Grand Challenge Research Articles

Human resources as a determining factor in ensuring the sustainability of facilities management

The role of human resources has been consistently advocated by experts to support organizations' proactive attempts to address environmental issues and/or wider corporate social responsibility while working hard not to diminish profitability. This includes the study of specific practices, for example, environmental training and wage incentives, as well as packages of "best practices" to be implemented in an integrated set of strategically targeted systems. However, the difficulty associated with working for organizational sustainability to enable companies to address society's grand challenges is that it requires the transformation from the current market-driven economic model to a new one in redefining the purpose, value and legitimacy of organizational activities. The aim of the research is to present how human resources practitioners can effectively contribute to the sustainability agenda.

Bi-AAE: A binary adversarial autoencoder deep neural network model for anomaly detection in system-levels marine diesel engines

Marine diesel engines typically use multivariate time series for health condition monitoring, the anomaly detection of which is fundamental and critical for an entity's operation and management. However, detecting anomalies in multivariate time series remains a grand challenge due to the complex behavior of marine machinery. Therefore, this research proposes a binary adversarial autoencoder model to overcome this issue. The framework is built upon the principles of reconstruction models by fusing Generative Adversarial Networks and Adversarial Autoencoder approaches to analyze the degree of anomaly in multivariate time series. Additionally, it identifies anomalies through a threshold obtained by a statistical distribution-based and unsupervised setting method. To emphasize the model's performance, multivariate condition parameters from actual ship diesel engines are utilized for validation. Two membership functions are recognized as the model metrics to characterize its best performance, evidenced by scores of 0.937 for F1 scores and 0.910 for precision. The proposed strategy can be applied to different types of monitoring systems in the ship's engine room to realize system-level operation and maintenance.

High-Volumetric Density Atomic Cobalt on Multishell ZnxCd1-xS Boosts Photocatalytic CO2 Reduction.

The volumetric density of the metal atomic site is decisive to the operating efficiency of the photosynthetic nanoreactor, yet its rational design and synthesis remain a grand challenge. Herein, we report a shell-regulating approach to enhance the volumetric density of Co atomic sites onto/into multishell ZnxCd1-xS for greatly improving CO2 photoreduction activity. We first establish a quantitative relation between the number of shell layers, specific surface areas, and volumetric density of atomic sites on multishell ZnxCd1-xS and conclude a positive relation between photosynthetic performance and the number of shell layers. The triple-shell ZnxCd1-xS-Co1 achieves the highest CO yield rate of 7629.7 μmol g-1 h-1, superior to those of the double-shell ZnxCd1-xS-Co1 (5882.2 μmol g-1 h-1) and single-shell ZnxCd1-xS-Co1 (4724.2 μmol g-1 h-1). Density functional theory calculations suggest that high-density Co atomic sites can promote the mobility of photogenerated electrons and enhance the adsorption of Co(bpy)32+ to increase CO2 activation (CO2 → CO2* → COOH* → CO* → CO) via the S-Co-bpy interaction, thereby enhancing the efficiency of photocatalytic CO2 reduction.

Electron Redistribution of Ru Site on MoO2@NiMoO4 Support for Efficient Ampere-Level Current Density Electrolysis of Alkaline Seawater.

Seawater electrolysis is a promising but challenging strategy to generate carbon-neutral hydrogen. A grand challenge for hydrogen evolution reaction (HER) from alkaline seawater electrolysis is the development of efficient and stable electrocatalysts to overcome the limitation of sluggish kinetics. Here, a 3D nanorod hybrid catalyst is reported, which comprises heterostructure MoO2@NiMoO4 supported Ru nanoparticles (Ru/ MoO2@NiMoO4) with a size of ≈5nm. Benefitting from the effect of strongly coupled interaction, Ru/MoO2@NiMoO4 catalyst exhibits a remarkable alkaline seawater hydrogen evolution performance, featured by a low overpotential of 184mV at a current density of 1.0 A cm-2, superior to commercial Pt/C (338mV). Experimental observations demonstrate that the heterostructure MoO2@NiMoO4 as an electron-accepting support makes the electron transfer from the Ru nanoparticles to MoO2, and thereby implements the electron redistribution of Ru site. Mechanistic analysis elucidates that the electron redistribution of active Ru site enhances the ability of hydrogen desorption, thereby promoting alkaline seawater HER kinetics and finally leading to a satisfactory catalysis performance at ampere-level current density of alkaline seawater electrolysis.

AI competitions as infrastructures of power in medical imaging

ABSTRACT This article examines how platform-based AI competitions structure power relations in medical imaging research. It focuses on two leading platforms, Kaggle and Grand Challenge, which provide organisational as well as infrastructural support to run AI competitions. In dialogue with critical AI and platform studies research, we investigate how such competitions are organised – under which infrastructural conditions and by whom – and how this shapes processes of model production and evaluation. To address these concerns, we have collected data from 118 medical image AI competitions on Kaggle and Grand Challenge, organised between January 2017 and May 2022. In addition, a variety of platform boundary resources – platform documentation, competition descriptions, dataset descriptions, and competition leaderboards – have been gathered. The analysis of these materials shows, first, that platforms direct the AI development process by requiring substantial financial resources, defining which institutions can host a competition and under which conditions. Second, competition organisers define dataset diversity and the generalisability of models. As most datasets are constructed with data from hospitals in North America, Western Europe and China, the application of models to different geographical contexts is potentially limited. Finally, competition participants influence model development through the institutional, demographic, and disciplinary contexts in which they operate. Overall, the examination demonstrates the importance of critically interrogating the entire medical AI research pipeline, including the definition of research problems, the construction of datasets as well as model production and evaluation.

From impact to impacting: A pragmatist perspective on tackling grand challenges

Scholars have long sought to impact management practice. However, the current conceptualization of impact is grounded in dualisms, separating researchers from managers, means from ends, and thought from action. Such a dualistic understanding of impact hampers researchers’ and managers’ ability to achieve impact. Nowhere is this issue more acute than in the context of grand challenges, which require researchers and managers to work together closely. As a way forward, we propose a pragmatist perspective on impact, where impact is not seen as a one-time, unidirectional event, but rather as a relational and recursive process. By overcoming dualisms in traditional approaches to impact, pragmatist impacting can help advance progress on grand challenges and our current understanding of co-creation. In this article, we illustrate pragmatist impact ing and reflect on its opportunities and challenges through our experience at Innovation North, an innovation laboratory that brought together researchers and managers to co-create a systems innovation process.

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

Competition Behind Competition: State-Sponsored Awards, Political Tournaments, and Corporate Participation in Poverty Alleviation in China

Research has thus far suggested mixed effects of awards on the behavior of nonwinning firms. We draw from research on interorganizational spillover to study under what conditions state-sponsored awards motivate nonwinning firms to increase their efforts. We contend that, when government officials—an important institutional agent—are under competitive pressure from their own rivals to pursue the state goal, they are more motivated to promote the awards and trigger peer emulation, resulting in interorganizational spillover following the bestowal of awards. In turn, nonwinning firms with a greater need for government-controlled resources are more responsive to the spillover effect promoted by local officials. We test our theory with government awards to firms in China’s state-initiated poverty alleviation program. Analysis of Chinese publicly listed firms from 2016 to 2019 shows that following antipoverty award announcements, nonwinning firms improved their poverty alleviation effort more when their local government officials faced stronger competitive pressure in poverty reduction from their own close-rival officials. Notably, such spillover effects did not occur when local officials faced low competitive pressure. Our study contributes to research on interorganizational spillover and awards as well as how the state can encourage firms to tackle grand challenges.

Impact of Hydrodynamic Interactions on the Kinetic Pathway of Protein Folding.

Protein folding is a fundamental process critical to cellular function and human health, but it remains a grand challenge in biophysics. Hydrodynamic interaction (HI) plays a vital role in the self-organization of soft and biological materials, yet its role in protein folding is not fully understood despite folding occurring in a fluid environment. Here, we use the fluid particle dynamics method to investigate many-body hydrodynamic couplings between amino acid residues and fluid motion in the folding kinetics of a coarse-grained four-α-helices bundle protein. Our results reveal that HI helps select fast folding pathways to the native state without being kinetically trapped, significantly speeding up the folding kinetics compared to its absence. First, the directional flow along the protein backbone expedites protein collapse. Then, the incompressibility-induced squeezing flow effects retard the accumulation of non-native hydrophobic contacts, thus preventing the protein from being trapped in local energy minima during the conformational search of the native structure. We also find that the significance of HI in folding kinetics depends on temperature, with a pronounced effect under biologically relevant conditions. Our findings suggest that HI, particularly the short-range squeezing effect, may be crucial in avoiding protein misfolding.

Rapid Construction of Double Crystalline Prussian Blue Analogue Hetero-Superstructure.

The controllable construction of complex metal-organic coordination polymers (CPs) merits untold scientific and technological potential, yet remains a grand challenge of one-step construction and modulating simultaneously valence states of metals and topological morphology. Here, a thiocyanuric acid (TCA)-triggered strategy is presented to one-step rapid synthesis a double-crystalline Prussian blue analogue hetero-superstructure (PBA-hs) that comprises a Co3[Fe(CN)6]2 cube overcoated with a KCo[Fe(CN)6] shell, followed by eight self-assembled small cubes on vertices. Unlike common directing surfactants, TCA not only acts as a trigger for the fast growth of KCo[Fe(CN)6] on the Co3[Fe(CN)6]2 phase resulting in a PBA-on-PBA hetero-superstructure, but also serves as a flange-like bridge between them. By combining experiments with simulations, a deprotonation-induced electron transfer (DIET) mechanism is proposed for formation of second phase in PBA-hs, differing from thermally and photo-induced electron transfer processes. To prove utility, the calcined PBA-hs exhibits enhanced oxygen evolution reaction performance. This work provides a new method to design of novel CPs for enriching chemistry and material science. This work offers a practical approach to design novel CPs for enriching chemistry and material science.

Fusion-Based 2.5D Face Recognition System

Face recognition is the dominant biometrics system used to authenticate an individual’s identity in various applications. Most commercial face recognition systems rely on 2D face images, but the changes in the environment lighting and a person's posture affect the accuracy of the 2D face recognition systems. Hence, the 2.5D face recognition system arises as the solution to eliminate the drawbacks of the 2D face recognition system. The depth feature in the 2.5D data (depth image) provides additional information that can help to improve the accuracy and robustness of 2.5D face recognition systems, particularly in challenging scenarios. This paper proposes a fusion-based approach for the 2.5D face recognition system to enhance the system’s performance, where feature fusion involves the combination of features extracted from the depth image. Gabor-based Region Covariance Matrices (GRCMs) that serve as face identifiers combine the depth and texture images in the structure of a covariance matrix. Several experiments on different fusions have been conducted in the Face Recognition Grand Challenge version 2 (FRGC v2.0) database. This paper shows that the max-min fusion applied to the surface normal (y-direction) and the mean curvature has achieved the best accuracy rate of 93.66% among the other fusion approaches used.

How does the problem-oriented innovation system (PIS) help in the management of cardiovascular diseases?

Cardiovascular diseases are a multifaceted and complex problem in the health system that can change the priorities of the economic, social, and even political systems of countries. Therefore, as a grand challenge (GC), its management requires adopting a systematic, interdisciplinary, and innovative approach. In Iran, the most common causes of death, have changed from infectious and diarrheal diseases to cardiovascular diseases since 1960. In this study, the novel framework of the problem-oriented innovation system (PIS) has been used, and cardiovascular diseases in Iran have been selected as a case study. To this end, first, the main challenges related to cardiovascular diseases in Iran were identified in two layers of "governance-centered" (including legal and policy gaps, insufficient education, financing, lack and unbalanced distribution of medical personnel) and "society driven" (including unhealthy diet and lifestyle, uncontrolled and hard-to-regulate factors, and high costs) through a library research. Then, the functional-structural framework of the problem-oriented innovation system was used to analyze cardiovascular diseases and provide policy recommendations. The findings indicate that based on the eight functions of the problem-oriented innovation system, an important part of cardiovascular diseases can be managed and controlled in three short-term, medium-term, and long-term periods. Increasing public awareness in the form of university courses, participation of the government with the private sector in building and equipping specialized cardiovascular centers, creating an electronic health record from birth, implementing a family health plan focusing on less developed areas, supporting agriculture and guaranteeing the purchase of agricultural products and healthy food, increasing the capacity of accepting students in medical and paramedical fields, and allocating pharmaceutical currency in the form of pharmaceutical subsidies directly to cardiovascular patients, are among the most important policy recommendations for this grand challenge.

How to Create and Foster Sustainable Smart Cities? Insights on Ethics, Trust, Privacy, Transparency, Incentives, and Success

This paper describes the motivation, framework, concepts, method, implementation, and results of the HCII2023 Design Café, a dedicated participatory highly interactive design workshop held in Copenhagen in July 2023. Motivated by the ubiquitous challenges our world is facing, this initiative had the goal to explore six main issues from an interdisciplinary perspective with a focus on the UN SDG 11: “Sustainable Cities and Communities” and the HCI Grand Challenge 2 “Human-Environment Interactions.” The six issues were formulated as questions and presented to the participants working in small groups. (1) How to create inclusive and ethical smart cities? (2) How to establish trust between people and smart environments? (3) How to address privacy concerns in smart environments that adopt the “disappearing computer” paradigm? (4) How to promote explainability and transparency of policies and measures to citizens of smart cities or in smart environments in general? (5) How to design incentives and rewards for engagement and sustainable behavior in smart cities at a personal as well as collective/corporate level? (6) How to measure success and impact in sustainable smart city projects? The method and approach of the Design Café is a tailored composition of a guided, structured format combined with and inspired by processes of informal communication and exchange of knowledge and ideas. Aligned moderation, a minimum set of rules, a set of relevant topics and an interdisciplinary group of motivated participants working in rotating formations provides the structure for achieving results. The results show that the six issues are not independent of each other, but require a holistic view, considering the various dependencies as well as synergies when exploring solutions. Nevertheless, the importance of first establishing higher-level goals based on ethical and inclusive approaches fostering human dignity and human rights were key. Acceptance of overall goals, processes, rules, and regulations was considered as the fundamental pre-requisite for sustainable change towards a declared goal. Acceptance needs trust and privacy as well as explainability and transparency of policies and measures. The role of incentives and rewards for engagement and sustainable behavior was twofold. Offering incentives must include planning on how to measure their impact. Effective measurement of success and impact depends heavily on how the institutions address privacy. Explainability and transparency should become one of the ethical guidelines that steer and control concepts, decision making, and implementations of all activities. The scale of these societal challenges still needs to be recognized by those responsible. It is essential to educate decision makers in the psychological needs and effects of sense making, comprehensibility, and finally acceptance and well-being moving towards a humanity-centered design.

Organizational Engagement With Poverty: A Review and Reorientation

Recognizing the potential contributions businesses can make to address the grand challenge of global poverty, management scholars have increasingly turned research attention to poverty. We conducted an integrative review of poverty studies in the organizational literature spanning from 1985 to 2022. Based on the review, we clarify poverty as a significant lack of market-oriented resources, opportunities, and capabilities. Further, we develop a framework that captures the ways in which organizational practices offer the poor, or deprive them of, resources, opportunities, and capabilities, and thereby contribute to poverty alleviation or aggravation respectively. Moreover, our framework identifies the necessary support conditions for an organization to effectively combat poverty. We conclude by proposing a research agenda aimed at advancing management scholarship on poverty.

Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials Under Heterogeneous Fields

AbstractMultifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM.

Transforming science journalism through collaborative research: a case study of the German “WPK Innovation Fund for Science Journalism”

Science journalism, a unique form of science communication, faces grand challenges requiring innovation for its sustainability. This practice insight delves into a research-practice collaboration addressing the “WPK Innovation Fund for Science Journalism”, a pioneering support infrastructure for innovation in German science journalism. Our transformative accompanying research project aims to both support the fund's development as well as advance science journalism research. This report, co-authored by researchers and practitioners, showcases opportunities and challenges, drawing from the three forms of knowledge generated in the collaboration: systems, target, and transformation knowledge. Each of these forms sheds light on specific lessons learned in our project on how to conduct transformative journalism research.

Tools Identification By On-Board Adaptation of Vision-and-Language Models

A robotic workshop assistant has been a long-standing grand challenge for robotics, speech, computer vision, and artificial intelligence (AI) research. We revisit the goal of visual identification of tools from human queries in the current era of Large Vision-and-Language models (like GPT-4). We find that current off-the-shelf models (that are trained on internet images) are unable to overcome the domain shift and unable to identify small, obscure tools in cluttered environments. Furthermore, these models are unable to match tools to their intended purpose or affordances. We present a novel system for online domain adaptation that can be run directly on a small on-board processor. The system uses Hyperdimensional Computing (HD), a fast and efficient neuromorphic method. We adapted CLIP to work with explicit ("I need the hammer") and implicit purpose-driven queries ("Drive these nails"), and even with depth images as input. This demo allows the user to try out various real tools and interact via free-form audio.

Facile in situ polymerization synthesis of poly(ionic liquid)-based polymer electrolyte for high-performance solid-state batteries

The rational one-step design of poly(ionic liquid) solid polymer electrolyte (PIL-SPE) with outstanding ionic conductivity and inferior interfacial resistance with Li-electrode to preclude the Li-dendrite growth and interfacial instability of solid-state lithium metal batteries (ASLMBs) remains a grand challenge. Herein, we provide a s facile, one-step, and productive approach for the synthesis of PIL-SPE via the in situ polymerization of 1-vinyl-3-butylimidazolium bis(trifluoromethanesulfony)imide ([VBIm][TFSI]) in the presence of bis(trifluoromethane) sulfonamide lithium salt (LiTFSI) using azodiisobutyronitrile (AIBN) as initiator. This approach endows a compact and compatible interface between Li-electrode and PIL-SPE with an excellent ionic conductivity that depends on the Li-salt concentration. Thereby, in situ polymerization of monomer with 40 wt% LiTFSI (PIL-SPE-LiTFSI-40) revealed the highest ionic conductivity of 1.35 × 10-3 S cm−1 at 303 K and 6.16 × 10-3 S cm−1 at 353 K, besides excellent lithium transference number (0.54), oxidative stability up to 5.3 V, and constant current plating/stripping cycles for 1000 h at 0.15 mA cm−2. Meanwhile, ASLMBs composed of PIL-SPE with LiFePO4 and LiNi0.8Co0.1Mn0.1O2 cathodes delivered discharge capacities of 164.3 mA h g−1 and 155.3 mA h g−1 at 0.1 C, respectively, along with 92 % capacity retention after 100 cycles. This study may revolutionize the synthesis of PIL-SPE electrolytes for the next generation of efficient and durable ASLMBs.

Towards Trustworthy Deep Learning

Deep neural networks (DNNs) have achieved unprecedented success across many scientific and engineering fields in the last decades. Despite its empirical success, unfortunately, recent studies have shown that there are various failure modes and blindspots in DNN models which may result in unexpected serious failures and potential harms, e.g. the existence of adversarial examples and small perturbations. This is not acceptable especially for safety critical and high stakes applications in the real-world, including healthcare, self-driving cars, aircraft control systems, hiring and malware detection protocols. Moreover, it has been challenging to understand why and when DNNs will fail due to their complicated structures and black-box behaviors. Lacking interpretability is one critical issue that may seriously hinder the deployment of DNNs in high-stake applications, which need interpretability to trust the prediction, to understand potential failures, and to be able to mitigate harms and eliminate biases in the model. To make DNNs trustworthy and reliable for deployment, it is necessary and urgent to develop methods and tools that can (i) quantify and improve their robustness against adversarial and natural perturbations, and (ii) understand their underlying behaviors and further correct errors to prevent injuries and damages. These are the important first steps to enable Trustworthy AI and Trustworthy Machine Learning. In this talk, I will survey a series of research efforts in my lab contributed to tackling the grand challenges in (i) and (ii). In the first part of my talk, I will overview our research effort in Robust Machine Learning since 2017, where we have proposed the first attack-agnostic robustness evaluation metric, the first efficient robustness certification algorithms for various types of perturbations, and efficient robust learning algorithms across supervised learning to deep reinforcement learning. In the second part of my talk, I will survey a series of exciting results in my lab on accelerating interpretable machine learning and explainable AI. Specifically, I will show how we could bring interpretability into deep learning by leveraging recent advances in multi-modal models. I'll present recent works in our group on automatically dissecting neural networks with open vocabulary concepts, designing interpretable neural networks without concept labels, and briefly overview our recent efforts on demystifying black-box DNN training process, automated neuron explanations for Large Language Models and the first robustness evaluation of a family of neuron-level interpretation techniques.

Porous high-entropy alloy coatings as highly efficient electrocatalysts for electrocatalytic water splitting

Developing highly efficient and stable non-noble metal electrocatalysts has always been a grand challenge for water splitting. Herein, the promising and highly efficient porous high entropy alloy (HEA) coatings is fabricated through the plasma spraying, which could be favorable to solve the above issues. Abased on the synergistic effect of different metals and the porous structure within the HEA coatings, it can provide the superior catalytic activities. Hence, the CoCrFeNiMo catalyst displays a low overpotential of 273 mV (for oxygen evolution reaction, OER) at 100 mA cm−2 in 1.0 M KOH electrolyte. As hydrogen evolution reaction (HER) catalysts, the CoCrFeNiMo presents a small overpotential. When the catalysts can be utilized to the overall water splitting, it delivers an excellent durable for 24 h. DFT result shows that CoCrFeNiMo is conducive to the hydrogen adsorption/desorption process, and it can enhance the transformation of *O to *OOH and improve the OER performance. This study not only offers a universal method to prepare high performance electrocatalysts, but also demonstrates the great potential of HEA coatings in energy conversion fields.