Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifrequency Coupled-Resonator Sensor for Dielectric Characterization of Liquids

A six-band microstrip sensor, based on a pair of electrically coupled resonators (ECRs), is proposed to perform multifrequency dielectric characterization of liquids. A pocket is milled through the substrate bottom of the sensor to contain the fluid sample, avoiding the need for a sample holder. The pocket has been designed to achieve a higher electric field magnitude in the sensing region and a mechanism to separate the resonant frequencies, which helps to ensure a high span between the first and the last resonant frequency. The microstrip sensor is implemented on a 1.905-mm-thick substrate with <inline-formula> <tex-math notation="LaTeX">$\varepsilon ^{\prime }_{r} = 12.2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\tan \delta = 0.0019$ </tex-math></inline-formula>. The sensor’s six resonance frequencies span the range between 1 and 3.5 GHz. Experimental tests show that the sensor can measure a wide range of <inline-formula> <tex-math notation="LaTeX">$\varepsilon ^{\prime }_{r}$ </tex-math></inline-formula> values between 2 and 79 resulting in close similarities to the dielectric constants published in previous works.
10 GHz Standing Wave Oscillator Based Clock Distribution Network Considering Distributed Capacitance

The paper presents 10 GHz standing wave oscillator (SWO) minimizing distributed capacitance Cd (distributed capacitance generated from multiple cross-coupled pairs (CCP)) for the clock distribution network. The conventional SWOs have been proposed for a negative resistance gd (an equivalent transconductance per CCP) to get rid of conductance in RLGC extracted from a transmission line. However, the SWOs have not considered its capacitance. The Cd can not only increase its phase constant but also decrease the unit length of the transmission line. In the proposed SWO, a MOS varactor, Cvar(V)(voltage controlled capacitance), is added to minimize cd(equivalent capacitance per CCP). To adjust the capacitance of the varactors, the external voltage, VB, is added. The impedance of the SWO can be represented as $2\left( { - \frac{1}{{{g_{m1}}}} - \frac{1}{{{g_{m2}}}} - s\frac{1}{{{C_{var}}\left( {{V_B}} \right)}} + s\frac{1}{{{C_{gs3,4}}}}} \right)$. The conductance for the proposed design is -4.83mS, which is reduced by 30% compared with the conventional design. The admittance of the capacitance in the proposed design is 3.76 mS while the conventional design has 6.07 mS, which shows approximately 2.29 mS (38%) reduction. The design is simulated by a 180nm CMOS technology node with 1.8V power supply, and the total power consumption is 5.4 mW for the proposed SWO.
Which Factors Impact Engagement on News Articles on Facebook?

Social media is increasingly being used as a news-platform. To reach their intended audience, newspapers need for their articles to be well ranked by Facebook's news-feed algorithm. The number of likes, shares and other reactions determine the lead scoring criteria. This paper will try to assess how the reaction volume is impacted by the following criteria: (1) Delay between event and post release; (2) Time of the day the post is published; and (3) Post format: video, photo or text. To isolate the effect of the publication time and post format on a post, we need to control for the news-event and the publishing newspaper. For that end, a news-aggregator is designed and implemented, to group together posts that relate to the same news-event. This tool gave some spin-off results, allowing the ability to map newspapers by similarity and to detect some topic omissions.
Extending Authentication Mechanism to Cooperate with Accountable Address Assignment

Lack of effective accountability mechanisms brings a series of security problems for Internet today. In Next Generation Internet based on IPv6, the system of identity authentication and IP verification is the key to accounting ability. Source Address Validation Improvement (SAVI) can protect IP source addresses from being faked. But without identity authentication mechanism and certain relationship between IP and accountable identity, the accountability is still unreliable. To solve this problem, most research focus on embedding accountable identity into IP address which need either changing DHCP client or twice DHCP request process due to the separate process of user authentication and address assignment. Different from previous research, this paper first analyzes the problems and requirements of combining Web Portal or 802.1X, two main identity authentication mechanism (AAA), with the accountable address assignment in SAVI frame-work. Then a novel Cooperative mechanism for Accountable IP address assignment (CAIP) is proposed based on 802.1X and SAVI, which takes into account the validation of IP address, the authenticity and accountability of identity at the same time. Finally, we build up prototype system for both Fat AP and Thin AP wireless scenarios and simulate the performance of CAIP through large-scale campus networks' data logs. The experiment result shows that the IP addresses and identities in CAIP are protective and accountable. Compared with other previous research, CAIP is not only transparent to the terminals and networks, but also low impact on network equipment, which makes CAIP easy deployment with high compatibility and low cost.
A Computational Model of Direction Selectivity in Macaque V1 Cortex Based on Dynamic Differences between On and Off Pathways

This paper is about neural mechanisms of direction selectivity (DS) in macaque primary visual cortex, V1. We present data (on male macaque) showing strong DS in a majority of simple cells in V1 layer 4Cα, the cortical layer that receives direct afferent input from the magnocellular division of the lateral geniculate nucleus (LGN). Magnocellular LGN cells are not direction-selective. To understand the mechanisms of DS, we built a large-scale, recurrent model of spiking neurons called DSV1. Like its predecessors, DSV1 reproduces many visual response properties of V1 cells including orientation selectivity. Two important new features of DSV1 are (1) DS is initiated by small, consistent dynamic differences in the visual responses of OFF and ON Magnocellular LGN cells, and (2) DS in the responses of most model simple cells is increased over those of their feedforward inputs; this increase is achieved through dynamic interaction of feedforward and intracortical synaptic currents without the use of intracortical direction-specific connections. The DSV1 model emulates experimental data in the following ways: (1) most 4Cα Simple cells were highly direction-selective but 4Cα Complex cells were not; (2) the preferred directions of the model's direction-selective Simple cells were invariant with spatial and temporal frequency (TF); (3) the distribution of the preferred/opposite ratio across the model's population of cells was very close to that found in experiments. The strong quantitative agreement between DS in data and in model simulations suggests that the neural mechanisms of DS in DSV1 may be similar to those in the real visual cortex. SIGNIFICANCE STATEMENT Motion perception is a vital part of our visual experience of the world. In monkeys, whose vision resembles that of humans, the neural computation of the direction of a moving target starts in the primary visual cortex, V1, in layer 4Cα that receives input from the eye through the lateral geniculate nucleus (LGN). How direction selectivity (DS) is generated in layer 4Cα is an outstanding unsolved problem in theoretical neuroscience. In this paper, we offer a solution based on plausible biological mechanisms. We present a new large-scale circuit model in which DS originates from slightly different LGN ON/OFF response time-courses and is enhanced in cortex without the need for direction-specific intracortical connections. The model's DS is in quantitative agreement with experiments.
Transcriptomic Analysis Reveals Candidate Genes Responding Maize Gray Leaf Spot Caused by Cercospora zeina

Gray leaf spot (GLS), caused by the fungal pathogen Cercospora zeina (C. zeina), is one of the most destructive soil-borne diseases in maize (Zea mays L.), and severely reduces maize production in Southwest China. However, the mechanism of resistance to GLS is not clear and few resistant alleles have been identified. Two maize inbred lines, which were shown to be resistant (R6) and susceptible (S8) to GLS, were injected by C. zeina spore suspensions. Transcriptome analysis was carried out with leaf tissue at 0, 6, 24, 144, and 240 h after inoculation. Compared with 0 h of inoculation, a total of 667 and 419 stable common differentially expressed genes (DEGs) were found in the resistant and susceptible lines across the four timepoints, respectively. The DEGs were usually enriched in ‘response to stimulus’ and ‘response to stress’ in GO term analysis, and ‘plant–pathogen interaction’, ‘MAPK signaling pathways’, and ‘plant hormone signal transduction’ pathways, which were related to maize’s response to GLS, were enriched in KEGG analysis. Weighted-Genes Co-expression Network Analysis (WGCNA) identified two modules, while twenty hub genes identified from these indicated that plant hormone signaling, calcium signaling pathways, and transcription factors played a central role in GLS sensing and response. Combing DEGs and QTL mapping, five genes were identified as the consensus genes for the resistance of GLS. Two genes, were both putative Leucine-rich repeat protein kinase family proteins, specifically expressed in R6. In summary, our results can provide resources for gene mining and exploring the mechanism of resistance to GLS in maize.
MicroRNA-196a promotes renal cancer cell migration and invasion by targeting BRAM1 to regulate SMAD and MAPK signaling pathways

Rationale: MicroRNAs (miRNAs) are endogenous ~22nt RNAs that play critical regulatory roles in various biological and pathological processes, including various cancers. Their function in renal cancer has not been fully elucidated. It has been reported that miR-196a can act as oncogenes or as tumor suppressors depending on their target genes. However, the molecular target for miR-196a and the underlying mechanism in miR-196a promoted cell migration and invasion in renal cancer is still not clear. Methods: The expression, survival and correlation between miR-196a and BRAM1 were investigated using TCGA analysis and validated by RT-PCR and western blot. To visualize the effect of Bram1 on tumor metastasis in vivo, NOD-SCID gamma (NSG) mice were intravenously injected with RCC4 cells (106 cells/mouse) or RCC4 overexpressing Bram1. In addition, cell proliferation assays, migration and invasion assays were performed to examine the role of miR-196a in renal cells in vitro. Furthermore, immunoprecipitation was done to explore the binding targets of Bram1. Results: TCGA gene expression data from renal clear cell carcinoma patients showed a lower level of Bram1 expression in patients' specimens compared to adjacent normal tissues. Moreover, Kaplan‑Meier survival data clearly show that high expression of Bram1correlates to poor prognosis in renal carcinoma patients. Our mouse metastasis model confirmed that Bram1 overexpression resulted in an inhibition in tumor metastasis. Target-prediction analysis and dual-luciferase reporter assay demonstrated that Bram1 is a direct target of miR-196a in renal cells. Further, our in vitro functional assays revealed that miR-196a promotes renal cell proliferation, migration, and invasion. Rescue of Bram1 expression reversed miR-196a-induced cell migration. MiR-196a promotes renal cancer cell migration by directly targeting Bram1 and inhibits Smad1/5/8 phosphorylation and MAPK pathways through BMPR1A and EGFR. Conclusions: Our findings thus provide a new mechanism on the oncogenic role of miR-196a and the tumor-suppressive role of Bram1 in renal cancer cells. Dysregulated miR-196a and Bram1 represent potential prognostic biomarkers and may have therapeutic applications in renal cancer.
Magnetic position sensors

Magnetic position sensors are popular in industrial and automotive applications since they are robust, resistant to dust and oil, and can be cheap. This was traditionally accompanied by low accuracy. However, new precise magnetic position sensors have been developed which can achieve 0.015% error and 10 nm resolution. The maximum achievable range is about 20 m. DC magnetic position sensors use a permanent magnet as a field source; a magnetic field sensor measures the field from that source, which is a function of distance. As a field sensor, magnetoresistors are often used instead of traditional Hall sensors. Eddy current position sensors also work with non-magnetic conduction targets. Magnetostrictive position sensors are based on the time of flight of the elastic waves excited in the magnetostrictive material. These sensors can be several meters long and their applications range from level meters to hydraulics. Magnetic trackers and long-range position sensors utilize AC field sources, which are detectable from distances up to 20 m. Compared to optical instruments, magnetic trackers do not need a direct view. Their applications include surgery, mixed reality, and underground and underwater navigation.
Development of Vertical Obstacle Detection System for Visually Impaired Individuals

Previously, an obstacle detection system for visually impaired people utilizing a smartphone was developed. However, this system lacked practicality because it depended on height information from a smartphone. In this study, ultrasonic, gyro, and acceleration sensors were installed in a white walking stick, creating a system independent of height information from smartphones. Results indicate that by using this fabricated system, it is possible to detect static obstacles, such as walls and stairs, which otherwise prevent visually impaired persons from walking. Furthermore, this new system was improved to be compatible with the slide technique performed by visually impaired persons during ambulation.
Exploring Multi-Objective Exercise Recommendations in Online Education Systems

Recommending suitable exercises to students in an online education system is highly useful. Existing approaches usually rely on machine learning techniques to mine large amounts of student interaction log data accumulated in the systems to select the most suitable exercises for each student. Generally, they mainly aim to optimize a single objective, i.e., recommending non-mastered exercises to address the immediate weakness of students. While this is a reasonable objective, there exist more beneficial multiple objectives in the long-term learning process that need to be addressed including Review & Explore, Smoothness of difficulty level and Engagement. In this paper, we propose a novel Deep Reinforcement learning framework, namely DRE, for adaptively recommending Exercises to students with optimization of above three objectives. In the framework, we propose two different Exercise Q-Networks for the agent, i.e., EQNM and EQNR, to generate recommendations following Markov property and Recurrent manner, respectively. We also propose novel reward functions to formally quantify those three objectives so that DRE could update and optimize its recommendation strategy by interactively receiving students' performance feedbacks (e.g., score). We conduct extensive experiments on two real-world datasets. Experimental results clearly show that the proposed DRE can effectively learn from the student interaction data to optimize multiple objectives in a single unified framework and adaptively recommend suitable exercises to students.
Restructured Fe−Mn Alloys Encapsulated by N‐doped Carbon Nanotube Catalysts Derived from Bimetallic MOF for Enhanced Oxygen Reduction Reaction

A set of novel catalysts FeMn/N‐CNTs that partly maintain the core‐shell structure have been prepared successfully by calcination of analogous MOF‐74, which has bimetallic species (Fe and Mn) and a cheap organic ligand (2, 5‐dihydroxylbenzoic acid, DHBA) with melamine as additional nitrogen source. These catalysts exhibit a distinctive microstructure of Fe−Mn alloys surrounded by N‐doped carbon nanotubes (CNTs). Electrochemical methods have been employed to investigate their activity in oxygen reduction reaction (ORR) in alkaline solution. The highest ORR performance of Fe3Mn1/N‐CNTs‐100 shows that the half wave potential is at 0.865 V and the kinetic current density (at 0.9 V) is 1.447 mA cm−2, which are higher than those of commercial Pt/C (0.855 V, 0.946 mA cm−2). In addition, Fe3Mn1/N‐CNTs‐100 is much more durable than commercial Pt/C under the conditions tested. The highly efficient ORR performance may be attributed to the unique microstructure and large surface area with appropriate pore size, as well as to the synergistic effects between the pyridinic N species and the Fe−Nx species that play important roles in ORR in alkaline solution. However, in acid medium, only Fe−Nx species catalyze ORR and pyridinic N species are limited to work as the active sites. This study may prompt others to explore the development of heteroatom‐doped CNTs surrounding particles as efficient catalyst for ORR and fuel cell applications.
Assaying Homodimers of NF-κB in Live Single Cells

NF-κB is a family of heterodimers and homodimers which are generated from subunits encoded by five genes. The predominant classical dimer RelA:p50 is presumed to operate as “NF-κB” in many contexts. However, there are several other dimer species which exist and may even be more functionally relevant in specific cell types. Accurate characterization of stimulus-specific and tissue-specific dimer repertoires is fundamentally important for understanding the downstream gene regulation by NF-κB proteins. In vitro assays such as immunoprecipitation have been widely used to analyze subunit composition, but these methods do not provide information about dimerization status within the natural intracellular environment of intact live cells. Here we apply a live single cell microscopy technique termed Number and Brightness to examine dimers translocating to the nucleus in fibroblasts after pro-inflammatory stimulation. This quantitative assay suggests that RelA:RelA homodimers are more prevalent than might be expected. We also found that the relative proportion of RelA:RelA homodimers can be perturbed by small molecule inhibitors known to disrupt the NF-κB pathway. Our findings show that Number and Brightness is a useful method for investigating NF-κB dimer species in live cells. This approach may help identify the relevant targets in pathophysiological contexts where the dimer specificity of NF-κB intervention is desired.
Distributed Fixed-Time Economic Dispatch under Time-Varying Topology and Uncertain Information

The centralized power generation infrastructure that defines the U.S. electric grid is slowly moving to the distributed architecture due to the explosion in use of renewable generation and distributed energy resources (DERs), such as residential solar, wind turbines and battery storage. Furthermore, variable pricing policies and profusion of flexible loads entail frequent and severe changes in power outputs required from each generation unit. In view of the above challenges, a fixed-time convergent, fully distributed economic dispatch algorithm for scheduling optimal power generation among a set of DERs is proposed. The proposed algorithm addresses both load balance, as well as generation capacity constraints. The algorithm subsumes and betters the existing economic dispatch algorithms on three fronts: (a) It is shown that the distributed algorithm converges to optimal dispatch solution in a fixed-time, regardless of the initial states of every generation unit. Additionally, the time of convergence can be user-prespecified. (b) Regardless of the physical topology of the power network, the proposed framework allows for time-varying communication topology. (c) Finally, we show that our algorithm is robust to uncertain information resulting from noisy communication with neighbors. Several case studies are presented that corroborate computational and robustness aspects of the proposed distributed economic dispatch algorithm.
High Q Resonant Graphene Absorber with Lossless Phase Change Material Sb2S3

Graphene absorbers have attracted lots of interest in recent years. They provide huge potential for applications such as photodetectors, modulators, and thermal emitters. In this letter, we design a high-quality (Q) factor resonant graphene absorber based on the phase change material Sb2S3. In the proposed structure, a refractive index grating is formed at the subwavelength scale due to the periodical distributions of amorphous and crystalline states, and the structure is intrinsically flat. The numerical simulation shows that nearly 100% absorption can be achieved at the wavelength of 1550 nm, and the Q factor is more than hundreds due to the loss-less value of Sb2S3 in the near-infrared region. The absorption spectra can be engineered by changing the crystallization fraction of the Sb2S3 as well as by varying the duty cycle of the grating, which can be employed not only to switch the resonant wavelength but also to achieve resonances with higher Q factors. This provides a promising method for realizing integrated graphene optoelectronic devices with the desired functionalities.
A NIRS-Aided Methodology to Elucidate the Nutrition of the Endangered Mountain Gazelle (Gazella gazella) Using Samples of Rumen Contents from Roadkills

The populations of the endangered mountain gazelle (Gazella gazella), which inhabit large parts of Israel, across various ecosystems and climatic conditions, shrunk drastically over the last decades. To date, data on gazelle nutrition, how these relate with individual characteristics and respond to seasonal and environmental changes, have not been available. We analyzed 110 samples from gazelle rumen contents collected throughout the country from occasional fatalities, mainly roadkills, and tested the feasibility of using them for near-infrared spectrometry (NIRS) calibrations. Although NIR calibrations for crude protein, in vitro dry matter digestibility, and ash were reasonable, we found that using calibrations based on local forage and feed plant species performed better, and used these to estimate several nutritional constituents in gazelle rumens, using NIRS. We tested how constituents relate to the sex, age-class, and weight of the individual gazelle, and to season and ecosystem type, and found that season plays a major role in gazelle nutrition. Winter is the most propitious season, when crude protein, ash and digestibility are highest, and acid detergent fiber (ADF), neutral detergent fiber (NDF), and the carbon to nitrogen (C:N) ratio are lowest. Autumn, being the harshest season, mirrors winter conditions, and summer and spring show intermediate levels. Yet the relative changes between seasons were mild: about 30%, for crude protein, digestibility, and ash, and 14–22% for ADF, NDF, and C:N ratio. Ecosystem type affected several constituents, and nutrition was slightly better in Mediterranean than in dry ecosystems. Gazelle sex, weight, and age-class had minor effects on nutrition. Overall, it seems that the adaptation of gazelles to their environment is germane to keeping relatively steady nutrition throughout the year. Our results, which do not show a dramatic decline in the quality of gazelle nutrition during any season or among the climatic regions that were studied, suggest that nutrition is not a major driver of the survival of gazelles in the populations surveyed.