Physical Sciences and Mathematics Commons^™

Seasonal Variations In Radar Backscatter And Corn Vegetation In The South Fork Iowa River Watershed During The Growing Season Of 2025, Monika Bhusal

Scholars Week

Remote sensing provides complementary information on crop development and surface conditions, but radar backscatter over agricultural areas can be influenced by vegetation and soil characteristics. This study examines seasonal relationships between Sentinel-1 radar backscatter and Landsat-derived vegetation greenness over corn-growing areas in the South Fork Iowa River Watershed, Iowa, during the 2025 growing season. Landsat 8/9 was used to derive the Normalized Difference Vegetation Index (NDVI), while OPERA RTC-S1 imagery provided VV and VH backscatter and a VH/VV polarization measure. USDA Cropland Data Layer was used to identify corn growing areas, and analysis was restricted to areas with common satellite …

Shadowable Diffeomorphisms With Dominated Splitting, Naoya Sumi

Journal of Stochastic Analysis

We study the shadowability for diffeomorphisms with dominated

splitting. In this paper we show that if a diffeomorphism f has a periodic

saddle q whose unstable manifold is compatible with dominated splitting in

some sense and if f is shadowable, then any other periodic point p has a large

local stable set. Our result does not require the hyperbolicity of p.

Assessor Experiences In Cmmc Level 2 Certification Assessments: An Interpretative Phenomenological Analysis Of Role Expectations, Samuel Heuchert, John Hastings

Research & Publications

The Cybersecurity Maturity Model Certification program requires that third-party assessments be conducted under a non-consultative model. The model is intended to ensure impartiality for organizations seeking certification. While this structure defines expectations for assessor behavior, assessor experiences and interpretations of these constraints remain underexamined. The study examines the lived experiences of CMMC-Certified Assessors and how they navigate role expectations within the non-consultative model. Using Role Conflict Theory as a guiding framework, the study applied Interpretative Phenomenological Analysis (IPA) to semi-structured interviews to explore how assessors make sense of their roles. The analysis identified experiential themes that describe how assessors construct …

Beyond Probability: An Analytical Exploration Of Fuzzy And Neutrosophic Paradigms In Binomial Acceptance Sampling, Uma G, Vela Muruga Anurahini P

Neutrosophic Systems with Applications

Acceptance sampling plans are widely used in Statistical Quality Control to determine the acceptance or rejection of production lots based on sample inspection. Classical sampling plans under the binomial distribution assume precise parameter values, which may not adequately represent uncertainty, vagueness, and indeterminacy encountered in practical inspection environments. This review summarizes acceptance probability formulations for single and double sampling plans developed using fuzzy and neutrosophic logic under the binomial distribution. The focus is on studies that model the fraction defective and related parameters as fuzzy or neutrosophic numbers, allowing imprecise and indeterminate quality information to be incorporated into sampling decisions. …

Reliability-Calibrated Q-Rung Orthopair Neutrosophic Soft Ranking Under Partial Observability: An Auditable Industrial-Process Benchmark, Nagham Ahmed Abdelmegeed, Zeshan Aslam Khan

Neutrosophic Systems with Applications

Multi-criteria decision systems often interpret disagreement among criteria as evidence that some criteria are unreliable. This is problematic when the disagreement reflects a genuine operational trade-off. We develop a reliability-and-admissibility-calibrated q-rung orthopair neutrosophic soft ranking procedure (RAC-qRON) that distinguishes recorded evidence quality from the direction of criterion preferences. An explicitly bounded interval for each alternative–criterion observation is mapped to truth, indeterminacy and falsity memberships, with an admissibility proof for every integer rung q ≥ 1. Criterion weights are adjusted according to recorded information coverage rather than cross-criterion correlation, and a fixed-reference ideal-distance calculation produces an auditable rank. The method is …

Risk Prioritization Of ``Digital Drug'' And Consumer Neurostimulation Modalities: A Reproducible Interval-Valued Fermatean Fuzzy Entropy–Topsis Framework, Rawan Emad El-Saadany, Ahmed Samy, Mohamed M. Abdelhafeez, Mahmoud M. Ismail

Neutrosophic Systems with Applications

Consumer audio and multisensory stimulation technologies are increasingly accessible through streaming platforms, mobile applications, and immersive media. The expression digital drug is sometimes used for binaural-beat content intended to alter subjective states, but the term is a consumer/cultural label and does not imply pharmacological equivalence. Risk prioritization in this area is difficult because evidence is heterogeneous, several relevant criteria are qualitative, and judgments may be imprecise rather than point-valued. This paper develops a transparent interval-valued Fermatean fuzzy (IVFF) group decision framework that combines interval-preserving aggregation, Shannon entropy weighting, and TOPSIS for prioritizing digital drug risk scenarios under uncertainty. The method …

Risk-Based Cybersecurity Threat Prioritization In Digital Twin-Enabled Cyber-Physical Systems, Nada Mohamed, Alshaimaa A. Tantawy, Ahmed M. Abdelmouty

Neutrosophic Systems with Applications

Digital Twin (DT)-enabled cyber-physical systems (CPS) face cybersecurity risks due to their complex connections and high levels of interdependence. Traditional methods, for assessing risk often fail to rank these threats. This is especially true when expert opinions include ambiguity, uncertainty or unclear information. A Type 2 Neutrosophic Entropy– Measurement of Alternatives and Ranking according to COmpromise Solution (Entropy- MARCOS) framework for cybersecurity threat prioritization in DT-enabled CPS is proposed in this paper. The uncertainty and indeterminacy present in expert evaluations are represented by Type 2 Neutrosophic Numbers (T2NNs). While the MARCOS technique is used to assess and rank cybersecurity threats …

Context-Regulated Q-Neutrosophic Soft Model Selection Under Input Perturbation: Evidence From Three Classification Benchmarks, Poom Kumam, Supak Phiangsungnoen

Neutrosophic Systems with Applications

Selecting a classifier using a single average metric can conceal instability across validation splits, poor lower-tail behavior, and sensitivity to input perturbations. This study formulates a context-regulated Q-neutrosophic soft model-selection procedure with two fixed evaluation conditions. For each classifier and performance criterion, mean attainment (truth), across-split dispersion (indeterminacy), and the complement of a lower empirical quantile (falsity) form an auditable three-component descriptor. A bounded lower-context regulator aggregates their penalized utilities; monotonicity and perturbation bounds follow directly from its convex representation. Four fixed classifiers are compared using five benefit-oriented criteria, three repetitions of stratified five-fold cross-validation, and paired test-feature perturbations. Three …

Electrocatalytic Oxidation Of Ethylene Glycol To Glycolic Acid: From Mechanism And Catalyst Design To Emerging Neutral Electrolyte Systems, Man Zhang, Xue Li, Xin-Yu Peng, Lin Wang, Wen-Wen Chen, Yao-Yue Yang

Journal of Electrochemistry

Polyethylene terephthalate (PET) waste represents both an environmental burden and a loss of carbon resources. Alkaline depolymerization of PET yields terephthalic acid and ethylene glycol (EG). Electrocatalytic oxidation of EG to glycolic acid (GA), the monomer of biodegradable polyglycolic acid (PGA), offers a sustainable route driven by renewable electricity. However, the ethylene glycol oxidation reaction (EGOR) involves competing pathways toward C2 products (GA and oxalic acid) and C1 products (formic acid and CO₂), making selective GA production a central challenge. This review provides a comprehensive overview of recent progress in selective EG-to-GA electrooxidation from catalyst design, mechanistic understanding, …

Regularization-Based Crystallite Size Distribution Analysis Of Pt/C Catalysts From X-Ray Diffraction, Cheng-Long Ma, Wei-Yu Shi, Zhong-Jun Hou

Journal of Electrochemistry

Accurate determination of the platinum crystallite size distribution in carbon-supported platinum (Pt/C) catalysts is critical for evaluating both the initial performance and long-term durability of proton exchange membrane fuel cells (PEMFCs). Conventional characterization approaches—including transmission electron microscopy (TEM), small-angle X-ray scattering (SAXS), and the Scherrer equation—are limited by restricted statistical sampling, sensitivity to support interference, or the inability to resolve the full breadth of the size distribution. In this work, we present a regularization-based inversion framework that extracts the volume-weighted crystallite size distribution directly from X-ray diffraction (XRD) line profiles, without requiring any a priori assumption regarding the functional form …

Metal Oxide Semiconductor Gas Sensors For Exhaled Breath Cancer Biomarker Detection, Guang-Lan Chen, Hui-Hui Luo, Wen-Wen Peng, Shi-Yu Xu, Yu-Ting Li, Hui-Jie Ke, Si-Hui Zhong, Jia-Ao Wei, Tong Chen, Xiao-Kun Li, You-Lin Zhang, Wei Chen

Journal of Electrochemistry

Early cancer screening can significantly improve therapeutic outcomes, volatile organic compounds (VOCs) and other related gases in exhaled breath, as cancer-specific biomarkers, offer a novel approach for non-invasive diagnosis. Metal oxide semiconductor (MOS)-based gas sensors are pivotal for exhaled breath VOCs detection, featuring compact size, cost-effectiveness, and rapid response. This review summarizes the most recent five-year research advances in cancer biomarker detection, and clarifies the core mechanisms of electrical property changes from sensing material-VOCs interactions and key performance metrics including sensitivity, selectivity, humidity tolerance, response/recovery time, stability, and operating temperature. This review also highlights the optimization strategies of sensing materials …

Autofield-Poa: A Hybrid Automl–Metaheuristic Framework For Automated Feature Selection In Recommender Systems, Mohammed Dheyaaldeen Taha, Waleed Kareem Ahmed, Rana Saad Mohammed, Zied O. Ahmed

Karbala International Journal of Modern Science

The problem of automated feature selection is one of the most important aspects of modern recommender systems, as overestimating the number of feature fields used, which can increase model complexity, inference latency, and performance degradation due to redundancy and noise. Although differentiable Automated Machine Learning (AutoML) based feature selection methods, including AutoField, are effective, they remain susceptible to initialization and local optima. Conversely, population-based metaheuristic algorithms are effective at global exploration, but are often inefficient in high-dimensional discrete spaces when randomly initialized. This paper presents AutoField-POA, a hybrid AutoML-metaheuristic framework that combines a differentiable controller with the Pelican Optimization Algorithm …

Data-Driven Discovery Of Canonical Models In Undergraduate Differential Equations Education, Devon L. Garls, Raina Majumder, Scott A. Strong

CODEE Journal

Roughly a decade ago, Brunton, Proctor, and Kutz built upon the re-

sults of classical control theory, compressed sensing, and symbolic regression

to create a method for Sparse Identification of Nonlinear Dynamics (SINDy),

allowing one to infer a dynamical system from time series and bring mathe-

matical dynamics into the modern era of large data. This paper is designed for

a reader concurrently enrolled in a first course in ordinary differential equa-

tions and begins with a technical overview of least-squares regression and

finite-difference approximations to derivatives, which is enough to perform

an example hand-calculation on three data points. The …

Resolving Satellite And In Situ Mismatches In Net Primary Production Using High-Frequency In Situ Bio-Optical Observations In The Subpolar Northwest Atlantic, Kitty Warsi Kam, Emmanuel Devred, Stephanie Alam Clay, Mohammad M. Amirian, Andrew J. Irwin, Dariia Atamanchuk, Uwe Send, Douglas W. R. Wallace

Publications and Research

Net primary productivity (NPP) plays a central role in biological carbon pump (BCP) and global carbon cycling, but NPP estimates in high-latitude regions remain highly uncertain despite their disproportional contribution to the global carbon sink. Previous studies identified discrepancies among satellite- and in situ-derived NPP estimates, but the drivers of disagreement remain poorly resolved due to limited high-frequency observations. Here, we present continuous depth-resolved estimates of in situ NPP from the Central Labrador Sea (56°N) in the subpolar Northwest Atlantic, reconstructed from daily bio-optical profiles collected between May and November 2016, and a 40-year dataset of photosynthesis parameters derived from …

A Yolov8x Framework For Breast Lesion Localization Using Real-World Mammographic Images, Hadeel Abdul Majeed Ghazi, Yarub Alazzawi, Hussein Hasan Nsaif

Karbala International Journal of Modern Science

Breast cancer is one of the leading causes of mortality among women worldwide, and early and accurate mammographic assessment is important for timely diagnosis and improved patient survival. However, accurate breast lesion detection requires overcoming the issues of low lesion contrast, dense breast tissue, image noise, and variation in mammographic acquisition. In this study, we present a YOLOv8x-based computer-aided detection framework for automated breast lesion localization in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) images acquired under heterogeneous clinical conditions. The framework was developed and evaluated using 501 clinical mammographic images obtained from multiple mammography systems, namely GE …

An Object-Level Semantic Video Transmission Framework For Iomt Based On Lightweight Yolo And Motion Triggering, Iman Kadhum Abbood, Alaa Shawqi Jaber

Karbala International Journal of Modern Science

In Internet of Multimedia Things (IoMT) environments, video capture and frame transmission consume substantial sensor energy and impose considerable network overhead because of redundant data transmission. Traditional approaches often rely on repeated pixel-level operations and frequent execution of deep segmentation models, thereby increasing computational and transmission costs. This paper proposes an Object-Level Semantic Video Transmission Framework (OSVT) for resource-constrained IoMT environments in which high-definition visual data must be transmitted over low-bandwidth wireless multimedia sensor networks (WMSNs). The proposed OSVT incorporates a dual-stage wake-up mechanism that remains in sleep mode during static scenes, thereby reducing transmission load. The high-power YOLOv11-seg module …

Data Without Borders, Sovereignty Without Guarantees: Cybersecurity Risks Of Cross-Border Data Hosting In The Global South, Evidence And Policy Lessons From Mozambique, Faztudo Languisse Eng.

Journal of Cybersecurity Education, Research and Practice

The digital transformation of public and private institutions across the Global South has produced a paradox: as states digitize government services, financial systems, and citizen records, an increasing share of this data is hosted on servers physically located in the United States, the European Union, and other developed economies. This article develops a taxonomy of the cybersecurity and sovereignty risks generated by this dependency, distinguishing deliberate risks, arising from extraterritorial legal instruments such as the United States CLOUD Act, foreign surveillance regimes, and a concentrated hyperscale cloud market, from non-deliberate risks, arising from undersea cable failures, hyperscaler outages, misconfiguration, and …

Visualizing The Regression Lines And The Correlation Coefficient For Bivariate Discrete Random Variables Authors, Mamunur Rashid, Jyotirmoy Sarkar

Mathematics Faculty Publications

Sarkar and Rashid (2024) visualized the joint probabilities of two discrete random variables using a 2D disc plot from which they graphically obtained the conditional distributions of 𝑌 given 𝑋=𝑎, and 𝑋 given 𝑌=𝑏; and the marginal distributions of 𝑋, 𝑌, 𝑋+𝑌, 𝑋−𝑌, and 𝑐𝑋+𝑑𝑌. Sarkar and Rashid (2025) visualized the mean, mean deviation, and standard deviation of any discrete random variable using a disc plot. Here, starting with the 2D disc plot, we visualize the correlation coefficient between X and Y, and the linear regression of one variable from the other. Such visualizations help users better comprehend these concepts. …

Summit County, Utah: Changes To Climate, Snowpack, And Wildfire (1979-2025), Scott Hotaling, Gordon O. Gianniny, Joseph D. Birch, Elizabeth Cohen, Kendall Becker

All Current Publications

Summit County is climatically unique from the rest of Utah due to its high mean elevation. It’s also economically unique, with world-class ski resorts near working agricultural lands. In this fact sheet, we synthesize how Summit County’s environment is changing, with an emphasis on the last 50 years. We focus on changes in temperature, precipitation, and snowpack; growing season length; and wildfire because of their impacts on the economy, natural resources, and local communities' well-being. Our goal is to support the region's future planning efforts. To that end, we highlight risks to Summit County’s future and opportunities that may arise …

Phenological Flexibility In Nesting Grassland Birds Over A Half Century Informs Timing Of Management, Kristin S. Ellis, Michael J. Anteau, Lawrence D. Igl, Garrett J. Macdonald, Neal D. Niemuth, Aaron T. Pearse, Jill A. Shaffer, Christine A. Ribic, Josh L. Vest, Masanori Abe, Daniel S. Ackerman, Marissa A. Ahlering, Johanna N. Alpert, Anahit Amiri, Kent Andersson, Jacqueline K. Augustine, Kristel K. Bakker, Jeffrey L. Beck, Adam C. Behney, Jacy S. Bernath-Plaisted, Todd R. Bogenschutz, Eric K. Bollinger, Lauren E. Bortolotti, Andy J. Boyce, Jeffrey D. Brawn, Fred C. Bryant, Andrew J. Campomizzi, Jay D. Carlisle, J Matthew Carroll, Rachel Carroll, John P. Carroll, Justin R. Clarke, Michael R. Conover, Reesa Y. Conrey, Jaime J. Coon, Kyle A. Cutting, Stephen K. Davis, Dawn M. Davis, Duane R. Diefenbach, Jonathan B. Dinkins, R Andrew Dreelin, Victoria J. Dreitz, Courtney J. Duchardt, Michael W. Eichholz, Susan N. Ellis-Felege, Kevin S. Ellison, Bradley C. Fedy, Samantha D. Fino, Robert J. Fletcher Jr, Dillon T. Fogarty, Samuel D. Fuhlendorf, Pamela R. Garrettson, Robert J. Gates, Erin L. Gelling, James J. Giacomo, Alex Glass, Cheri L. Gratto-Trevor, Ryan D. Haffele, Christian A. Hagen, Vanessa B. Harriman, Samuel W H Harryman, David A. Haukos, Robert T. Haynam, Heather A. Herakovich, James R. Herkert, Fidel Hernandez, Jason M. Hill, H Jeffrey Homan, Torre J. Hovick, Jonathan A. Jenks, William E. Jensen, Aaron N. Johnston, Sydney-Marie Jones, Carly S. Kelly, Kaylan M. Kemink, Silka L F Kempema, Dylan C. Kesler, Christopher P. Kirol, Robert W. Klaver, Page E. Klug, Jesse L. Kolar, Nicola Koper, Kyle J. Kuechle, Jonathan D. Lautenbach, Zoe M. Lebrun-Southcott, David W. Londe, Scott R. Loss, Cullen M. Mackenzie, Patrick A. Magee, Douglas L. Manzer, John P. Mccarty, Gywm Mckee, Ann E. Mckellar, Lance B. Mcnew, James R. Miller, Megan C. Milligan, Ashlee K. Minor, Rocco J. Murano, Les D. Murray, David E. Naugle, Clayton K. Nielsen, Christopher J. Norment, Sarah B. Ogden, Lindsay A. Parsons, Brian D. Peer, Noah G. Perlut, James C. Pittman, Christopher D. Pollentier, Larkin A. Powell, Aaron C. Pratt, John P. Pulliam, Nancy M. Raginski, Dan R. Reinking, Kaitlyn M. Reintsma, Terry Z. Riley, Kevin M. Ringelman, Joseph R. Robb, Scott K. Robinson, Andres N. Rosales, Charlotte L. Roy, Kayla A. Ruth, Brett K. Sandercock, Kevin K. Sedivec, Steve K. Sherrod, Justin J. Shew, Mason L. Sieges, Jenny L. Sika, Susan K. Skagen, Cassandra G. Skaggs, Joseph Smith, Jennifer A. Smith, Kurt D. Smith, Alex J. Solem, Joshua D. Stafford, Glenn E. Stauffer, Matthew D. Stephenson, Lee Sutcliffe, Amber E. Swicegood, Jason Tack, Evan P. Tanner, Catrina V. Terry, Wendy P. Tori, Ashley M. Unger, Kelly R. Vanbeek, William E. Vetter, Jay A. Vonbank, Brett L. Walker, Michael D. Whitson, Maiken Winter, Kimberly A. With, Donald H. Wolfe, Amy A. Yakel Adams, Benjamin Zuckerberg

United States Department of Agriculture Wildlife Services: Staff Publications

The timing of biological events for wildlife species reflects and influences ecological and evolutionary processes and helps guide management actions to meet conservation objectives. Climate change may cause phenological shifts across trophic levels and ecosystems, which can have broad ecological impacts. Shifts may also result in mistiming between management practices based on historical conditions and current biological events, reducing the effectiveness of natural resource management and conservation efforts. We analyzed nesting phenology patterns over recent decades across North America for 44 species of grassland-nesting birds, including waterfowl, upland gamebirds, shorebirds, and passerines, many of which have experienced precipitous population declines. …

Dynamic Graph Mamba Model For Crime Count Forecasting Based On Adaptive Geographic Fusion, Wadhah R. Baiee

Journal of Intelligent Informatics, Networking, and Cybersecurity

Daily crime counts are hard to predict due to variations in the patterns of crime across neighborhoods and over time, as well as that the patterns vary by offence type. Areas with a shared border can give different information for forecasts than areas with similar crime histories. This study investigates if the joint use of these two relationships would give better prediction than using one or the other, and whether the algorithm can be adapted to the frequent occurrence of zeros and variance greater than the mean. It evaluates the functionality of Dynamic Graph Mamba for Crime Prediction (DG-Mamba-CP), a …

Optimized Fully Sharded Data Parallel For Efficient Distributed Training Of Bilstm With Multi-Head Attention For Network Intrusion Detection Systems, Noor Al-Huda H. Oleiwi, Ameer K. Hadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The expanding volume of network traffic, the growing size of cybersecurity datasets, and the increasing complexity of deep learning models present major computational challenges to training deep learning-based Network Intrusion Detection Systems (NIDSs). Fully Sharded Data Parallel (FSDP) enables distributed training by sharding model parameters across multiple GPUs; however, the synchronization and communication overhead can limit its training efficiency. To overcome these limitations, this paper proposes an Optimized Fully Sharded Data Parallel (Optimized FSDP) framework that improves distributed training efficiency by dynamic gradient accumulation and synchronization scheduling. The proposed framework is integrated with a Bidirectional Long Short-Term Memory (BiLSTM) network …

Calculation Of Alpha-Decay Half-Lives Using A Numerical Approach, Daniel Suescún-Díaz, Rosemberg Gómez-Noguera, Diego Rasero-Causil

Karbala International Journal of Modern Science

This study introduces a numerical approach for estimating alpha-decay half-lives of alpha-emitting nuclei using a modified five-parameter Woods-Saxon potential. The radial Schrödinger equation is solved with the Numerov method to calculate the wave function and the barrier transmission probability, without relying exclusively on the WKB approximation. The calculations were performed for 398 nuclides taken from the NUBASE2020 database. Of these, 264 nuclides were used for the MWS5 parameter-adjustment stage, whereas the remaining 134 nuclides were reserved for an independent evaluation of the model. The results were compared with those obtained from two reference models: the modified Gamow-Like Model (MGLM) and …

Antioxidant, Anti-Tyrosinase, Anti-Elastase, And Anti-Collagenase Activities Of Propolis Extracts From The Stingless Bee Tetragonula Laeviceps (Smith, 1857), Tri Hadi Handayani, Dimas Andrianto, Sih Kahono, Pamungkas Rizki Ferdian, Raden Lia Rahadian Amalia, Muhamad Ridwan

Karbala International Journal of Modern Science

Stingless bees incorporate propolis into their nest as a structural and protective material. Information remains limited on how solvent fractionation affects the antioxidant and enzyme-inhibitory activities of propolis produced by Indo nesian Tetragonula laeviceps. This study examined a crude extract prepared with 70% ethanol together with the n- hexane, ethyl acetate, and water fractions obtained from it. The analysis comprised total phenolic content (TPC), total flavonoid content (TFC), three antioxidant assays (ABTS, CUPRAC, TBARS), and inhibition of tyrosinase, elastase, and collagenase. The largest TPC and TFC values were obtained from the ethyl acetate fraction, at 85.93 ± 1.28 mg GAE/g …

Xenoliths In Meteorites: Insights Into The Dynamics Of Material Exchange Between Asteroids, J. Gattacceca, B. Devouard, L. Bonal, Y. Marrocchi, A. Greshake, R. Tartese, M. Gounelle, D. Auyang, C. Sonzogni, M. Hutson, Multiple Additional Authors

School of Earth, Environment, and Society Faculty Publications and Presentations

A minor fraction of meteorites contains xenoliths, that is, clasts that were formed on a different parent body than that of the host lithology. The occurrence of xenoliths is direct evidence for the exchange of material between asteroids, including exchanges between asteroids from the carbonaceous (CC) and non-carbonaceous (NC) reservoirs. Overall xenolith population statistics may therefore inform us about the dynamical mixing of these two asteroid reservoirs. Xenoliths are also intrinsically interesting because they may sample parent bodies that are not represented in the meteorite collection. In this work, we conducted a critical and exhaustive review of xenoliths described in …

Ruins In Reverse: Landscape Painting, Time, And The Galleria At Crystal Run | Srps Fall '26 10/21, Kristopher Benedict

Sustainability Research & Practice Seminar Presentations

Professor Kristopher Benedict, Art + Design, presents "Ruins in Reverse: Landscape Painting, Time, and the Galleria at Crystal Run".

A Biased Variant Of The Ladybug Clock Problem, Jacob Vogelpohl, Russell May

Communications on Number Theory and Combinatorial Theory

Instead of the common assumption of uniform probabilities for steps in a random walk on a graph, we generalize the steps to be biased and compute several statistics related to biased random walks on a cycle graph, more fancifully known as the ``ladybug clock problem." We obtain new explicit formulas for the distribution of probabilities for a walk to end on a particular node, the expected cover time for the cycle, and other similar statistics. We carry out these computations solely with generating functions related to the classic gambler's ruin problem.

Innovative Development Of Synthetic Biology Industry From Perspective Of Deep Integration Of Four Chains, Qin Guo, Chao Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Future industries represent the development direction of technological innovation and emerging industries, and are the main path to accelerate the formation of new quality productivity. As an important subsector of the future industry, synthetic biology industry is becoming a new track and new format for new quality productivity, and various regions are making cutting-edge directions such as synthetic biology industry a hot topic for future industrial layout. Based on the analysis of typical cities such as Shenzhen, Beijing, Tianjin, Shanghai, Hangzhou, and Changzhou, the current integration of the Four Chains in the synthetic biology industry is facing a four-dimensional dilemma …

Ai-Driven Drug Innovation Research: Trends, Challenges, And Countermeasures, Tian Miao, Qi Zhou, Lianzheng Zhou, Weijun Xu, Siyuan Liu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is profoundly transforming the innovation paradigm, industrial ecosystem, and business model of drug research and development. In recent years, the global AI-driven pharmaceutical market has grown rapidly. Major economies, multinational pharmaceutical enterprises, and technology giants are accelerating their strategic deployment. AI pharmaceutical industry has entered a critical stage of transitioning from proof-of-concept to commercial-application. China attaches great importance to the integrated development of AI and the biopharmaceutical industry, but it still faces challenges, including data scarcity, talent shortages, algorithmic black-boxes, regulatory lag, and weak foundations. This study proposes measures from the aspects of data supply, talent cultivation, …

Artificial Intelligence For Scientific Research: Strategic Landscape And Development Recommendations, Jiaofeng Pan, Feng Zhang, Jing Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence is currently driving the evolution of scientific research toward a “fifth paradigm” characterized by intelligence. Major global economies are elevating the integration of AI into scientific research (AI for Science, AI4S) to the level of national strategy, rapidly rolling out strategic plans and policy measures amidst intensifying global strategic competition in this field. Based on analyzing the typical paradigms and strategic value of AI4S, this study examines the global strategic landscape by focusing on major economies such as the United States, the European Union, and Japan; compares and analyzes their policy frameworks regarding organizational coordination, platform development, and …