George F. Corliss

Background: Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions. Methods: We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention. Results: Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed the same pattern: lower reductions under the network than the all‑to‑all model at modest efficacy, converging at high efficacy/coverage. Findings were robust across baseline prevalence scenarios. Conclusions: Accounting for social networks can attenuate projected impacts for sub‑optimal TB interventions. Forecasts and target‑setting should include sensitivity to social network structure.

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency data and correlated variables, followed by an update phase that redistributes low-frequency observations across high-frequency periods. This process repeats, refining estimates with each iteration cycle. The ISD algorithm’s key contribution is its ability to disaggregate multiple, nonuniformly spaced time series with overlapping intervals into a single daily representation. In two case studies using natural gas data, ISD successfully disaggregates billing cycle and grouped residential customer data into daily time series, achieving a 1.4–4.3% WMAPE improvement for billing cycle data and a 4.6–10.4% improvement for residential data over existing methods.

Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction in hourly mean absolute scaled error and a 3% reduction in daily mean absolute scaled error.

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. Based on ten-fold Monte Carlo cross-validation, an ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0 % for An. gambiae, and 90.2 ± 1.7 % for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae, and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae, and 88.7 ± 1.1% for An. arabiensis. Conclusion Training both regression and binary classification age models using ANNs yields models with higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers score higher accuracy on classifying age of mosquitoes than a regression model translated as binary classifier. Therefore, we recommend training models to estimate age of An. gambiae and An. arabiensis using ANN model architectures and direct training of binary classifier instead of training a regression model and interpret it as a binary classifier.

Extreme cold events in natural gas demand are characterised by unusual dynamics that makes modelling the characteristics of the gas demand during extreme cold events a challenging task. This unusual dynamics is in the form of hysteresis, possibly due to human behavioural response to extreme weather conditions. To natural gas distribution utilities, extreme cold events represent high risk events given the associated huge demand of gas by their customers. To understand the nature of the unusual dynamics and help utilities in their decision-making process, we present a semi-supervised learning algorithm that identifies extreme cold events in natural gas time series data. Using phase space reconstruction, the input space is mapped into a phase space. In the reconstructed phase space, events with similar dynamics are closer together, while events with different dynamics are far apart. A cluster containing extreme cold events is identified by finding the nearest neighbours to an observed cold event. The learning algorithm was tested on natural gas consumption data obtained from natural gas local distribution companies. Our RPS-kNN algorithm was able to identify extreme cold events in the data.

Extreme cold events in natural gas demand are characterised by unusual dynamics that makes modelling the characteristics of the gas demand during extreme cold events a challenging task. This unusual dynamics is in the form of hysteresis, possibly due to human behavioural response to extreme weather conditions. To natural gas distribution utilities, extreme cold events represent high risk events given the associated huge demand of gas by their customers. To understand the nature of the unusual dynamics and help utilities in their decision-making process, we present a semi-supervised learning algorithm that identifies extreme cold events in natural gas time series data. Using phase space reconstruction, the input space is mapped into a phase space. In the reconstructed phase space, events with similar dynamics are closer together, while events with different dynamics are far apart. A cluster containing extreme cold events is identified by finding the nearest neighbours to an observed cold event. The learning algorithm was tested on natural gas consumption data obtained from natural gas local distribution companies. Our RPS-kNN algorithm was able to identify extreme cold events in the data.

Dry eye syndrome is a relatively common disease of the tears and ocular surfaces that results in discomfort, visual disturbance, and tear film instability with possible damage to the ocular surfaces. Microvascular submandibular gland (SMG) transfer offers a surgical alternative for a permanent autologous substitution of tears using the basal secretion of a transplanted SMG. Long-term follow-up reveals that this technique is a lasting and effective solution for patients with severe dry eye syndrome. The uncomfortable symptoms were relieved, and the frequency of use of pharmaceutical tear substitutes was reduced. Objective examination showed significant improvement in tear film and some features of ocular surface such as breakup time of tear film and corneal staining. Patients may suffer from obstruction of Wharton's duct or epiphora after surgery. Activation of secretion-related receptors could improve the early hypofunction of the denervated SMG and prevent the duct obstruction. Reduction surgery, partial SMG transplantation, uses of atropine gel or Botulinum toxin A could be the choices of treatment for epiphora.