• This continues to be the area most affected by a lack of water quality, particularly in rural areas and in the slums that surround the major cities.
• Detta område är fortfarande hårdast drabbat av dålig vattenkvalitet, särskilt i stadsområden och i de slumområden som omger de större städerna.

• Today I want to deal with the effect they are having on the regional areas of Europe, rather than the major cities like London, Birmingham and Paris, because they will survive and receive support.
• I dag vill jag ta upp det sätt på vilket dessa händelser påverkar de regionala områdena i Europa, snarare än de stora städerna som London, Birmingham och Paris, eftersom de kommer att överleva och få stöd.

SET search_path TO f9miniensv;

WITH
	list AS (SELECT
		         t11.token_id AS t11,
		         t12.token_id AS t12,
		         t21.token_id AS t21,
		         t22.token_id AS t22,
		         r1.dep_id AS dep1,
		         r2.dep_id AS dep2
		         FROM
			         deprel r1
			         JOIN depstr s1 ON s1.dep_id = r1.dep_id
			         JOIN word_align a1 ON a1.wsource = r1.head AND a1.wsource < a1.wtarget
			         JOIN word_align a2 ON a2.wsource = r1.dependent
			         JOIN deprel r2 ON r2.head = a1.wtarget AND r2.dependent = a2.wtarget
			         JOIN depstr s2 ON s2.dep_id = r2.dep_id
			         JOIN token t11 ON t11.token_id = r1.head
			         JOIN token t21 ON t21.token_id = r2.head
			         JOIN token t12 ON t12.token_id = r1.dependent
			         JOIN token t22 ON t22.token_id = r2.dependent
		         WHERE
				         s1.val = 'amod' AND
				         s2.val = 'AT' AND
				         t11.ctag = 'NOUN' AND
				         t21.ctag = 'NOUN' AND
				         t12.ctag = 'ADJ' AND
				         t22.ctag = 'ADJ' AND
				         t11.lemma_id = 13734 AND
				         t12.lemma_id = 60115 AND
				         t21.lemma_id = 57456 AND
				         t22.lemma_id = 35323),
	stats AS (SELECT
		          sentence_id,
		          count(DISTINCT token_id) AS c,
		          count(*) AS c_aligned,
		          count(DISTINCT wtarget) AS c_target
		         FROM
			         token
			         LEFT JOIN word_align ON wsource = token_id
		         WHERE
				         sentence_id IN (
					                        SELECT sentence_id
						                        FROM
							                        list
							                        JOIN token ON token_id IN(t11, t21)
					                        )
		         GROUP BY sentence_id),
	numbered AS (SELECT row_number() OVER () AS i, *
		         FROM
			         list),
	sentences AS (SELECT *, .2 * (1 / (1 + exp(max(c) OVER (PARTITION BY i) - min(c) OVER (PARTITION BY i)))) +
			                        .8 * (1 / log(avg(c) OVER (PARTITION BY i))) AS w
		         FROM
			         (
				         SELECT i, 1 AS n, sentence_id, ARRAY[t11,t12] AS tokens
					         FROM
						         numbered
						         JOIN token ON token_id = t11
				         UNION SELECT i, 2 AS n, sentence_id, ARRAY[t21,t22] AS tokens
					         FROM
						         numbered
						         JOIN token ON token_id = t21
				         ) x
			         JOIN stats USING (sentence_id)
		         ORDER BY i, n)
	SELECT
		i,
		n,
		w,
		c,
		c_aligned,
		c_target,
		sentence_id,
		string_agg(CASE WHEN lpad THEN ' ' ELSE '' END || '<span class="token' ||
			           CASE WHEN ARRAY[token_id] <@ tokens THEN ' hl' ELSE '' END || '">' || val || '</span>',
		           '' ORDER BY token_id ASC) AS s
	FROM
		sentences
		JOIN token USING (sentence_id)
		JOIN typestr USING (type_id)
	GROUP BY i, n, w, c, c_aligned, c_target, sentence_id
	ORDER BY w DESC, i, n;
;