Miracle-dz/UniFAR-FTUs huggingface dataset:
Macromodeling of Reconfigurable Intelligent Surface Based on Microwave Network Theory

Reconfigurable intelligent surfaces (RISs) are of potential use in wireless communication systems due to their ability to perform beam steering, harmonic wave manipulation, and polarization conversion. However, the main challenge in designing RISs is to obtain their electromagnetic (EM) responses, which is time-consuming and computationally prohibitive. In this work, an efficient modeling method is proposed for fast evaluation of the reflection coefficient and far-field patterns of RISs. The proposed method utilizes the microwave network theory to compute the accurate reflection coefficient of RISs elements under the illumination of incident waves from arbitrary directions. Compared to the classical equivalent circuit model, it enables the separate design of meta-atoms and tunable devices at the network level. The proposed method is verified by the full-wave simulation results of two RIS elements and experimental results of a 3-bit RIS. This work forms a fast and feasible route to obtain the responses of RISs, which allows the RISs to find extensive applications in many scenarios.
Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

BACKGROUND Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. METHODS This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). RESULTS This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. CONCLUSIONS A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.
Clinical Application of the Food Compass Score: Positive Association to Mediterranean Diet Score, Health Star Rating System and an Early Eating Pattern in University Students

Nutrient profiling systems (NPS) assist consumers in food choices. Several scores based on NPS have been proposed, but data on their clinical application are lacking. The food compass score (FCS) is a newly developed NPS per 100 kcal (from 1 “least healthy” to 100 “most healthy”). We examined the correlations of FCS with other indices, food groups, and meal patterns. A total of 346 students of the University of the Peloponnese (269 women and 77 men) participated. Dietary habits were evaluated with a food frequency questionnaire, and FCS, health star rating score (HSR), and MedDietScore were computed. Meal and snack frequency consumption was reported. Principal component analysis revealed three meal patterns: “early eater” (breakfast, morning snack and afternoon snack), “medium eater” (lunch and dinner), and “late eater” (bedtime snack). Pearson partial correlations between ranked variables were used to test the correlation coefficients between FCS, other scores, and meal patterns, after adjustment for age, sex, BMI, and underreporting. FCS was positively correlated to HSR (rho = 0.761, p ≤ 0.001) in a multi-adjusted analysis. In the highest tertile of MedDietScore FCS was also positively correlated to MedDietScore (rho = 0.379, p < 0.001). The FCS was positively correlated with juices, high-fat dairy, vegetables, legumes, fruits, and olive oil and negatively correlated with sodas, alcoholic drinks, red meat, refined grains, sweets, fats other than olive oil, fast foods, and coffee. In addition, it related positively to the “early eater” pattern (rho = 0.207, p < 0.001). The FCS was associated with other quality indices and better nutritional habits, such as being an early eater.
Digital inequality beyond the digital divide: conceptualizing adverse digital incorporation in the global South

ABSTRACT Digital systems are significantly associated with inequality in the global South. That association has traditionally been understood in terms of the digital divide or related terminologies whose core conceptualization is the exclusion of some groups from the benefits of digital systems. However, with the growing breadth and depth of digital engagement in the global South, an exclusion worldview is no longer sufficient. What is also needed is an understanding of how inequalities are created for some groups that are included in digital systems. This paper creates such an understanding, drawing from ideas in the development studies literature on chronic poverty to inductively build a model of a new concept: ‘adverse digital incorporation’, meaning inclusion in a digital system that enables a more-advantaged group to extract disproportionate value from the work or resources of another, less-advantaged group. This new model will enable those involved with digital development to understand why, how and for whom inequality can emerge from the growing use of digital systems in the global South. It creates a systematic framework incorporating the processes, the drivers, and the causes of adverse digital incorporation that will provide detailed new insights. The paper concludes with implications for both digital development researchers and practitioners that derive from the model and its exposure to the broader components of power that shape the inclusionary connection between digital and inequality.
Cloud Computing: Arduino Cloud IoT Integration with REST API

Cloud computing is a hot topic in the field of computer science nowadays. Due to the rapid growth of serverless functions also known as Lambda functions, it is easier than ever for people to utilize resources that would otherwise be out of reach because of both hardware and software requirements. It used to be, if you wanted to develop a website, for instance, you would have to either build your server or pay a hosting company to use their servers. And if you went with the option of building your own server, then you were responsible for security, scalability, maintenance, and the cost was high. However, those obstacles have been eliminated with the introduction of cloud computing and server-less function. Living in the world with these amazing technologies with the backing of cloud computing, we could not help but wonder how far we can integrate the software with hardware. Arduino IoT makes such technology possible.
Overexpression of DfRaf from Fragrant Woodfern (Dryopteris fragrans) Enhances High-Temperature Tolerance in Tobacco (Nicotiana tabacum)

Heat stress seriously affects medicinal herbs’ growth and yield. Rubisco accumulation factor (Raf) is a key mediator regulating the activity of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), which plays important roles in carbon assimilation and the Calvin cycle in plants. Raf has been studied in many plants, but has rarely been studied in the important medicinal plant fragrant woodfern (Dryopteris fragrans). The aim of this study was to analyze the effects of Raf on carbohydrate metabolism and the response to heat stress in medicinal plants. In this study, high temperature treatment upregulated the expression of DfRaf, which was significantly higher than that of phosphoribokinase (DfPRK), Rubisco small subunits (DfRbcS), Rubisco large subunits (DfRbcL) and Rubisco activase (DfRCA). The subcellular localization showed that the DfRaf proteins were primarily located in the nucleus; DfPRK, DfRbcS, DfRbcL and DfRCA proteins were primarily located in the chloroplast. We found that overexpression of DfRaf led to increased activity of Rubisco, RCA and PRK under high-temperature stress. The H2O2, O2− and MDA content of the DfRaf-OV-L2 and DfRaf-OV-L6 transgenic lines were significantly lower than those of WT and VC plants under high-temperature stress. The photosynthetic pigments, proline, soluble sugar content and ROS-scavenging ability of the DfRaf-OV-L2 and DfRaf-OV-L6 transgenic lines were higher than those of WT and VC plants under high-temperature stress. The results showed that overexpression of the DfRaf gene increased the Rubisco activity, which enhanced the high-temperature tolerance of plants.
Explaining automatic answers generated from knowledge base embedding models.

While many chatbot systems rely on templates and shallow semantic analysis, advanced question-answering devices are typically produced with the help of largescale knowledge bases such as DBpedia or Freebase. Information extraction is often based on embedding models that map semantically rich information into low-dimensional vectors, allowing computationally efficient calculations. When producing new facts about the world, embeddings often provide correct answers that are very hard to explain from a human perspective as they are based on operations performed in the low-dimensional vector space, thus bearing no meaning to human users. Although interpretability has become a central concern in machine learning, the literature so far has focused on non-relational classifiers (such as deep neural networks); embeddings, however, require a whole range of different approaches. In this work we improve an existing method designed to provide explanations for predictions made by embedding models.
A Back-Stepping Sliding Mode Control Strategy Based on Extended Observer for Photovoltaic Grid-Connected Inverter

In the photovoltaic power generation system, the grid-connected inverter is an important link to realize system connection with the electricity grid, but the existence of various interferences of the external environment and the system itself brings a series of problems. In this paper, the circuit of the LCL is used to filter of photovoltaic grid-connected inverter output, and step-sliding mode control and the extended state observer are combined to complete the tracking control of the inverter system. Three state variables of the control strategy are the output voltage of inverter system its first and second derivative, back-stepping and sliding mode control are combined, and the extended state observer is designed to estimate the composite interference, then its output compensates to the back-stepping sliding mode controller. The effectiveness of the control strategy is proved by the simulation of photovoltaic grid-connected inverter system in Matlab. Keywords-grid-connected inverter system; LCL filter circuit; back-stepping sliding mode control; extended state observer
Additively Manufactured “Smart” RF/mm-Wave Packaging Structures: A Quantum Leap for On-Demand Customizable Integrated 5G and Internet of Things Modules

5G and Internet of Things (IoT) technologies are rapidly expanding fields, utilizing cutting edge processes and manufacturing techniques that enable wide connectivity coverage and promise to connect billions of devices in all different types of environments. This critical requirement of ubiquitous implementation necessitates an adaptive system that can be easily modified on demand to be utilized in different areas of deployment.
Measurements of ground motion and SSC dipole vibrations

The results of seismic ground measurements at the Superconducting Super Collider (SSC) site and investigations of vibrational properties of superconducting dipoles for the SSC are presented. Spectral analysis of the data obtained in the large frequency band from 0.05 Hz to 2000 Hz is done. Resonant behavior and the dipole-to-ground transform ratio are investigated. The influence of measured vibrations on SSC operations is considered.
Constraining non-local dependencies in tree-adjoining grammar: computational and linguistic perspectives

Working on Information Extraction and management of the TIDES (“Translingual Information Detection, Extraction and Summarization”) program at Penn. Helped develop Penn’s first contribution to the Automatic Content Extraction evaluation (pipelined statistical system). Current research is investigation of whether the notion of locality inherent in Tree Adjoining Grammar (TAG) will allow for an efficient approach to automatic extraction of predicate-argument structure. Developing software for tagging predicate argument structure of Chinese Treebank parse trees.
Acute Effects of Focused Ultrasound-Induced Blood-Brain Barrier Opening on Anti-Pyroglu3 Abeta Antibody Delivery and Immune Responses

Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and hyperphosphorylated tau in the brain. Currently, therapeutic agents targeting amyloid appear promising for AD, however, delivery to the CNS is limited due to the blood-brain-barrier (BBB). Focused ultrasound (FUS) is a method to induce a temporary opening of the BBB to enhance the delivery of therapeutic agents to the CNS. In this study, we evaluated the acute effects of FUS and whether the use of FUS-induced BBB opening enhances the delivery of 07/2a mAb, an anti-pyroglutamate-3 Aβ antibody, in aged 24 mo-old APP/PS1dE9 transgenic mice. FUS was performed either unilaterally or bilaterally with mAb infusion and the short-term effect was analyzed 4 h and 72 h post-treatment. Quantitative analysis by ELISA showed a 5–6-fold increase in 07/2a mAb levels in the brain at both time points and an increased brain-to-blood ratio of the antibody. Immunohistochemistry demonstrated an increase in IgG2a mAb detection particularly in the cortex, enhanced immunoreactivity of resident Iba1+ and phagocytic CD68+ microglial cells, and a transient increase in the infiltration of Ly6G+ immune cells. Cerebral microbleeds were not altered in the unilaterally or bilaterally sonicated hemispheres. Overall, this study shows the potential of FUS therapy for the enhanced delivery of CNS therapeutics.
The Ameliorative Effect of COST on Diet-Induced Lipid Metabolism Disorders by Regulating Intestinal Microbiota

Background: Chitosan oligosaccharides, with an average molecular weight ≤ 1000 Da (COST), is a natural marine product that has the potential to improve intestinal microflora and resist lipid metabolism disorders. Methods: First, by establishing a mice model of lipid metabolism disorder induced by a high fat and high sugar diet, it is proven that COST can reduce lipid metabolism disorder, which may play a role in regulating intestinal microorganisms. Then, the key role of COST in the treatment of intestinal microorganisms is further confirmed through the method of COST-treated feces and fecal bacteria transplantation. Conclusions: intestinal microbiota plays a key role in COST inhibition of lipid metabolism disorder induced by a high fat and high sugar diet. In particular, COST may play a central regulatory role in microbiota, including Bacteroides, Akkermansia, and Desulfovibrio. Taken together, our work suggests that COST may improve the composition of gut microbes, increase the abundance of beneficial bacteria, improve lipid metabolism disorders, and inhibit the development of metabolic disorders.
Data analytics and global logistics performance: an exploratory study of informatization in the logistics sector

: Background: Informatization has enabled global logistics and supply chains (LSC) to capitalize on data-driven analytics to improve logistics performance. At the country level, logistics performance is gauged through the logistics performance index (LPI), where globally 61.25% or 98 countries perform below the mean LPI score. Previous studies focused on logistics informatization in high and moderate LPI rank economies. The paper aims to conduct an exploratory case study in a low LPI performing country to assess the informatization practices of logistics entities and develop a logistics informatization continuum to unlock data analytics for other countries. Methods: The study implements qualitative methods to develop strategic recommendations to reduce global logistics imbalance. We employ a two-layer methodology consisting of thematic analysis and a novel strategic choice approach (SCA) to involve stakeholders for recommendations on obstruction. For thematic analysis, 16 semi-structured interviews were conducted from logistics companies, also onboard 10 trade associations and government representatives for the SCA analysis. Results: We observed many obstructions in informatization; low willingness on informatization, fear of information leakage by humans, low-reciprocity for collaboration, the myth of information and communication technologies (ICT) as an expensive tool, self-interest, and opportunistic behavior. Conclusion: Information-centric and integrated LSC enables data-driven technologies for real-time decision making, vigilance, and data analytics to distinguished the success of a country’s logistics performance. Originality: This study explores the informatization conformity in the logistics sector to connect data analytics. We introduced a novel strategic choice approach in the technology domain for problem structuring. The paper further contributes by suggesting a logistics informatization continuum for low LPI countries to straighten digitalization in the logistics sector.
Efficient and Secure Deep Learning Inference in Trusted Processor Enabled Edge Clouds

Edge intelligence has emerged as a prevalent enabling technology to support various intelligent applications. Along with the prosperity, it also raises great concern on the security and privacy since the edge servers are usually shared and untrusted. The security-sensitive code (i.e., the pre-trained model) and data may be easily stolen by malicious tenants, and even untrusted infrastructure providers. To this end, Software Guard Extensions (SGX) is proposed to provide an isolated Trust Execution Environment (TEE) for security and privacy guarantee. However, we find that running tasks in SGX suffer certain performance degradation due to the limited Enclave Page Cache (EPC) size. This further leads to frequent page swapping operations and the high enclave call overhead, which are also influenced by the task (i.e., DNN layer) dispatching and scheduling. To this end, in this paper, we design <bold>Lasagna</bold>, as an SGX based secure DNN inference acceleration framework, which explores the layered-structure of DNN models to well balance the usage of the scarce EPC resources and the computation resources. Lasagna mainly consists of a global task balancer and a local task scheduler, responding for task dispatching across distributed edge servers and task scheduling in local server, respectively. We evaluate Lasagna over different well-known DNN models, and the results show that Lasagna effectively speeds up the inference performance by <inline-formula><tex-math notation="LaTeX">$1.11\times -1.51\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>11</mml:mn><mml:mo>×</mml:mo><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>51</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zeng-ieq1-3187772.gif"/></alternatives></inline-formula>.
Comparação de modelos perceptron multicamadas na estimava da evapotranspiração de referência

O estudo teve como objetivo modelar por redes neurais artificiais perceptron multicamadas (RNA-MLP) a evapotranspiração de referência , em função das variáveis climáticas para o município de Ariquemes – RO. Para a caracterização das séries temporais das temperaturas do ar (médias, máximas e mínimas), umidade do ar e precipitação pluviométrica foram utilizados os dados disponíveis no Instituto Nacional de Meteorologia - INMET, obtidos na estação meteorológica automática de Ariquemes/RO, no período de janeiro de 2011 até dezembro de 2013. Os dados da evapotranspiração utilizados foram calculados seguindo o modelo Penman-Monteith-FAO. Dentre os resultados, observou-se que a utilização de modelos RNA-MLP com variáveis de mais simples obtenção, entre elas a precipitação chuvosa, temperatura do ar e umidade foram satisfatórias para estimar a , pois foram consideradas como “muito boas” no índice de concordância.
Angular symmetrical components-based anti-islanding method for solar photovoltaic-integrated microgrid

ABSTRACT This article examines how an innovative Angular Symmetrical Components of Voltages be applied to islanding detection and voltage unbalance factor estimation at a photovoltaic inverter-based distribution unit. Positive and Negative Sequence Components are converted into a polar form such as Line Aggregate RMS (LARMS) Voltage and Tangent Angle of Unbalance (TAU) which are determined from two line voltage signals. Three voltage relays are replaced with a single relay which compares the LARMS voltage and TAU with threshold limits to identify the condition of balance. ASCOV relay does not generate the trip signal in a non-islanding situation such as linear load switching and nonlinear load switching. The Non-Detection Zone (NDZ) of this ASCOV relay is very low compared to other relays. The proposed anti-islanding method is tested in a SPV powered microgrid using MATLAB/SIMULINK and the performance was studied under islanding and non-islanding events. Consequently, the simulation outcome indicates that the proposed technique effectively identifies the islanding condition with less computation time as 1 s and the performance of the proposed relay is compared with existing relays. The voltage unbalance factor determined by the proposed method is very close to the actual value in all types of unbalance conditions.
Stability and Dynamic Analysis of the PMSG-Based WECS With Torsional Oscillation and Power Oscillation Damping Capabilities

The direct-drive permanent-magnet synchronous generator (PMSG) based wind energy conversion system (WECS) with a soft drivetrain shaft is vulnerable to torsional oscillation when it participates in power oscillation damping (POD) services for the AC grid. This paper investigates three possible scenarios of incorporating both torsional oscillation damping (TOD) and POD controllers into PMSG-based WECS. A model of a PMSG-based offshore wind farm (OWF) connected to the IEEE 39-bus AC grid is developed and utilized to analyze the stability and dynamic performances of the WECS within these three scenarios. Comprehensive modal analysis is carried out to analyze the interaction between the torsional and power oscillations, and to tune the TOD and POD controllers to damp these oscillations simultaneously in each scenario. Furthermore, the dynamic performances of the PMSG-based WECS within these scenarios are evaluated through time-domain simulations. Then an optimal scenario for PMSG-based WECS to damp both oscillation types with close frequencies is identified, which is modulating the DC-link voltage for the TOD and the grid-side active power for the POD. This result can provide useful guidance for the future industrial application of the TOD and POD control in the PMSG-based WECS.