OpenPinch: An Open-Source Python Library for Advanced Pinch Analysis and Total Site Integration

Energy Integrated Distillation Column Sequence by Driving Force Method and Pinch Analysis

Pinch analysis approach to carbon-constrained energy sector planning

A multiperiod approach for waste heat and renewable energy integration of industrial sites

Pinch analysis method as applied in the Al-Khairat power plan

The James River Camas mill produces 500,000 tons/year of paper from an integrated operation comprising both Kraft and Magnefite pulping, bleaching, recovery and paper machines. In addition to liquor recovery boilers the mill burns hog fuel and gas to supplement steam requirements. Electrical power demands on the site are met by a combination of on site back pressure turbines and purchased power. The mill was studied by the pinch analysis method and revealed scope to reduce thermal energy requirements by 12% on the Nos. 15, 16 and 20 paper machines and 7% in the cross pinch heat transfer in the existing mill configuration. These savings are equivalent to approximately 73 MBtu/hr in process steam, or 6.2% of total energy use. Pinch techniques reveal additional potential to save between 11--16 MBtu/hr in steam by appropriate process change, principally to black liquor concentrator and evaporator configuration. More significantly, about 36 MBtu/hr steam savings could be achieved by vapor compression heat pumping at the expense of about 1.3 MW electrical power. This amounts to 2.5% of total energy use. To realize these savings appropriate heat recovery networks are required downstream of the modified process equipment. Further scope was identified but not quantified on the remaining paper machines both to make heat savings and to integrate gas turbine power generation. Projects were identified to save up to 38 MBtu/hr of steam by process integration (i.e. 2.7% of total energy use). With appropriate process changes to concentrator configuration this saving can be increased by 9 MBtu/hr. Alternatively, vapor compression heat pumping can achieve 16 MBtu/hr in additional steam savings (1.8% of total energy use). 30 figs., 1 tab.

Deforestation and climate change are two interdependent factors lying at the foundation of global ecosystems and human societies. In this research, the link between climate change and deforestation is searched by employing high-resolution satellite data and advanced analytic methods. Deforestation rates are quantified, changes in land use are assessed, and the emission of greenhouse gases over time is monitored using satellite imagery and remote sensing data. Also, the study examines the thermal variations and precipitation patterns on a regional and global scale as well as carbon recruitment capabilities in the affected areas. Moreover, this research paper makes a thorough analysis of how deforestation activities cause global climate changes by integrating different metrics derived from satellite data and climate models. The results underline the importance of forest conservation in the fight against climate change and put forward practical proposals for the development of policies and land use sustainable practices. The ultimate objective of this study is to create a better understanding of the complex interactions between climate change and deforestation, thus creating a basis for addressing in a data-driven way such challenges as the ones that affect our planet on a global level.

Quantitative imaging of organelle dynamics provides crucial insights into cellular function, state, and organization; however, existing analysis workflows often require advanced coding expertise and multiple software tools. AutoMorphoTrack is an open-source Python toolkit that automates organelle detection, morphology classification, motility tracking, and colocalization from multichannel fluorescence microscopy image stacks. The platform includes adaptive segmentation, organelle trajectory reconstruction, and pixel-level overlap quantification within a unified, reproducible framework that can be executed as an interactive Jupyter notebook, a modular Python package, or through AI-assisted natural-language commands. Each analysis step outputs publication-ready images, time-lapse videos, and standardized quantitative data tables. To complement the main pipeline, an accompanying script— AMTComparison.py—is provided to demonstrate how AutoMorphoTrack’s outputs can be extended for comparative analysis across individual neurons or experimental conditions. Together, these tools provide an accessible framework for high-content, reproducible quantification of subcellular morphology, motility, and interactions at single-cell resolution.

Molecular simulations are invaluable for analysing molecular systems, but existing post-processing tools are often limited by a lack of customization, interactivity, and efficiency with large datasets. To address this, we developed CRISP (Comprehensive Repository for Insightful Simulation Post-Processing), an open-source Python toolkit designed to enhance workflows within the Atomic Simulation Environment (ASE). CRISP provides a versatile platform for detailed analysis and visualization, featuring a customizable and modular design, various static analysis methods, interactive 3D visualizations, and parallel processing capabilities optimized for high-performance computing. We demonstrate its effectiveness through case studies, including the analysis of statistical convergence in zeolite simulations, subsampling large datasets for machine learning, and analysing the dynamic stability of atomic clusters. CRISP effectively bridges the gap between raw simulation data and actionable insights, offering an efficient solution for researchers and saving significant time in code development.

To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English. We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation. For candidate ranking, we incorporate a trainable cross-encoder (CE) model if annotations for the target task are available. To balance the output of general-purpose candidate generators with subsequent trainable re-rankers, we introduce a novel rank regularization term in the loss function for training CEs. For re-ranking without gold-standard annotations, we introduce multiple new weakly labeled datasets using machine translation and projection of annotations from a high-resource language. xMEN improves the state-of-the-art performance across various benchmark datasets for several European languages. Weakly supervised CEs are effective when no training data is available for the target task. We perform an analysis of normalization errors, revealing that complex entities are still challenging to normalize. New modules and benchmark datasets can be easily integrated in the future. xMEN exhibits strong performance for medical entity normalization in many languages, even when no labeled data and few terminology aliases for the target language are available. To enable reproducible benchmarks in the future, we make the system available as an open-source Python toolkit. The pre-trained models and source code are available online: https://github.com/hpi-dhc/xmen.

Filippo Vicentini introduces the open-source Python toolkit NetKet, which implements machine learning methods for the study of quantum many body physics.

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Electricity Pinch Analysis Method and Application for Flexibility Supply-Demand Balance

A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrument‐independent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support on‐the‐fly orbit determination, and cleanly span file breaks. Pysat's functions and higher‐level scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages community‐driven development.

anaklasis constitutes a set of open-source Python scripts that facilitate a range of specular neutron and X-ray reflectivity calculations, involving the generation of theoretical curves and the comparison/fitting of interfacial model reflectivity against experimental data sets. The primary focus of the software is twofold: on one hand to offer a more natural framework for model definition, requiring minimum coding literacy, and on the other hand to include advanced analysis methods that have been proposed in recent work. Particular attention is given to the ability to co-refine reflectivity data and to the estimation of model-parameter uncertainty and covariance using bootstrap analysis and Markov chain Monte Carlo sampling. The compactness and simplicity of model definition together with the streamlined analysis do not present a steep learning curve for the user, an aspect that may accelerate the generation of reproducible, easily readable and statistically accurate reports in future neutron and X-ray reflectivity related literature.