Transactions on Intelligent Computing Systems - Articles
Browse published articles from Transactions on Intelligent Computing Systems
Álvarez, R., Sørensen, B., Aaltonen, H., Rahman, Y., Wiśniewski, E.
Understanding static analysis is of central importance, yet existing approaches rely on restrictive assumptions. We show that the approach remains accurate under severe label noise. The analysis uses a combination of rigorous arguments and extensive numerical or empirical checks. In contrast to earlier studies, our approach accounts for heterogeneity and avoids the usual simplifying assumptions. We show that the approach remains accurate under severe label noise. These results clarify the role of the underlying mechanisms and suggest several directions for future work.
Towards reproducible edge inference with formal guarantees
Álvarez, R., Sørensen, B., Aaltonen, H., Rahman, Y., Wiśniewski, E.
Understanding static analysis is of central importance, yet existing approaches rely on restrictive assumptions. We show that the approach remains accurate under severe label noise. The analysis uses a combination of rigorous arguments and extensive numerical or empirical checks. In contrast to earlier studies, our approach accounts for heterogeneity and avoids the usual simplifying assumptions. We show that the approach remains accurate under severe label noise. These results clarify the role of the underlying mechanisms and suggest several directions for future work.
Wang, H., Yin, S., Lian, H., Cao, M., Hou, E., Wang, X., Xing, G.
The mining of extremely thick coal seams causes strong disturbances to the floor strata. When a strong aquifer exists beneath the coal seam and the effective aquiclude is weak, the risk of floor water inrush increases significantly. In recent years, microseismic (MS) monitoring technology has been widely applied, and using MS monitoring to assess floor water inrush risk has proven to be a reliable and effective approach. To address the high-risk problem of floor water inrush during the extraction of extremely thick coal seams, this study employed MS monitoring to investigate the evolution characteristics of floor MS activity and its associated water inrush risk. The MS response characteristics induced by thick seam mining were analyzed, and a floor water inrush risk evaluation model was established based on the Analytic Hierarchy Process–Entropy Weight Method (AHP–EWM). The evaluation results were consistent with field monitoring data. The results indicate that most floor MS events in the 61607 working face of the Longwanggou Coal Mine were concentrated in the Taiyuan Formation. The MS monitoring indicators, such as the extent of the advanced influence zone, floor failure depth, and total daily released energy, showed strong correlations with observed data including water inflow and mine pressure. By summarizing the key controlling factors of floor water-inrush risk, this study departs from traditional static geological parameter indicators and instead focuses on dynamic indicators derived from the activity, spatiotemporal characteristics, and intensity characteristics of floor MS events. Integrating multiple influential factors of floor water inrush, a quantitative evaluation system for floor water-inrush hazard in extremely thick coal seam mining was established using the AHP–EWM combined subjective–objective weighting method, which shows good consistency with field monitoring results. This study provides theoretical and data support for analyzing the dynamic process and influencing factors of floor damage and fracture evolution under thick seam mining. It offers a new approach for predicting and forecasting water inrush risk, enriches the application of MS monitoring in mine floor water hazard prevention, and holds significant practical importance for promoting safe coal mine production.
Investigation on the dynamic evolution and evaluation of floor microseismic responses during extra-thick coal seam mining
Wang, H., Yin, S., Lian, H., Cao, M., Hou, E., Wang, X., Xing, G.
The mining of extremely thick coal seams causes strong disturbances to the floor strata. When a strong aquifer exists beneath the coal seam and the effective aquiclude is weak, the risk of floor water inrush increases significantly. In recent years, microseismic (MS) monitoring technology has been widely applied, and using MS monitoring to assess floor water inrush risk has proven to be a reliable and effective approach. To address the high-risk problem of floor water inrush during the extraction of extremely thick coal seams, this study employed MS monitoring to investigate the evolution characteristics of floor MS activity and its associated water inrush risk. The MS response characteristics induced by thick seam mining were analyzed, and a floor water inrush risk evaluation model was established based on the Analytic Hierarchy Process–Entropy Weight Method (AHP–EWM). The evaluation results were consistent with field monitoring data. The results indicate that most floor MS events in the 61607 working face of the Longwanggou Coal Mine were concentrated in the Taiyuan Formation. The MS monitoring indicators, such as the extent of the advanced influence zone, floor failure depth, and total daily released energy, showed strong correlations with observed data including water inflow and mine pressure. By summarizing the key controlling factors of floor water-inrush risk, this study departs from traditional static geological parameter indicators and instead focuses on dynamic indicators derived from the activity, spatiotemporal characteristics, and intensity characteristics of floor MS events. Integrating multiple influential factors of floor water inrush, a quantitative evaluation system for floor water-inrush hazard in extremely thick coal seam mining was established using the AHP–EWM combined subjective–objective weighting method, which shows good consistency with field monitoring results. This study provides theoretical and data support for analyzing the dynamic process and influencing factors of floor damage and fracture evolution under thick seam mining. It offers a new approach for predicting and forecasting water inrush risk, enriches the application of MS monitoring in mine floor water hazard prevention, and holds significant practical importance for promoting safe coal mine production.
Ortiz, C., Alva, J., Raucana, R., Chipana, M., Oliden, J., Huarcaya, N., Riveros, G., Valverde, J.
Accelerographs are essential instruments for quantifying strong ground motion, serving as the foundation of modern earthquake engineering. In Peru, the first accelerographic station was installed in Lima in 1944; since then, various institutions have promoted the expansion of the national network. However, this network’s spatial coverage and instrumentation remain insufficient to properly characterize strong motion and support seismic risk reduction policies. In this context, the KUYUY accelerograph is presented as a low-cost, low-noise device equipped with real-time telemetry and high-performance MEMS sensors. Its interoperability with the Intelligent Automatic Processing System (SIPA) enables real-time monitoring and automated signal analysis for seismic microzonation studies and rapid damage assessment, contributing to seismic risk reduction in Peru. The validation process included static gravity calibration, field comparison with a reference accelerograph, and an initial deployment in Lima and Yurimaguas. The results demonstrate the proposed accelerograph’s linear response, temporal stability, and amplitude consistency with respect to high-end instruments, with differences below 5–10%.
The KUYUY Accelerograph and SIPA System: Towards Low-Cost, Real-Time Intelligent Seismic Monitoring in Peru
Ortiz, C., Alva, J., Raucana, R., Chipana, M., Oliden, J., Huarcaya, N., Riveros, G., Valverde, J.
Accelerographs are essential instruments for quantifying strong ground motion, serving as the foundation of modern earthquake engineering. In Peru, the first accelerographic station was installed in Lima in 1944; since then, various institutions have promoted the expansion of the national network. However, this network’s spatial coverage and instrumentation remain insufficient to properly characterize strong motion and support seismic risk reduction policies. In this context, the KUYUY accelerograph is presented as a low-cost, low-noise device equipped with real-time telemetry and high-performance MEMS sensors. Its interoperability with the Intelligent Automatic Processing System (SIPA) enables real-time monitoring and automated signal analysis for seismic microzonation studies and rapid damage assessment, contributing to seismic risk reduction in Peru. The validation process included static gravity calibration, field comparison with a reference accelerograph, and an initial deployment in Lima and Yurimaguas. The results demonstrate the proposed accelerograph’s linear response, temporal stability, and amplitude consistency with respect to high-end instruments, with differences below 5–10%.
Abdullahi, M., Piro, R., Suratanee, A., Plaimas, K.
Identifying disease-relevant biological pathways is critical for understanding the molecular mechanisms underlying specific disease phenotypes. Traditional techniques, such as gene set analysis, often neglect topological interactions among genes within actual biological pathways. To address this limitation, we introduce GenPath-PPH, a novel framework that integrates gene expression data with directed biological pathway networks using persistent path homology (PPH), a topological tool for analyzing directional relationships. GenPath-PPH tracks changes in correlation strength between interacting genes across two conditions (e.g., disease and control) and interprets these differences as topological, disease-related alterations in the pathway network. Within pathways, it identifies changes in connected components (co-expression clusters) as well as in higher-order structures (directed cycles), which are not detectable by conventional homology methods. By combining connectivity and cyclic features, GenPath-PPH highlights significantly altered pathways, using permutation testing to assess statistical significance. When applied to peripheral blood mononuclear cell (PBMC) samples from hepatocellular carcinoma (HCC) patients, GenPath-PPH not only identifies well-known cancer-associated pathways (e.g., JAK-STAT signaling, p53 signaling and the pentose phosphate pathway) in accordance with other techniques, but also reveals additional pathways (e.g., NF-κB signaling, sphingolipid signaling and aminoacyl-tRNA biosynthesis) that are either missed by other techniques, despite their known relevance to HCC, or represent novel candidate pathways for experimental evaluation. Our work bridges network topology and biological function, offering a new analytical approach capable of uncovering previously overlooked connections between topological structure and functional activity.
GenPath-PPH: Integrating gene expression and pathway networks via persistent path homology enhances detection of disease-relevant pathways
Abdullahi, M., Piro, R., Suratanee, A., Plaimas, K.
Identifying disease-relevant biological pathways is critical for understanding the molecular mechanisms underlying specific disease phenotypes. Traditional techniques, such as gene set analysis, often neglect topological interactions among genes within actual biological pathways. To address this limitation, we introduce GenPath-PPH, a novel framework that integrates gene expression data with directed biological pathway networks using persistent path homology (PPH), a topological tool for analyzing directional relationships. GenPath-PPH tracks changes in correlation strength between interacting genes across two conditions (e.g., disease and control) and interprets these differences as topological, disease-related alterations in the pathway network. Within pathways, it identifies changes in connected components (co-expression clusters) as well as in higher-order structures (directed cycles), which are not detectable by conventional homology methods. By combining connectivity and cyclic features, GenPath-PPH highlights significantly altered pathways, using permutation testing to assess statistical significance. When applied to peripheral blood mononuclear cell (PBMC) samples from hepatocellular carcinoma (HCC) patients, GenPath-PPH not only identifies well-known cancer-associated pathways (e.g., JAK-STAT signaling, p53 signaling and the pentose phosphate pathway) in accordance with other techniques, but also reveals additional pathways (e.g., NF-κB signaling, sphingolipid signaling and aminoacyl-tRNA biosynthesis) that are either missed by other techniques, despite their known relevance to HCC, or represent novel candidate pathways for experimental evaluation. Our work bridges network topology and biological function, offering a new analytical approach capable of uncovering previously overlooked connections between topological structure and functional activity.
Real-Time Synchronized Multi-Axis Platform for Microscopic Image Acquisition and Matrix-Based Fusion
YALÇIN, C., Ersoy, M.
This study presents the design, implementation, and validation of a novel real-time synchronized multi-axis microscopy platform engineered for the acquisition and fusion of high- resolution images from large specimens, such as lamellae. Traditional microscopes face inherent limitations in imaging expansive samples and offering versatile movement capabilities, necessitating systems that provide a wide field of view, high resolution, and detailed analytical capacity (Smith, 2021; Johnson & Thompson, 2020). The developed platform incorporates a precision-controlled stage capable of movement along the X, Y, and Z axes while maintaining a constant focal height. This enables automated, matrix-based image acquisition, allowing the platform to execute automatic capture operations by dividing the specimen into matrices (e.g., 3x7, 4x8) and following user-defined patterns, including serpentine scans. Captured images are stored with unique identifiers. The accompanying software suite facilitates robust image fusion using conventional image processing techniques. This fusion process leverages optimized feature detection algorithms such as SIFT, ORB, and AKAZE, coupled with RANSAC-based outlier rejection, precise image warping, and seamless blending methods like multi-band or gradient-domain techniques. Beyond fusion, the system integrates comprehensive, non-AI- driven image enhancement functionalities, including CLAHE for local contrast improvement, various noise reduction filters, and computational super-resolution methods. Furthermore, interpretable, non-AI quantitative analysis modules for anomaly detection, difference identification, and object counting/shape analysis are incorporated. The research rigorously validates the platform's sub-micron precision and repeatability, the
Real-Time Synchronized Multi-Axis Platform for Microscopic Image Acquisition and Matrix-Based Fusion
YALÇIN, C., Ersoy, M.
This study presents the design, implementation, and validation of a novel real-time synchronized multi-axis microscopy platform engineered for the acquisition and fusion of high- resolution images from large specimens, such as lamellae. Traditional microscopes face inherent limitations in imaging expansive samples and offering versatile movement capabilities, necessitating systems that provide a wide field of view, high resolution, and detailed analytical capacity (Smith, 2021; Johnson & Thompson, 2020). The developed platform incorporates a precision-controlled stage capable of movement along the X, Y, and Z axes while maintaining a constant focal height. This enables automated, matrix-based image acquisition, allowing the platform to execute automatic capture operations by dividing the specimen into matrices (e.g., 3x7, 4x8) and following user-defined patterns, including serpentine scans. Captured images are stored with unique identifiers. The accompanying software suite facilitates robust image fusion using conventional image processing techniques. This fusion process leverages optimized feature detection algorithms such as SIFT, ORB, and AKAZE, coupled with RANSAC-based outlier rejection, precise image warping, and seamless blending methods like multi-band or gradient-domain techniques. Beyond fusion, the system integrates comprehensive, non-AI- driven image enhancement functionalities, including CLAHE for local contrast improvement, various noise reduction filters, and computational super-resolution methods. Furthermore, interpretable, non-AI quantitative analysis modules for anomaly detection, difference identification, and object counting/shape analysis are incorporated. The research rigorously validates the platform's sub-micron precision and repeatability, the
MacNish, T., Al-Mamun, H., Bergmann, T., Bestry, M., Marsh, J., Edwards, D.
Summary Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. Availability and implementation HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license.
HaploVar: an R package for defining local haplotype variants for trait association and trait prediction analyses (JOS DOCX counterpart)
MacNish, T., Al-Mamun, H., Bergmann, T., Bestry, M., Marsh, J., Edwards, D.
Summary Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. Availability and implementation HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license.
MacNish, T., Al-Mamun, H., Bergmann, T., Bestry, M., Marsh, J., Edwards, D.
Summary Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. Availability and implementation HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license.
HaploVar: an R package for defining local haplotype variants for trait association and trait prediction analyses
MacNish, T., Al-Mamun, H., Bergmann, T., Bestry, M., Marsh, J., Edwards, D.
Summary Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar, a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. Availability and implementation HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub (https://github.com/TessaMacNish/HaploVar). HaploVar is available under an MIT license.
Osei, N.
Identifying disease-relevant biological pathways is critical for understanding the molecular mechanisms underlying specific disease phenotypes. Traditional techniques, such as gene set analysis, often neglect topological interactions among genes within actual biological pathways. To address this limitation, we introduce GenPath-PPH, a novel framework that integrates gene expression data with directed biological pathway networks using persistent path homology (PPH), a topological tool for analyzing directional relationships. GenPath-PPH tracks changes in correlation strength between interacting genes across two conditions (e.g., disease and control) and interprets these differences as topological, disease-related alterations in the pathway network. Within pathways, it identifies changes in connected components (co-expression clusters) as well as in higher-order structures (directed cycles), which are not detectable by conventional homology methods. By combining connectivity and cyclic features, GenPath-PPH highlights significantly altered pathways, using permutation testing to assess statistical significance. When applied to peripheral blood mononuclear cell (PBMC) samples from hepatocellular carcinoma (HCC) patients, GenPath-PPH not only identifies well-known cancer-associated pathways (e.g., JAK-STAT signaling, p53 signaling and the pentose phosphate pathway) in accordance with other techniques, but also reveals additional pathways (e.g., NF- κ B signaling, sphingolipid signaling and aminoacyl-tRNA biosynthesis) that are either missed by other techniques, despite their known relevance to HCC, or represent novel candidate pathways for experimental evaluation. Our work bridges network topology and biological function, offering a new analytical approach capable of uncovering previously overlooked connections between topological structure and functional activity.
Revisiting: GenPath-PPH: Integrating gene expression and pathway networks via persistent path homology enhances detection of disease-relevant pathways
Osei, N.
Identifying disease-relevant biological pathways is critical for understanding the molecular mechanisms underlying specific disease phenotypes. Traditional techniques, such as gene set analysis, often neglect topological interactions among genes within actual biological pathways. To address this limitation, we introduce GenPath-PPH, a novel framework that integrates gene expression data with directed biological pathway networks using persistent path homology (PPH), a topological tool for analyzing directional relationships. GenPath-PPH tracks changes in correlation strength between interacting genes across two conditions (e.g., disease and control) and interprets these differences as topological, disease-related alterations in the pathway network. Within pathways, it identifies changes in connected components (co-expression clusters) as well as in higher-order structures (directed cycles), which are not detectable by conventional homology methods. By combining connectivity and cyclic features, GenPath-PPH highlights significantly altered pathways, using permutation testing to assess statistical significance. When applied to peripheral blood mononuclear cell (PBMC) samples from hepatocellular carcinoma (HCC) patients, GenPath-PPH not only identifies well-known cancer-associated pathways (e.g., JAK-STAT signaling, p53 signaling and the pentose phosphate pathway) in accordance with other techniques, but also reveals additional pathways (e.g., NF- κ B signaling, sphingolipid signaling and aminoacyl-tRNA biosynthesis) that are either missed by other techniques, despite their known relevance to HCC, or represent novel candidate pathways for experimental evaluation. Our work bridges network topology and biological function, offering a new analytical approach capable of uncovering previously overlooked connections between topological structure and functional activity.
Duarte, M., Yoon, A., Olsen, M.
Preemptive kidney transplantation (PKT) is associated with reduced dialysis-related morbidity and improved graft survival, which has led to an increase in the prevalence of PKT. The distinct clinical pathways of PKT and non-preemptive kidney transplantation (NPKT) patients may affect their ability to perform self-care, a key factor in post-transplant recovery. However, there is ongoing controversy regarding which group demonstrates better self-care abilities, highlighting the need to explore the factors influencing self-care in each group. This study aims to identify and compare predictors of self-care in PKT and NPKT groups.
Predictors of self-care in kidney transplant patients according to preoperative dialysis: A comparative study: methods and sensitivity checks
Duarte, M., Yoon, A., Olsen, M.
Preemptive kidney transplantation (PKT) is associated with reduced dialysis-related morbidity and improved graft survival, which has led to an increase in the prevalence of PKT. The distinct clinical pathways of PKT and non-preemptive kidney transplantation (NPKT) patients may affect their ability to perform self-care, a key factor in post-transplant recovery. However, there is ongoing controversy regarding which group demonstrates better self-care abilities, highlighting the need to explore the factors influencing self-care in each group. This study aims to identify and compare predictors of self-care in PKT and NPKT groups.
Ivanova, D., Eriksen, I., Tembo, O.
Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar , a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub ( https://github.com/TessaMacNish/HaploVar ). HaploVar is available under an MIT license.
HaploVar: an R package for defining local haplotype variants for trait association and trait prediction analyses: methods and sensitivity checks
Ivanova, D., Eriksen, I., Tembo, O.
Marker assisted breeding (MAB) supports breeding by identifying individuals or molecular markers associated with important traits. MAB methods include genome-wide association studies (GWAS) and genomic selection (GS). Local haplotypes are regions of DNA that are inherited together due to high levels of linkage disequilibrium. Local haplotypes can improve the prediction accuracy and power of GS and GWAS. Currently available local haplotyping tools improve GWAS power through fine-mapping of candidate regions or through haplotype-based GWAS. However, no local haplotyping tools utilize the benefits of haplotypes for GS. Here we present HaploVar , a local haplotyping tool designed to improve both GWAS and GS pipelines by identifying local haplotypes and formatting the output to be compatible with all major GWAS and GS tools. HaploVar can be used in any haplotype-based MAB study. HaploVar can be downloaded from CRAN with R >4.0.0 (DOI: 10.32614/CRAN.package.HaploVar). HaploVar and a tutorial vignette is available on GitHub ( https://github.com/TessaMacNish/HaploVar ). HaploVar is available under an MIT license.