Our Work

Version 3.1 of the Lexicon C19 dataset: 3,760 performance-relevant entries extracted from eight nineteenth-century European music, dance, and encyclopedic sources. The package includes the main CSV da…

Version 3.1 of the Lexicon C19 dataset: 3,760 performance-relevant entries extracted from eight nineteenth-century European music, dance, and encyclopedic sources. The package includes the main CSV dataset, a multilingual concept lexicon, semantic relations, a GraphML knowledge graph, metadata, reproducibility scripts, notebooks, and publication figures. Acknowledgement: this dataset publication acknowledges support from ANR MELODY (ANR-24-IAS1-0001).

Precision agriculture has emerged as a transformative approach to modern farming by integrating Artificial Intelligence, the Internet of Things, data analytics, and intelligent automation to enhance a…

Precision agriculture has emerged as a transformative approach to modern farming by integrating Artificial Intelligence, the Internet of Things, data analytics, and intelligent automation to enhance agricultural productivity and sustainability. Unlike conventional farming practices, precision agriculture utilizes real-time data collected from smart sensors, Unmanned Aerial Vehicles, satellite imagery, and weather monitoring systems to optimize irrigation, fertilization, pest control, and crop management. AI-based predictive models and machine learning algorithms analyze large volumes of agricultural data to support informed decision-making, improve crop yield, and reduce resource consumption. IoT-enabled monitoring systems facilitate continuous observation of soil health, environmental conditions, and crop growth, enabling farmers to implement timely interventions and predictive maintenance. Furthermore, the integration of cloud computing, robotics, digital twins, and precision farming technologies contributes to sustainable agricultural development by minimizing water usage, reducing chemical inputs, lowering operational costs, and improving food security. This chapter presents a comprehensive review of AI- and IoT-enabled precision agriculture technologies, their architectures, applications, sustainable farming practices, engineering challenges, and future research directions. The chapter highlights the role of intelligent farming systems in achieving climate-resilient, resource-efficient, and environmentally sustainable agricultural production.

This project includes: Metabolomics data using LC-MS/MS for Bacterial combination inoculated BRRI DHAN67 & Fungus inoculated BRRI DHAN28 roots. 100mM salt stress was applied for 48 hours. Total R…

This project includes: Metabolomics data using LC-MS/MS for Bacterial combination inoculated BRRI DHAN67 & Fungus inoculated BRRI DHAN28 roots. 100mM salt stress was applied for 48 hours. Total RNA-Sequencing Data for Bacterial combination inoculated BRRI DHAN67 & BRRI DHAN28 shoot (80mM salt stress was applied for 48 hours); Total RNA-Sequencing Data for Fungus inoculated BRRI DHAN28 shoot (100mM salt stress was applied for 48 hours) Hormone Profiling Data for Bacterial combination inoculated BRRI DHAN67 roots and Fungus inoculated BRRI DHAN28 roots. 100mM salt stress was applied for 72 hours. Raw RNA-Sequencing files have been deposited to NCBI SRA under BioProject Accession PRJNA1499441 (for Bacteria) and PRJNA1437529 (For Fungi).

This reproducibility package supports a spatial ecological analysis of associations between persistent green vegetation exposure and area-level mental health burden across 599 Population Health Areas …

This reproducibility package supports a spatial ecological analysis of associations between persistent green vegetation exposure and area-level mental health burden across 599 Population Health Areas (PHAs) in five Australian metropolitan regions. The study evaluates green vegetation exposure derived from Digital Earth Australia (DEA) Fractional Cover Percentiles (photosynthetic vegetation median, pv_pc_50) averaged over 2020–2022, and area-level mental health indicators obtained from the Population Health Information Development Unit (PHIDU). The analysis integrates ordinary least squares regression with HC3 robust standard errors, spatial error models, spatial lag models, and sensitivity analyses evaluating alternative exposure windows, spatial specifications, outcome definitions, multiple-testing correction, and covariate treatment. The package includes derived analytical datasets, Python scripts, model outputs, publication tables, supplementary materials, and final figures required to reproduce the reported analyses. Raw third-party datasets are not redistributed. Original datasets should be obtained directly from Digital Earth Australia, PHIDU, and the Australian Bureau of Statistics according to their respective access conditions and licensing terms. The associated manuscript will provide the scientific interpretation of these results.

What's Changed perf: reduce file I/O and allocations in alignment projection by @AndreaGuarracino in https://github.com/pangenome/impg/pull/225 ci: run local_compression testbed on macOS via GNU core…

What's Changed perf: reduce file I/O and allocations in alignment projection by @AndreaGuarracino in https://github.com/pangenome/impg/pull/225 ci: run local_compression testbed on macOS via GNU coreutils by @AndreaGuarracino in https://github.com/pangenome/impg/pull/226 Revert temporary local_compression testbed diagnostics by @AndreaGuarracino in https://github.com/pangenome/impg/pull/227 deps: update wfmash-rs (wfmash v0.14.1) via sweepga, bump seqwish by @AndreaGuarracino in https://github.com/pangenome/impg/pull/228 release: 0.5.0 with graph fixes and a packageable layout by @AndreaGuarracino in https://github.com/pangenome/impg/pull/229 deps: one lib_wfa2 everywhere, fix aarch64 and the conda build by @AndreaGuarracino in https://github.com/pangenome/impg/pull/230 release: 0.5.1 by @AndreaGuarracino in https://github.com/pangenome/impg/pull/231 deps: fix aarch64 and macOS builds by @AndreaGuarracino in https://github.com/pangenome/impg/pull/232 release: back to 0.5.0 by @AndreaGuarracino in https://github.com/pangenome/impg/pull/234 deps: drop the macOS 10.15 requirement by @AndreaGuarracino in https://github.com/pangenome/impg/pull/235 deps: re-vendor wfmash to v0.14.1 head by @AndreaGuarracino in https://github.com/pangenome/impg/pull/236 Full Changelog: https://github.com/pangenome/impg/compare/v0.4.1...v0.5.0

The Taqrib Diagnostic–Decision Model (TDDM) is proposed as a multi-dimensional, design–science framework to overcome the descriptive and fragmented character of current studies on Islamic …

The Taqrib Diagnostic–Decision Model (TDDM) is proposed as a multi-dimensional, design–science framework to overcome the descriptive and fragmented character of current studies on Islamic intra-faith rapprochement (taqrib). Drawing on an extensive typology of 25 families and more than 200 modalities of taqrib across social, institutional, legal–fiqh, doctrinal–kalamic, epistemic, historical, civilizational, educational, digital, media–discursive, and interfaith domains, the model pursues three main objectives: (1) to classify diverse rapprochement practices through a shared codebook; (2) to diagnose real-world tensions across multiple levels of analysis (micro, meso, macro, civilizational) and architectural layers (foundational, strategic, operational, civilizational); and (3) to guide the selection and evaluation of context-appropriate taqrib interventions. Methodologically, the article employs a design–science approach that integrates framework-building, typology construction, diagnostic matrices, and decision trees into a coherent research-ready artefact. The model specifies core axes (type of taqrib, level of analysis, architectural layer, temporal horizon, and mode of intervention) and links them to a diagnostic matrix and decision tree that translate problem profiles into recommended families and modalities of rapprochement. A measurement logic is introduced through input, process, output, and outcome indicators, compatible with qualitative, quantitative, and AI-assisted analytics (including textual corpora, network analysis, and platform data). The article concludes that TDDM provides a transferable, data-friendly platform for scholars and policy-makers to design, compare, and evaluate taqrib strategies, and for institutions to embed rapprochement governance into policies, programmes, and digital infrastructures. Concrete implications are outlined for research design, institutional assessment, and the governance and regulation of sectarian diversity.

Background: Objective: To describe the clinical presentation, diagnostic approach, surgical treatment, and outcomes of a patient with plantar eccrine poroma presenting as a long-standing pigmented les…

Background: Objective: To describe the clinical presentation, diagnostic approach, surgical treatment, and outcomes of a patient with plantar eccrine poroma presenting as a long-standing pigmented lesion with recent symptomatic changes. Introduction: Eccrine poroma is a rare benign adnexal neoplasm originating from the intraepidermal portion of eccrine sweat glands. Case Description: A 67-year-old female presented with a pigmented plantar lesion of 47 years' evolution that recently became painful and bled intermittently. Histopathological examination confirmed eccrine poroma. Complete surgical excision and reconstruction with a local advancement flap were performed successfully. Conclusion: Histopathological examination remains essential for diagnosis, and complete surgical excision remains the treatment of choice.   Keywords: Eccrine poroma, case report.

Labor shortages in the hospitality sector have persisted well beyond the post-pandemic recovery period, with 65% to 76% of U.S. hotels reporting unfilled positions across three survey waves in 2024, a…

Labor shortages in the hospitality sector have persisted well beyond the post-pandemic recovery period, with 65% to 76% of U.S. hotels reporting unfilled positions across three survey waves in 2024, according to surveys conducted by the American Hotel & Lodging Association. Despite growing recognition of this challenge, academic literature has predominantly examined workforce management from an internal human resources perspective, leaving the role of external staffing agencies as strategic operational partners largely unaddressed. This study analyzes how advanced staffing models, specifically the integration of permanent core staff, contingent agency-supplied labor, and AI-assisted scheduling, relate to revenue optimization and operational efficiency in high-demand hospitality environments. Analysis draws on a systematic review of peer-reviewed publications from 2020 to 2024 and industry reports issued by the American Hotel & Lodging Association, Staffing Industry Analysts, the U.S. Bureau of Labor Statistics, and the World Travel & Tourism Council. Study findings suggest that hospitality firms operating with integrated staffing frameworks demonstrate measurably lower labor cost variability and higher service capacity alignment during demand peaks compared to those relying on traditional permanent-hire models. The novelty of this study lies in reframing the staffing agency from a transactional labor supplier to a strategic revenue-enabling partner, and in proposing a three-component integrated staffing model designed for high-demand service environments.

Added option to supply paths to SVR and RF models. Full Changelog: https://github.com/adamyhe/pydreg/compare/v0.2.5...v0.2.6