Convection heat transfer enhancement techniques play a vital role in many industrial thermal processing applications, including food thermal processing, and the pharmaceutical, and chemical manufacturing industries. These techniques contribute to reducing the size and cost of heat exchangers, conserving energy, improving product quality, and enhancing both energy efficiency and thermal performance. Among passive solutions, corrugated wall tubes are widely adopted in heat exchangers for such applications. This study applies the inverse heat conduction problem (IHCP) method combined with infrared thermography data to estimate the local temperature and convective heat transfer coefficient distributions for forced convection in a transversally corrugated wall tube with high viscosity fluid flow under laminar conditions. The IHCP is solved within the corrugated wall domain using measured external wall temperatures as input. Thermal performance was evaluated over a Reynolds number range of 290–1200. The findings showed that at Re $<$ 350, irregular local temperature and convective heat transfer distributions led to reduced thermal efficiency, unreliable sterilization, and increased microbial risk, whereas for 650 $<$ Re $<$ 1200, thermal efficiency improved significantly. These findings support the development of more efficient heat exchanger designs, offering significant benefits to industries requiring precise thermal management.
Perovskite solar cells (PSCs) continue to advance toward higher efficiencies, yet the geometrical design of functional layers remains a critical bottleneck for device optimization and manufacturability. This work establishes a hybrid physics-data framework that integrates three-dimensional finite-element modeling with machine-learningbased surrogate prediction to accelerate PSC thickness optimization. A full 3D COMSOL Multiphysics model was developed to resolve charge-transport behavior, spatial electric fields, and recombination profiles within TiO2/MAPbI3/Spiro-OMeTAD architectures. Systematic variations in electron transport layer (ETL), perovskite absorber, and hole transport layer (HTL) thicknesses reveal that device power conversion efficiency (PCE) is governed by a trade-off between optical absorption, interface recombination, and resistive losses. A multi-layer perceptron regressor was trained using simulation data and achieved strong predictive fidelity (R2 ≈ 0.98) with a mean absolute error below 0.3%. The resulting surrogate model rapidly identifies optimal structural configurations without requiring additional high-cost simulations, demonstrating a reduction of design time by more than an order of magnitude. The proposed workflow provides a transferable route toward digital-twin-driven photovoltaic design and offers practical guidance for high-performance PSC engineering with reduced material consumption and enhanced computational efficiency.
Despite their influence on the stability of underground excavations, mineralized veinlets, particularly those composed of pyrite and chalcopyrite, are often underestimated in traditional geomechanical models. The lack of experimental data on their tensile behavior under direct stress represents a critical gap in rock mass characterization. This study experimentally evaluated the direct tensile strength of pyrite and chalcopyrite veinlets from the El Teniente mine, in order to enhance the accuracy of geotechnical models for complex geological contexts. Following the Organization for Economic Cooperation and Development (OECD) 203 (2019) guidelines, a fully randomized experimental design was employed to conduct direct tensile testing of 19 veinlet samples. The results showed that chalcopyrite veinlets exhibited greater internal cohesion with significantly higher tensile strength, reaching up to 3.17 MPa, compared to pyrite veinlets of lower values. Furthermore, chalcopyrite veinlets demonstrated a more homogeneous and cohesive failure behavior compared to pyrite, which displayed greater surface roughness and interfacial failure. This study highlights the importance of incorporating veinlet mineralogy into geotechnical models to improve underground design and safety.
Modern image processing systems deployed on embedded and heterogeneous platforms face increasing pressure to deliver high performance under strict energy and real-time constraints. The rapid growth in image resolution and frame rates has significantly amplified computational demand, making uniform full-precision processing increasingly inefficient. This paper presents a significance-driven adaptive approximate computing framework that reduces energy consumption by tailoring computational precision and resource allocation to the spatial importance of image content. We introduce a statistical importance metric that captures local structural variability using low-complexity deviation-based analysis on luminance information. The metric serves as a lightweight proxy for identifying regions that are more sensitive to approximation errors, enabling differentiated processing without the overhead of semantic or perceptual saliency models. Based on this importance classification, the proposed framework dynamically orchestrates heterogeneous CPU–GPU resources, applies variable kernel sizes, and exploits dynamic voltage and frequency scaling (DVFS) to reclaim timing slack for additional energy savings. The framework is validated through two complementary case studies: (i) a heterogeneous software implementation for adaptive convolution filtering on an Odroid XU-4 embedded platform, and (ii) a hardware-level approximate circuit allocation approach using configurable-precision arithmetic units. Experimental results demonstrate energy reductions of up to 60\% compared to uniform-precision baselines, while maintaining acceptable visual quality. Image quality is evaluated using both PSNR and the perceptually motivated SSIM metric, confirming that the proposed approach preserves structural fidelity despite aggressive approximation.
Open Access
ririt dwiputri permatasari 
, m. ansyar bora , luki hernando , vitri aprilla handayani , taufiq rahman , larisang , m. ropianto , tommy saputra , fitri mehdini addieningrum , dukhroni ali , alhamidi , haidil fauzan , nur shilah , muhamad andrian yudhistira , shafira putri rheyna , fani rahma yanti , anisa fitrianti |
Available online: 12-30-2025
This study investigates how cognitive ergonomics-based interface design can enhance user experience and reduce cognitive workload in digital da’wah applications, using the MASJIDA mobile application as a case study. While existing digital da’wah platforms primarily emphasize functional features and content dissemination, limited attention has been given to systematic evaluations of usability and cognitive load. To address this gap, this study integrates cognitive ergonomics principles into the design and evaluation of MASJIDA, a mobile application developed to support mosque management and congregational engagement. A pre-test and post-test experimental design was employed involving mosque administrators and congregants. System usability was measured using the System Usability Scale (SUS), while cognitive workload was assessed using the NASA Task Load Index (NASA-TLX). The results demonstrate a substantial improvement in usability, with SUS scores increasing from 55.1 to 79.3 for congregants and from 55.5 to 85.4 for mosque administrators. In parallel, NASA-TLX results reveal a significant reduction in mental demand, effort, and frustration, indicating lower cognitive workload after implementation. These findings confirm that applying cognitive ergonomics principles contributes not only to improved usability but also to more cognitively efficient user interactions. This study provides empirical evidence and analytical insights for the development of user-centered digital religious applications that balance functional effectiveness with cognitive accessibility.
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