* @param minutes the minute of the hour (0-59)
         * @param day the day of the week (1=Sunday, 7=Saturday)
         * @return true if this rule applies, false otherwise
         */
        public boolean isTarget(final int hours, final int minutes, final int day) {
            if (!reverse) {

      require(hour in 0..23)
      require(minute in 0..59)
      require(second in 0..59)

      GregorianCalendar(UTC).apply {
        isLenient = false
        set(Calendar.YEAR, year)
        set(Calendar.MONTH, month - 1)
        set(Calendar.DAY_OF_MONTH, dayOfMonth)
        set(Calendar.HOUR_OF_DAY, hour)
        set(Calendar.MINUTE, minute)
        set(Calendar.SECOND, second)

    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();
      if (values.hasAnyNaN()) {
        assertWithMessage("mean of %s", values).that(mean).isNaN();

        try {
            IntervalControlHelper.parseTime("12:60");
            fail("Should throw FessSystemException");
        } catch (FessSystemException e) {
            assertEquals("Invalid minute value: 60 in time: 12:60. Minute must be between 0 and 59", e.getMessage());
        }

        try {
            IntervalControlHelper.parseTime("-1:30");
            fail("Should throw FessSystemException");

   *   computeInPlace;
   * - (except that, for non-finite values, we don't do all combinations exhaustively);
   * - percentiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace.
   */

  private static final double ALLOWED_ERROR = 1.0e-10;

  /**
   * A {@link Correspondence} which accepts finite values within {@link #ALLOWED_ERROR} of each
   * other.
   */

   *   computeInPlace;
   * - (except that, for non-finite values, we don't do all combinations exhaustively);
   * - percentiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace.
   */

  private static final double ALLOWED_ERROR = 1.0e-10;

  /**
   * A {@link Correspondence} which accepts finite values within {@link #ALLOWED_ERROR} of each
   * other.
   */

  public void testSchedule() {
    MockExecutor mock = new MockExecutor();
    TestExecutor testExecutor = new TestExecutor(mock);

    Future<?> unused1 = testExecutor.schedule(() -> {}, 10, MINUTES);
    mock.assertLastMethodCalled("scheduleRunnable", 10, MINUTES);

    Future<?> unused2 = testExecutor.schedule(() -> null, 5, SECONDS);
    mock.assertLastMethodCalled("scheduleCallable", 5, SECONDS);
  }

  public void testSchedule_repeating() {

    // Test edge cases to understand the validation behavior
    @Test
    public void test_edgeCaseBehavior() {
        String[] edgeCases = { "0 0 25 * * ?", // Invalid day of month
                "0 60 * * * ?", // Invalid minute
                "0 0 0 32 1 ?", // Invalid day
                "0 0 0 1 13 ?", // Invalid month
        };

        for (String cron : edgeCases) {
            boolean result = validator.determineValid(cron);

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();