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We present a set of Grand Challenges for predictive modeling in small molecule drug discovery, with the goal of defining, prioritizing, and quantifying the areas where computation can have transformative impact. Rather than offering another broad survey of methods, this paper articulates specific scientific and technical problems that limit progress today and proposes measurable criteria by which advances can be judged. Our objective is to align researchers, investors, and industry leaders around the challenges that matter most for advancing real drug discovery programs. This work seeks to broaden participation in drug discovery by providing a clear roadmap for contributors from the growing areas of artificial intelligence, machine learning, robotics, high-performance computing, and quantum computing, where tools are rapidly advancing but are often disconnected from the practical realities of medicinal chemistry and pharmacology. The insights presented here draw on extensive discussions with experienced drug hunters, computational method developers, and thought leaders across biotech, pharma, software, and venture capital, as well as lessons learned from our own drug discovery efforts. While there is substantial enthusiasm (particularly around AI) for revolutionizing drug discovery, this moment demands sharper problem definition. Without clearly articulated challenges and performance metrics, computational innovation risks optimizing for benchmarks rather than for translational impact. We therefore identify Grand Challenges across four domains: Chemistry, Structure, Energy, and Pharmacology. For each domain, we present a common framework: a well-defined challenge, the underlying physical principles, its relevance to drug discovery decision-making, the current state of the field, and quantitative metrics that define progress. By grounding computational ambition in concrete scientific problems, we aim to catalyze sustained, measurable advances rather than episodic waves of enthusiasm.

The major issues with business cycle value prediction are that the data sources are heterogeneous, complex spatiotemporal dependencies, and a lack of long-term prediction accuracy. The model of dynamic prediction, which is developed in this paper, is founded on the fusion of multimodal data and deep learning and incorporates four kinds of heterogeneous data, financial indicators, market transactions, macroeconomic data, and textual sentiment. The former applies a Temporal Convolutional Network (TCN) to learn the temporal features of each modality and a Multi-Head Attention (MHA) mechanism to learn cross-modal interaction relationships. Second, gated fusion unit is intended to weigh and fuse heterogeneous features dynamically and Bidirectional Long Short-Term Memory (BiLSTM) network is employed to model cycle evolution law. Lastly, residual connections with Dropout regularization are also added to enhance the generalization capacity of the model. The model on an empirical dataset of 3500 companies in the 2010-2024 period had a mean absolute percentage error (MAPE) of 6.2% and root mean square error (RMSE) of 0.043 in the expansion phase and 8.7% in the contraction phase, giving it a useful tool and theoretical foundation on dynamic monitoring of business cycle and corporate decision-making.

The emerging phenomenon of using AI in regulated markets (healthcare, finance, and critical infrastructure) requires protected, expandable, and regulatory compliant data analytics pipelines across edge devices to backends (cloud environments). This paper describes a new Edge-to-Cloud Security Fabric to secure AI workflows in heterogeneous environments that maintain end-to-end confidentiality, integrity, and regulations of data. The suggested architecture will combine decentralized identity management, on-demand policy enforcement, and federated learning approaches to guard sensitive information throughout its entire lifecycle. Critical results indicate that the security fabric does not only alleviate the threat on both the edge and cloud, but it also improves the state of transparency and traceability without adverse effects on the system performance level. Experimental validation on representative regulated-industry representative attests to an observable increase in attack resistance, auditability, and secure model updates. Keen on filling the security gap in distributed AI pipelines, this study provides a sound platform of trustworthy AI deployment in high-stakes, compliance-oriented global locations.

SummaryBackgroundKnowledge of the contribution of the pathogen to the heterogeneity of the host response to infection is limited. We aimed to compare the host response in critically ill patients with a bloodstream infection (BSI).MethodsRNA profiles were determined in blood obtained between one day before and after a positive blood culture. Differential expression and pathway analyses were performed on independent patients’ samples by RNA sequencing (discovery) or microarray (validation). Additional patients were included for the discovery and validation of transcriptome classifiers of pathogen-specific BSIs. Twenty biomarkers reflecting key host response pathways were measured in blood.FindingsWe included 341 patients, among which 255 with BSI, 25 with viral infection and 61 non-infectious controls. The cultured pathogen explained 41·8% of the blood transcriptomic variance in patients with BSI. Gene set enrichment analysis showed a global resemblance between monomicrobial BSIs caused by Streptococcus, Staphylococcus aureus and Escherichia coli, which were clearly different from BSI caused by coagulase-negative staphylococci or Enterococcus. BSI by Streptococcus was associated with the highest number of differentially expressed genes, indicating strong innate and adaptive immune activation. An eight-gene streptococcal classifier performed well across different Streptococcus species, and was validated in external cohorts. Plasma biomarker profiling showed that E. coli BSI was associated with the strongest response in the cytokine and systemic inflammation domain, and S. aureus BSI with the strongest endothelial cell activation.InterpretationThe causative pathogen explains a substantial part of the heterogeneity of the host response in critically ill patients with BSI.Funding10.13039/501100006020Center for Translational Molecular Medicine and the 10.13039/501100000780European Commission.

Wind turbines that are consistently exposed to the air displaced by moving trains have a high potential for energy generation. Researchers have developed mathematical models to simulate wind energy generation from turbines on moving trains but there are significant gaps in the developed model theory. Most models do not consider the negative effects that additional aerodynamic drag, increased weight, and modified dimensions can have on the train’s operation. To overcome the drawbacks of existing models, this work proposes a novel approach of modeling the wind turbines on trains by considering wind turbine exposure only when the train is decelerating or stationary. There are no models that consider all of these realistic physical effects as a function of time. Real-time analysis and power-system simulations showed that the proposed model could produce over 3 MJ of net energy for favorable train trips. The simulated load profile met the demand of a 1 KW generator connected to onboard electrical components. Some recommendations on possible future research on wind turbines on trains are explained.

How can students prepare for potential business disruptions? The approach shown in this paper uses optimization modeling with parametric sensitivity analysis and further broadens the search for insights with structural sensitivity analysis in a series of contextual exercises. The exercises prompt students to identify critical resources for business impact analysis by extending production planning to include scenario analyses and contingency considerations. During virtual office hours, one participant described the exercises in terms of a “mystery” to solve. For an open-ended contingency planning question, some participants even proposed new product design decisions. Upon conclusion of the study, 25 of the 28 participants recommended the exercises for future semesters. Funding: Graduate assistant support for P. E. Billings and N. C. Belford was provided by the College of Business at the University of West Florida. Supplemental Material: The supplemental material is available at https://doi.org/10.1287/ited.2022.0279 .

Blockchain is a technology discussion, but not only a technology discussion. The technology works. It’s not yet fast, scalable, or mature. And it is not as good as other enterprise-ready databases; but where authenticity matters, it will be game-changing—everywhere in our world. What many are not yet realizing is how to scale it. We have spent more than a decade watching people try to monetize blockchain using the same monetization model as the Internet, with little to no success. In the global commerce space, consortia models won’t scale—there is too much friction in the process. With that realization, the other fork in that road is that ‘open’ is inevitable. The transformative nature of peer-to-peer technology, smart contracts, and standards means that it is not just individuals competing against each other to ‘win’ in this space. Our success in this area will be measured by how we work together as a global team. Yes, blockchain technology will change many things, but the greatest impact of blockchain technology is that it will change the rules and subsequently change many business models. Explain that to the C-Suite or others who may still be trying to marginalize blockchain only as a fringe technology. What we are talking about is ‘coopetition,’ which may mean the difference between success and failure in years to come.KeywordsCoopetitionBlockchain consortiumBlockchain standards

The aims of this paper are the estimation of demographic and socio-economic characteristics of the Ukrainian diaspora in Canada, and the description of methodological problems arising in the process of its implementation. Population censuses, which represent the entire population of Canada, including those of Ukrainian origin, are considered as the most comprehensive, informative and reliable source of information on Ukrainians in Canada. The paper shows that methodological problems are caused both by the specifics of the ethnic group and by changes in census procedures. The article focuses on the changes in Canadian ethnic terminology in early population censuses. It is noted that census statistics experienced a significant change in the definition of ethnicity. Starting in 1986, the question on ethnicity includes descendants of mixed ethnic marriages; this makes it impossible to compare numbers of Ukrainians in previous years. A map of the distribution of ethnic Ukrainians was constructed based on data from the 2016 Canadian population census. The paper analyzes changes in the age and sex structures of the population of Ukrainian origin and their language characteristics. Trends in the number of persons of Ukrainian origin, who consider Ukrainian as their mother tongue, are assessed. We show that the proportion of persons over 65 years of age with Ukrainian mother tongue is constantly increasing, while the respective proportion of younger persons is decreasing. The number of Canadian Ukrainians who know Ukrainian is decreasing. Their distribution by age groups has a clearly defined right-sided asymmetry, i.e., the top of the curve is constantly shifting towards the older age groups. The article shows that the overwhelming majority (90 % and more) of ethnic Ukrainians know and use English, while French is much less prevalent. The proportion ISSN 2072ISSN -9480. , 2019, 3 (37) , 3 (37) of persons who know both official languages is increasing and this proportion among ethnic Ukrainians is similar to the proportion for the whole population of Canada. The proportion of persons who do not speak any of the official language is decreasing and is less than one percent. We show that Ukrainians have fairly good socio-economic positions relative to the Canadian population as an ethnic group. For example, their position in the labor market is in some cases more advantageous compared to the national average.

Channel routing and crosstalk minimization are important issues while we talk about high-performance circuits for VLSI physical design automation. Interconnection among the net terminals satisfying constraints in an intelligent way is a necessity to realize a circuit in a minimum possible area, as this is a primary requirement to reduce cost as well as to increase yield. In this paper, along with area minimization, the performance of the computed circuits has also been enhanced by computing routing solutions with a specified amount of bottleneck crosstalk in two-layer channel routing. Usually, crosstalk is measured by the amount of overlapping of a pair of nets assigned to adjacent tracks. The crosstalk minimization problem in the reserved two-layer Manhattan channel routing model is NP-hard. Thus, in this paper, heuristic algorithms have been devised to optimize bottleneck crosstalk in order to augment circuit performance in two-layer channel routing. Experimental results obtained are highly encouraging.