Risks And Possibilities: The Poetry Of A. R. Ammons, Peter Stevens

Ontario Review

PETER STEVENS, whose Pavase sequence appeared in Ontario Review #2, is former poetry editor of Canadian Forum and the author of numerous articles, poems, and reviews. His most recent book is And the Dying Sky Like Blood, a cycle of poems for Norman Bethune. He teaches at the University of Windsor.

Multimedia-Based Interventions To Improve Science Learning Outcomes For Deaf Students At Wangsel Institute For The Deaf., Cheten Tshering Mr., Tshering Wangchuk

Journal of Science Education for Students with Disabilities

Efforts have been made to ensure inclusive learning opportunities for all students, including those from diverse backgrounds. One key strategy to enhance children’s learning and academic performance involves the thoughtful selection and application of instructional tools tailored to meet learners' specific needs. This study examines the impact of multimedia-based instruction on science achievement among deaf students at Wangsel Institute for the Deaf in Bhutan. Using a quasi-experimental design, the research compared pretest and post-test results between a control group and an experimental group. Data analysis included descriptive statistics (t-test) and ANCOVA. The findings indicated a statistically significant differences (p = …

Factors Impacting Elementary School Students’ Engineering Design And Optimization Decisions, Elaine Silva Mangiante, Ilana Haliwa

Journal of Pre-College Engineering Education Research (J-PEER)

This mixed methods study explored possible factors that could impact elementary school students’ engineering design and optimization decisions. Data were collected from six teachers and 117 students in six fourth grade classrooms that implemented the Engineering is Elementary geotechnical engineering unit, A Stick in the Mud: Evaluating a Landscape. Students were to recommend to villagers their site decision of where to build a TarPul bridge based on four properties: soil type, villager preference, amount of compaction needed, and location less prone to erosion. Data sources included a pre-and post-assessment question, students’ documentation of property choices for their first and optimized …

From Desire To Decision (A Syllabic Synopsis Of A Short Story), Ken Stange

Ontario Review

KEN STANGE, a psychology lab instructor in North Bay, Ontario, edits Nebula Magazine and has poems published or forthcoming in The Canadian Forum, The Dalhousie Review, Event, Waves, and elsewhere.

The Man Who Changed Overnight, Fielding Dawson

Ontario Review

FIELDING DAWSON, a resident of New York City, is the author of The Dream/Thunder Road (stories), The Greatest Story Ever Told (novella), and The Sun Rises into the Sky (stories), and other books. "The Man Who Changed Over-night" will be the title story of a new collection of fiction to be published by The Black Sparrow Press this year.

Handwritten Piece Of Paper

Josie Green Washington Papers

Small strip of paper with multiple accounting calculations.

Hand-Written Program Notes

Josie Green Washington Papers

Scraps of paper with hand-written notes of a program scheduled for March 2, 1950. Math operations are present on the back of the paper.

Rme-1: A Formal Specification For Geometric Semantic Refinement, Samuel J. Church

Defensive Publications Series

RME-1: A Formal Specification for Geometric Semantic Refinement

Document ID: RME-1-2026-PRIOR-ART-01

Status: Public Disclosure for Defensive Publication

Core Methodology: Non-Euclidean Mapping of Abstract Logic to Sensory-Grounded Vectors

I. Mathematical Framework: The RME Hilbert Space

The RME-1 engine operates by projecting linguistic units into a multi-axial Hilbert Space. Unlike standard token-probability models, RME-1 enforces a Geometric Constraint (GC) on the output, defined by the relationship between four primary variables:

1. Symbolic Density ($SD$): The concentration of domain-specific semantic tokens per unit of syntax.

2. Sensory Gravity ($SG$): The coefficient of tactile or physical-structural grounding in the output string.

3. Intent …

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Theoretical Analysis Of Mir-Based Differential Photoacoustic Spectroscopy For Noninvasive Glucose Sensing, Tasnim Ahmed, Khan Mahmud, Md Rejvi Kaysir, Shazzad Rassel, Dayan Ban

Electrical and Computer Engineering Faculty Research

Diabetes is a developing global health concern that cannot be cured, necessitating frequent blood glucose monitoring and dietary management. Photoacoustic Spectroscopy (PAS) in the mid-infrared (MIR) region has recently emerged as a viable noninvasive blood glucose monitoring technique. However, MIR-PAS confronts significant challenges: (i) Water absorption, which reduces light penetration, and (ii) interference from other blood components. This paper systematically analyzes the background of photoacoustic signal generation and proposes a differential PAS (DPAS) in the MIR region for removing the background signals arising from water and other interfering components of blood, which improves the overall detection sensitivity. A detailed mathematical …

Cover And Contents

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Exploitation Prioritization And Residual Risk Assessment Based On Hybrid Mcdm Model, Zibo Wang, Yaofang Zhang, Sicai Lv, Yingzhou Wang, Hongri Liu, Bailing Wang

Turkish Journal of Electrical Engineering and Computer Sciences

Exploitation is one of the most significant ways to launch attacks using vulnerabilities. The increasing number of vulnerabilities and limited allocation of security resources make it impossible to eliminate all exploitations. Because not every vulnerability can be fixed, it is necessary to rank exploitations and subsequently assess the residual risk, which is defined as the remaining threat potential after each elimination. In this paper, a structured and flexible decision support framework based on a hybrid multicriteria decision-making model is proposed for prioritizing exploitations and assessing residual risk. Metrics are treated as criteria in the model. The hybrid model is developed …

A Deep Learning-Based Real-Time No-Reference Image Decolorization Network With Perceptual Preservation, Mengjuan Zhao, Yitao Liang, Weiya Shi, Juan Xia

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, grayscale images are preferred as input data for some specific vision tasks. Decolorization is the transformation of a color image into a grayscale image. Efficient decolorization algorithms can improve the overall task efficiency, while perceptual preservation in decolorization can provide more information for further processing. In recent research, traditional methods focus on preserving contrast or detail information with little attention to perceptual features. Deep-learning methods are beginning to consider perceptual preservation, but they run inefficiently. In addition, the decolorization methods lack the optimal target grayscale images for reference. Therefore, we propose a new deep learning-based real-time no-reference decolorization network …

Noninvasive Condition Monitoring For Eccentricity Fault Detection In Large Hydro Generators, Atena Tazikeh Lemeski, Di̇dem Tekgün, Ozan Keysan, Kemal Leblebi̇ci̇oğlu, Murat Göl

Turkish Journal of Electrical Engineering and Computer Sciences

Eccentricity faults in electric machines remain a critical concern, as they generate uneven magnetic forces that increase vibration and noise, ultimately raising the risk of premature motor failure. This study proposes a method for the early detection of dynamic eccentricity (DE) faults in hydropower plants through an advanced optimization-based parameter identification technique integrated with finite element analysis (FEA). Finite element modeling (FEM) is first used to analyze an existing salient-pole synchronous generator (SPSG) from a hydroelectric power plant in Türkiye. The effects of DE faults on the SPSG’s magnetic equivalent circuit parameters are then examined under various fault severities. A …

A Deep Learning Model For Accurate Tomato Leaf Disease Identification, Maheen Shahzad, Muhammad Abdullah Javed, Erum Ashraf, Hafiz Ishfaq Ahmad, Sabeen Masood

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources …

Influence Of Sinr And Noise Variance On Outage Probability For Mimo-Noma System In 5g And Beyond, Sadiq Ur Rehman, Jawwad Ahmed, Muhammad Zubair, Syed Sajjad Hussain Rizvi

Turkish Journal of Electrical Engineering and Computer Sciences

Nonorthogonal multiple access (NOMA) communication presents a promising solution to the limitations of traditional orthogonal multiple access techniques, offering potential improvements in achievable rates. Multiple-input multiple-output (MIMO), when combined with NOMA (MIMO-NOMA), further enhances these benefits by leveraging the diversity advantages of multiple antennas. Looking ahead, the future of wireless communication hinges on deploying heterogeneous networks (HetNets), facilitating the coexistence of various wireless access networks in a hierarchical fashion. However, the advent of 5G and 6G communications brings shorter channel coherence times, rendering channel reciprocity unreliable. Consequently, conventional channel estimation methods relying on uplink (UL) pilots for downlink (DL) transmission …

A Novel Approach To Maximum Weighted Traffic Flow Method For Effective Signal Control, Zülal Hi̇lal Yildiz Budak, Seyi̇t Alperen Çeltek, Aki̇f Durdu

Turkish Journal of Electrical Engineering and Computer Sciences

Traffic signal management is a critical challenge due to its environmental, economic, and public health impacts. The maximum weighted flow method (MaxWeightedFlow) was developed to optimize traffic flow at isolated and coordinated urban intersections. This study proposes a new method, the novel MaxWeightedFlow, which includes two key strategies to enhance the classical approach. The first strategy reduces computational burden by estimating vehicle approach times based on instantaneous speeds, improving real-time performance. The second employs regression analysis to optimize the alpha parameter, representing the vehicle waiting coefficient. The proposed approach, the novel MaxWeightedFlow, was evaluated using real-world traffic data from Kilis, …

Improving Rail System Signaling Efficiency Through Ai-Based Driving Profile Generation: A Comparative Performance Analysis, Mehmet Taci̇ddi̇n Akçay, Abdurrahi̇m Akgündoğdu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a dataset comprising 3600 discrete operational snapshots (rather than continuous time-series data) derived from real-field operations is used to obtain a high-accuracy driving profile equation using a second-degree Polynomial Regression method. This equation demonstrates the model’s interpretability. The performance metrics obtained with the second-degree polynomial regression model’s equation are as follows: a coefficient of determination (R2) of 0.84, a Pearson Correlation Coefficient of 0.91, and an RMSE of 11.13. These results indicate the effectiveness of artificial intelligence-based approaches in improving the efficiency of the railway signaling system. The same dataset is also utilized with other machine learning …

First Results On The Search For Lepton Number Violating Neutrinoless Double-Β Decay With The Legend-200 Experiment, H. Acharya, N. Ackermann, M. Agostini, A. Alexander, C. Andreoiu, G. R. Araujo, Frank Avignone Iii, M. Babicz, W. Bae, A. M. Bakalyarov, M. Balata, A. S. Barabash, C. J. Barton, L. Baudis, C. Bauer, E. Bernieri, L. Bezrukov, K. H. Bhimani, V. Biancaccu, E. Blalock, Et. Al.

Faculty Publications

The LEGEND Collaboration is searching for neutrinoless double-beta (0νββ) decay by operating high-purity germanium detectors enriched in ⁷⁶Ge in a low-background liquid argon environment. Building on key technological innovations from the GERmanium Detector Array (GERDA) experiment and the MAJORANA DEMONSTRATOR experiment, LEGEND-200 has performed a first 0νββ decay search based on 61.0 kg yr of data. Over half of this exposure comes from our highest performing detectors, including newly developed inverted-coaxial detectors, and is characterized by an estimated background level of 0.5^+0.3_−0.2 cts/(keV ton yr) in the 0νββ decay signal region. A combined …

Law Schools Should Teach How To Integrate Ai Tools Into Practice, Robert A. Mackenzie, David J. Reiss

Cornell Law Faculty Publications

Now that artificial intelligence tools for lawyers are widely available, we decided to integrate them for a semester in our Entrepreneurship Clinic. We have some important takeaways for legal education in general and the transactional practice of law in particular.

First, employers and educators need to account for law students who already are using AI tools in their legal work and guide new lawyers about how to use such tools appropriately.

Second, different AI products lead to wildly different results. Just demonstrating this to law students is very valuable, as it dispels the notion that AI responses can replace their …

Coastal Conservation And Blue Carbon: Willingness To Pay For Changes To Nearshore Management In Oregon, Arthur Caplan, Marcelo Pignatari, Sarah Klain, Kreg Lindberg

Browse all Datasets

This paper reports results from a discrete choice experiment conducted with Oregon residents regarding possible policy changes in spatial management of nearshore habitat. We evaluate public preferences across several policy scenarios, each characterized by varying levels of marine reserve size (bounded areas where extractive activities are prohibited), coastal jobs generated or lost, and carbon sequestration by seagrass beds, tidal marshes and kelp forests(blue carbon habitat expansion), with models estimated in both utility and willingness to pay (WTP) space. Each of these attributes across all models displays positive, monotonic marginal WTP. Scenario analysis reveals that an “optimistic” policy package (+50 % …

Human Capital And Development, Philippe Aghion, Ingvild Almås, Costas Meghir

Cowles Foundation Discussion Papers

Human capital is central to efforts to promote growth, convergence, and the elimination of poverty. Drawing on seminal macroeconomic frameworks by Nelson-Phelps, Lucas, and subsequent developments, alongside macro and microeconomic evidence, the chapter examines the role of human capital in driving innovation and growth, emphasizing how different types of human capital matter at different stages of development, and discussing obstacles to accumulation and evidence from policy interventions.

Distribution System Reliability Evaluation Considering Protection Coordination Using Petri Nets, Rani Kumari, Bhukya Krishna Naick

Turkish Journal of Electrical Engineering and Computer Sciences

Maintaining reliable and high-quality power delivery becomes increasingly complex with expanding power grids. The lack of protection coordination poses a significant threat, compromising overall system reliability. This research addresses this challenge by proposing a method for coordinating protective devices within the distribution system, specifically during network faults. The proposed approach utilizes a stochastic timed Petri net (STPN) based methodology to model protective device coordination across various fault scenarios. This technique effectively captures the dynamic behavior and interactions of protective equipment, allowing for the anticipation of potential disturbances. This proactive insight facilitates preventative measures to address prewarning situations, thereby preventing cascading …

Butte Priority Soils Operable Unit (Bpsou) Data Summary Report (Dsr) Statement Of Authenticity Updates, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

A Robust Deep Learning Ensemble Framework For Waterbody Detection Using High-Resolution X-Band Sar Under Data-Constrained Conditions, Soyeon Choi, Seung Hee Kim, Son V. Nghiem, Menas Kafatos, Minha Choi, Jinsoo Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, …

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

Explaining Transformer-Based Classification Of Radiology Reports, M Courtman, G Abdelhalim, L Sun, E Ifeachor, S Mullin, M Thurston

Peninsula Medical School

ObjectivesDeep learning models developed for the classification of radiological reports have lacked explainability. We aimed to validate and explain a pretrained classification model by applying it to the removal of confounding data from a radiological dataset.MethodsTwo radiologists categorized 2038 anonymized MRI head free-text radiology reports for abnormality and for small vessel disease presence. Of these reports, 80% (n = 1630) were used to fine-tune pretrained transformer models to classify scans. Five-fold cross-validation was used in model development. The models were tested on the remaining 20% of the reports (n = 408). SHapley Additive exPlanations (SHAP) were used to explain the …

Forest Aboveground Biomass In The Southwestern U.S. From Misr And Gedi: Assessment With Nasa Carbon Monitoring System Data, Mark J. Chopping, Zhousen Wang, Crystal B. Schaaf, Michael Bull

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Forest aboveground biomass (AGB) density mapping initiatives generally use one of three remote sensing approaches: lidar, radar, or near-nadir multispectral imaging leveraging machine learning methods, or a combination thereof. However, the active instrument record is limited and near-nadir multispectral imaging data are relatively insensitive to canopy physical structure. Multiangle imaging enables annual wall-to-wall mapping with a global record that extends back to 2000 as these data are highly sensitive to forest AGB. This paper describes work to validate estimates in a published annual, wall-to-wall record of forest AGB on a 250 m grid, derived using 672 nm imagery from the …

Chasing Disinfection Byproducts Through The Pipes: How 66 Dbps Change Over Time In Chlorinated Vs. Chloraminated Distribution Systems, Erik Niehaves, Patrick T. Justen, Ashley A. Perkins, Alexandria L.B. Forster, Caroline O. Granger, Susan D. Richardson

Faculty Publications

While disinfection byproducts (DBPs) are typically measured at drinking water treatment plants, levels can change dramatically within the distribution system before reaching the consumer. In this study, the spatio-temporal trends of 66 DBPs across 9 different classes were examined in two drinking water distribution systems with similar source waters, but different pretreatments and residual disinfectants. One system uses residual chlorine in the distribution system, and the other uses chloramine, allowing for examination of how DBP concentrations change over time in distribution systems with different residual disinfectants. Four routes were sampled for each system with six time points over three days …

Complexity Mindsets, Complex Flow, And More: A Qualitative Grounded Theory Study On The Phenomenon Of Practitioners Working Within Complex Social Systems, Emma D. Stellman

Educational Studies Dissertations

This qualitative grounded theory study addressed gaps in the literature regarding the application of complexity theory to complex social phenomena (CSP), for which no straightforward solutions or simple interventions exist. While complexity theory has played an important role in groundbreaking innovations in quantitative fields such as medicine, engineering, and finance, its application remains limited in human-centered developmental domains including education, social work, and healthcare. Using a grounded theory design informed by phenomenological approaches, this study examined the lived experiences of a group of six CSP practitioners, each of whom participated in two individual, semi-structured video interviews. Interview data were analyzed …

Reliability Of Approaches For Measuring Soil Organic Carbon And Implications For Results-Based Payments For Smallholder Carbon Farming, Friederike Schilling, Dennis Beesigamukama, Chrysantus M. Tanga

All Peer-Reviewed Publications

Soil organic carbon (SOC) measurement is critical for result-based payments for carbon sequestration in agriculture. This study examined (i) the consistency of SOC measurements from a portable near-infrared (NIR) spectroscopy soil scanner and laboratory analyses (Walkley-Black method) using samples from 151 plots across Western Kenya, and (ii) explored the implications of measurement error for SOC stock change estimates in group-based carbon farming schemes. Scanner predictions showed a weak correlation with laboratory results (R² = 0.10–0.11; RMSE = 5.6–7.1 g/kg). Comparing results between the two laboratories showed only moderate agreement (R² = 0.34) with a systematic bias of 7.5 …