Showing papers by "Worcester Polytechnic Institute published in 2019"

Blockchain technology and its relationships to sustainable supply chain management

Abstract: Globalisation of supply chains makes their management and control more difficult. Blockchain technology, as a distributed digital ledger technology which ensures transparency, traceability, and sec...

Highly stable and efficient all-inorganic lead-free perovskite solar cells with native-oxide passivation.

Abstract: There has been an urgent need to eliminate toxic lead from the prevailing halide perovskite solar cells (PSCs), but the current lead-free PSCs are still plagued with the critical issues of low efficiency and poor stability. This is primarily due to their inadequate photovoltaic properties and chemical stability. Herein we demonstrate the use of the lead-free, all-inorganic cesium tin-germanium triiodide (CsSn0.5Ge0.5I3) solid-solution perovskite as the light absorber in PSCs, delivering promising efficiency of up to 7.11%. More importantly, these PSCs show very high stability, with less than 10% decay in efficiency after 500 h of continuous operation in N2 atmosphere under one-sun illumination. The key to this striking performance of these PSCs is the formation of a full-coverage, stable native-oxide layer, which fully encapsulates and passivates the perovskite surfaces. The native-oxide passivation approach reported here represents an alternate avenue for boosting the efficiency and stability of lead-free PSCs. Replacing the toxic lead in the state-of-the-art halide perovskite solar cells is highly desired but the device performance and stability are usually compromised. Here Chen et al. develop inorganic cesium tin and germanium mixed-cation perovskites that show high operational stability and efficiency over 7%.

A supply chain sustainability innovation framework and evaluation methodology

Abstract: Sustainability is hinged on innovation. The importance of sustainable innovation management in sustainable supply chain management (SSCM) cannot be underestimated. Studies on SSCM have emphasised t...

ZombieLoad: Cross-Privilege-Boundary Data Sampling

Abstract: In early 2018, Meltdown first showed how to read arbitrary kernel memory from user space by exploiting side-effects from transient instructions. While this attack has been mitigated through stronger isolation boundaries between user and kernel space, Meltdown inspired an entirely new class of fault-driven transient-execution attacks. Particularly, over the past year, Meltdown-type attacks have been extended to not only leak data from the L1 cache but also from various other microarchitectural structures, including the FPU register file and store buffer. In this paper, we present the ZombieLoad attack which uncovers a novel Meltdown-type effect in the processor's fill-buffer logic. Our analysis shows that faulting load instructions (i.e., loads that have to be re-issued) may transiently dereference unauthorized destinations previously brought into the fill buffer by the current or a sibling logical CPU. In contrast to concurrent attacks on the fill buffer, we are the first to report data leakage of recently loaded and stored stale values across logical cores even on Meltdown- and MDS-resistant processors. Hence, despite Intel's claims, we show that the hardware fixes in new CPUs are not sufficient. We demonstrate ZombieLoad's effectiveness in a multitude of practical attack scenarios across CPU privilege rings, OS processes, virtual machines, and SGX enclaves. We discuss both short and long-term mitigation approaches and arrive at the conclusion that disabling hyperthreading is the only possible workaround to prevent at least the most-powerful cross-hyperthread attack scenarios on current processors, as Intel's software fixes are incomplete.

Fallout: Leaking Data on Meltdown-resistant CPUs

Abstract: Meltdown and Spectre enable arbitrary data leakage from memory via various side channels. Short-term software mitigations for Meltdown are only a temporary solution with a significant performance overhead. Due to hardware fixes, these mitigations are disabled on recent processors. In this paper, we show that Meltdown-like attacks are still possible on recent CPUs which are not vulnerable to Meltdown. We identify two behaviors of the store buffer, a microarchitectural resource to reduce the latency for data stores, that enable powerful attacks. The first behavior, Write Transient Forwarding forwards data from stores to subsequent loads even when the load address differs from that of the store. The second, Store-to-Leak exploits the interaction between the TLB and the store buffer to leak metadata on store addresses. Based on these, we develop multiple attacks and demonstrate data leakage, control flow recovery, and attacks on ASLR. Our paper shows that Meltdown-like attacks are still possible, and software fixes with potentially significant performance overheads are still necessary to ensure proper isolation between the kernel and user space.

Social sustainable supplier evaluation and selection: a group decision-support approach

Abstract: Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their de...

Bridging the Gap between Training and Inference for Neural Machine Translation.

Abstract: Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the way. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese->English and WMT’14 English->German translation tasks demonstrate that our approach can achieve significant improvements on multiple datasets.

An Overview of the Status and Challenges of CO2 Storage in Minerals and Geological Formations

Abstract: Since the Industrial Revolution, anthropogenic carbon dioxide (CO2) emissions have grown exponentially, accumulating in the atmosphere and leading to global warming. According to the IPCC (IPCC Special Report 2018), atmospheric warming should be less than 2 ℃ to avoid the most serious consequences associated with climate change. This goal can be achieved in part by reducing CO2 emissions, together with capturing and sequestering CO2 from point sources. The most mature storage technique is sequestration in deep saline aquifers. In addition, CO2 can be mineralized and sequestered in solid form by various techniques: ex-situ, surficial and in situ mineralization. Ex situ and surficial approaches may produce valuable products while mitigating environmental hazards. In-situ mineralization uses ultramafic and mafic geological formations for permanent, solid storage. A portfolio that limits warming to less than 2 ℃ by 2100 will include avoiding CO2 emissions and removal of CO2 from air. Regardless of the specific mix of approaches, it will be essential to permanently sequester tens of billions of tons of CO2. Maximizing the potential of all of these storage technologies will help to meet global climate goals. The research agenda published by the National Academy of Science (NASEM 2019) calls for about $1 billion over a 10-20 year time period to advance deployment of CO2 sequestration in deep sedimentary reservoirs at the GtCO2/yr scale and develop CO2 mineralization at the MtCO2/yr scale. This overview study presents the advantages, drawbacks, cost, and CO2 storage potential of each technique, the current and future projects in this domain, and potential sequestration options in geologic formation around the world.

Expert recommendations on the assessment of wall shear stress in human coronary arteries: existing methodologies, technical considerations, and clinical applications

Abstract: Abstract

The Continued Use of Mobile Health Apps: Insights From a Longitudinal Study.

Abstract: Background Mobile health (mHealth) apps that support individuals pursuing health and wellness goals, such as weight management, stress management, smoking cessation, and self-management of chronic conditions have been on the rise. Despite their potential benefits, the use of these tools has been limited, as most users stop using them just after a few times of use. Under this circumstance, achieving the positive outcomes of mHealth apps is less likely. Objective The objective of this study was to understand continued use of mHealth apps and individuals' decisions related to this behavior. Methods We conducted a qualitative longitudinal study on continued use of mHealth apps. We collected data through 34 pre- and postuse interviews and 193 diaries from 17 participants over two weeks. Results We identified 2 dimensions that help explain continued use decisions of users of mHealth apps: users' assessment of mHealth app and its capabilities (user experience) and their persistence at their health goals (intent). We present the key factors that influence users' assessment of an mHealth app (interface design, navigation, notifications, data collection methods and tools, goal management, depth of knowledge, system rules, actionable recommendations, and user system fit) and relate these factors to previous literature on behavior change technology design. Using these 2 dimensions, we developed a framework that illustrated 4 decisions users might make after initial interaction with mHealth apps (to abandon use, limit use, switch app, and continue use). We put forth propositions to be explored in future research on mHealth app use. Conclusions This study provides insight into the factors that shape users' decisions to continue using mHealth apps, as well as other likely decision scenarios after the initial use experience. The findings contribute to extant knowledge of mHealth use and provide important implications for design of mHealth apps to increase long-term engagement of the users.

At the Nexus of Blockchain Technology, the Circular Economy, and Product Deletion

Abstract: The circular economy (CE) is an emergent concept to rethink and redesign how our economy works. The concept recognizes effective and efficient economic functioning at multiple scales—governments and individuals, globally and locally; for businesses, large and small. CE represents a systemic shift that builds long-term resilience at multiple levels (macro, meso and micro); generating new business and economic opportunities while providing environmental and societal benefits. Blockchain, an emergent and critical technology, is introduced to the circular economy environment as a potential enabler for many circular economic principles. Blockchain technology supported information systems can improve circular economy performance at multiple levels. Product deletion, a neglected but critical effort in product management and product portfolio management, is utilized as an illustrative business scenario as to blockchain’s application in a circular economy research context. Product deletion, unlike product proliferation, has received minimal attention from both academics and practitioners. Product deletion decisions need to be evaluated and analyzed in the circular economy context. CE helps address risk aversion issues in product deletions such as inventory, waste and information management. This paper is the first to conceptualize the relationships amongst blockchain technology, product deletion and the circular economy. Many nuances of relationships are introduced in this study. Future evaluation and critical reflections are also presented with a need for a rigorous and robust research agenda to evaluate the multiple and complex relationships and interplay amongst technology, policy, commerce and the natural environment.

ZombieLoad: Cross-Privilege-Boundary Data Sampling

Abstract: In early 2018, Meltdown first showed how to read arbitrary kernel memory from user space by exploiting side-effects from transient instructions. While this attack has been mitigated through stronger isolation boundaries between user and kernel space, Meltdown inspired an entirely new class of fault-driven transient execution attacks. Particularly, over the past year, Meltdown-type attacks have been extended to not only leak data from the L1 cache but also from various other microarchitectural structures, including the FPU register file and store buffer. In this paper, we present the ZombieLoad attack which uncovers a novel Meltdown-type effect in the processor's previously unexplored fill-buffer logic. Our analysis shows that faulting load instructions (i.e., loads that have to be re-issued for either architectural or microarchitectural reasons) may transiently dereference unauthorized destinations previously brought into the fill buffer by the current or a sibling logical CPU. Hence, we report data leakage of recently loaded stale values across logical cores. We demonstrate ZombieLoad's effectiveness in a multitude of practical attack scenarios across CPU privilege rings, OS processes, virtual machines, and SGX enclaves. We discuss both short and long-term mitigation approaches and arrive at the conclusion that disabling hyperthreading is the only possible workaround to prevent this extremely powerful attack on current processors.

Investing in lean manufacturing practices: an environmental and operational perspective

Abstract: Lean manufacturing practices (LMPs) and corporate environmental sustainability are becoming inextricably linked. Throughout the lean and green debate, many organisations have recognised that LMPs h...

High-Performance Graphite Recovered from Spent Lithium-Ion Batteries

Abstract: With the wide usage of Li-ion batteries (LIBs) in portable electronics, electric vehicles, and grid storage, recycling and reusing LIBs have attracted wide attention. However, due to the low added ...

A divalent siRNA chemical scaffold for potent and sustained modulation of gene expression throughout the central nervous system

Abstract: Sustained silencing of gene expression throughout the brain using small interfering RNAs (siRNAs) has not been achieved. Here we describe an siRNA architecture, divalent siRNA (di-siRNA), that supports potent, sustained gene silencing in the central nervous system (CNS) of mice and nonhuman primates following a single injection into the cerebrospinal fluid. Di-siRNAs are composed of two fully chemically modified, phosphorothioate-containing siRNAs connected by a linker. In mice, di-siRNAs induced the potent silencing of huntingtin, the causative gene in Huntington’s disease, reducing messenger RNA and protein throughout the brain. Silencing persisted for at least 6 months, with the degree of gene silencing correlating to levels of guide strand tissue accumulation. In cynomolgus macaques, a bolus injection of di-siRNA showed substantial distribution and robust silencing throughout the brain and spinal cord without detectable toxicity and with minimal off-target effects. This siRNA design may enable RNA interference-based gene silencing in the CNS for the treatment of neurological disorders. A divalent siRNA architecture enables sustained silencing of gene expression in deep regions of the brain.

Automatic emotion detection in text streams by analyzing Twitter data

Abstract: Techniques to detect the emotions expressed in microblogs and social media posts have a wide range of applications including, detecting psychological disorders such as anxiety or depression in individuals or measuring the public mood of a community A major challenge for automated emotion detection is that emotions are subjective concepts with fuzzy boundaries and with variations in expression and perception To address this issue, a dimensional model of affect is utilized to define emotion classes Further, a soft classification approach is proposed to measure the probability of assigning a message to each emotion class We develop and evaluate a supervised learning system to automatically classify emotion in text stream messages Our approach includes two main tasks: an offline training task and an online classification task The first task creates models to classify emotion in text messages For the second task, we develop a two-stage framework called EmotexStream to classify live streams of text messages for the real-time emotion tracking Moreover, we propose an online method to measure public emotion and detect emotion burst moments in live text streams

Haptic display for virtual reality: progress and challenges

Abstract: Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

Degrowth within – Aligning circular economy and strong sustainability narratives

Abstract: This perspective calls for building greater understanding of overlapping and conflicting considerations between the sustainability principles that inform current conceptions of circular economy and degrowth. We contend that scholars and practitioners need to be pragmatic and to recognize evident ideological differences, but simultaneously to acknowledge beneficial similarities and complements. The common aim of both frameworks – to change business-as-usual and to enable human society to operate within ecological planetary boundaries – will likely engender opportunities to formulate new solutions. Management of the inherent tensions, such as the scale and scope of rebound effects, will continue to pose challenges. However, with thoughtful dialogue, commitment to respectful discourse, and more refined articulation we are confident that progress will be made. By building on synergies and seeking holistic strategies, the academic community, along with its transdisciplinary partners, can advance strong global sustainability efforts.

Blockchain Technology, Supply Chain Information, and Strategic Product Deletion Management

Abstract: Products and associated materials are important supply chain flows. Product management greatly influences supply chain performance. Supply chain information is also critical for sound product management. Product deletion, rationalization, or discontinuation research is an important dimension often overlooked in product management. It is a critical issue for many managerial reasons, many espoused in this article. Product deletion is typically a multi-staged process including recognition, analysis and revitalization, evaluation and decision formation, and implementation. Each stage requires complicated information and data support from supply chain activities. Failure in information generating, understanding, and accuracy can prove risky for rational product deletion. Blockchain technology may help address information challenges. Blockchain technology provides traceability, transparency, security, accuracy, and smart execution, which can all contribute to the product deletion and rationalization decision. Application recommendations and managerial insights into product deletion decision making processes with blockchain technology are provided.

Graph Convolutional Networks with Motif-based Attention

Abstract: The success of deep convolutional neural networks in the domains of computer vision and speech recognition has led researchers to investigate generalizations of the said architecture to graph-structured data. A recently-proposed method called Graph Convolutional Networks has been able to achieve state-of-the-art results in the task of node classification. However, since the proposed method relies on localized first-order approximations of spectral graph convolutions, it is unable to capture higher-order interactions between nodes in the graph. In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks, which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods. A novel attention mechanism is used to allow each individual node to select the most relevant neighborhood to apply its filter. We evaluate our approach on graphs from different domains (social networks and bioinformatics) with results showing that it is able to outperform a set of competitive baselines on the semi-supervised node classification task. Additional results demonstrate the usefulness of attention, showing that different higher-order neighborhoods are prioritized by different kinds of nodes.

Employee proenvironmental behavior in Russia: The roles of top management commitment, managerial leadership, and employee motives

Abstract: Despite Russia’s large ecological footprint, there has been limited examination of environmental sustainability initiatives in Russian corporations Drawing on research on the importance of employee-level behaviors for the success of corporate sustainability initiatives, we focus on the proenvironmental behaviors (PEBs) of Russian employees We integrate scholarship on employees’ PEBs and the Russian cultural context to offer theory regarding three potentially important antecedents of employees’ PEBs: top management commitment to sustainability, the immediate manager’s environmental leadership, and the employee’s motivation Using self-report data from management development program attendees in Russia (N = 165), we examined the links between these factors and employees’ PEBs We also tested whether top management commitment moderated the impact of immediate managers’ leadership on employees’ PEBs We found that the immediate manager’s active environmental leadership (ie, transformational, contingent reward, and active management by exception) was positively related to employees’ PEBs Managers’ passive-avoidant environmental leadership (ie, passive management by exception and laissez-faire) was negatively related to PEBs, but only when top management was committed to sustainability Employees’ motives were linked to PEBs, but the nature of the relationship varied across motives External motivation was negatively related to PEBs, suggesting that using rewards to motivate PEBs may be detrimental Motivation that came from a desire to fulfill one’s values or avoid feeling bad about oneself was positively associated with PEBs Our work provides a foundation for future research on PEBs in Russia, and suggests new directions for research on employees’ PEBs in other settings

CB-Planner: A bus line planning framework for customized bus systems

Abstract: A customized bus (CB) system is an emerging public transportation that aims to provide direct and efficient transit services for groups of commuters with similar travel demands. Existing CB systems aggregate similar travel demands and plan bus lines manually, which is inefficient and costly. In this paper, we propose a CB line planning framework called CB-Planner, which is applicable to multiple travel data sources. A mathematical programming formulation is proposed to simultaneously optimize bus stop locations, bus routes, timetables and passengers’ probabilities of choosing CB. We then developed a heuristic solution framework that includes a grid-density based clustering method for discovering potential travel demands efficiently, a bus stop deployment algorithm to minimize the number of stops and walking distance, and dynamic programming based routing and timetabling algorithms for maximizing estimated profit. We conduct an experiment on a small-scale network to verify the performance gap between the optimal solution and our proposed heuristic solution. A case study is then conducted on one-month taxi trajectory data in Nanjing, China. The study demonstrates that CB lines generated by our CB-Planner can achieve higher profit compared with baseline methods, and they also provide efficient transit services with short walk distances and small departure time adjustments. The moderate increase in travel time is paid off by the significant savings in travel fare.

The role of innovation in the implementation of green supply chain management practices

Abstract: How organizational green practices become routinely embedded in supply chains remains underexplored in the literature. Based on the practice‐based view and normalization process theory, this study adopts a novel perspective on green supply chain management (GSCM) practices implementation and suggests that innovation is a crucial mechanism in such process. Specifically, we theorize and test the mediating effect of product and process innovation on the relationship between GSCM practices and sustainability performance. Survey data from 173 manufacturing firms were used to test the model hypotheses. Our findings show that product and process innovation mediate the relationship between GSCM practices and sustainability performance. The findings also suggest that the relevance of different innovation mechanisms depends on the stage of the operational lifecycle within which the practices are implemented. Our study provides insights for managers and scholars seeking to define innovation strategies to ensure the successful implementation of GSCM practices.

Enhanced Cas12a editing in mammalian cells and zebrafish

Abstract: Type V CRISPR-Cas12a systems provide an alternate nuclease platform to Cas9, with potential advantages for specific genome editing applications. Here we describe improvements to the Cas12a system that facilitate efficient targeted mutagenesis in mammalian cells and zebrafish embryos. We show that engineered variants of Cas12a with two different nuclear localization sequences (NLS) on the C terminus provide increased editing efficiency in mammalian cells. Additionally, we find that pre-crRNAs comprising a full-length direct repeat (full-DR-crRNA) sequence with specific stem-loop G-C base substitutions exhibit increased editing efficiencies compared with the standard mature crRNA framework. Finally, we demonstrate in zebrafish embryos that the improved LbCas12a and FnoCas12a nucleases in combination with these modified crRNAs display high mutagenesis efficiencies and low toxicity when delivered as ribonucleoprotein complexes at high concentration. Together, these results define a set of enhanced Cas12a components with broad utility in vertebrate systems.