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Н Новости

ИИ и прогнозирование микроэлементов для борьбы с загрязнением воды

В последние десятилетия угроза загрязнения водных ресурсов стала одной из наиболее беспокоящих экологических проблем. С ростом мирового потребления фармацевтических препаратов в 2020 году оно достигло 4 миллиардов доз, и как следствие, водные системы сталкиваются с увеличением количества и разнообразия микроэлементов, попадающих в очистные сооружения. Эти вещества, часто неизвестные и трудно поддающиеся анализу, могут оказать вредное воздействие на окружающую среду и здоровье человека, включая канцерогенез и эндокринные нарушения.

В условиях, где традиционные методы анализа требуют дорогостоящего оборудования, опытных специалистов и затрат времени, наука стремится к эффективным и инновационным подходам. В этом контексте исследовательская команда Корейского Института Науки и Технологии (KIST), под руководством Хон Сок-вона, главы Центра исследования водных ресурсов и цикла, и старшего исследователя Сон Муна, представляет новаторскую методологию, основанную на искусственном интеллекте (AI), для борьбы с вызовами загрязнения воды.

В данной статье мы рассмотрим уникальный подход команды KIST, объединяющий в себе методы самоорганизующихся карт (SOM) для кластеризации и случайных лесов (RFC) в машинном обучении для прогнозирования свойств и поведения микроэлементов. Результаты этого исследования проливают свет на возможности применения искусственного интеллекта в экологии, предоставляя быстрый и точный инструмент для анализа и прогнозирования воздействия микроэлементов в водных системах.

Приятного прочтения(:

Кластеризация с самоорганизующимися картами (SOM)

Обучение самоорганизующихся карт (SOM)

С использованием физико-химических свойств, функциональных групп и биотрансформационных правил в качестве входных параметров, SOM были обучены выделять схожие особенности между трассовыми веществами. Процесс обучения позволил картам выстраивать кластеры, где элементы внутри кластера демонстрировали схожие характеристики.

Развитие SOM: credit

Выделим ключевые шаги:

Инициализация: Процесс начинается с инициализации карты, представляющей изначальный вес каждого нейрона. Эти веса обычно устанавливаются случайным образом или используются некоторые методы, например, PCA (Principal Component Analysis).
Выборка данных: Затем из обучающего набора данных случайным образом выбирается вектор. Этот вектор является примером из пространства входных данных, который будет использоваться для коррекции весов нейронов.
Конкуренция: Выбранный вектор сравнивается с весами всех нейронов, и нейрон с наилучшим соответствием (наименьшим расстоянием) считается победителем. Этот нейрон называется "победителем сети".
Адаптация весов: Веса всех нейронов адаптируются в направлении вектора обучения, сосредотачиваясь вокруг победителя. Этот процесс приводит к тому, что соседние нейроны в пространстве карты начинают представлять схожие характеристики.
Итерации: Эти шаги повторяются множество раз для различных векторов из обучающего набора данных. Каждая итерация приближает SOM к описанию структуры входных данных.
Снижение размерности: В процессе обучения размерность карты обычно уменьшается, что позволяет сжать информацию о данных и выделить главные черты их структуры.

Кластеризация и группировка микроэлементов

Получившиеся SOM-карты обеспечили интуитивно понятное представление о структуре данных, где цветовое кодирование и близость элементов на карте отражали степень их схожести. Это сделало процесс группировки микроэлементов более понятным и визуально доступным для дальнейшего анализа.

Определение маркерных компонентов

Ward’s метод использовался для определения границ между кластерами на SOM-картах. Этот подход выявил маркерные компоненты в каждом кластере, обеспечивая представление о типичных представителях данной группы. Маркерные компоненты стали ключевыми элементами в дальнейшем прогнозировании свойств и концентраций микроэлементов.

Процесс Варда в кластеризации SOM:

Обучение SOM: Сначала проводится обучение Самоорганизующихся Карт на входных данных, включающих физико-химические свойства, функциональные группы и биотрансформационные правила микроэлементов.
Вычисление расстояний: После обучения проводится расчет расстояний между узлами карты. Эти расстояния используются для определения степени схожести между кластерами.
Формирование кластеров: Метод Варда выстраивает кластеры, объединяя узлы карты с наименьшими значениями расстояний между ними. Это происходит с учетом минимизации общего расстояния между всеми узлами.
Оптимизация кластеров: Процесс объединения кластеров продолжается до тех пор, пока не достигнута оптимальная структура, при которой внутрикластерное расстояние минимально, а межкластерное максимально.

Математическая формула метода Варда:

Для каждого шага метода Варда используется следующая формула для вычисления расстояния между кластерами:

Dij=ninjni+njdij2

где:

Dij – расстояние между кластерами i и j,
ni, nj – количество узлов в соответствующих кластерах,

dij– евклидово расстояние между центроидами кластеров.

Преимущества метода Варда в кластеризации SOM:

Четкость границ: Метод Варда способствует формированию компактных и четко выделенных кластеров.
Учет расстояний: Значение расстояния учитывает внутрикластерные и межкластерные взаимодействия, обеспечивая баланс между компактностью и различимостью.

Применение SOM для классификации

С учетом обученных SOM и маркерных компонентов, мы можем классифицировать 13 новых трассовых веществ с высокой точностью. Карта SOM служила своеобразной "картой памяти", позволяя предсказывать свойства и концентрации новых микроэлементов, основываясь на их сходстве с ранее исследованными компонентами.

Преимущества кластеризации с SOM

Этот подход к кластеризации с использованием SOM обладает несколькими преимуществами. Во-первых, он позволяет визуализировать и понять структуру данных, что облегчает интерпретацию результатов. Во-вторых, маркерные компоненты предоставляют конкретные представления о характеристиках каждого кластера. Эти преимущества сделали SOM мощным инструментом в первичном этапе анализа трассовых веществ и обеспечили основу для последующего машинного обучения.

Листинг кода для обучения SOM

Представим пример кода с использованием библиотеки MiniSOM, которая предоставляет реализацию самоорганизующихся карт:

from minisom import MiniSom

import numpy as np

# Загрузка данных для обучения SOM (пример)

data = np.random.rand(100, 5) # Здесь необходимо загрузить фактические данные

# Определение размеров карты

map_size = (10, 10)

# Создание и обучение SOM

som = MiniSom(map_size[0], map_size[1], data.shape[1], sigma=0.5, learning_rate=0.5)

som.random_weights_init(data)

som.train_random(data, 1000, verbose=True)

# Получение карты после обучения

trained_map = som.get_weights()

# Дополнительные шаги могут включать визуализацию карты или использование ее для кластеризации данных

В реальном исследовании код, вероятно, был бы более сложным и включал бы дополнительные шаги для предобработки данных, настройки параметров SOM и анализа результатов.

Математическая модель SOM

Математически, Самоорганизующиеся Карты (SOM) представляют собой искусственные нейронные сети, организованные в двумерную сетку, предназначенную для кластеризации данных. Процесс обучения SOM связан с поиском оптимальных весов между нейронами, чтобы точнее представить структуру входных данных. Математическая модель SOM выражается следующей системой уравнений:

Инициализация весов:

Wi,j=Xrand,

где Wi,j – веса нейрона на позиции (i, j), а Xrand – случайно выбранный вектор из входных данных.

Вычисление близости:

di,j=nk=1(Xk-Wi,j,k)2,

где di,j – мера близости между входным вектором Х и весами нейрона Wi,j, n – размерность входных данных.

Определение победителя:

(iwin,jwin) =argi,jmindi,j,

где (iwin,jwin) – координаты нейрона, который обладает наименьшей близостью к входному вектору.

Обновление весов:

Wновi,j=Wстарi,j+hi,j(X-Wстарi,j),

где – коэффициент обучения, hi,j – функция окрестности, определяющая степень влияния на соседние нейроны.

Эти уравнения описывают основной процесс обучения SOM, который нацелен на организацию данных в пространстве таким образом, чтобы схожие элементы оказывались близкими по координатам на SOM-карте.

Химический контекст

В области исследований микроэлементов и микрозагрязнителей, химический контекст выступает важным аспектом, который определяет взаимодействие веществ и их дальнейшие судьбы в природной среде. Самоорганизующиеся карты (SOM) в этом контексте предоставляют мощный инструмент для анализа и классификации химических соединений.

Физико-химические свойства: SOM, обученные на физико-химических характеристиках микроэлементов, способны выявлять схожие особенности в их структуре. Например, водорастворимость, коэффициенты распределения в системе октанол-вода (log Kow), и другие параметры могут служить основой для группировки веществ схожей природы.
Функциональные группы: SOM прекрасно подходят для выделения взаимосвязи между веществами, содержащими определенные функциональные группы. Например, аминокислоты, содержащие аминогруппу, или эфиры с определенными биотрансформационными маршрутами, могут быть эффективно кластеризованы на SOM-карте.
Биотрансформационные правила: Обучение SOM на основе биотрансформационных правил добавляет в модель знание о том, как микроэлементы претерпевают изменения под воздействием биологических процессов. Это включает в себя процессы, такие как гидролиз, окисление, деметилирование и другие, которые могут влиять на устойчивость вещества в окружающей среде.

Прогнозирование со случайными лесами (RFC)

Билдинг и оптимизация RFC

Процесс построения и оптимизации модели с использованием классификации случайного леса (Random Forest Classifier, RFC) представляет собой критический этап в обеспечении точности и эффективности прогнозирования микроэлементов. Давайте рассмотрим этот процесс в деталях.

Листинг кода

В данном разделе представлен листинг кода, отражающий реализацию алгоритма RFC для классификации микроэлементов. Далее представлены ключевые шаги, выполняемые в ходе построения и оптимизации модели.

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score

# Разделение данных на тренировочные и тестовые

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# Инициализация и настройка RFC модели

rfc_model = RandomForestClassifier(n_estimators=100, random_state=42)

# Обучение модели

rfc_model.fit(X_train, y_train)

# Предсказание на тестовых данных

predictions = rfc_model.predict(X_test)

# Оценка точности модели

accuracy = accuracy_score(y_test, predictions)

Разбор кода и шагов

Разделение данных: Первый шаг включает разделение данных на тренировочные (X_train, y_train) и тестовые (X_test, y_test) наборы с использованием train_test_split.
Инициализация и настройка модели: Модель RFC инициализируется с использованием RandomForestClassifier с параметром n_estimators=100, указывая количество деревьев в лесу. Этот параметр можно настраивать в зависимости от конкретных требований.
Обучение модели: Происходит обучение модели на тренировочных данных с использованием fit.
Предсказание на тестовых данных: Модель предсказывает метки классов для тестовых данных с использованием predict.
Оценка точности модели: Точность модели оценивается сравнением предсказанных меток с фактическими метками тестовых данных, используя accuracy_score.

Немного математики

Метод RFC основан на ансамбле решающих деревьев. Вероятность классификации объекта x в RFC определяется как голосование деревьев:

P(y|x)=1NNi=1I(fi(x)=y),

где:

P(y|x) – вероятность класса y для объекта x,
N – количество деревьев в лесу,
fi(x) – прогноз i-го дерева для объекта x,
I() – индикаторная функция.

Построение дерева решений

Каждое дерево RFC строится с использованием подвыборки данных и случайного выбора признаков на каждом разделении. Критерий, такой как "индекс Джини" или "энтропия", используется для измерения качества разделения в узлах дерева.

Признаки выбираются случайным образом, что обеспечивает разнообразие между деревьями и уменьшает переобучение.

Больше математических концепций можно найти в библиотеке Scikit-learn.

Преимущества Использования RFC

Ансамблевый подход: RFC объединяет прогнозы нескольких деревьев, что улучшает устойчивость модели.
Гибкость и универсальность: Модель RFC применима для задач классификации и регрессии.
Устойчивость к переобучению: Благодаря случайному выбору признаков на каждом шаге, RFC устойчив к переобучению.

Оптимизация Параметров

Оптимальные параметры RFC, такие как количество деревьев (n_estimators) и глубина деревьев (max_depth), могут быть настроены с использованием кросс-валидации или других методов оптимизации для достижения наилучшей производительности модели.

Кластеризация на основе физико-химических свойств и групп функциональных элементов

Анализ свойств функциональных групп

В контексте исследования, анализ физико-химических свойств и функциональных групп микроэлементов представляет собой важный этап, направленный на выделение схожих характеристик между молекулами. Этот этап играет ключевую роль в создании основы для последующей кластеризации и классификации микроэлементов.

Пример использования

# Пример использования SOM для кластеризации

from minisom import MiniSom

# Инициализация SOM

som = MiniSom(x=5, y=5, input_len=len(features.columns), sigma=1.0, learning_rate=0.5)

# Обучение SOM на физико-химических свойствах и функциональных группах

som.random_weights_init(features.values)

som.train_batch(features.values, num_iteration=1000)

# Получение карты кластеров

cluster_map = som.labels_map(features.values)

В данном листинге кода:

MiniSom представляет реализацию мини-самоорганизующейся карты,
som.random_weights_init инициализирует веса SOM на основе физико-химических свойств и функциональных групп,
som.train_batch обучает SOM на данных.

Группировка по физико-химическим характеристикам

Физико-химические Свойства:

Гидрофобность (Octanol-Water Partitioning Coefficient): Одним из важных физико-химических параметров является гидрофобность молекулы, выраженная коэффициентом разделения октанол-вода. Этот параметр определяет способность микроэлемента к разделению между органической и водной фазами.
Функциональные группы: Анализ наличия определенных функциональных групп, таких как эфир, эстеры и амины, может дополнительно характеризовать молекулярные свойства. Например, аминогруппы могут указывать на биологическую активность.

Функциональные Группы:

Эфиры: Молекулы, содержащие эфирные группы, могут обладать особыми свойствами, такими как устойчивость к биологическому разложению или специфическое взаимодействие с микроорганизмами.
Эстеры: Наличие эфирных групп может влиять на реакцию биотрансформации микроэлементов, а также на их токсичность.
Амины: Аминогруппы могут быть связаны с биологической активностью и влиять на процессы разложения водных сточных вод.

Анализ этих свойств и функциональных групп позволяет эффективно разделить микроэлементы на группы схожих характеристик. Этот этап подготавливает почву для последующего использования методов машинного обучения, таких как кластеризация с самоорганизующимися картами и прогнозирование с использованием случайных лесов.

Применение методов кластеризации и визуализации

В этом разделе мы рассмотрим процесс применения методов кластеризации, в частности, самоорганизующихся карт (SOM), и визуализации результатов для эффективного анализа микроэлементов.

Кластеризация с Самоорганизующимися Картами (SOM)

Выбор входных параметров: Для обучения SOM используются физико-химические свойства, функциональные группы и биотрансформационные правила микроэлементов в качестве входных параметров. Это позволяет учесть разнообразные характеристики молекул и определить схожие черты между ними.

# Пример использования физико-химических свойств, функциональных групп и биотрансформационных правил в качестве входных параметров для SOM

som_input = prepare_som_input(physical_properties, functional_groups, biotransformation_rules)

Обучение SOM: Самоорганизующиеся карты обучаются на подготовленных данных, выстраивая кластеры микроэлементов в пространстве с учетом их схожести.

# Инициализация и обучение SOM

som_model = SelfOrganizingMap()

som_model.train(som_input)

Выделение кластеров: После обучения SOM каждый кластер представляет собой группу микроэлементов с схожими характеристиками.

# Получение кластеров из обученной SOM

clusters = som_model.get_clusters()

Визуализация Результатов

Карта кластеров: Визуализация самоорганизующихся карт позволяет наглядно представить структуру кластеров и взаимное распределение микроэлементов.

# Визуализация карты кластеров

visualize_som_clusters(clusters)

Характеристики кластеров: После выделения кластеров важно проанализировать характеристики микроэлементов в каждом кластере.

# Анализ характеристик микроэлементов в каждом кластере

analyze_cluster_characteristics(clusters)

Визуализация этих результатов существенна для понимания внутренней структуры данных, выявления закономерностей и последующего использования этой информации в моделях машинного обучения для прогнозирования поведения микроэлементов и классификации новых данных.

Кластеризация на основе биотрансформационных правил

В этом разделе мы более детально рассмотрим процесс кластеризации микроэлементов на основе биотрансформационных правил и проведем сравнение с физико-химическим подходом.

Анализ и группировка на основе биотрансформации

Этап анализа и группировки микроэлементов на основе биотрансформации является ключевым в контексте прогнозирования их поведения в водных системах. Здесь рассматриваются биологические механизмы взаимодействия среды с микроэлементами, что позволяет учесть их динамические изменения под воздействием организмов. Такой подход отражает реальные процессы, происходящие в природных условиях.

Метод биотрансформации позволяет выявить, какие элементы подвергаются изменениям в результате метаболизма биологических систем. Это важно для понимания, каким образом микроэлементы взаимодействуют с организмами и какие метаболиты образуются в процессе их обработки. Группировка на основе биотрансформации обеспечивает более точное представление о долгосрочных эффектах микроэлементов на экосистемы водных объектов.

Подготовка Данных: Входные данные включают биотрансформационные правила для каждого микроэлемента. Эти правила предоставляют информацию о возможных биологических превращениях.

# Подготовка данных для кластеризации на основе биотрансформационных правил

biotransformation_data = prepare_biotransformation_data(microelements, biotransformation_rules)

Кластеризация с использованием RFC: Random Forest Classifier (RFC) применяется для классификации микроэлементов на основе их биотрансформационных правил.

# Инициализация и обучение RFC модели на биотрансформационных данных

rfc_model_biotransformation = RandomForestClassifier(n_estimators=100, random_state=42)

rfc_model_biotransformation.fit(X_biotransformation_train, y_biotransformation_train)

Сравнение с физико-химическим подходом

В сравнении с традиционными физико-химическими методами, фокусирующимися на свойствах вещества и его взаимодействии с окружающей средой, биотрансформационный подход призван улучшить прогнозы в условиях, приближенных к реальным. Физико-химические методы не всегда учитывают сложные биологические механизмы, происходящие в системе, и могут оставаться ограниченными в способности предсказать последствия воздействия микроэлементов на живые организмы.

Важно отметить, что совмещение биотрансформационных и физико-химических подходов может предложить комплексный взгляд на поведение микроэлементов. Такие интегрированные методы обеспечивают более глубокое понимание процессов и позволяют уточнить прогнозы, что в конечном итоге важно для разработки эффективных стратегий управления качеством воды и снижения негативного воздействия на окружающую среду.

Представим два рисунка, демонстрирующие результаты кластеризации на основе:

физико-химического подхода и функциональных групп микрозагрязнителей,
биотрансформационных правил.

Карты расстояний, полученные из SOM, иллюстрированы разными цветами в зависимости от относительного расстояния между каждым нейроном карты(а). Микрозагрязнители (MPs) интерпретировались как “с аналогичными характеристиками”, если размещались близко на карте расстояний.

*Палитра показывает расстояние между соседними единицами карты

Машинное обучение в прогнозировании новых микроэлементов

В условиях постоянного увеличения потребления фармацевтических препаратов, важно иметь инструменты, способные предсказывать поведение новых микроэлементов в водных системах. Исследование сосредотачивается на применении методов машинного обучения для решения этой проблемы. Ключевым этапом является оценка точности и эффективности предложенной модели.

Оценка точности и эффективности

Модель была подвергнута тщательной оценке с целью определения ее точности в прогнозировании и общей эффективности.

1. Оценка Точности:

Производится анализ точности модели, измеренной через метрики, такие как accuracy score.

from sklearn.metrics import accuracy_score

# Оценка точности модели на тестовых данных

accuracy_model = accuracy_score(y_test, predictions_model)

print(f"Accuracy: {accuracy_model}")

2. Эффективность Модели:

Изучение эффективности модели в прогнозировании характеристик микроэлементов.

# Предсказание на тестовых данных

predictions_model = model.predict(X_test)

# Анализ эффективности модели

efficiency_analysis = analyze_model_efficiency(y_test, predictions_model)

print(f"Model Efficiency: {efficiency_analysis}")

Применение RFC к 13 новым микроэлементам

Для проверки эффективности модели, RFC был успешно применен к 13 новым микроэлементам. Этот этап исследования был важен для проверки способности модели предсказывать поведение и свойства ранее неизвестных микроэлементов. Результаты показали, что модель смогла достичь высокой точности прогнозирования, приблизительно 75%. Это свидетельствует о том, что модель успешно обобщает свои знания на новые микроэлементы, обеспечивая точные предсказания, даже в условиях ограниченной информации.

Сравнение с традиционными методами

Для оценки превосходства предложенной модели проведено сравнение с традиционными методами. Подход, использующий машинное обучение с использованием RFC, был сравнен с традиционными методами, основанными на биологической информации. Результаты указывают на значительное преимущество модели машинного обучения: точность ее прогнозов составила около 75%, в то время как традиционные методы обеспечивали точность порядка 40%. Таким образом, предложенная модель не только превосходит традиционные методы, но и значительно повышает точность прогнозов, что делает ее важным инструментом для предсказания свойств и поведения микроэлементов в водных системах.

Преимущества ИИ в точности прогнозирования

Искусственный интеллект в данной модели демонстрирует высокую точность в прогнозировании свойств микроэлементов. Это достигается благодаря использованию сложных алгоритмов машинного обучения, таких как случайные леса (RFC), способных выявлять нелинейные зависимости в данных. Точность в предсказаниях оказывает влияние на эффективность принимаемых решений в области водных технологий и экологической безопасности.

Анализ применения к реальным ситуациям

Применение разработанной модели к реальным ситуациям подтвердило ее практическую применимость. Модель эффективно предсказывала поведение микроэлементов, а результаты соответствовали данным, полученным в реальных условиях сточных вод и водных систем. Например, при анализе влияния фармацевтических микроэлементов, точность прогнозирования в аналогичных условиях сравнительно высока, что подтверждает преимущества предложенной модели.

Однако использование модели не ограничивается только научными исследованиями. Ее прикладное использование может охватывать области водных и сточных систем, предоставляя быструю и точную информацию о микроэлементах. Перспективы включают в себя расширение области применения модели на различные типы веществ и условия окружающей среды, а также дальнейшее совершенствование методов обучения.

В итоге, использование ИИ, такого как разработанная модель, в прогнозировании микроэлементов предоставляет современный и эффективный инструмент для экологических и инженерных приложений в области водных ресурсов.

Заключение

Результаты проведенного исследования подчеркивают важность применения искусственного интеллекта (ИИ), особенно в виде разработанной модели AI KIST, для прогнозирования поведения микроэлементов в водных системах. Сочетание методов кластеризации и машинного обучения позволяет не только эффективно классифицировать известные микроэлементы, но также успешно прогнозировать свойства новых веществ.

Влияние и инновации AI KIST

AI KIST представляет собой инновационный подход к решению проблем загрязнения воды. Модель, основанная на таких алгоритмах как, самоорганизующиеся карты Кохонена и случайных лесах, демонстрирует превосходство в прогнозировании, что имеет важное значение для управления качеством воды и охраны окружающей среды. Разработанная методология становится инструментом для эффективного контроля за воздействием микроэлементов.

Дальнейшие шаги и возможное развитие

Несмотря на успешные результаты, исследование оставляет множество возможностей для дальнейшего развития. В следующих этапах исследования можно углубить анализ воздействия микроэлементов на биологические системы и продолжить расширение базы данных для повышения точности прогнозирования. Важно также обратить внимание на адаптацию модели к различным географическим и климатическим условиям.

Как мне кажется, с постоянным увеличением глобального потребления фармацевтических препаратов неотложной становится задача эффективного контроля и обработки сточных вод, содержащих следы этих веществ. Модель, представленная в данном исследовании, предоставляет перспективу для широкого внедрения в системы водоочистки и мониторинга, способствуя оптимизации процессов и минимизации экологических рисков.

Таким образом, с учетом динамики роста фармацевтической индустрии и повышенного внимания к экологическим проблемам, прогностический потенциал модели AI KIST становится ключевым фактором в обеспечении устойчивого управления водными ресурсами в будущем.

Резюмируем: использование AI KIST не только улучшает наше понимание водных систем, но и предоставляет эффективные инструменты для борьбы с вызовами, связанными с загрязнением воды. Это внедрение инновационных методов в практику, направленных на устойчивое управление водными ресурсами и снижение негативного воздействия на окружающую среду.

Я была очень рада подготовить этот материал, поскольку тема действительно крайне важна в наше время, поэтому с удовольствием почитаем фидбэк в комментариях, спасибо(:

Автор: Вероника Веселова

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3 февр. 2024 г.

07.09.23 16:24 CherryTeam

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08.10.23 01:30 davec8080

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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27.08.24 12:50 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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26.11.24 23:12 rickrobinson8

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27.11.24 09:10 Michal Novotny

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01.12.24 17:21 KollanderMurdasanu

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04.12.24 22:24 andreygagloev

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12.12.24 00:35 amandagregory

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27.12.24 00:21 swiftdream

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