Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compact Surface Plasmonic Band-Pass Filter with Sharp Out-of-Band Rejection

We propose a compact band-pass filter based on spoof localized surface plasmons (LSPs), which consists of four rectangular metal rings with corrugated grooves. It has been shown that the couplings between the resonators produce the flat-top transmission response and sharper out-of-band rejection. The simulation results validate the functionalities. It is believed that the proposed filter could be applied in the high-performance circuit design with compact size.
Energy Optimal Wireless Data Transmission for Wearable Devices: A Compression Approach

Wearable devices are designed to have a small size and be lightweight. Consequently, the battery life is constrained and becomes a crucial limitation. In this paper, we use both data compression and wireless transmission speed control to minimize the energy consumption of wearable devices for data transmission, subject to a deadline constraint. We consider both an off-line setting where future channel gains are known ahead of time and a stochastic setting where channel gains change stochastically according to a Markov process. For the first case, we present an efficient <inline-formula><tex-math notation="LaTeX">$(1+\epsilon)$</tex-math></inline-formula> approximation algorithm for an arbitrarily small <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>, while in the latter case we give a stochastic algorithm to minimize the total expected energy use. We also conduct experimental studies on the proposed algorithms and show that the stochastic algorithm, despite not knowing future channel gains, closely approximates the performance of the nearly optimal off-line solution with less than 0.1% difference in energy consumption on an average. We also compared the stochastic algorithm with several other practical algorithms and showed that our algorithm achieves significant improvements in the overall energy use.
Alzheimer's Disease — Perspective from Political Science: Public Policy Issues

The paper outlines the policy context and summarizes the numerous policy issues that AD raises from the more generic to the unique. It posits that strong public fears of AD and its future prevalence projections and costs, raise increasingly difficult policy dilemmas. After reviewing the costs in human lives and money and discussing the latest U.S. policy initiatives, the paper presents two policy areas as examples the demanding policy decisions we face. The first focuses on the basic regulatory function of protecting the public from those who would exploit these fears. The second centers on the well-debated issues of advance directives and euthanasia that surround AD. Although more dialogue, education and research funding are needed to best serve the interests of AD patients and families as well as society at large, this will be challenging because of the strong feelings and divisions AD engenders.
Highly Isotropic Terfenol-D/PZT Magnetoelectric Sensor Based on Nested Ring Structure

In this paper, we studied a highly isotropic sensor for scalar magnetic field measurements and developed a Terfenol-D/PZT composite magnetoelectric sensor based on a nested ring structure. The effect of applied magnetic field, direction, and bias magnetic field on the output characteristics of the structure was studied, and it was observed that it has good linearity and a wide measurement range. According to a linear theoretical model suitable for small-signal excitation, when the direction of the applied ac magnetic field changes, the ratio of the maximum and minimum output voltages is only 3:1. Both experimental and theoretical results demonstrate that the nested-ring-structured sensor is highly isotropic. It is observed that the isotropy of the magnetoelectric sensor depends not only on the shape isotropy of the piezoelectric and magnetostrictive materials but also on the amplitude of the bias magnetic field.
Dimensionality Reduction via Multiple Locality-Constrained Graph Optimization

In recent years, graph-based dimensionality reduction methods became increasingly more significant since they have been successfully applied in various computer vision and machine learning problems. The key point in graph-based dimensionality reduction methods is how to construct an appropriate graph to reflect the underlying distribution of data. However, most existing methods usually consider graph construction and dimensionality reduction as two separate processes. To overcome this limitation, a multiple locality-constrained graph optimization for dimensionality reduction (MLGODR) algorithm is proposed in this paper. The proposed MLGODR possesses two characteristics. First, MLGODR integrates graph optimization and dimensionality reduction into a unified framework. Thus, a graph that characterizes the distribution of input data and a matrix that projects the input data into a low-dimensional subspace can be learned simultaneously. Second, to better exploit the local structure of input data, a locality constraint that adaptively combines multiple distance measurements is introduced into our objective function. Moreover, an effective updating algorithm is also designed to solve the proposed MLGODR. Extensive experiments are performed on four image databases and four UCI data sets. The experimental results demonstrate that our method outperforms the compared approaches in both classification and cluster tasks.
Query Intent Recognition Based on Multi-Class Features

In order to enhance the user search experience of the search engine, an intent recognition search based on natural language input is proposed. By using reality mining technology to obtain the potential consciousness information from the query expression, search engines can better predict the query results that meet users’ requirements. With the development of conventional machine learning and deep learning, it is possible to further improve the accuracy of prediction results. This paper adopts a similarity calculation method based on long short-term memory (LSTM) and a traditional machine learning method based on multi-feature extraction. It is found that entity features can significantly improve the accuracy of intention classification. Second, the accuracy of intention classification based on the feature sequence constructed by key entities is up to 94.16% in the field of manual labeling by using the BiLSTM classification model.
The effect of community based health insurance on catastrophic health expenditure in Northeast Ethiopia: A cross sectional study

Introduction Moving towards the goal of universal health coverage requires strengthening service delivery and overcoming significant financial barriers. The Government of Ethiopia is rolling out community based health insurance to protect the rural community from high out of pocket health expenditure and improve health service utilization. We investigated the effect of community based health insurance on catastrophic health expenditure in Northeast Ethiopia. Methods A community based cross sectional study was conducted. A Multi stage sampling technique was used to get a total of 454 (224 insured and 230 uninsured) households. The data were entered using EPI info version 7 and analyzed using SPSS version 20 and STATA version 13 for binary logistic regression analysis and propensity score matching analysis respectively. Wealth status of the households was computed by Principal Component Analysis (PCA). A multivariable logistic regression analysis was done to identify the predictors of catastrophic health expenditure. Propensity score matching analysis was used to determine the effect of community based health insurance on catastrophic health expenditure. The average treatment effect on the treated (ATT) was calculated to compare the means of outcomes across insured and uninsured households. Results A total of 454 household heads were included in the study, making a response rate of 91.2%. The total level of catastrophic health expenditure was found to be 20%. Among the households with catastrophic health expenditure, 4.41% were insured, whereas the remaining 15.64% were noninsured. Insured households (AOR = 0.19, 95% CI: 0.11–0.34), rich households (AOR = 1.98; 95% CI: 1.07–3.66), having member with chronic illness (AOR = 2.13, 95% CI: 1.01–4.51) and having member encountered any illness during the past 3 months (AOR = 2.44, 95% CI: 1.35–4.40) were statistically associated with catastrophic health expenditure. Community based health insurance contributed to 23.2% (t = -5.94) (95% CI: -0.31_-0.15) reduction of catastrophic health expenditure. Conclusion The overall level of catastrophic health expenditure was high among noninsured households. Community based health insurance has significant financial protection from catastrophic health expenditure in northeast Ethiopia. Thus, the government need to scale up community based health insurance to protect the noninsured households from catastrophic health expenditure.
Made in Academia: The Effect of Institutional Origin on Inventors' Attention to Science

Inventors cannot exploit new scientific discoveries if they do not pay attention to them. However, allocating attention to science is difficult because the scientific literature is vast, fast-changing, and often unreliable. Inventors are therefore likely to rely on informational cues when screening new publications. I posit that inventors pay significantly less attention to discoveries “made in academia” than to those “made in industry” because they believe that the work of academic scientists will be less useful to them. I test this proposition by examining inventors’ patent references to the scientific literature in the case of simultaneous discoveries made by at least one team based in academia and another based in industry. I find that inventors are 23% less likely to cite the academic paper than its twin from industry. My results highlight the importance of inventors’ attention as a hitherto underexplored bottleneck shaping the translation of science into new technologies.
A New Look at Optimum Macro Base Station Deployment in Heterogeneous Massive MIMO Networks for Eliminating Interference

Massive multiple input multiple output (MIMO) Heterogeneous Networks (HetNets) are integral part of 5G systems. They can be viewed in three dimensional (3D) space where, each slice represents a unique tier that has its own Base stations (BS) and user equipments (UEs). Different slices cooperate with each other for mutual benefit. Data can be interactively exchanged among the tiers, and UEs have the flexibility to switch between the tiers. Poisson point process (PPP) has been widely used to allocate BSs and UEs among various tiers so far. However, BS locations using PPP approach may not be optimum to reduce interference. In this paper, we develop a new algorithm to find the optimum locations of Macro BSs in HetNets where every Macro BS along with its associate Micro BSs is treated as an independent unit after almost eliminating interference from neighboring Macro and Micro BSs. Hence, this approach provides increased coverage area and enhanced quality of service. Simulation results show the validity of our approach and promises enhancement to system performance.
A 0.85mm2 BLE Transceiver with Embedded T/R Switch, 2.6mW Fully-Passive Harmonic Suppressed Transmitter and 2.3mW Hybrid-Loop Receiver

This paper presents a miniaturized Bluetooth Low-Energy transceiver for short-range IoT applications in 65-nm CMOS. A T/R switch embedded with transmitter harmonic-suppression and on-chip impedance matching is proposed. The BLE transceiver delivers −6dBm output while consuming 2.6mW. 18.5% TX efficiency is achieved at OdBm output power. A −94dBm receiver sensitivity is achieved with 2.3mW receiver power consumption. Thanks to the T/R switch embedded with harmonic suppression, −56dBc of 2nd-order harmonic distortion (HD2) and −48dBc of 3rd-order harmonic distortion (HD3) suppressions are achieved. The fabricated BLE transceiver occupies 0.85mm2 active area. This transceiver satisfies the BLE radio specification without the need for external filters, that enables minimum size modules and short time to market.
Investigating Plausible Reasoning Over Knowledge Graphs for Semantics-Based Health Data Analytics

Plausible reasoning reflects the "plasticity" element of human reasoning, which, by leveraging the semantics of relevant concepts, allows dealing with incomplete data during decision making. We propose the SEmantics-based Data ANalytics (SeDan) framework that integrates plausible reasoning with expressive, fine-grained biomedical ontologies. Using this framework, an unresolvable query can be rewritten to explore the semantic knowledge graph and infer new knowledge. While the gained insights may be plausible, i.e., not supported by crisp deductive reasoning, they may still aid complex medical decision making by recommending plausible solutions. In this paper, we investigate the efficiency of SeDan in a real-world medical setting to pose intelligent medical queries from BioASQ challenges over the MEDLINE database. Experimental results show that SeDan can expand the query answering coverage by resolving up to 45% of initially unresolvable queries. The correctness of the inferred answers, as well as the underlying plausible reasoning processes, was verified by a domain expert.
State Estimation in Power Distribution Systems Based on Ensemble Kalman Filtering

In this paper, a past-aware state estimation (PASE) method (for static state estimation) is proposed for power distribution systems, which takes previous estimates into account to improve the accuracy of the current one, using an Ensemble Kalman Filter (EnKF). Fewer phasor measurements units are needed to achieve the same estimation error target than snapshot-based methods. Furthermore, contrary to existing methods, the proposed approach does not embed power flow equations into the state estimator, thus making it a versatile technique. The theoretical formulation of the EnKF-based PASE presented in the paper has been validated considering a 33-bus distribution system and using power consumption traces from real households.
Confirmation of Hard Magnetic L10 FeNi Phase Precipitated in FeNiSiBPCu Alloy by Anomalous X-Ray Diffraction

There is a growing interest in the development of rare-earth element-free hard magnets. The L10 FeNi phase found in Fe-based meteorites is promising based on its high-magnetocrystalline anisotropy and saturation magnetization. Not only the production, but also the characterization of L10 FeNi phase is challenging due to similar X-ray scattering factors of Fe and Ni. Here, we report on the confirmation of L10 FeNi phase precipitated in a multi-phase FeNiSiBPCu alloy by anomalous X-ray diffraction (AXRD). This is a powerful technique, which can differentiate between ordered and disordered phases along with the elements present in a phase. We measured integrated X-ray intensity with energy near the Fe (~7–7.2 keV) and Ni (8.25–8.4 keV) absorption edges for superlattice and fundamental reflections of L10 FeNi. A drastic change in integrated intensity with energy of X-ray was observed. Increase in intensity around superlattice reflection at Ni-absorption edge clearly confirms the presence of a chemically ordered L10 FeNi phase, but the results obtained at Fe edge are surprising. The analysis of AXRD results suggested that (110) diffraction peak of Fe2B phase overlaps with (001) of L10 FeNi. Understanding gained from AXRD results also allows us to estimate the long-range order parameter (S) from X-ray diffraction measurements.
Overview on Moving Target Network Defense

Moving Target Defense (MTD) is a research hotspot in the field of network security. Moving Target Network Defense (MTND) is the implementation of MTD at network level. Numerous related works have been proposed in the field of MTND. In this paper, we focus on the scope and area of MTND, systematically present the recent representative progress from four aspects, including IP address and port mutation, route mutation, fingerprint mutation and multiple mutation, and put forward the future development directions. Several new perspectives and elucidations on MTND are rendered.
Asthma–COPD overlap: identification and optimal treatment

Asthma and chronic obstructive pulmonary disease (COPD) are both highly prevalent conditions that can coexist in the same individual: the so-called ‘asthma -COPD overlap’ (ACO). Its prevalence and prognosis vary widely depending on how ACO is defined in each publication, the severity of bronchial obstruction of patients included and the treatment they are receiving. Although there is a lack of evidence about the biology of ACO, the overlap of both diseases should express a mixture of a Th1 inflammatory pattern (characteristic of COPD) and a Th2 signature (characteristic of asthma). In this review we support a novel algorithm for ACO diagnosis proposed by the Spanish Respiratory Society (SEPAR), based on a sequential evaluation that considers: (a) the presence of chronic airflow limitation in a smoker or ex-smoker patient ⩾35 years old; (b) a current diagnosis of asthma; and (c) the existence of a very positive bronchodilator test (PBT; ⩾15% and ⩾400 ml) or the presence of eosinophilia in blood (⩾300 eosinophils/μl). This algorithm can identify those patients who may benefit from a treatment with inhaled corticosteroids (ICSs) and maybe from biological drugs in a near future. In addition, it is easily applicable in clinical practice. The major disadvantage is that it groups patients with very different characteristics under the ACO’s umbrella. In view of this heterogeneity, we recommend a strategy of defining specific and measurable therapeutic objectives for every single patient and identifying the traits that can be treated to achieve those objectives.
A Flexible Mobility System Based on CHIP Architectures: The NETCHIP Research Project

This paper describes the main features and the objectives of a research project that is in an initial phase. The project, namely NETCHIP, aims to propose a flexible mobility system based on small and low-emission vehicles (from 4 to 10/12 seats), as a step towards the Mobility as a Service (MaaS) paradigm. The paper summarises the main characteristics of the proposed service from the user viewpoint, the technologic aspects to consider for its implementation, the main challenges to afford and the possible solutions.
Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges

With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog computing for real- time analytics of crowdsourced dash camera video. Instead of forwarding all the video to the cloud, we propose to turn commercial fleets (e.g., buses and taxis) into vehicular fog nodes, and to utilize these nodes to gather and process the video from the vehicles within communication ranges. We assess the feasibility of our proposal in two steps. First, we analyze the availability of vehicular fog nodes based on a real-world traffic dataset. Second, we explore the serviceability of vehicular fog nodes by evaluating the networking performance of fog-enabled video crowdsourcing over two mainstream access technologies, DSRC and LTE. Based on our findings, we also summarize the challenges to largescale real-time analytics of crowdsourced videos over vehicular networks.
A Framework for Evaluation of Power Grid Resilience Case Study: 2016 South Australian Blackout

Evaluation of a system resilience has a multidimensional nature that includes multifarious qualitative and quantitative aspects. This paper focuses on energy resilience in power systems and presents a novel quantitative metric, which aims to quantify the entire system behavior from pre-outage to post-outage stage. On September 28 2016, fierce tornado and lightning strikes caused multiple faults on the South Australian (SA) transmission system and led to a blackout in the entire state of South Australia. Load restoration data related to this event is collected from the Australian Energy Market Operator (AEMO) and is considered as the case study in this work. The proposed metric is applied to the real-world restoration data related to this disruptive event to measure and evaluate energy resilience in the SA grid. Furthermore, the effectiveness of the proposed framework in energy-resilience optimization is shown by detailed resilience-oriented problem formulation in power grids.
A Power Sector in Transition Understanding Transition Towards a Cleaner Grid and how Distributed Energy Resources Affect the Design and Operation of Electric Power Systems

Important changes in the supply and the demand side of electricity apparatus are now underway, triggered by a combination of drivers dramatically affecting the transmission sector of power systems: greenhouse gas emissions, distributed generation, energy efficiency and the full integration of the European energy market. A variety of accelerating factors - including active consumer, aggregators, energy storage, e-vehicles, etc.- are creating a new era for the many stakeholders, incl. system operators, regional entities, industry and consumer organizations, producers or their trade bodies, Regulators, public decision makers and the society at large. Entraining this background, ENTSO-E and the TSO Community strive for a secure, resilient, flexible, adequate and well interconnected pan-European power system, and they are acting unanimously to develop the most suitable responses to the challenges of the energy transition and digital transformation. This paper highlights several matters of importance in the above system operations and grid development processes, especially focusing on the use case of Italy.
A Systematic Approach to Customer Segmentation and Buyer Targeting for Profit Maximization

Nowadays, maintaining customer loyalty and attention span of the customers are major challenges faced by the retail industry. This leads to the need for reinforcement of marketing strategies from time to time. This paper proposes a systematic approach for targeting customers and providing maximum profit to the organizations. An important initial step is to analyze the data of sales acquired from the purchase history and determine the parameters that have the maximum correlation. Based on respective clusters, proper resources can be channeled towards profitable customers using machine learning algorithms. K-Means clustering is used for customer segmentation and Singular Value Decomposition is used for providing appropriate recommendations to the customers. This paper also deals with the drawbacks of the recommender system like cold start problem, sparsity, etc and how they can be overcome.
Daily Operation Optimization for Grid-Connected Hybrid System Considering Short-Term Electricity Price Forecast Scheme

With an increasing public and governmental awareness regarding environment protection and sustainable resources, hand-in-hand with an escalating electricity demand, the exponential growth of renewable energy generation capacity has been the “answer”. Mitigating the environmental harms associated with the more conventional energy sources such as: coal, oil, gas and nuclear. Nonetheless, some challenges remain, particularly concerning the integration of these technologies into the conventional generation mix. Hybrid energy systems allow a paradigm shift from a concentrated conventional generation to a more distributed one. This paper discusses the optimized PV-wind with hydro and battery storage capabilities for a grid-connected application considering the Short-Term Price Forecast information. The proposed technique has been tested in different scenarios, and results demonstrate the effectiveness of the proposed approach.