2026-06-01T17:17:05Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32533266 2026-06-01T17:14:17Z category_26407 category_26509 portal_0 item_type_3 month_year_06_2026

Resilient Cyber-Physical Sensor Fusion Filter and Windowed Watchdog Guard Architecture for Bare-Metal Firmware Applications Jamie Davis (23014132) Signal processing Microelectronics complementary filter, sensor fusion, windowed watchdog, bare-metal resilience, fixed-point math, 16.16 DSP, embedded systems An advanced safety-critical firmware module dataset addressing the complex challenges of algorithmic drift and clock synchronization anomalies inside rigorous embedded systems. This source architecture implements: 1. A deterministic Complementary Sensor Fusion Filter engineered purely with 16.16 fixed-point mathematical structures. It blends high-rate gyroscope angular velocities with steady-state accelerometer signals using branchless arithmetic, eliminating the memory and processing overhead associated with heavy Kalman filters or floating-point calculations. 2. A strict Windowed Hardware Watchdog Monitoring loop designed to map directly to physical microcontroller hardware configuration registers. Unlike traditional watchdogs that only check for deadlocks, this system enforces minimum and maximum timing thresholds, triggering immediate defensive hardware resets if code executes prematurely or stalls due to clock drift. Features full standalone structural unit tests validating signal tracking accuracy and runtime verification checks without external dependencies. 2026-06-01T17:14:17Z Dataset Dataset 10.6084/m9.figshare.32533266.v1 https://figshare.com/articles/dataset/Resilient_Cyber-Physical_Sensor_Fusion_Filter_and_Windowed_Watchdog_Guard_Architecture_for_Bare-Metal_Firmware_Applications/32533266 CC BY 4.0

oai:figshare.com:article/32533260 2026-06-01T17:06:51Z category_26407 category_26509 portal_0 item_type_3 month_year_06_2026

Deterministic Inter-Core Streaming and Fixed-Point Matrix Engine for Safety-Critical Navigation Pipelines Jamie Davis (23014132) Signal processing Microelectronics lock-free queue, SPSC ring buffer, fixed-point matrix, zero-copy parsing, DMA memory alignment, real-time safety An ultra-low latency extension dataset for real-time safety-critical software engineering architectures. This package addresses micro-architectural processing bottlenecks through three integrated software sub-modules: 1. A lock-free, single-producer single-consumer (SPSC) ring-buffer queue engineered with explicit 64-byte boundaries (alignas(64)) to completely neutralize multi-core CPU cache-line false sharing degradation. 2. A fixed-point 3x3 matrix multiplication engine operating on 16.16 signed integer arithmetic, achieving perfectly deterministic computation cycle times for 3D coordinate system transforms. 3. A zero-overhead structural interpretation pipeline using compilation byte-packing directives (#pragma pack(push, 1)) to map incoming Direct Memory Access (DMA) raw network buffers directly to memory arrays without consuming processor instruction cycles for byte translations. The collection features embedded automated unit verification testing scripts ensuring zero-allocation memory constraints are fully maintained. 2026-06-01T17:06:51Z Dataset Dataset 10.6084/m9.figshare.32533260.v1 https://figshare.com/articles/dataset/Deterministic_Inter-Core_Streaming_and_Fixed-Point_Matrix_Engine_for_Safety-Critical_Navigation_Pipelines/32533260 CC BY 4.0

oai:figshare.com:article/32533182 2026-06-01T17:00:49Z category_28864 category_28921 category_29071 portal_0 item_type_3 month_year_06_2026

AI Use Among Middle-Aged Adults in Bangladesh Major Challenges and Policy Implications Ritesh Karmaker (14780170) Artificial intelligence not elsewhere classified System and network security Human-centred computing not elsewhere classified Artificial Intelligence human dignity digital justice adult education Bangladesh grey digital divide The adoption of Artificial Intelligence (AI) among middle-aged adults (40-55 years) in Bangladesh poses an ethical challenge. The aim is to analyze how the systematic exclusion of this cohort situated in the demographic vacuum between the National Youth Policy (2017) and the National Policy on Older Persons (2013) constitutes a serious violation of distributive justice and human dignity. The objective is to examine the ethical barriers that hinder AI adoption, integrating UTAUT2, Innovation Resistance Theory, Social Shaping of Technology, and Digital Divide Theory. The theoretical framework grounds the study in Sen's capability approach, Kantian ethics, Honneth's recognition theory, Rawlsian distributive justice, and Heidegger's phenomenology. Through a qualitative research design with descriptive quantitative elements, the study deeply analyzes the lived experiences of 229 participants in the Sherpur region. The identified barriers include the traditional barrier, symbolic annihilation, and institutional exclusion that reduce mature professionals to mere means for the ends of a youth-centered digital economy. The results reveal four principal ethical determinants: effort expectancy as a condition of autonomy, social influence as intergenerational recognition, facilitating conditions as digital distributive justice, and innovation resistance as defense of professional identity. It is concluded that the transition toward Smart Bangladesh 2041 requires an urgent reconceptualization of digital literacy as a fundamental human right, ensuring that middle-aged workers are treated as ends in themselves in the design of inclusive and equitable technology policies. 2026-06-01T17:00:49Z Dataset Dataset 10.6084/m9.figshare.32533182.v1 https://figshare.com/articles/dataset/AI_Use_Among_Middle-Aged_Adults_in_Bangladesh_Major_Challenges_and_Policy_Implications/32533182 CC BY 4.0

oai:figshare.com:article/32533194 2026-06-01T16:54:02Z category_26407 portal_0 item_type_3 month_year_06_2026

High-Efficiency Fixed-Point Multi-Axis Neuro-Navigation Pipeline with FNV-1a Hashing and CORDIC Trigonometric Rotations Jamie Davis (23014132) Signal processing neuro-navigation, fixed-point math, embedded systems, real-time pipeline, CORDIC engine, FNV-1a hash A high-efficiency, deterministic real-time processing pipeline written in C++ for multi-axis spatial hardware telemetry processing. The architecture utilizes 16.16 fixed-point math to bypass floating-point hardware constraints, making it highly optimized for deep embedded microcontrollers and safety-critical neuro-navigation environments. Key architecture layers include a multi-axis parser, a bit-shifted CORDIC trigonometric engine, a 32-bit FNV-1a bitwise integrity hashing checker, a branchless sensor fallback routine, and a NIST-core velocity guard to prevent signal artifacts. Includes a deterministic, non-allocating ring-buffer diagnostic logger and embedded unit tests. 2026-06-01T16:54:02Z Dataset Dataset 10.6084/m9.figshare.32533194.v1 https://figshare.com/articles/dataset/High-Efficiency_Fixed-Point_Multi-Axis_Neuro-Navigation_Pipeline_with_FNV-1a_Hashing_and_CORDIC_Trigonometric_Rotations/32533194 CC BY 4.0

oai:figshare.com:article/28871060 2025-04-25T17:31:44Z category_24559 portal_0 item_type_3 month_year_04_2025

Beyond Single-Score Matching: A Two-Dimensional Propensity Score Method for Mixed Covariate Types Kostiantyn Botnar (21181664) Clinical sciences not elsewhere classified Machine Learning Propensity Score Matching Electronic Health Records Propensity score matching (PSM) reduces confounding bias in observational studies, yet traditional single-score approaches assume linear covariate-treatment relationships. This assumption often fails when applied to complex electronic health record data that contain mixed categorical and continuous variables and nonlinear interactions. We introduce two-dimensional PSM (2D-PSM), which estimates separate propensity scores for categorical and numerical confounders and then performs matching in two-dimensional space under an elliptical caliper constraint. We compared 2D-PSM with traditional single-score PSM using five machine learning classifiers across 5 real-world (RW) clinical datasets (n=460-61,926) and 21 synthetic datasets (n=10,000) with systematically varied confounder complexity and categorical-to-continuous ratios. Matching employed 1:1 greedy nearest-neighbor algorithm with adaptive calipers (0.1× to 0.5× propensity score interquartile range). Balance was assessed using univariate and multivariate metrics. In multivariate assessment and the smallest caliper (0.1), 2D-PSM performed comparably to conventional PSM on RW datasets and consistently outperformed it in synthetic data experiments. With larger calipers, 2D-PSM outperformed single-score methods in 85% of datasets. Our 2D-PSM approach provides superior multivariate balance for datasets with heterogeneous covariate types and complex interactions, particularly with moderate calipers.These datasets have been used for the 2D-PSM evaluation. 2025-04-25T17:31:44Z Dataset Dataset 10.6084/m9.figshare.28871060.v2 https://figshare.com/articles/dataset/Assessment_of_ML_Models_for_PSM_RW_Clinical_Data_Collection/28871060 GPL 3.0+

oai:figshare.com:article/22220824 2024-04-18T20:18:54Z category_26893 portal_0 item_type_3 month_year_04_2024

Database published in The global contribution of soil mosses to ecosystem services | Nature Geoscience Manuel Delgado-Baquerizo (4886419) David Eldridge (4816788) Ecosystem services (incl. pollination) Moss Plants Ecosystems Database published in <a href="https://www.nature.com/articles/s41561-023-01170-x" target="_blank">The global contribution of soil mosses to ecosystem services | Nature Geoscience</a> 2024-04-18T20:18:54Z Dataset Dataset 10.6084/m9.figshare.22220824.v5 https://figshare.com/articles/dataset/MUSGONET_ES/22220824 CC BY 4.0

oai:figshare.com:article/31112554 2026-01-21T13:16:50Z category_28864 category_29173 category_30166 portal_0 item_type_3 month_year_01_2026

Machine learning dataset, model weights, and training logs for the publication "Broken neural scaling laws in learning the optical properties of solids" Max Großmann (23030383) Malte Grunert (19957977) Erich Runge (20022605) Artificial intelligence not elsewhere classified Machine learning not elsewhere classified Condensed matter modelling and density functional theory Neural Scaling Laws Graph Neural Networks Materials Informatics Dielectric Functions Dataset Intermetallics This repository contains a ready-to-use machine learning dataset, model weights, and training logs associated with the publication "Broken neural scaling laws in learning the optical properties of solids".The code implementing the graph neural network models and training workflows used with this dataset is available at: https://github.com/MaxGrossmann/optimetalThe data are provided in the form of compressed archives that are organized as follows.Dataset archive:The archive "dataset.zip" contains the following three HDF5 files: "train.h5", "val.h5", and "test.h5". These files store the training, validation, and test splits used for the reported models. Details on the data format and parsing are provided in the associated code repository.The underlying raw dataset was generated using a high-throughput ab initio workflow and is distributed across two repositories due to its size: https://doi.org/10.6084/m9.figshare.31111798 and https://doi.org/10.6084/m9.figshare.31112491.Graph archives:The "graph.zip" and "graph_e3.zip" archives each contain three corresponding PyTorch files: "train.pt", "val.pt", and "test.pt". These files store the corresponding graph representations used as input for the invariant and equivariant graph neural networks, respectively. Parsing and usage of these files are documented in the associated code repository.Model weights archive:The "adapted_uma_model_state_dicts.zip" archive contains the trained model state dictionaries for the equivariant graph neural networks used in the publication. The corresponding model weights for the invariant OptiMetal architectures are available in the aforementioned code repository.Training logs archive:The "training_logs.zip" and "training_logs_e3.zip" archives contain directories corresponding to specific groups of training runs. Within each directory, the subdirectories are named according to the model configuration, random seed, and (where applicable) additional hyperparameters. These subdirectories contain TensorBoard log files, job submission scripts (LSF files), and other outputs produced during training.Together, these data, model weights, graph representations, and training logs enable reproduction of the training, evaluation, and neural scaling law analyses reported in the publication. 2026-01-21T13:16:50Z Dataset Dataset 10.6084/m9.figshare.31112554.v2 https://figshare.com/articles/dataset/Graph_neural_network_dataset_and_training_logs_for_the_publication_Broken_neural_scaling_laws_in_materials_science_/31112554 CC BY 4.0

oai:figshare.com:article/32533143 2026-06-01T16:29:48Z category_24127 portal_0 item_type_3 month_year_06_2026

heatwave-orchard-stemwatercontent Yair Mau (2824523) Other agricultural, veterinary and food sciences not elsewhere classified Heatwave stem water storage VPD variability extreme event This folder contains the CSV inputs used to generate the four figures in the Results section of the paper:Laura Rez, Justine Missik, Gil Bohrer, and Yair Mau. Stem water content is crucial to support fruit tree functioning during heatwaves in a Mediterranean climate, Agricultural and Forest Meteorology, 2026. 2026-06-01T16:29:48Z Dataset Dataset 10.6084/m9.figshare.32533143.v1 https://figshare.com/articles/dataset/heatwave-orchard-stemwatercontent/32533143 MIT

oai:figshare.com:article/32533137 2026-06-01T16:25:06Z category_64 category_379 category_380 category_381 category_382 category_383 category_384 category_385 category_386 category_387 category_388 portal_316 item_type_3 month_year_06_2026

DataSheet_1_RETRACTED: Exosome Component 4 Promotes Epithelial Ovarian Cancer Cell Proliferation, Migration, and Invasion via the Wnt Pathway.pdf Chang Xiong (11331110) Zhongfeng Sun (4909822) Jinjin Yu (788889) Yaying Lin (10630818) Cancer Cancer Cell Biology Cancer Diagnosis Cancer Genetics Cancer Therapy (excl. Chemotherapy and Radiation Therapy) Chemotherapy Haematological Tumours Molecular Targets Radiation Therapy Solid Tumours Oncology and Carcinogenesis not elsewhere classified exosome component 4 (EXOSC4) epithelial ovarian cancer (EOC) Wnt pathway proliferation migration invasion BackgroundOf gynecologic malignancies, ovarian cancer is the leading cause of death, mainly due to the lack of sensitive tumor markers, which means it almost always presents at an advanced stage. Exosome Component 4 (EXOSC4) is involved in RNA degradation, but its role in epithelial ovarian cancer (EOC) is unclear.MethodsThe expression levels of EXOSC4 in EOC and normal ovarian tissue specimens were determined by immunohistochemical staining. The overall survival (OS) and progression-free survival (PFS) of patients with EOC were evaluated after patients were classified into high and low EXOSC4 expression groups, and the Cox regression model was established to identify independent predictors of patient prognosis. The effects of EXOSC4 on proliferation, colony formation, migration, and invasion were examined in the SKOV-3 and HO8910 cell lines by lentivirus-mediated shRNA knockdown. Flow cytometry was used to detect cell cycle changes. The mRNA levels of cyclin D1, CDK4, and c-myc were detected by RT-PCR. The protein expression levels of β-catenin, cyclin D1, CDK4, c-myc, vimentin, N-cadherin, and E-cadherin were assessed by western blot. Wnt/β-catenin activation was measured by TCF/LEF reporter assay.ResultsEXOSC4 was significantly elevated in EOC tissues and cell lines. High EXOSC4 expression was correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage and pathological grade, and identified as an independent predictor of shorter OS and PFS. EXOSC4 knockdown suppressed proliferation, migration, and invasion in EOC cell lines. Cells were arrested at G0/G1 phase after EXOSC4 knockdown. The mRNA levels of cyclin D1, CDK4, and c-myc were decreased. β-catenin, cyclin D1, CDK4, c-myc, vimentin, and N-cadherin protein expression levels were reduced, while those of E-cadherin was increased. Wnt/β-catenin activity was suppressed after the EXOSC4 knockdown.ConclusionsEXOSC4 is involved in EOC. Knockdown of EXOSC4 can inhibit the proliferation, migration, and invasion ability of EOC by suppressing the Wnt pathway. EXOSC4 is expected to be a novel biomarker and molecular target in EOC. 2026-06-01T16:25:06Z Dataset Dataset 10.3389/fonc.2021.797968.s001 https://figshare.com/articles/dataset/DataSheet_1_RETRACTED_Exosome_Component_4_Promotes_Epithelial_Ovarian_Cancer_Cell_Proliferation_Migration_and_Invasion_via_the_Wnt_Pathway_pdf/32533137 CC BY 4.0

oai:figshare.com:article/32533140 2026-06-01T16:25:06Z category_64 category_379 category_380 category_381 category_382 category_383 category_384 category_385 category_386 category_387 category_388 portal_316 item_type_3 month_year_06_2026

Table_1_RETRACTED: Exosome Component 4 Promotes Epithelial Ovarian Cancer Cell Proliferation, Migration, and Invasion via the Wnt Pathway.xlsx Chang Xiong (11331110) Zhongfeng Sun (4909822) Jinjin Yu (788889) Yaying Lin (10630818) Cancer Cancer Cell Biology Cancer Diagnosis Cancer Genetics Cancer Therapy (excl. Chemotherapy and Radiation Therapy) Chemotherapy Haematological Tumours Molecular Targets Radiation Therapy Solid Tumours Oncology and Carcinogenesis not elsewhere classified exosome component 4 (EXOSC4) epithelial ovarian cancer (EOC) Wnt pathway proliferation migration invasion BackgroundOf gynecologic malignancies, ovarian cancer is the leading cause of death, mainly due to the lack of sensitive tumor markers, which means it almost always presents at an advanced stage. Exosome Component 4 (EXOSC4) is involved in RNA degradation, but its role in epithelial ovarian cancer (EOC) is unclear.MethodsThe expression levels of EXOSC4 in EOC and normal ovarian tissue specimens were determined by immunohistochemical staining. The overall survival (OS) and progression-free survival (PFS) of patients with EOC were evaluated after patients were classified into high and low EXOSC4 expression groups, and the Cox regression model was established to identify independent predictors of patient prognosis. The effects of EXOSC4 on proliferation, colony formation, migration, and invasion were examined in the SKOV-3 and HO8910 cell lines by lentivirus-mediated shRNA knockdown. Flow cytometry was used to detect cell cycle changes. The mRNA levels of cyclin D1, CDK4, and c-myc were detected by RT-PCR. The protein expression levels of β-catenin, cyclin D1, CDK4, c-myc, vimentin, N-cadherin, and E-cadherin were assessed by western blot. Wnt/β-catenin activation was measured by TCF/LEF reporter assay.ResultsEXOSC4 was significantly elevated in EOC tissues and cell lines. High EXOSC4 expression was correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage and pathological grade, and identified as an independent predictor of shorter OS and PFS. EXOSC4 knockdown suppressed proliferation, migration, and invasion in EOC cell lines. Cells were arrested at G0/G1 phase after EXOSC4 knockdown. The mRNA levels of cyclin D1, CDK4, and c-myc were decreased. β-catenin, cyclin D1, CDK4, c-myc, vimentin, and N-cadherin protein expression levels were reduced, while those of E-cadherin was increased. Wnt/β-catenin activity was suppressed after the EXOSC4 knockdown.ConclusionsEXOSC4 is involved in EOC. Knockdown of EXOSC4 can inhibit the proliferation, migration, and invasion ability of EOC by suppressing the Wnt pathway. EXOSC4 is expected to be a novel biomarker and molecular target in EOC. 2026-06-01T16:25:06Z Dataset Dataset 10.3389/fonc.2021.797968.s002 https://figshare.com/articles/dataset/Table_1_RETRACTED_Exosome_Component_4_Promotes_Epithelial_Ovarian_Cancer_Cell_Proliferation_Migration_and_Invasion_via_the_Wnt_Pathway_xlsx/32533140 CC BY 4.0 eyJtZXRhZGF0YVByZWZpeCI6ICJvYWlfZGMiLCAidmVyYiI6ICJMaXN0UmVjb3JkcyIsICJvYWlfaGFuZGxlciI6IHRydWUsICJkZWZpbmVkX3R5cGUiOiAzLCAicGFnZSI6IDIsICJ4Q3Vyc29yIjogIklsc3hOemd3TXpNeE1UQTJNREF3TENBek1qVXpNekUwTUYwaS5SOXo4NHhoeVB6ZmNGUlFQVTkwWVZadUdEQjgiLCAiZXhwaXJhdGlvbiI6ICIyMDI2LTA2LTAxIDE4OjE3OjA1In0=