Fragmentation Data Research Articles

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

Impact of nuclear fragmentation on the stopping power ratio of12C ion beams.

Nuclear fragmentation generates a diverse dosimetric environment in the path of 12C ion beams. Concise parametrization of the beam's composition is paramount for determining key correction factors in clinical dosimetry. This study sets out to provide such a parametrization based on detailed Monte Carlo simulations of clinically relevant 12C beams. Special attention was paid to the products of nuclear fragmentations and their importance in determining the stopping power ratios. Using the Monte Carlo simulation package GATE, the spectral fluence of all primary and secondary particles in water were computed at different depths for selected clinically relevant incident energies. Collision-stopping power data was taken from the ICRU90, SRIM and MSTAR database, as well as from previous publications. The choice of stopping power data was shown to have a bigger impact on the resulting stopping power ratio than the choice of physics lists for the simulations. A comprehensive analysis of the relationship between fragmentation and dosimetric data has been provided. This study compared different methods for determining spectral fluence-based stopping power ratios, which is essential for accurate ion beam dosimetry.

Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1.

Pharmacogenomics (PGx) holds the promise of personalizing medical treatments based on individual genetic profiles, thereby enhancing drug efficacy and safety. However, the current landscape of PGx research is hindered by fragmented data sources, time-consuming manual data extraction processes, and the need for comprehensive and up-to-date information. This study aims to address these challenges by evaluating the ability of Large Language Models (LLMs), specifically Llama3.1-70B, to automate and improve the accuracy of PGx information extraction from the FDA Table of Pharmacogenomic Biomarkers in Drug Labeling (FDA PGx Biomarker table), which is well-structured with drug names, biomarkers, therapeutic area, and related labeling texts. Our primary goal was to test the feasibility of LLMs in streamlining PGx data extraction, as an alternative to traditional, labor-intensive approaches. Llama3.1-70B achieved 91.4% accuracy in identifying drug-biomarker pairs from single labeling texts and 82% from mixed texts, with over 85% consistency in aligning extracted PGx categories from FDA PGx Biomarker table and relevant scientific abstracts, demonstrating its effectiveness for PGx data extraction. By integrating data from diverse sources, including scientific abstracts, this approach can support pharmacologists, regulatory bodies, and healthcare researchers in updating PGx resources more efficiently, making critical information more accessible for applications in personalized medicine. In addition, this approach shows potential of discovering novel PGx information, particularly of underrepresented minority ethnic groups. This study highlights the ability of LLMs to enhance the efficiency and completeness of PGx research, thus laying a foundation for advancements in personalized medicine by ensuring that drug therapies are tailored to the genetic profiles of diverse populations.

AI-enabled intelligent inventory and supply chain optimization platform for SMEs

An AI-enabled intelligent inventory and supply chain optimization platform offers SMEs a transformative solution to streamline operations, improve forecasting accuracy, and reduce costs. SMEs often face challenges in managing inventory efficiently, forecasting demand, and maintaining strong supplier relationships, largely due to limited resources and fragmented data. This platform integrates data from multiple sources such as sales, ERP, and supplier systems into a centralized system, enabling comprehensive visibility across inventory and supply chain functions. The platform employs machine learning algorithms to analyze historical data, identify trends, and predict demand patterns, resulting in more accurate forecasting and optimized stock levels. With predictive analytics, SMEs can set dynamic reorder points, minimizing overstock and stockouts while maintaining optimal safety stock. Additionally, the platform provides real-time tracking of shipments and supplier performance, allowing SMEs to monitor logistics in real-time and make informed, proactive decisions. Intelligent automation tools further enhance productivity by automating reorder processes, handling order generation, and reducing manual intervention in restocking. Benefits of this AI-driven platform include increased operational efficiency, reduced inventory holding and ordering costs, and enhanced customer satisfaction through timely order fulfillment. By optimizing stock levels and supply chain flows, SMEs can adapt quickly to market fluctuations and demand shifts, gaining a competitive edge. While challenges remain, such as data integration and employee training, the platform’s scalable design allows SMEs to start small and expand as needed. As AI technology advances, SMEs adopting these intelligent systems will position themselves for sustainable growth, improved resilience, and higher customer satisfaction in a rapidly evolving market landscape.

Dephosphorylation-related signature predicts the prognosis of papillary renal cell carcinoma.

Phosphorylation-dephosphorylation is one of the most common and critical cellular activities. It is essential for cell cycle control and leads to large changes in protein conformation, which can alter protein function and coordinate multiple functions such as cell metabolism, gene transcription and translation, signaling, growth, differentiation, and apoptosis. Alterations in the phosphorylated proteome have been shown in many cancers. Many phosphatases that catalyze dephosphorylation have been described as oncogenes and tumor suppressors. Papillary renal cell carcinoma (PRCC) is the second most common subtype of kidney cancer, in which most patients diagnosed with PRCC are already in advanced stages with a poor prognosis. It is necessary to identify reliable predictors associated with early diagnosis and prognosis of PRCC. The study used PRCC patients data from The Cancer Genome Atlas (TCGA) database to evaluate dephosphorylation-related genes and build a panel of prognostic gene signatures which predicts accurately the outcome of PRCC patients. The mutation data, and the fragments per kilobase of exon model per million mapped fragments (FPKM) data together with the corresponding clinical information were downloaded from TCGA database for 288 PRCC patients. Lasso regression algorithm (LASSO) and multivariate Cox regression analysis were performed to produce a panel of risk-related genetic signatures. We analyzed 417 dephosphorylation-associated genes and, finally, identified 9 genes (ADORA1, CDKN3, CRY2, PLPPR4, PPA2, PPP2R2B, PPP6R2, PTP4A1, TPTE2) and constructed a panel of signatures associated with prognosis. The area under the receiver operating characteristic curve (AUC) value was 0.833 for the prognostic risk score signature. It was confirmed that the risk score was an independent predictor of prognosis [hazard ratio (HR) =1.013, 95% confidence interval (CI): 1.002-1.024, P=0.02]. We identified 9 genes associated with dephosphorylation differentially expressed in PRCC tumor tissues and established the first prognostic model based on dephosphorylation-associated genes in PRCC patients. It was shown to be a valid and reliable prognostic indicator that could predict the prognosis of PRCC patients accurately. This study has a lot of potential value for future studies.

New insights in understanding the fiber-matrix interface and its reinforcement behavior using single fiber fragmentation data

New insights in understanding the fiber-matrix interface and its reinforcement behavior using single fiber fragmentation data

GoFCards: an integrated database and analytic platform for gain of function variants in humans.

Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from>150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research.

The value of blockchain for shipping platforms: a perspective from multi-homing and network effects

Traditional information structures within shipping supply chain platforms often grapple with inefficient decision-making processes, hindering overall efficiency. Blockchain is increasingly utilised in shipping platforms, employing decentralised distributed ledgers to transform the information architecture of supply chains, which are typically characterised by centralised management and fragmented data. However, the ceasing of operations such as TradeLens and 300cubits due to high operating costs, user privacy concerns, and multiple bets also makes investing in blockchain an obscure choice. Motivated by this dilemma about the value of blockchain to platforms, we investigate the impact of user behaviour on blockchain investments in shipping platforms through a game-theoretic model of heterogeneous platform competition. First, our results show that the relationship between the intrinsic value and the option value of blockchain is a crucial determinant of its applicability to platforms. Second, we establish that, although the network effects enhance platform profits, higher network effects exacerbate price competition and hinder the value of the blockchain. Moreover, multi-homing can weaken the strategic dependency between platforms and users, thereby reducing the profitability of platforms. Finally, it is observed that blockchain investment in shipping platforms brings more consumer surplus and social welfare, with moderate network effects and multi-homing behaviour.

Life prediction of on-board supercapacitor energy storage system based on gate recurrent unit neural network using sparse monitoring data

With the increasing use of supercapacitor in transportation and energy sectors, service life prediction becomes an important aspect to consider. As the aging process of onboard supercapacitors is closely related to practical working conditions, the actual service life may be inconsistent with the cycle life measured in the laboratory. However, the low-quality onboard monitoring data recording the historical working conditions is usually sparse and fragmented, making it difficult to extract valuable information. In our previous study, we successfully obtained the characteristic parameters from sparse and fragmented data, whereas those characteristic parameters change periodically and couldn't be used directly for life prediction. In this paper, we firstly extract the degradation trend term of supercapacitor by a composite sine and polynomial time series decomposition model from the characteristic parameters. Secondly, in order to make up for the lack of data, a GRU network is designed to generate more sample data which is in consistent with historical data evolution trends. The combination of input characteristic variables including the extracted historical characteristic capacitance C, temperature T and the time fitting sequences CtD are selected to improve the accuracy of GRU predictions. The predictive error of the characteristic capacitance C is 2.36 %. Finally, the life prediction of on-board supercapacitors based on actual working conditions is realized.

Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa.

Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and projections suggest this could rise to 10 million annually by 2050 without effective interventions. Sub-Saharan Africa (SSA) faces considerable challenges in managing AMR due to insufficient surveillance systems, resulting in fragmented data. Technological advancements, notably artificial intelligence (AI), offer promising avenues to enhance AMR biosurveillance. AI can improve the detection, tracking and prediction of resistant strains through advanced machine learning and deep learning algorithms, which analyze large datasets to identify resistance patterns and develop predictive models. AI's role in genomic analysis can pinpoint genetic markers and AMR determinants, aiding in precise treatment strategies. Despite the potential, SSA's implementation of AI in AMR surveillance is hindered by data scarcity, infrastructural limitations and ethical concerns. This review explores what is known about the integration and applicability of AI-enhanced biosurveillance methodologies in SSA, emphasizing the need for comprehensive data collection, interdisciplinary collaboration and the establishment of ethical frameworks. By leveraging AI, SSA can significantly enhance its AMR surveillance capabilities, ultimately improving public health outcomes.

Abstract B053: NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing

Abstract Introduction: Cell-free DNA (cfDNA) is emerging as a valuable cancer biomarker, enabling both the detection and epigenetic characterization of malignancies through sequencing. cfDNA sequencing can reveal cancer-specific nucleosome occupancy patterns, which provide insights into tumor type and differentiation status, which define prognosis and therapy recommendations. Low-coverage whole-genome cfDNA sequencing is a cost-efficient approach to assay multiple cfDNA samples, however, the low coverage limits its sensitivity. We developed NucAE, a denoising autoencoder model, to enhance nucleosome occupancy signals from low-coverage cfDNA-sequencing. Methods: We designed NucAE to estimate nucleosome occupancy genome- wide from low-coverage sequencing data. To train the model, we undersampled high-coverage cfDNA-sequencing data and produced high- and low-coverage sample pairs at varying coverages (1x-90x). Inputs to NucAE include cfDNA fragment data and the corresponding genomic nucleotide sequence, with the model leveraging a symmetrical architecture comprising two convolutional layers in both the encoder and decoder, connected by ReLU activations. Mean squared error relative to the high-coverage signal was employed as the loss function. To evaluate the model independently, we performed peak detection from the nucleosome occupancy signals. We conducted a grid search to optimize hyperparameters. Results: We found that NucAE enhances the signal to noise ratio by detecting periodicities in nucleosome occupancy data. Denoising was improved by supplementing the cfDNA fragment signals with the genomic nucleotide sequences as input, which demonstrates that the model learned the nucleotide- sequence dependence of nucleosome positioning. Independent validation through peak detection on previously unseen samples demonstrated a significant improvement (p<0.001) in accuracy compared to raw signals across all tested coverages (1x-45x). Remarkably, at extremely low coverages (1x-2x), denoised signals allowed for more accurate peak detection than raw signals from 10-20x deeper sequencing. Conclusions: By using advanced machine-learning, we were able to enhance low-coverage cfDNA sequencing data to assay nucleosome positions at a nucleotide resolution. NucAE achieves an order of magnitude improvement over the low- coverage signals and greatly improves the utility of cfDNA-sequencing data to identify cancer- specific nucleosome occupancy, while preserving its low cost. Our model is the first to use autoencoders for genome-wide signal enhancement with potential use cases in many other areas of genomics. Citation Format: Zsolt Balázs, Jean Radig, Noah Wolford, Manuel Schürch, Michael Krauthammer. NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B053.

DECOMPOSITION MECHANISM OF ALKYLIDENE BRIDGED TETRAZOLES WITH DIFFERENT CARBON CHAIN LENGTHS.

Nitrogen-rich heterocycles, particularly tetrazole-based high-energy density materials (HEDMs) offer high performance, low sensitivity, and are eco-friendly. Despite the diversity of nitrogen-rich energetic heterocycles, many are sensitive to external stimuli, and the introduction of a methylene, ethylene, or C-C linkage between nitrogen-rich heterocycles is a successful strategy to improve mechanical sensitivity and thermal stability. Understanding the potential anomalous thermal or kinetic behavior of such molecules is crucial for the design of new HEDMs and practical applications of these molecules. We have investigated the influence of introducing an alkylidene bridge between the energetic nitrogen heterocycles on the decomposition mechanism and pathway of different bridged tetrazoles, namely 5,5'-Bis-1H-tetrazole, 1,2-Bis(5-tetrazolo)methane, and 1,2-Bis(5-tetrazolo)ethane, using thermal experiments, mass spectrometry, and computational analysis. Kinetic parameters were evaluated using a non-linear integral method, and decomposition pathways were proposed based on mass fragmentation data. Stability comparisons were made using HOMO-LUMO gap and electrostatic potential (ESP) values from computational calculations.

Peptaloid: A Comprehensive Database for Exploring Peptide Alkaloid.

Peptaloid is the first dedicated database for peptide alkaloid molecules, a unique class of naturally derived compounds known for their structural diversity and significant biological activities. Despite their promising potential in drug discovery and therapeutic development, research on peptide alkaloids has been limited by the absence of a comprehensive and centralized resource. Fragmented data across various sources have posed a significant challenge, underscoring the need for a specialized database to facilitate more efficient research and application. Peptaloid addresses this critical gap by providing a database with over 161,000 peptide alkaloid entries, each detailed with structural, physicochemical, and pharmacological properties. By leveraging advanced computational tools and machine learning, Peptaloid generates ADMET profiles, aiding in identifying and optimizing therapeutic candidates. Designed for versatility, the database supports various applications beyond drug discovery, including ecology and material sciences. Peptaloid (as a specialized database for peptide alkaloids) will play a crucial role in innovation and collaboration across scientific disciplines. Peptaloid is accessible at https://peptaloid.niser.ac.in.

Research on the Factors That Influence and Improve the Quality of Informal Learning Spaces (ILS) in University Campus

Currently, with the intensification of global education competition, how to cultivate innovative and versatile talents has become the focus of education today. Informal learning spaces (ILS) have received widespread attention due to their benefits for interdisciplinary interaction. Many forms of ILS have been built, and coffee shops are one of them. However, there is no uniform standard for the design of cafes as ILS, and there are many problems with the spatial quality. This study took a university cafe as a case study and conducted research on the environmental quality and influencing factors from the perspective of environmental behavior. In terms of research methods, this study adopted a combination of ultra-wideband positioning, questionnaire surveys, and environmental data measurements. This research method could obtain the complete relationship between user behavior, time, and location, which improved the shortcomings of traditional research methods of fragmented and accidental data acquisition, and helped to obtain more accurate research results. The study identified the impact proportions of four criteria layer elements and 26 scheme layer factors, which was also the main contribution of this study. In terms of four criteria layer elements, the physical elements had the highest proportion, accounting for 32.46%. The ontology elements ranked second, accounting for 27.07%. The atmosphere elements ranked third, accounting for 24.19%. Finally, facility elements accounted for 16.29%. The top three scheme layer factors were equipment and facilities, noise, and illumination, with weights of 11.18%, 10.71%, and 8.35%, respectively. The study summarized the development directions of cafes as ILS, such as considering the learning needs of different groups, installing sound-absorbing and soundproof devices, adding grilles to external windows, and improving glare problems.

Urban health data in India: mapping gaps and solutions

Inadequacies of current health information systems in capturing the diverse health needs and vulnerabilities within urban populations, emphasizing the need to move beyond fragmented traditional data collection approaches. By incorporating emerging technologies and enhancing governance structures, such as strengthening coordination mechanisms and promoting transparency, urban health stakeholders can overcome data gaps and better address the evolving health landscape.

11.A. Scientific session: Overcoming Real-World Challenges in Setting up Health Data Linkages for Public Health Policy

Abstract Background Linking available administrative data has gained momentum in the last years to enhance public health research and public health formulation. Linkage of diverse data sources can be leveraged to understand social-determinants of health, the cost-effectiveness of an intervention, and to evaluate impact of policies. For instance, data linkage has been proven to be a powerful approach to periodically analyze real-world progress of the COVID-19 pandemic. Today, we are witnessing an unparalleled increase in electronic health data stemming from citizens who interact with services such as healthcare providers, employers, sick funds, and other governmental structures. This enormous amount of data opens many innovative possibilities in epidemiological research. The main advantages of the secondary use of data are the following: 1) it is enriching for new collected data (e.g. survey study), 2) it provides comprehensive information of the target population, and 3) it is cheaper than new data collection. Data linkage between data sources is, however, often a herculean task, and it comes with considerable challenges such as: 1) juridical complexity related to intra-national and cross-border data governance procedures, 2) fragmented data integration, 3) data delivery delays. Addressing these challenges is essential to unlocking the true potential of data linkages for timely responses to emerging public health questions, thereby contributing to evidence-based policy and care and transforming our health research into a One Health approach. Objectives The workshop’s main aim is to present the researcher’s lessons-learned and approaches in applying data linkage from an European, and a country-level perspective through case-studies while creating a space for the public to share their own view. In this workshop we aim to 1) provide a helicopter overview of current data linkages approaches in Europe, 2) illustrate this with case studies from an international, and a country-level perspective, and 3) emphasize the challenges and disadvantages of those data linkages, while sharing potential solutions. Workshop outline The workshop is divided into a knowledge translation, and an interactive part. In the first part of the workshop, three speakers will give a presentation of each 10 minutes with a Q&A of 5 minutes between each presentation where data linkage will be introduced, and three case studies on how data linkage can be used will be presented. The first presentation will introduce approaches to data linkages in Europe, and will delve into how AI can be used during routine data linkage. The second presentation will show a case-study on how an international data linkages can be done in an European federated manner. The third presentation will further illustrate how nationally data can be linked to investigate health inequalities. In the second part of the workshop, the audience will be invited to interact with the speakers and share their experiences. Key messages • A comprehensive overview of different current data linkages approaches are provided, setting the stage for discussions on methods and best practices. • Administrative data linkages are a powerful tool to answer important public health challenges, but important challenges related to data governance need to be addressed to realize the full potential.

High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.

Experimental and numerical investigation on rock fracturing and fragmentation under coupled static pressure and blasting

The blasting technique is widely adopted to break rock in civil and mining engineering, and its operation is generally subjected to static pressure due to tectonic and gravitational stresses. In the present study, the rock fracturing and fragmentation under coupled static pressure (uniaxial pressure and confining pressure) and blasting are experimentally and numerically investigated. First, 11 blast tests with 100-mm cubic red sandstone samples are carried out based on a biaxial loading system. Then, the blast-produced rock fragmentation in blast testing is numerically modeled using finite element method with LS-DYNA, and the explosion pressure attenuation and fracture evolution in rock samples are numerically reproduced. The simulated rock fragmentation data are obtained by image processing in ImageJ, and the fragment size distribution of red sandstone under combined static stress and blasting is characterized using Weibull distribution. Accordingly, the effects of uniaxial pressure and confining pressure on blast-created rock fragmentation are quantitatively compared and analyzed, and the corresponding mechanisms are discussed and revealed. The current findings indicate that the static pressure plays a role in increasing blast-induced compressive stress and reducing tensile stress in both radial and hoop directions and thus results in a decrease in the length and number of fractures propagating vertically to the direction of applied pressure, further leading to the creation of coarser fragmentation. Blasting under confining pressure produces larger fragments than that under uniaxial pressure. The average fragment size increases quickly at first and then increases slowly with the increase of static stress, which can be well characterized using a logarithm equation. Based on current findings, increasing the free surface is crucial in practical blasting to improve rock fragmentation performance under blasting.

High-resolution modeling and projection of heat-related mortality in Germany under climate change.

Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate changescenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014-2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7-14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning.

Conceptualizing the Antecedents and Individual Impact of Business Intelligence in the Public Sector: The Technology-Organisation-Authoritative Framework

Business intelligence (BI) refers to a technological tool and process that transforms large and fragmented data into well-informed graphical insights to aid managerial decision-making. Recognizing the significant benefits of BI in leveraging extensive amounts of data, governments have been progressively investing in BI tools to improve efficiency in public offices. Nevertheless, despite a significant body of knowledge related to BI adoptions and their advantages, there is a dearth of theoretical understanding of how the effective use of the system affects employees' work performance, particularly among civil servants who have distinctive work natures compared to businesses. Most studies have also ignored external and institutional factors that influence individual usage, whilst studies on the effective use of BI tools remain limited. The paper thus proposed a new conceptual framework for examining the factors that influence the effective use of BI and its impact on individual job performance. The suggested propositions could provide a theoretical contribution by integrating technological, organizational and authoritative dimensions that are novel and unique to the public sector. It would also contribute to practical understandings for public managers and policymakers in ensuring the investment made on BI is worthwhile. Ultimately, the paper seeks to bridge the gap in BI studies related to public organizations.