseInt(hash.slice(0, 8), 16);
    return number / 0xffffffff;
}
```

2. Using the `seedrandom` library for test environments:

```typescript:backend/server/src/utils/random.ts!information
import seedrandom from 'seedrandom';

let rng: seedrandom.PRNG | null = null;
if (process.env.NODE_ENV === 'test') {
    // Initialize a seedable RNG with a fixed seed
    rng = seedrandom('fixed-seed');
}

export function MATH_RANDOM_PLEASE_CONSIDER_HOW_THIS_AFFECTS_TEST_COVERAGE(): number {
    if (process.env.NODE_ENV === 'test') {
        if (rng === null) {
            throw new Error('rng is null');
        }
        return rng();
    }
    return Math.random();
}
```

3. For array shuffling with an optional seed:

```typescript:backend/server/src/base/common/arrays.ts!information
export function shuffle<T>(array: T[], _seed?: number): void {
    let rand: () => number;

    if (typeof _seed === 'number') {
        let seed = _seed;
        // Seeded random number generator in JS
        rand = () => {
            const x = Math.sin(seed++) * 179426549;
            return x - Math.floor(x);
        };
    } else {
        rand = Math.random;
    }
    // ... Fisher-Yates shuffle implementation
}
```

The main approaches are:

1. `deterministicMathRandom`: Uses SHA-256 hashing to generate a deterministic random number from a string seed and salt
2. `seedrandom`: Used in test environments to ensure deterministic randomness
3. `shuffle` with seed: Uses a simple mathematical formula based on sine to generate deterministic random numbers

For test environments, the codebase uses a fixed seed ('fixed-seed') with seedrandom to ensure tests are deterministic. For production code, you can use either `deterministicMathRandom` if you need string-based seeds, or the `shuffle` function's seeding mechanism if you need number-based seeds.

The most robust approach would be to use `deterministicMathRandom` as it uses cryptographic hashing to generate the random values, making it more uniformly distributed than the sine-based approach used in `shuffle`.