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Food insecurity, defined as insufficient access to food for a healthy and active life, affected approximately 12.8% of U.S. households in 2022. A concerted effort from both the government and non-government organizations is underway to address this challenge in the United States. This study centers on the Foodbank of Central and Eastern North Carolina (FBCENC), a nonprofit hunger relief organization pivotal in collecting and distributing food donations to local agencies serving individuals in need. However, despite the critical role of food banks, nutritional considerations are often overlooked. To address this gap, the study employs the Healthy Eating Research (HER) Nutrition Guideline, categorizing nutrition types (Red, Yellow, and Green) to assess and enhance the nutritional equity of the current distribution system. A linear programming model is proposed, and equity is adopted as the performance measure. The study aims to develop a model that strategically reduces nutritional disparities across the network. By incorporating HER guidelines and emphasizing equity in distribution, this research contributes to the broader objective of creating a more nutritionally equitable response to food insecurity within the non-profit sector. 

Food insecurity hinders individuals from the healthy and sustainable life they truly deserve. Unfortunately, food insecurity and chronic health diseases affect millions of people across the United States. Food banks are constantly fighting the uphill battle against food insecurity to supply adequate, relevant, healthy meals to those who need them. Oftentimes hunger relief organizations lack data and software tools that could aid strategic decision-making. A local food bank faces this exact problem and is struggling to find clients that face chronic health diseases in their service area. This study uses data from the local food bank to develop visualizations that investigate the health considerations of the population they serve. The results easily found a specific county with the largest number of health considerations and a zip code with the highest number of individuals facing hypertension. Dominant chronic health considerations highlight the importance for food banks to diversify their food selections. It is important for the local food bank to know what foods are essential within each county and zip code area to provide services that will be valuable to those who need them. This study benefits food banks so they can yield better service to the community. 

Food insecurity is a serious problem in America and the pandemic makes the problem even worse. Feeding America has more than 200 food banks. that These food banks and their partner agencies are the key players in the battle against food insecurity. Partner agencies may vary in size and location depending on the service area and the variety of the partner agencies and the complexities of their operations make equitable food distribution very challenging. There is a need for a meaningful to group those partner agencies to assist food bank operations managers to make informed decisions. This study uses data from a local food bank and its partner agencies. Each agency is unique in terms of its behavior. Therefore, k-means clustering was used to categorize agencies into groups based on the number of persons served and the amount of food received. The results of the study will provide evidence-based information to assist the food bank in making informed decisions. 

The focus of this paper is the development, numerical simulation and parameter analysis of a model of the transcription of ribosomal RNA in highly transcribed genes. Inspired by the well-known classic Lighthill-Whitham-Richards (LWR) traffic flow model, a linear advection continuum model is used to describe the DNA transcription process. In this model, elongation velocity is assumed to be essentially constant as RNA polymerases move along the strand through different phases of gene transcription. One advantage of using the linear model is that it allows one to quantify how small perturbations in elongation velocity and inflow parameters affect important biology measures such as Average Transcription Time (ATT) for the gene. The ATT per polymerase is the amount of time an individual RNAP spends traveling through the DNA strand. The numerical treatment for model simulations includes introducing a low complexity and time accurate method by adding a simple linear time filter to the classic upwind scheme. This improved method is modular and requires a minimal modification of adding only one line of code resulting in increased accuracy without increased computational expense. In addition, it removes the overdamping of upwind. A stability condition for the new algorithm is derived, and numerical computations illustrate stability and convergence of the filtered scheme as well as improved ATT estimation. 

Predictive modeling of a rare event using an unbalanced data set leads to poor prediction sensitivity. Although this obstacle is often accompanied by other analytical issues such as a large number of predictors and multicollinearity, little has been done to address these issues simultaneously. The objective of this study is to compare several predictive modeling techniques in this setting. The unbalanced data set is addressed using four resampling methods: undersampling, oversampling, hybrid sampling, and ROSE synthetic data generation. The large number of predictors is addressed using penalized regression methods and ensemble methods. The predictive models are evaluated in terms of sensitivity and F1 score via simulation studies and applied to the prediction of food deserts in North Carolina. Our results show that balancing the data via resampling methods leads to an improved prediction sensitivity for every classifier. The application analysis shows that resampling also leads to an increase in F1 score for every classifier while the simulated data showed that the F1 score tended to decrease slightly in most cases. Our findings may help improve classification performance for unbalanced rare event data in many other applications. 

In the fight against hunger, Food Banks must routinely make strategic distribution decisions under uncertain supply (donations) and demand. One of the challenges facing the decision makers is that they tend to rely heavily on their prior experiences to make decisions, a phenomenon called cognitive bias. This preliminary study seeks to address cognitive bias through a visual analytics approach in the decision-making process. Using certain food bank data, interactive dashboards were prepared as an alternative to the customary spreadsheet format. A preliminary study was conducted to evaluate the effectiveness of the dashboard and results indicated dashboards reduced the amount of confirmation bias. 

Abstract We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz, where we assume the gravitational-wave emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy 1 × 10⁻⁴M_⊙c²and luminosity 2.6 × 10⁻⁴M_⊙c²s⁻¹for a source emitting at 82 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as 1.08, at frequencies above 1200 Hz, surpassing past results. 

Abstract Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of general relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO–Virgo–KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering single-harmonic and dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is 6.4 × 10⁻²⁷for the young energetic pulsar J0537−6910, while the lowest constraint on the ellipticity is 8.8 × 10⁻⁹for the bright nearby millisecond pulsar J0437−4715. Additionally, for a subset of 16 targets, we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of nonstandard polarizations as predicted by the Brans–Dicke theory. 

Abstract We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO–Virgo–KAGRA network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received with low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum-likelihood Non-imaging Transient Reconstruction and Temporal Search pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15–350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10⁻³Hz, we compute the GW–BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers.