Muslima Academy is an educational and motivational initiative founded by young leader Muslima Olimova. The project aims to equip students with skills in technology, global education, scholarships, and leadership. So far, it has reached over 1,000 young people in Uzbekistan and abroad.
Muslima Academy held an inspiring youth presentation in Andijan, Uzbekistan. The goal of the event was to inspire, educate, and empower young people with opportunities that stretch from local to global.
Although traditional media did not attend and some invited guests couldn’t make it, the energy and passion in the room were undeniable. The event was filled with hope, determination, and the belief that true change begins with us.
During the presentation, young participants learned about how to apply for international programs, ways to earn certificates, how to develop skills, and how to build self-confidence. The day was enriched with live workshops, motivational speeches, and moments that lit up hearts.
“Even if the cameras didn’t show up, the world will continue to hear our voice. What we’re doing truly matters.”
This event was a powerful reminder that to make an impact, we don’t need an audience — we need action. And this time, the youth of Uzbekistan took a bold step onto the global stage.
Abstract: Rapid urbanization, industrial activities, and unsustainable land use have intensified environmental issues such as air pollution and deforestation, leading to biodiversity loss and health risks. Traditional environmental monitoring approaches are often reactive, delayed, and spatially limited. Therefore, developing a real-time predictive framework that combines multiple data sources is essential for timely interventions and sustainable environmental management. This study introduces a comprehensive Environmental Impact Prediction Model that integrates satellite remote sensing data and ground-based sensor feeds. Sentinel-2 and MODIS satellites provided NDVI, LST, and AOD data, while 12 on-site sensors collected air quality metrics including PM2.5, NO2, and CO2.
Preprocessing steps such as cloud masking, normalization, and temporal alignment ensured data quality. LSTM neural networks were applied for air quality forecasting, and Random Forest algorithms were used for deforestation classification. Visual outputs were presented via dynamic geospatial dashboards developed with Python (Dash, Plotly). The model demonstrated high performance: LSTM-based air quality predictions achieved a Mean Absolute Error of 4.2 AQI units and R² of 0.88. Deforestation detection using Random Forest showed 91% accuracy and 89% precision. The system identified early warning signals for both pollution peaks and forest degradation before they were confirmed by drone inspections and sensor validation, proving its reliability and responsiveness.
In recent decades, the growing complexity and intensity of environmental challenges such as air pollution, deforestation, climate change, and ecosystem degradation have raised global concerns over the sustainability of human development. These changes, often triggered by industrialization, urban expansion, and unsustainable land use, have led to significant alterations in natural cycles and biodiversity loss.
One of the most critical barriers to mitigating these challenges is the lack of timely and localized environmental data that enables prediction rather than reaction. Traditional environmental monitoring systems—although accurate in limited contexts—are usually retrospective, static, costly to scale, and often fail to provide dynamic, real-time insight into the rapidly changing state of ecosystems. This shortcoming necessitates the development of more agile and predictive frameworks that leverage technological advancements in remote sensing, satellite imagery, and ground-based sensor networks.
Within this context, the application of artificial intelligence (AI) and machine learning (ML) techniques, combined with big environmental data, offers an innovative and proactive approach to environmental conservation. The integration of satellite data and in-situ sensor feeds—complemented by AI-driven analytics—creates new opportunities to predict critical changes in environmental quality indicators, such as air pollution levels or patterns of forest loss, with a high degree of spatial and temporal granularity. This study proposes a novel Environmental Impact Prediction Model aimed at forecasting local environmental changes—particularly air quality fluctuations and deforestation trends—through the fusion of satellite imagery (such as NDVI, AOD, LST) and real-time environmental sensor data (e.g., PM2.5, NO2, CO2 measurements).
The model is grounded in a multidisciplinary methodology that combines environmental science, geospatial information systems (GIS), computer vision, and time- series forecasting. Prior research in the field has highlighted the potential of remote sensing to monitor deforestation on a global scale, as demonstrated in the Global Forest Change dataset (Hansen et al., 2013), and machine learning techniques such as Random Forest and Convolutional Neural Networks (CNNs) have been increasingly adopted to classify and detect land use change.
Similarly, Long Short- Term Memory (LSTM) networks have shown promising results in predicting air quality trends by learning temporal dependencies within large-scale data. However, the novelty of this study lies in the combined, real-time application of these tools within a unified predictive platform that operates at the local level and is capable of adapting to new data as it arrives. Moreover, by visualizing predictions on interactive geospatial dashboards, the model enhances the accessibility of environmental information for policymakers, conservationists, urban planners, and citizens. The scientific gap this research addresses is the absence of a comprehensive, real-time environmental prediction system that seamlessly integrates both satellite-derived and sensor-based data streams to inform rapid conservation actions.
Most existing systems either rely on satellite data alone, which can be delayed due to cloud cover or orbiting schedules, or on localized sensors, which lack broader contextual visibility. By merging both, this model aims to overcome such limitations and present a robust predictive solution. Furthermore, the study contributes to the theoretical understanding of how spatiotemporal data fusion and AI can enhance environmental resilience and adaptive management strategies. From a methodological standpoint, this research utilizes a hybrid approach where Random Forest is applied for land cover classification, LSTM is employed for air quality time-series forecasting, and CNNs are used for extracting visual features from satellite images. Data preprocessing steps include cloud masking, spectral band normalization, and sensor calibration.
The selected test region represents a biodiversity-sensitive area experiencing growing anthropogenic pressure, thus serving as a relevant case study for model evaluation. Ultimately, the purpose of this study is not merely technical innovation but environmental impact—specifically, to provide early warnings and insights that facilitate preventive action, reduce ecological damage, and support evidence-based policy formulation in environmental governance. By enabling fine-grained, near real-time forecasting of air pollution and deforestation risks, the proposed model aligns with global environmental protection efforts under frameworks such as the United Nations Sustainable Development Goals (SDGs), particularly Goals 13 (Climate Action) and 15 (Life on Land). This introduction sets the stage for an in-depth exploration of how cutting-edge technologies can be harnessed to forecast, rather than merely observe, the future trajectory of our planet’s environmental health.
Methodology
The methodology of this study is based on the integration of satellite remote sensing data and ground- based environmental sensors to develop a predictive model for local environmental changes, focusing on air quality and deforestation. Data were collected from Sentinel-2 and MODIS satellites to extract vegetation indices (NDVI), land surface temperature (LST), and aerosol optical depth (AOD). In parallel, real-time air quality data—such as PM2.5, NO2, and CO2—were gathered from 12 strategically placed ground sensors within the selected region. The raw data underwent preprocessing, including cloud masking, radiometric correction, normalization, and time alignment.
For air quality prediction, we used Long Short-Term Memory (LSTM) neural networks to analyze time-series patterns based on both satellite and sensor data. For detecting deforestation, a Random Forest classifier was trained using temporal changes in NDVI and other spectral features extracted from satellite images. The models were trained using an 80/20 train-test split and evaluated through Mean Absolute Error (MAE) and accuracy scores. Additionally, spatial outputs were visualized through interactive geospatial dashboards developed with Python libraries such as Plotly and Dash. Ground-truthing through limited field visits validated the model’s predictions. This combined methodology offers a dynamic, scalable, and cost-effective solution to anticipate environmental changes and guide conservation efforts.
Results
The Environmental Impact Prediction Model developed in this study yielded significant and promising results in both major research domains: air quality forecasting and deforestation detection. In the first direction, real-time sensor feeds measuring PM2.5, NO2, and CO2 were processed using an LSTM (Long Short-Term Memory) neural network, which was specifically trained on historical air quality data combined with meteorological and satellite-derived variables such as aerosol optical depth (AOD), humidity, and wind speed.
The model demonstrated a strong capacity to learn temporal dependencies within the dataset, achieving a Mean Absolute Error (MAE) of 4.2 AQI units and a Root Mean Square Error (RMSE) of 5.9, which is considered excellent in environmental time-series forecasting contexts. The R² score, a statistical measure that indicates how well the predicted values match the actual values, reached 0.88, showing a high level of precision and model reliability.
These metrics were consistent across all twelve sensor nodes deployed across industrial, residential, and peri-urban zones. The model’s predictive accuracy was especially high in urban regions with relatively stable meteorological patterns and consistent historical data, while in high-altitude or mountainous areas with less predictable microclimates, the model experienced a slight decline in precision. Temporal prediction windows ranged from one day to seven days, with the best performance achieved in short-term forecasts (1–3 days). Longer-term predictions retained acceptable error margins but displayed greater variance.
One of the most significant advantages observed was the model’s responsiveness: as new data were streamed in real-time from sensors, the model updated its forecast continuously, allowing for adaptive responses to rapid environmental changes, such as sudden pollution spikes caused by industrial activities or traffic congestion. In the second research direction—deforestation monitoring—the Random Forest classifier trained on Sentinel-2 imagery and NDVI-derived time-series data yielded compelling results. The classification model reached an overall accuracy of 91%, with a precision of 89%, a recall of 87%, and a F1-score of 88%, confirming its robustness in identifying both existing deforested areas and early-stage degradation zones. A confusion matrix analysis revealed low rates of false positives, meaning that the model did not overstate deforestation risk.
In practical application, the system was tested on a 150 km² area known to experience illegal logging activity. Within this pilot region, the model successfully identified six separate zones of active deforestation over a two-month period. These zones were later confirmed via drone-based aerial inspection, thus verifying the reliability of satellite-derived predictions. Furthermore, the model was able to detect subtle changes in canopy density, particularly areas transitioning from dense forest to sparse coverage — a sign of early disturbance.
These changes were not yet evident to the naked eye or on static satellite snapshots, which reinforces the importance of using temporal sequences and machine learning to amplify observational capacity. In addition to classification outputs, the model generated a risk probability heatmap overlay, allowing environmental officers to prioritize field inspections based on zones with the highest likelihood of environmental degradation. The integrated dashboard also allowed users to overlay pollution data with deforestation risks, revealing important spatial correlations — for example, regions with increasing deforestation also demonstrated upward trends in PM2.5 concentrations, possibly due to increased dust and combustion emissions.
This kind of multi-dimensional visualization made it easier for policymakers and conservation agencies to understand the broader ecological consequences of land use change. Moreover, model outputs were made accessible through a web-based geospatial interface with daily updates, making the system suitable for use not only by researchers but also by field rangers, non-governmental environmental organizations, and urban planners working on climate adaptation strategies.
Overall, the results of this study confirm the feasibility and effectiveness of integrating satellite remote sensing, sensor feeds, and machine learning models to predict environmental impacts at a local scale. While challenges remain in terms of data coverage, cloud interference, and occasional sensor calibration issues, the hybrid framework proposed here demonstrates that near real-time forecasting and monitoring are within reach, even in data-sparse regions. The strength of the model lies not only in its predictive accuracy but also in its modularity and adaptability, meaning it can be scaled to other regions or adjusted to monitor additional indicators such as soil moisture, water quality, or temperature anomalies. These findings suggest that data-driven environmental intelligence systems can become vital tools in national and regional conservation planning. As a result, the presented model offers both scientific value and practical applicability in achieving the goals of proactive environmental governance and sustainable resource management.
Discussion
The findings of this study provide compelling evidence that environmental forecasting through the integration of satellite data and ground-level sensor feeds is not only feasible but also highly effective in addressing pressing ecological challenges at the local scale. Our model, designed to predict air quality and deforestation events, demonstrated a high level of accuracy, responsiveness, and adaptability. The LSTM-based component successfully forecasted PM2.5, NO2, and CO2 fluctuations with low error margins, particularly in urban and industrial regions.
These results underline the power of time-series neural networks to capture both periodic and irregular environmental dynamics. Unlike traditional forecasting models which rely solely on historical pollutant trends, the inclusion of meteorological data such as humidity, wind speed, and temperature, along with satellite-derived aerosol optical depth (AOD), allowed the model to generate more nuanced and situationally aware predictions. This integrated approach increased robustness in areas where sensor coverage was limited or inconsistent. The system’s real-time updating feature made it highly responsive to environmental perturbations, such as sudden emissions, temperature inversions, or meteorological shifts, thereby enabling early-warning capabilities.
Importantly, the spatial granularity of the forecasts, tied to specific geolocations, provided actionable insights not just for researchers but also for local authorities and public health institutions tasked with managing air quality standards. In the field of deforestation monitoring, the use of Random Forest classification combined with satellite- derived vegetation indices (NDVI and EVI) proved particularly effective. The model not only identified areas where tree cover had been visibly lost but also detected subtle patterns of vegetation thinning, indicative of early-stage environmental degradation.
These early signals often escape conventional classification systems that depend on static snapshots or threshold analysis. Our model’s ability to process temporal sequences allowed it to track how vegetation health deteriorates over time—an essential feature in contexts where illegal logging or unsanctioned land clearing occurs gradually. Comparisons with similar studies in the domain support this assertion. For instance, Hansen’s global forest change maps emphasize the value of high-frequency monitoring to identify slow-moving deforestation fronts, and our model builds upon this logic by incorporating dynamic risk heatmaps. These visualizations provide color-coded overlays indicating where vegetation stress is likely to progress, based on temporal modeling and land use history.
Moreover, our study revealed that regions flagged for high deforestation risk also tended to display rising concentrations of PM2.5, suggesting a link between biomass burning or soil exposure and air pollution. This multidimensional correlation, while requiring further investigation, presents a new avenue for holistic environmental risk modeling— one that sees forest loss not just as a biodiversity issue but also a public health concern. It also illustrates how integrating multiple environmental indicators within a single system can yield richer, more interconnected insights.
Despite these promising results, the predictive model is not without its limitations. For example, satellite data remains vulnerable to cloud cover, especially during rainy seasons or in tropical regions. While cloud masking algorithms have improved significantly, they are not always perfect, which can delay or distort the environmental signal. In areas with limited sun exposure or high atmospheric interference, the quality of satellite images can deteriorate, impacting model reliability.
Additionally, while the Sentinel- 2 satellite provides high-resolution imagery, its revisit cycle of five days may not be sufficient for areas undergoing rapid change. Another technical limitation involves sensor infrastructure: real-time air quality monitoring depends on the maintenance and calibration of ground-based devices, which can degrade over time or produce inconsistent data due to environmental exposure. In this study, we performed regular calibration and filtering, but in broader applications, especially in low-resource regions, maintaining such data integrity could prove challenging.
Furthermore, while the LSTM and Random Forest models achieved high accuracy in test environments, their performance may vary when applied to unfamiliar regions or under different climatic and ecological conditions. Generalizing the model requires retraining and tuning based on regional datasets, which may not always be readily available or standardized. On a broader scale, the implications of this study are both practical and theoretical. Practically, the predictive model can be embedded into regional environmental monitoring frameworks, offering daily updates to decision-makers, environmental watchdogs, and NGOs.
The interactive geospatial dashboard, for instance, allows users to overlay real-time data, track historical trends, and project future outcomes, all within a user-friendly visual format. This tool can be particularly valuable in environmental hot spots where quick action is needed to prevent irreversible damage, such as forest encroachment in protected areas or air quality declines in urban corridors. The system also holds promise for educational and participatory governance initiatives, where citizens can engage with environmental data and contribute observations to improve model accuracy—what is sometimes referred to as “citizen science.”
Theoretically, our findings support the growing body of literature that advocates for data fusion approaches in environmental monitoring. Studies by Zhang, Li, and others have demonstrated the superiority of models that combine multiple data streams (satellite, ground, meteorological) over single- source systems. This study contributes to that discourse by providing a scalable, modular, and open- ended framework that can be adapted to different use cases, including water quality prediction, wildfire risk assessment, and urban sprawl analysis. Moreover, by incorporating AI components such as LSTM and CNN within the environmental sciences, the model bridges the gap between computational intelligence and ecological sustainability. Looking forward, several avenues exist for expanding and strengthening the system.
First, integration of drone-based imagery could enhance spatial resolution, especially in areas with persistent cloud cover or terrain shadow. Second, extending the model to include additional variables—such as hydrological flow, soil moisture, or anthropogenic activity data (e.g., night-time lights)—could provide a more comprehensive understanding of environmental change drivers. Third, collaboration with local authorities and community organizations could improve ground validation efforts and encourage policy alignment with model insights. Lastly, ethical considerations must be addressed. While predictive models offer tremendous benefits, they also raise concerns around data privacy, especially if linked to land ownership or resource exploitation.
Transparency in how predictions are made, who controls the data, and how alerts are acted upon will be crucial for public trust and long-term adoption. In conclusion, the environmental impact prediction model developed in this study demonstrates strong potential as a decision-support tool for conservation and sustainable development efforts. By accurately forecasting air pollution and deforestation patterns using cutting-edge data science and Earth observation technologies, it empowers stakeholders to shift from reactive to proactive strategies in environmental governance.
Conclusion
This study presents a comprehensive and innovative approach to forecasting environmental changes by integrating satellite remote sensing data with real-time sensor feeds through machine learning models. The proposed Environmental Impact Prediction Model demonstrated high predictive accuracy in both air quality forecasting and deforestation detection, using LSTM and Random Forest algorithms respectively. With Mean Absolute Error values below 5 AQI units and deforestation classification accuracy above 90%, the model proved effective in generating localized, timely, and actionable insights.
One of the model’s most significant contributions lies in its ability to combine spatial and temporal data into a unified analytical framework that allows for early warning, trend analysis, and decision support. By incorporating NDVI, AOD, and other spectral indices along with ground-level pollutant data, the system provides a more nuanced understanding of environmental dynamics than traditional methods. Moreover, the model’s modular structure, adaptability to various regions, and user-friendly visualization tools make it highly applicable for policymakers, urban planners, conservation agencies, and researchers.
Despite certain limitations such as dependency on cloud-free satellite imagery and sensor maintenance requirements, the study confirms the model’s potential to be scaled and customized across diverse ecological and climatic zones. Future research may focus on enhancing model granularity, integrating additional variables like land use patterns or socioeconomic data, and leveraging citizen science inputs for validation. In conclusion, the predictive framework developed in this research marks a significant step toward proactive environmental governance by enabling data-driven monitoring and sustainable decision-making in response to rapidly evolving ecological challenges.
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A soft knock on the front door. I opened the door. It was a man I had never seen before.
What can I do you for, I said
He said, I need someone to talk to. I need help. Well no one is here right now, I said. I really need someone to talk to, he said
I said, Well come on in. You don’t look like you’re feeling too well.
Would you like a glass of water?
Do you have a beer?
I got a beer from the refrigerator. We sat down at my kitchen table
Well, come on, I said, I don’t have all day. What do you want to talk about?
I did have all day but sometimes a man has to feel important. What do you have to do that’s so important, he asked? Never mind, I said. Long story short, he said, I’m afraid of my girlfriend’s husband. She told him we’re having an affair and he wants to kill me.
Why would she do something so crazy, I said. Because she loves to be spanked on the ass, he said.
She loves me and wants to marry me. Why don’t you marry her?
Because I’m married, he said. Someone knocked an angry knock on the front door, again and again and again.
Mars, an angry man yelled from my front porch, I ’m going to kill you. I know You’re in there.
Whoever you are, I yelled, go away or I’ll call the police. I looked out the window, saw a guy in flip-flops walk across my lawn into the house next door without knocking.
You’re having an affair with a woman who lives next door to you? I asked
Do you have any weed, he asked? Are you in love with this woman?
No, he said, but she’s so damn hot. Does your wife know about this affair? Why should I tell her? That would be damn stupid and just cause trouble.
Knowing her, she would just tell all the neighbors.
What’s your plan, pendejo? I moved to Kansas City but she telephoned me every night, begging me to come back. I told her I was dating a new girlfriend, an intelligent, compassionate women that doesn’t appeal to me like you do because you are the best lover I’ve ever had.
Have you had many lovers? she asked Do you mean lovers I was in love with?
Yes, she said. That memory has been wiped away, I said, laughing into my iPhone. It was a joke. She didn’t think it was funny.
Do you like fish? she asked. Yes, I said, I do.
Well take a perch, she said. I never want to talk to you again. She hung up.
I moved back home to Tulsa, Moved back in with my wife, next door to my former lover. Her husband and I have become damn good friends. He chugged his beer, turned his baseball cap backwards, Got up from the table, shook my hand, said, Thanks old man. I couldn’t have ended this affair without your advice. You’re the best neighbor I’ve ever had.
TAMARISK TREE
The tamarisk tree wears a green diaphanous gown & needs a shave
A ladder of stars beneath an arroyo feeds her children
The tamarisk tree traces her genealogy to angels & the eternal pilgrimage of fish
The tamarisk tree near La Joya, near a cemetery where I hear a baby rabbit cry itself to death
The tamarisk tree is not Christian, has never read the Bible
She loves to chant Buddhist mantras
Jesus Christ was once her secret lover
She loves hawks, psychedelic mushrooms, Wittgenstein, Gertrude Stein, Buffy St.-Maria, Patti Smith, Pablo Picasso and the old Bob Dylan
Dear dear tamarisk tree
Tiny glaciers slide beneath her holy bark
Blue porcelain dolphins frolic in her white gloved hands
She is surrounded by the shadow of Einstein
The tamarisk tree plays tungsten horn in her spare time
She has eyes for Little Orphan Annie
She is terrified surrounded by America’s fear and greed
She remembers when and where music became transparency solidified
Blind fish swim in her iridescent roots
A lather of clouds in her hair of ocean foam
Tiny winged beings in her superluminal womb
She is a breathing grave
Dances for the sun
Loves the moon
Is bi-sexual and horny
Sunlight glides over her skin like the soft mouth of a dream lover
The drum in her leaves skips a beat
Poor tree
She has never seen the transcendental light of Taos
She has never seen a cubist painting or Marx Brother’s movie
Exploring Imagination, Innovation, and Inspiration: My Journey at ANIM CAMP 2025
In the heart of the majestic Bo‘stonliq mountains in Tashkent region, something extraordinary unfolded in May 2025 — ANIM CAMP, a vibrant week-long animation and creativity camp that brought together 200 talented and curious young minds. Organized by United Soft in partnership with the Youth Affairs Agency of Uzbekistan, the camp promised much more than a break from daily routine — it offered a chance to dive into the world of animation, technology, and personal growth.
As one of the selected participants, I had the privilege to live, learn, and create in an inspiring environment that blended natural beauty with digital exploration. Here’s a detailed glimpse into what made this camp such a transformative and unforgettable experience.
Days 1–3: The Future is Here — Artificial Intelligence and Creativity
Our journey began with a deep dive into one of the most talked-about and rapidly evolving technologies of our time — Artificial Intelligence (AI). The first three days of the camp were dedicated to understanding the foundations of AI and how it’s revolutionizing the creative industry, especially animation.
We didn’t just study theory — we explored how AI tools can be used to design posters and visual content. It was fascinating to learn how machine learning models can assist artists, generate visuals from prompts, and turn simple ideas into stunning artwork. We experimented with AI-driven design platforms and created our own posters, blending technology with artistic expression.
These sessions opened up a new world of possibilities for us. For many participants, including myself, it was the first time seeing AI as a creative companion — not just a technical tool, but a new way of thinking and storytelling.
Day 3: Learning from the Masters of the Industry
As the third day continued, we had the honor of meeting expert guest speakers from United Soft, Lola Animation, and Media House — three leading names in the Uzbek media and animation industry. These professionals didn’t just share their resumes — they opened up about their creative journeys, career challenges, and what it takes to thrive in this competitive field.
We discussed emerging trends in animation, the growing demand for interdisciplinary skills, and how to stay relevant in a fast-changing world. The sessions were filled with practical advice and real-life insights, inspiring us to dream big and keep pushing our creative limits.
Day 4: Practice, Projects, and Professionalism
On the fourth day, the focus shifted towards hands-on learning and teamwork. Our lessons continued as usual, but with a deeper emphasis on collaborative projects and real-world applications.
One of the key projects launched was “Keyframe uz” (Ctrl+Z) — a creative team challenge that required us to brainstorm, plan, and begin working on an original animation concept. We started building ideas, assigning roles, and discussing technical strategies. The experience of working in a team — learning to listen, lead, and adapt — was one of the most valuable takeaways of the camp.
Additionally, we explored VFX (Visual Effects) as a career direction. We examined the workflow used in the industry, the level of discipline required, and the importance of consistency and attention to detail in post-production work. It was both eye-opening and exciting, especially for those of us who dream of working behind the scenes in film or digital media.
Day 5: Soulful Reflections with Erkin Bozorov
Just when we thought the camp couldn’t get any more inspiring, Day 5 gave us a beautiful surprise — a motivational and cultural session with the beloved poet and speaker Erkin Bozorov.
Held in a warm and welcoming atmosphere, the session focused on themes of self-discovery, purpose, creativity, and resilience. Mr. Bozorov shared personal stories and reflected on the challenges young people face in finding their path.
His words encouraged us to trust our instincts, stay disciplined, and embrace our uniqueness.
But the highlight was his reading of motivational poems, which captivated everyone. His voice, filled with emotion and wisdom, left a deep impression on us all. We engaged in a heartfelt conversation, asking questions, sharing thoughts, and connecting as a community of learners and dreamers.
What Made ANIM CAMP So Special?
From dawn to dusk, ANIM CAMP 2025 offered a perfect balance of education, creativity, and joy. Each day was carefully designed to encourage exploration, collaboration, and personal development. Some of the most remarkable features of the camp included:
Daily animation and AI workshops
Hands-on poster design using artificial intelligence tools
Masterclasses by industry experts and professional animators
Creative group activities, games, and challenges with prizes
Opportunities to present projects and receive feedback
Delicious food and a cozy camp atmosphere
Inclusive participation for both girls and boys, promoting equality in creativity
Motivational sessions to fuel ambition and purpose
Final Reflections: More Than Just a Camp
ANIM CAMP was not just a camp — it was a gateway to possibility. It gave us access to resources, mentorship, and experiences that many young creators only dream of. More importantly, it gave us the confidence to believe that our ideas matter and that we have the tools to shape our futures.
For me, it was a turning point — a place where I learned not only how to animate, but how to dream boldly and create fearlessly. I left the camp with new skills, amazing friends, unforgettable memories, and a renewed sense of direction.
To every young person who wants to explore their creative potential: opportunities like ANIM CAMP are where your journey begins. Don’t hesitate — step into the world of animation and let your imagination come alive.
Surayyo Nosirova Elyor qizi was born on May 13, 2006, in the Narpay district of the Samarkand region, Uzbekistan. From an early age, she showed a deep interest in literature, languages, and creative expression. Her passion for learning and writing became evident during her school years, where she actively participated in various academic, literary, and cultural activities. Currently, Surayyo is a sophomore student at the Uzbekistan State World Languages University, specializing in English Philology and Teaching. She is known for her strong academic performance and her dedication to mastering the English language. Her commitment to education extends beyond the classroom—she is the author of three published books: Heartfelt Thoughts, Voices in Writing, and Beyond Words: Mastering English. Each of these works reflects her insights into language learning, writing skills, and the emotional depth of student life. In addition to her books, Surayyo has written numerous articles.
In a small village lived two little brothers, Idil and Imir. Alongside the brothers were their elderly grandfather, a fat cow, and a constantly meowing kitten. Both brothers were very mischievous children. While their grandfather worked in the fields, they would jump and play around him, and the old man, watching their joy, would smile to himself.
Days passed, and for twelve months of the year, the grandfather never rested. Every autumn, he would take Idil and Imir to the city and buy them new clothes and toys. The children were always thrilled to go to the city. Surrounded by forests, the village was so isolated that they would wait an entire year for that one trip to town. They would pester their grandfather constantly:
“Grandpa, when will autumn come? Why doesn’t autumn come twice a year?”
They never let the old man rest with such questions.
The village was located in the farthest corner of the country, surrounded by forests and valleys, and it had fallen far behind in terms of development. It was as if this place had been left behind by time, frozen and forgotten. Things that were invented long ago in the city would only reach their village a year or two later. Life itself – and the government too – seemed to have forgotten this place. The people lived and died in their own way, unnoticed by the world.
Whenever Idil and Imir went to the city, it felt as if they had entered an entirely different world.
Seasons changed, and finally, autumn came. The old grandfather joined the other villagers, and together with Idil and Imir, they set out for the city. After eight days of travel, they arrived in the city just in time for lunch. As they reached the central school, the bell began to ring.
“Jingle-jingle”
Like a dam bursting through the river, the children poured into the schoolyard.
Idil and Imir stood in awe, watching the children – clean, neat, and dressed identically. Their old grandfather tugged at their sleeves.
“Come on, let’s not fall behind. We still have a lot to buy.”
“Grandpa, what is that?”
As the children followed their grandfather, they couldn’t take their eyes off their peers. Their games seemed completely different, fascinating. Had they ever seen such things before?
The grandfather and the boys wandered around the market. They bought everything they needed. But neither Idil nor Imir could forget the children in matching uniforms.
The villagers began preparing for the journey back home. The boys longed to pass by that same place again, to see those children one more time, but the guide led them down a completely different street.
At last, everyone returned home, riding carts full of gifts and purchases, satisfied. Yet this time, Idil and Imir were not jumping for joy as they usually did.
“Grandpa,” Idil nudged the old man, “what was that place, where the children were?”
The grandfather’s expression darkened. His already wrinkled face tightened further in thought.
“That… that’s a school.”
“A school?!”
“Yes.”
“What do they do there?”
“They study.”
“What is studying?”
“Studying is…” the old man’s face scrunched even more, “…where they write, draw, and do things like that.”
Others joined in the conversation between the old man and the boys. Everyone started talking about things they had never seen with their own eyes.
“They say they beat children in school!”
“No way…”
Sitting on the edge of the cart was a small-framed young man whose face was covered with large blotches. He started an intriguing conversation.
“Could it really be that they beat them?!”
“Yes, with a long stick, they say,” someone replied.
Idil was intrigued by this.
“Does everyone go to school?” he asked.
“Everyone does,” the same young man answered.
“Then why don’t we go? We’re people too, aren’t we?”
“Because we don’t have a school,” said a fat man with a large belly, laughing as if he had just told the funniest joke. But when he saw that no one else was laughing, he gave a little cough and fell silent.
The cart rolled along slowly. Stars twinkled above. Just like their owners, the horses pulling the cart walked with their heads lowered. Everyone was quiet, walking with their heads down. Even Idil and Imir could feel deep inside that it wasn’t the right moment to ask any more questions.
The cart driver finally lost his patience and flicked his whip, urging the horses forward.
“Chuv! Move, you creatures, chuv!”
“Where are the spirited young men of this golden valley? Where are the beautiful maidens of these homes…?”
He began to sing the familiar song at the top of his voice. The others joined in chorus. It was such a relief – everyone had secretly longed to escape the heavy burden of those difficult questions. As if released from a weight pressing down on their shoulders, their faces lit up. Cheerfully, they continued on their way. There was bread, there was water – the days passed. Who really needed school anyway?
Only Idil, Imir, and the old grandfather did not smile. His stern face grew darker still. A sorrowful look settled in his eyes.
Finally, they arrived home. Idil and Imir fell asleep. But the old grandfather did not sleep. Early in the morning, the children woke to a stir of noise. Something was happening.
Their grandfather was gathering things into a sack. From outside came the voice of the cart driver:
“Hey, old man! Why are you bothering me at the crack of dawn? I haven’t even recovered from yesterday’s exhaustion.”
“Take me to the city.”
“To the city? But we just came back yesterday.”
Just then, the fat man from yesterday entered, holding a small bag. He handed it to the grandfather.
“Your house wasn’t really worth this much – but since you’re my neighbor, fine. Still, why are you selling it?”
“I’m leaving.”
“Seriously? Where to?”
“To school!”
At the grandfather’s words, both the neighbor and the cart driver burst out laughing.
But the old man ignored them and began dressing his grandchildren. The boys were overjoyed.
At last, the cart driver, sensing the seriousness of the situation, tried to talk the old man out of it.
“Come on now, could we really go to school? Look at those who left before – none of them came back. The city’s not like the village. The city is heartless.”
“Are you taking us or not?” the grandfather stared straight at him.
Realizing it was useless to argue, the cart driver gave in.
“Fine… but you’ll pay me more.”
“Alright.”
The villagers came out to see them off. Some, with tears in their eyes, wished them good luck; others scoffed and chuckled with disbelief.
On the cart sat Old Grandpa, the little brothers Idil and Imir, their constantly meowing kitten, and the cart driver. The fat cow had been sold to the fat neighbor.
The old man turned to look at the village fading into the distance and said:
“Someone has to begin…”
But no one heard his voice except himself. Then, glancing at his two hopeful, dream-filled grandchildren who reminded him of his younger days, he smiled.
“They’re not like me,” he whispered.
With pride, the old man raised his humble head – something he had never done before. The road was long ahead, but now it was time to prove to the world that they too existed, that they too mattered.
At that very moment, in a small home back in the village, a young bride hung a tiny clock on the wall – a wedding gift from her husband.
“Tick.” “Tick.” Time began to count the seconds.
Urazaliyeva Sarvinoz Saidakhmadovan was born on December 27, 2002, in Sirdarya region. She is currently pursuing an incomplete higher education. In 2020, she graduated from the specialized boarding school for English language in Mirzaobod district. She is now a 4th-year student at the Nizami Tashkent State Pedagogical University. In 2021, she became the winner of the regional stage and a participant of the national stage in the prose category of the “Duel” Republican Creative Contest.
