Miracle-dz/UniFAR-FTUs huggingface dataset:
Early engineering through service-learning: Adapting a university model to high school

The challenges of this next century require a new generation of engineering talent. In the United States, interest in engineering has remained flat and many groups within remain underrepresented relative to the overall population, specifically women and ethnic minorities. Attracting the next generation of diverse engineers requires a diverse set of pre-college experiences to connect diverse pathways leading to an engineering degree. One exciting approach is the use of service-learning to expose students to design and engineering. Servicelearning has been well established in many disciplines with positive impacts on interest, motivation, student satisfaction, personal success, desire, and retention of students who participated in service-learning projects. Service-learning is pedagogically consistent with literature on the recruitment and retention of women and other underrepresented groups in science and engineering. These benefits have been studied at the higher education level and show promise for pre-college as well. Service-learning connected to engineering also has an enormous potential for capitalizing on the wave of interest in community engagement among teenagers nationally. Connecting service to our community with engineering aligns perfectly with the National Academy’s Changing the Conversation. This paper describes the adaptation of a successful university model to high school having been disseminated to more than 50 schools in 10 states. This paper highlights high school programs that have been integrated into the school day and are supported by a large Midwestern university and two large Southwestern universities. Example projects are described as well as the academic structure and teacher training processes. Demographic data shows that the model is attracting more female students and students from groups traditionally underrepresented in engineering. Data also shows that students are becoming more interested in engineering as a result of their experience in the service-learning programs.
Spatial Markov Model for the Prediction of Travel‐Time‐Based Solute Dispersion in Three‐Dimensional Heterogeneous Media

In recent years, Spatial Markov Models have gained popularity in simulating solute transport in heterogeneous formations. They describe the transition times of particles between equidistant observation planes by statistical distributions, assuming correlation of the transit times of individual particles between subsequent steps. By this, the approach naturally captures preasymptotic solute dispersion. In this study, we analyze Spatial Markov Models assuming bivariate log‐normal distributions of the particle slowness (i.e., the inverse velocity) in subsequent transitions. The model is fully parameterized by the mean Eulerian velocity, the variance of the log‐slowness, and the correlation coefficient of log‐slowness in subsequent steps. We derive closed‐form expressions for distance‐dependent ensemble dispersion, which is defined in terms of the second‐central moments of the solute breakthrough curves. We relate the coefficients to the properties of the underlying log‐hydraulic conductivity field assuming second‐order stationarity. The results are consistent with linear stochastic theory in the limit of small log‐conductivity variances, while the approach naturally extends to high‐variance cases. We demonstrate the validity of the approach by comparison to three‐dimensional particle‐tracking simulations of advective transport in heterogeneous media with isotropic, exponential correlation structure for log‐conductivity variances up to five. This study contributes to relating solute dispersion to metrics of the porous‐medium structure in cases of strong heterogeneity.
aiMSE: Toward an AI-Based Online Mental Status Examination

There is a lack of automated tools that utilize artificial intelligence to monitor mental health. The mental status examination (MSE) is an important tool used by mental health providers for assessing mental health. Currently, MSEs are conducted by licensed professionals, which is a barrier for patients in low income and remote areas. We propose an AI-based personal online mental status examination (aiMSE), the first interactive MSE platform. Users can use aiMSE to self-administer MSEs at home through a web browser, using only a camera and microphone. aiMSE uses multimodal image, speech, and natural language processing algorithms to detect signs of abnormalities in mental functioning and recommend them for further examination by a mental health specialist. We conducted a 14-person study, which supports the feasibility of detecting a wide range of signs commonly found in patients with changes in mental or cognitive capacity.
Dysregulated Gene Expression in Lymphoblasts from Parkinson’s Disease

Parkinson’s disease is the second largest neurodegenerative disease worldwide and is caused by a combination of genetics and environment. It is characterized by the death of neurons in the substantia nigra of the brain but is not solely a disease of the brain, as it affects multiple tissues and organs. Studying Parkinson’s disease in accessible tissues such as skin and blood has increased our understanding of the disease’s pathogenesis. Here, we used lymphoblast cell lines generated from Parkinson’s disease patient and healthy age- and sex-matched control groups and obtained their whole-cell transcriptomes and proteomes. Our analysis revealed, in both the transcriptomes and the proteomes of PD cells, a global downregulation of genes involved in protein synthesis, as well as the upregulation of immune processes and sphingolipid metabolism. In contrast, we discovered an uncoupling of mRNA and protein expression in processes associated with mitochondrial respiration in the form of a general downregulation in associated transcripts and an upregulation in proteins. Complex V was different to the other oxidative phosphorylation complexes in that the levels of its associated transcripts were also lower, but the levels of their encoded polypeptides were not elevated. This may suggest that further layers of regulation specific to Complex V are in play.
Development of a hybrid autoencoder model for automated edible bird’s nest impurities inspection

Abstract. There is a dire need for vision automation for edible bird’s nest (EBN) hygiene inspection. To date, an effective impurities detection method for EBNs has yet to be realized owing to the inhomogeneous optical properties, various types and sizes of impurities, and limited sample size. The impurities inspection was formulated as an anomaly detection task, and a hybrid autoencoder model that contains an autoencoder and a single layer convolutional network is proposed. The model was trained to reconstruct only nonimpurity regions of the EBN for impurities segmentation and detection as anomalies. The results showed that with only 50 EBN sample images, the hybrid model achieved a recall of 0.9282, a precision of 0.7718, and a 5.63% undetected rate for impurities. Furthermore, a misclassification rate of 21.53% was recorded due to artifacts mostly with sizes <0.20 mm that were detected as false positive. Nonetheless, the applicability of the proposed autoencoder model was confirmed, with >92 % of successful impurities detected. Therefore, the hybrid autoencoder model is further explored for improvement and practical application.
A toughening design for stretchable composite embedded with strain-rate stiffening elastomer

Herein, a macroscopic double-network (DN) design for stretchable composite is proposed by embedding strain-rate stiffening elastomer (SSE) reinforced bars into the Ecoflex matrix. Due to the complementarity of mechanical properties between the soft-phase Ecoflex and hard-phase SSE as well as the strong interfacial bond strength, the Ecoflex/SSE composite achieves higher toughness and fatigue threshold than traditional single-network elastomers. Besides, based on the crack propagation characterization, strain-field evolution investigation, and finite element analysis, the fracture toughness of the Ecoflex/SSE composite is proved to be enhanced with the increase of the external strain rates. Interestingly, a sideway crack-arrest morphology is observed above 0.1 s−1, displaying a better anti-crack ability of the Ecoflex/SSE composite at the higher strain rate. As a fundamental research in macroscopic DN composite, the method in this work provides a new idea for the toughening optimization of the strain-rate stiffening material. With the high fracture toughness, fatigue threshold, and flexibility, the Ecoflex/SSE composite has broad application prospects in designing high-performance stretchable elastomers.
Reduced Cardiotoxicity of Ponatinib-Loaded PLGA-PEG-PLGA Nanoparticles in Zebrafish Xenograft Model

Tyrosine kinase inhibitors (TKIs) are the new generation of anti-cancer drugs with high potential against cancer cells’ proliferation and growth. However, TKIs are associated with severe cardiotoxicity, limiting their clinical value. One TKI that has been developed recently but not explored much is Ponatinib. The use of nanoparticles (NPs) as a better therapeutic agent to deliver anti-cancer drugs and reduce their cardiotoxicity has been recently considered. In this study, with the aim to reduce Ponatinib cardiotoxicity, Poly(D,L-lactide-co-glycolide)-b-poly(ethyleneoxide)-b-poly(D,L-lactide-co-glycolide) (PLGA-PEG-PLGA) triblock copolymer was used to synthesize Ponatinib in loaded PLGA-PEG-PLGA NPs for chronic myeloid leukemia (CML) treatment. In addition to physicochemical NPs characterization (NPs shape, size, size distribution, surface charge, dissolution rate, drug content, and efficacy of encapsulation) the efficacy and safety of these drug-delivery systems were assessed in vivo using zebrafish. Zebrafish are a powerful animal model for investigating the cardiotoxicity associated with anti-cancer drugs such as TKIs, to determine the optimum concentration of smart NPs with the least side effects, and to generate a xenograft model of several cancer types. Therefore, the cardiotoxicity of unloaded and drug-loaded PLGA-PEG-PLGA NPs was studied using the zebrafish model by measuring the survival rate and cardiac function parameters, and therapeutic concentration for in vivo efficacy studies was optimized in an in vivo setting. Further, the efficacy of drug-loaded PLGA-PEG-PLGA NPs was tested on the zebrafish cancer xenograft model, in which human myelogenous leukemia cell line K562 was transplanted into zebrafish embryos. Our results demonstrated that the Ponatinib-loaded PLGA-PEG-PLGA NPs at a concentration of 0.001 mg/mL are non-toxic/non-cardio-toxic in the studied zebrafish xenograft model.
SconeKV: a Scalable, Strongly Consistent Key-Value Store

For decades, relational databases provided a strong foundation for constructing applications due to their ACID properties. However, distributed applications reached a scale, both in terms of data volume and number of concurrent clients, that traditional databases cannot accommodate without severe performance degradation. NoSQL databases addressed this problem by trading off consistency for scalability, namely through horizontal scalability schemes supported by optimistic replication protocols, which only guarantee eventual consistency. In this paper, we explore a novel design between the two extremes, that is able to scale to large deployments while still offering strong consistency guarantees in the form of serializable transactions. Our key insight is to leverage recent advances in membership services that provide strongly consistent views at scale. Those assurances from the membership layer simplify building efficient and consistent storage protocols. Our evaluation of the resulting system, SconeKV, in a realistic scenario shows that it scales and performs better than CockroachDB while being competitive with Cassandra.
Polarization Converter Surface Integrated MIMO Antenna for Simultaneous Reduction of RCS and Mutual Coupling

This letter provides an effective solution to deal with the radar cross section (RCS) and mutual coupling of the multiple-input–multiple-output (MIMO) antenna simultaneously. A wideband reflective type polarization converter (PC) is designed and its strategic placement in between the antenna elements of the two-element MIMO antenna has been conceived to mitigate these two issues concurrently. The antenna elements of the proposed MIMO antenna are placed at a proximal distance of 0.088 λ from each other. The proposed MIMO architecture ensures a maximum coupling reduction of 46 dB compared to the conventional MIMO across 4.4–8 GHz. The PC surface reduces the reflection of copolarized waves and enhances cross-polarized reflection. A supercell formed by a unit cell of PC and its 90° rotated counterpart causes phase cancellation of cross- polarized reflected waves, which contributes to wideband RCS reduction by a maximum value of 7 dB. The alignment of PC cells in a column between the adjacent antenna elements causes a reduction in the mutual coupling due to introduction of an extra coupling path and cross-polarization conversion. A good agreement between the simulated and measured results validates this concept of the proposed MIMO design with low RCS and mutual coupling.
Prioritisation of quality management principles based on critical success factors of TQM using integrated approach of MCDM and IPA - the case of Marjan Tile Company

The aim of this research is to propose a framework for classifying eight quality management principles in ISO 9001:2008 based on the critical success factors of TQM. For this purpose, multi-criteria decision making (MCDM), importance performance analysis (IPA) and a survey on 13 process owners of Marjan Tile Company have been conducted. Based on the findings, four packages have been proposed as priorities of quality assurance included: 1) systemic approach in management; 2) employees involvement, process approach, continuous improvement; 3) leadership; 4) realistic approach in decision making.
Circulating HOTAIR potentially predicts hepatocellular carcinoma in cirrhotic liver and prefigures the tumor stage

Aim of the study Homeobox transcript antisense intergenic RNA (HOTAIR) is a long non-coding RNA classified as an oncogene and has been implicated in liver cancer initiation and progression. This study investigated the clinical usefulness of serum HOTAIR to predict hepatocellular carcinoma (HCC) and prefigure the tumor stage. Material and methods This study included 80 patients with de novo HCC divided into 40 late-stage HCC patients (group IA) and 40 early-stage HCC patients (group IB), 40 patients with non-tumorous liver cirrhosis (group II), and 20 healthy controls (group III). Serum HOTAIR was measured using real-time quantitative polymerase chain reaction. Serum α-fetoprotein (AFP) was measured via enzyme-linked immunosorbent assay. Results Serum HOTAIR was significantly higher in groups IA, IB and II compared to healthy subjects. Serum HOTAIR was significantly higher in group IA than group IB, and in groups IA and IB compared to group II. Serum HOTAIR at cut-off value > 15.45 (AUC = 0.71) showed 66% sensitivity and 78% specificity in discriminating HCC patients of group IB from HCC patients of group IA. When combined with AFP, the discriminative sensitivity and specificity increased to 74% and 90% respectively (AUC = 0.85). Serum HOTAIR at cut-off value > 9.42 (AUC = 0.823) showed 67.5% sensitivity and 93.3% specificity in discriminating HCC patients of group IB from patients with non-tumorous cirrhotic liver. When combined with AFP, the discriminative sensitivity and specificity increased to 80% and 98.3% respectively (AUC = 0.954). Conclusions Circulating HOTAIR is a potential biomarker which may be used solely, or preferably in combination with AFP, to help HCC detection in cirrhotic liver and prefigure the tumor stage.
Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.
Tuning Polymer/TiO2 Nanocomposites Morphology by In Situ Non-Hydrolytic Sol-Gel Syntheses in Viscous Polymer Medium: Influence of the Polymer Nature and Oxygen Donor

Herein, we reported the synthesis of TiO2 through different non-hydrolytic sol-gel (NHSG) routes in viscous polymer media. For the first time, the influence of the polymer nature (Polystyrene (PS) or Polypropylene (PP)) on the morphology of synthesized inorganic domains was investigated. The non-hydrolytic sol-gel reactions between titanium isopropoxide Ti(OiPr)4 and acetic anhydride in molten polypropylene lead to the formation of microfillers with a mean diameter of about 1 μm, while the same synthesis carried out in viscous polystyrene lead to the formation of nanofillers with diameter lower than 10 nm forming aggregates of approximately 200 nm. We have also investigated the influence of the oxygen donor nature on the morphology of synthesized fillers using aromatic oxygen donors in a polystyrene matrix. The use of benzoic anhydride or acetophenone as oxygen donors with Ti(OiPr)4 in viscous polystyrene lead to respectively platelet-like morphology or aggregated nanofillers. We demonstrated that the affinity between polymer, reactants, and/or by-products had an influence on the morphology and the size of in situ synthesized TiO2 fillers. These results evidenced for the first time the possibility to control and to tune the morphology of in situ grown inorganic objects through the NHSG process by the appropriate choice of solvent, here a viscous polymer medium, and reactants.
Based on outcomes? Challenges and (missed) opportunities of measuring social outcomes in outcome-based contracting

Abstract Although outcome measurement is a key element of outcome-based contracting, the main challenges related to the measurement processes in these programs are still under-investigated. To fill this gap, this article examinates the challenges and opportunities of measuring social outcomes in outcome-based contracting, focusing particularly on three main areas: the design of measurement processes; the definition of outcomes, metrics, and targets; and the practical implications of the measurement processes. The analysis focuses on four case studies in the UK. Regarding the measurement of payment outcomes, our results bring into question the viability of outcome-based contracting for providing evidence that can inform policymaking. However, the results highlight the opportunities emerging from the measurement of outcomes not related to the payment mechanism and from involving different stakeholders in the measurement process. Finally, we discuss future research on the influence of governance and bargaining power on the measurement of outcome-based contracting.
Hawkes processes in finance: market structure and impact

In response to recent financial crises, financialmarkets have experienced rapid and profound changes. For example, the belief that global banks are ‘too big to fail’ has awoken the Federal Reserve to lean toward passing the Dodd–Frank Wall Street Reform and Consumer Protection Act1 (https://www.congress.gov/111/plaws/publ2 03/PLAW-111publ203.pdf). The deep reasons that led to the 2008 financial crisis following overzealous lending to an overheated housing market remain an unresolved long-term problem. The creation of new insurance instruments against risky mortgage products and mortgage-backed securities further increased the complexity of the market. The Troubled Asset Relief Program (https://home.treasury.gov/data/troubled-assets-relief-pro gram) was proposed to deal with the aftermath of the crises. Classic issues in finance, such as systemic risk modelling, inevitably become more complex. Further, the advance in financial technology has enabled trading to take place at the microscopic level (see Huang and Li 2017). High-frequency trading means that market structure is presented in much more profound ways: the price formation processes and price discovery of financial assets are driven by information flows that are highly interactive and very fast moving. Other market features such as order book dynamics, liquidity provisions and resilience also affect price movements. Thus, trading has become both extremely complicated and dynamic. Markets that are interconnected through the new trading dynamic have become more complex and are exposed to a greater number of and more variable types of extreme events. For example, the rampant risk inherent in proprietary trading across Wall Street firms has prompted the regulators’ proposal of the Volcker Rule (https://www.sec.gov/divisions/marketreg/faq-volcker-rule-section13.htm) to potentially legislate to prevent banks from taking on too much risk and incurring the consequent risk of default. A major lesson learnt from these market events is that more rigorous scientific approaches, including modelling techniques, are urgently needed to understand and interpret complex market and financial phenomena. Despite their simplicity and past efficiency, classical asset pricing theory and risk modelling have limitations in their ability to capture the new features of contemporary finance in today’s markets. For instance, stock prices almost certainly do not evolve as a random walk and return distributions often deviate far from the normal. Features observed in return time series such as fat tails, high peaks and extreme skewness are accepted stylized facts of financial markets in the post-crisis era. The occurrence of mini-flash crashes or so-called black swan events is no stranger to traders. They result in many practitioners seeking to neutralize their positions before the closing bell of a trading day. Price movements are also deeply influenced by news that often dominates investment sentiment and leads to either excessive risk-taking or contagion.