Browse Articles

Assessing carbon–neutral supercapacitors in renewable energy systems with self-improving agent-based molecular fuzzy intelligent algorithms

Acoustic standing wave driven bubble dynamics in Oldroyd-B fluids using a semi analytical approach

Application of concentrated solar energy in postprocessing of selective laser melted Ti6Al4V alloy through simultaneously gas nitriding and heat treatment

The impact of embedded global innovation networks on manufacturing value chains

Using machine learning to forecast conflict events for use in forced migration models

Abstract Forecasting the movement of populations during conflict outbreaks remains a significant challenge in contemporary humanitarian efforts. Accurate predictions of displacement patterns are crucial for improving the delivery of aid to refugees and other forcibly displaced individuals. Over the past decade, generalized modeling approaches have demonstrated their ability to effectively predict such movements, provided that accurate estimations of conflict dynamics during the forecasting period are available. However, deriving precise conflict forecasts remains difficult, as many existing methods for conflict prediction are overly coarse in their spatial and temporal resolution, rendering them inadequate for integration with displacement models. In this paper, we propose a hybrid methodology to enhance the accuracy of conflict-driven population displacement forecasts by combining machine learning-based conflict prediction with agent-based modeling (ABM). Our approach uses a coupled model that combines a Random Forest classifier for conflict forecasting with the Flee ABM—a model of the movements of refugees and internally displaced persons (IDPs). The coupled model is validated using case studies from historical conflicts in Mali, Burundi, South Sudan, and the Central African Republic. Our results demonstrate comparable predictive accuracy over traditional methods without the need for manual conflict estimations in advance, thus reducing the effort and expertise needed for humanitarian professionals to provide urgent displacement forecasts.

Efficient temporary pollen storage techniques for quality kiwifruit production

A fast retrieval method for multilevel redundant data in grid resource business middle office based on improved decision tree algorithm

High prevalence of vector-borne protozoa and bacteria in dogs from Chad determined using a targeted next-generation sequencing approach

Comparison of performance of cervical cancer grading based on acetowhite areas

Emerging applications of sustainable modified CdO/Ag-CdO NPs for electrochemical sensitive and selective detection of mercury (Hg+) heavy metal

The genomic history of Iberian horses since the last Ice Age

TLR4+group 2 innate lymphoid cells contribute to persistent type 2 immunity in airway diseases

Broad neuraminidase antibodies confer protection against seasonal and avian influenza viruses

Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states

Mechanically reliable and electronically uniform monolayer MoS2 by passivation and defect healing

Peptide-based rigid nanorod-reinforced gelatin methacryloyl hydrogels for osteochondral regeneration and additive manufacturing

KNN-based frequency-adjustable ferroelectric heterojunction and biomedical applications

Abstract High-performance lead-free K0.5Na0.5NbO3 piezoelectric ceramics present a practical alternative to lead-containing counterparts by effectively reducing potential environmental hazards. This advancement is particularly relevant to the development of ferroelectric heterojunction devices for biomedical applications. Here, we design and fabricate a frequency-adjustable ferroelectric heterojunction based on the developed K0.5Na0.5NbO3 piezoelectric ceramics with a high piezoelectric coefficient (d 33 = 680 pC/N). By leveraging flexible encapsulation, the heterojunction achieves miniaturization (φ = 13.3 mm, h = 2.28 mm) and suitability for implantation. After penetrating the rat skull, the ultrasound generated by the heterojunction at a frequency of 3 MHz reaches a focal depth of about 7.9 mm, a focal width of approximately 480 μm at −6 dB, and millimeter-scale continuous focal tuning (1.5 mm) within a narrow frequency range (2.7–3.3 MHz). Additionally, the implanted heterojunction enables long-term and high-precision transcranial neuromodulation, and consequently yields therapeutic effects in a myocardial infarction animal model. Collectively, this study highlights a viable strategy for developing and applying lead-free ferroelectric heterojunctions, expanding their potential in brain modulation, and providing new insights into clinical treatments of myocardial infarction.

Sporadic ALS induced pluripotent stem cell derived neurons reveal hallmarks of TDP-43 loss of function

High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction Trees (BERT)

Abstract Data from high-throughput technologies assessing global patterns of biomolecules (omic data), is often afflicted with missing values and with measurement-specific biases (batch-effects), that hinder the quantitative comparison of independently acquired datasets. This work introduces batch-effect reduction trees (BERT), a high-performance method for data integration of incomplete omic profiles. We characterize BERT on large-scale data integration tasks with up to 5000 datasets from simulated and experimental data of different quantification techniques and omic types (proteomics, transcriptomics, metabolomics) as well as other datatypes e.g., clinical data, emphasizing the broad scope of the algorithm. Compared to the only available method for integration of incomplete omic data, HarmonizR, our method (1) retains up to five orders of magnitude more numeric values, (2) leverages multi-core and distributed-memory systems for up to 11 × runtime improvement (3) considers covariates and reference measurements to account for severely imbalanced or sparsely distributed conditions (up to 2 × improvement of average-silhouette-width).

Skeletal editing of 4-arylpyrimidines into diverse nitrogen heteroaromatics via four-atom synthons