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All Journal World Psychology Journal Markcount Finance Rechtsnormen Journal of Law Sharia Oikonomia Law Journal Journal of Islamic Education Students Journal of Social Science Utilizing Technology International Journal of Language and Ubiquitous Learning Journal of Social Science Utilizing Technology Darussalam: Journal of Psychology and Educational Journal of Moeslim Research Technik International Journal of Educatio Elementaria and Psychologia Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Journal of Biomedical and Techno Nanomaterials Journal of Humanities Research Sustainability
Santos, Luis
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Quantum Computing to Forecast Extreme Weather Rith, Vicheka; Vann, Dara; Santos, Luis
Journal of Tecnologia Quantica Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i3.1970

Abstract

The background of this research focuses on the challenges in forecasting extreme weather that is increasingly frequent due to climate change. Conventional weather models still face limitations in terms of accuracy and computational time, especially in predicting extreme weather phenomena. The purpose of this study is to explore the potential of quantum computing in predicting extreme weather by improving prediction accuracy and accelerating computational processes. The research method used involves the development and testing of weather prediction models based on quantum algorithms on extreme weather phenomena such as tropical storms, heavy rains, and heat waves. The results show that the quantum model is able to improve prediction accuracy by up to 92% for tropical storms and accelerate the computational time from 48 hours to 5 hours. The conclusion of the study is that quantum computing offers a more efficient and accurate solution in forecasting extreme weather, with great potential for practical applications in early warning and mitigation of weather disasters.
Identification of Non-Invasive Biomarkers for Early Detection of Ovarian Cancer Takahashi, Haruto; Tanaka, Kaito; Santos, Luis
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i2.2017

Abstract

Ovarian cancer is one of the most lethal gynecologic malignancies due to late diagnosis. Early detection is critical for improving survival rates, yet current screening methods are inadequate. To identify and validate non-invasive biomarkers for the early detection of ovarian cancer, focusing on improving diagnostic accuracy and patient outcomes. This study utilized proteomic and genomic approaches, including mass spectrometry for protein profiling and next-generation sequencing for analyzing cfDNA and miRNAs. Blood samples from patients with early-stage ovarian cancer, healthy controls, and individuals with benign conditions were analyzed. The combination of CA-125 and HE4 biomarkers significantly increased sensitivity (85%) and specificity (90%) for early detection of ovarian cancer compared to CA-125 alone. Proteomic analysis identified significant differences in protein profiles between cancer patients and healthy controls. Genomic analysis revealed specific mutations in cfDNA associated with ovarian cancer. The study demonstrates that a combination of CA-125 and HE4, along with multi-omic approaches, can enhance the early detection of ovarian cancer, providing a basis for the development of more accurate diagnostic tests. Further clinical trials are necessary to validate these findings.
The Role of Subcultures in Defining Urban Identities Hidayat, Rahmat; Santos, Luis; Reyes, Maria Clara
Journal of Humanities Research Sustainability Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jhrs.v1i5.1786

Abstract

identities is often overlooked in urban studies, which place more emphasis on mainstream culture. Purpose. This study aims to identify and analyze how subcultures affect urban identity, especially in the context of social interaction and public policy in the city of Padang.   Method. The research uses a qualitative approach with case studies, involving in-depth interviews and participatory observations. Results. The results show that subcultures have a significant influence in shaping urban identity through cultural expression, social participation, and more inclusive urban policies. Subcultures not only enrich the visual and aesthetic dimensions of cities, but they also play an important role in creating more welcoming and participatory public spaces.   Conclusion. This study concludes that subcultures can be a strategic component in more sustainable and adaptive urban planning, albeit with limitations in scope and research methods that need to be further developed.
Beyond Symptom Reduction: The Role of Mindfulness-Based Lifestyle Interventions in Cultivating Flourishing and Psychological Capital in Urban Populations Santos, Luis; Reyes, Maria Clara; Khan, Ali
Research Psychologie, Orientation et Conseil Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/rpoc.v2i5.2297

Abstract

Urban populations often face high levels of stress, anxiety, and burnout, which can negatively affect their overall well-being. While mindfulness-based interventions (MBIs) have been widely studied for their effectiveness in reducing symptoms of mental health disorders, less attention has been paid to their potential in fostering positive psychological outcomes such as flourishing and psychological capital. Flourishing, characterized by life satisfaction, meaning, and engagement, alongside psychological capital, which includes hope, resilience, self-efficacy, and optimism, are essential for long-term well-being and adaptive functioning. This study aims to explore the role of mindfulness-based lifestyle interventions (MBLIs) in cultivating flourishing and psychological capital among urban populations, beyond the reduction of negative symptoms. The research utilized a mixed-methods design, involving pre- and post-assessments using the Flourishing Scale, Psychological Capital Questionnaire, and qualitative interviews. A sample of 100 urban residents participated in an 8-week MBLIs program. Results indicated significant improvements in flourishing (mean increase of 1.2 points) and psychological capital (mean increase of 1.2 points). Participants reported higher levels of self-efficacy, optimism, and emotional resilience. In conclusion, mindfulness-based interventions can serve as a valuable tool in enhancing psychological resilience and well-being, particularly in urban populations, supporting long-term mental health and flourishing.
Quantum Machine Learning for Drug Discovery: Accelerating the Simulation of Molecular Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) Devices Santos, Luis; Reyes, Maria Clara; Gonzales, Samantha; Anurogo, Dito
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2796

Abstract

Drug discovery increasingly relies on accurate simulation of molecular Hamiltonians, yet classical computational methods face exponential scaling barriers when modeling complex quantum systems. Recent advances in quantum machine learning (QML) and the availability of Noisy Intermediate-Scale Quantum (NISQ) devices offer new opportunities to accelerate molecular simulation despite hardware noise and qubit limitations. This study aims to evaluate the effectiveness of QML-based variational algorithms in improving the efficiency and accuracy of Hamiltonian simulation for drug-relevant molecules on NISQ platforms. A hybrid quantum–classical methodology was employed, combining variational quantum eigensolvers, noise-aware circuit optimization, and supervised learning models trained to predict energy landscapes. Experimental simulations were performed using IBM-Q and Rigetti NISQ architectures, supported by classical benchmarks for validation. The results demonstrate that QML-enhanced variational circuits significantly reduce computational depth while maintaining competitive accuracy compared to classical methods, particularly for medium-sized molecular systems. The findings also reveal that noise-adaptive training improves algorithm robustness, enabling more reliable energy estimation under realistic quantum noise conditions. The study concludes that QML provides a promising pathway for accelerating early-stage drug discovery by enabling efficient molecular Hamiltonian simulation on current-generation quantum hardware. Further integration of error mitigation and scalable QML frameworks will be essential for future advancements.
Coherent Coupling Between a Superconducting Qubit and a Spin Ensemble in a Hybrid Quantum System for Microwave-to-Optical Transduction Gomez, Raul; Rocha, Thiago; Santos, Luis
Journal of Tecnologia Quantica Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i6.3198

Abstract

The coupling of superconducting qubits with spin ensembles has emerged as a promising solution to bridge the microwave-optical frequency gap in hybrid quantum systems. These systems are crucial for advancing quantum communication, quantum networks, and integrated quantum technologies. However, achieving coherent coupling between these two platforms remains a significant challenge due to the differences in their operational frequency regimes and their susceptibility to decoherence. This research aims to explore the coherent coupling between a superconducting qubit and a spin ensemble, specifically focusing on its potential for efficient microwave-to-optical transduction. The primary objective of this study is to develop a hybrid quantum system that enables the transfer of quantum information between microwave and optical domains with minimal loss of coherence. Experimental and theoretical approaches were used, involving superconducting qubits and nitrogen-vacancy (NV) centers in diamonds as the spin ensemble. The results demonstrate that the coupling mechanism is efficient, achieving high transduction efficiencies and long coherence times, particularly at optimized coupling strengths. These findings suggest that the hybrid system can be used for scalable quantum communication systems, facilitating quantum information transfer across different frequency domains. In conclusion, this study provides a robust method for microwave-to-optical transduction, opening new avenues for quantum network development and hybrid quantum technologies.
A GIS-BASED DECISION SUPPORT SYSTEM FOR SUSTAINABLE LAND USE PLANNING AND CLIMATE CHANGE ADAPTATION IN THE BRANTAS RIVER WATERSHED Hakim, Dani Lukman; Santos, Luis; Reyes, Maria Clara
Techno Agriculturae Studium of Research Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i3.2522

Abstract

The Brantas River Watershed, a vital socio-economic region in East Java, Indonesia, faces escalating environmental pressures from unplanned urbanization and intensive agriculture. These challenges are exacerbated by climate change, leading to increased land degradation, soil erosion, and severe flood events, which threaten the watershed’s long-term sustainability and the livelihoods of millions. This study aimed to develop and validate a Geographic Information System (GIS)-based Decision Support System (DSS) to aid policymakers in formulating integrated, evidence-based strategies for sustainable land use planning and climate change adaptation within this critical watershed. The DSS was constructed by integrating a multi-criteria evaluation (MCE) framework within a GIS environment. Key geospatial datasets (land cover, soil type, slope, rainfall projections) were weighted using the Analytical Hierarchy Process (AHP). The system models land suitability and vulnerability to environmental hazards under various climate change scenarios. The developed DSS successfully generated high-resolution maps identifying priority zones for conservation, reforestation, and sustainable development. The model revealed that 22% of the upper watershed area is at high risk of landslides under projected rainfall patterns. The optimized land use plan proposed by the DSS demonstrated a potential to reduce surface runoff by up to 35%, significantly mitigating flood risk. The GIS-based DSS is a powerful and effective tool for integrated watershed management. It provides a dynamic, scientifically-grounded platform for strategic planning, enabling policymakers to balance ecological protection with socio-economic needs and enhance the climate resilience of the Brantas River Watershed.
An Electrical Engineering Analysis of Energy-Efficient Lighting and Climate Control Systems for Modern Mosques Santos, Luis; Sulaiman, Sulaiman; Lan, Thi
Journal of Moeslim Research Technik Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i5.2504

Abstract

The increasing demand for energy-efficient solutions in public buildings has led to a growing interest in sustainable technologies for modern mosques. As places of worship with high foot traffic and long operating hours, mosques represent an ideal setting for implementing energy-efficient lighting and climate control systems. However, there is limited research on optimizing energy usage in these religious spaces while maintaining comfort and functionality. This study aims to analyze the energy efficiency of lighting and climate control systems in modern mosques and propose improvements that align with sustainable energy practices. Using an electrical engineering approach, this research employs simulation models and energy consumption data to evaluate current systems in selected mosques. Various energy-efficient technologies, including LED lighting, smart thermostats, and automated climate control systems, are assessed for their potential in reducing energy consumption. The results indicate that integrating energy-efficient lighting and climate control solutions can significantly reduce energy use, with potential savings of up to 40%. The study concludes that adopting these systems not only promotes sustainability but also aligns with the Islamic principles of conservation and stewardship of resources. Recommendations for mosque administrators include investing in smart energy solutions and regular monitoring to optimize energy consumption.
Co-Authors Aji, Wahyu Trisno Dito Anurogo, Dito Dony Darma Sagita Flores, Josefa Gomez, Raul Gonzales, Samantha gunawan, mohamad sigit Iqbal, Kiran Iriana Bakti Izzati, khofifah Nurul Khan, Ali Lan, Thi Malik, Fatima Muh. Safar, Muh. Nampira, Ardi Azhar Nia Kurniati Nofirman, Nofirman Rahmat Hidayat Reyes, Maria Clara Rith, Vicheka Rocha, Thiago Simarmata, Sari Wardani Sulaiman Sulaiman Sultana, Momena Takahashi, Haruto Tanaka, Kaito Tariq, Usman Tu, Nguyen Minh Uliya Khoirun Nisa Vann, Dara Yeni, Putri