Miracle-dz/UniFAR-FTUs huggingface dataset:
Precoding with adaptive subcarrier grouping in MIMO-OFDM systems

With the increase of physical antenna and subcarrier numbers in MIMO-OFDM systems, the signal precoding complexity overburden and channel side information feedback amount would become enormous and intolerable. To solve this problem, previous works mainly focused on fixed subcarrier grouping size and MIMO precoded signals in the same group. It could reduce the system overburden, but such a process would lead to system capacity loss due to the channel mismatch in the precoding procedure. In this paper, we consider a MIMO-OFDM system over the frequency self-correlation of i.i. d. Rayleigh channels and investigate the relationship between group size and capacity loss. By exploiting our developed theoretical results, we also propose an adaptive subcarrier grouping algorithm, which not only enables to have a good control of system service quality but also to reduce system overburden significantly. Numerical results are shown to validate our theoretical results, which also provide valuable insights on the system design of MIMO-OFDM systems.
Numerical investigation of marine diesel engine turbocharger compressor tonal noise

The compressor tonal noise of a low-speed marine diesel engine turbocharger is investigated with numerical method. Nine operating conditions on four different rotating speed lines are analyzed; the conditions include near-choke conditions, near-surge conditions, and peak efficiency conditions. Numerical predictions of compressor global variables are compared with experimental measurements, and a good agreement is achieved. The pressure fluctuation at all operating conditions presents strong periodicity, and the prominent peaks of pressure amplitude appear at the blade passing frequency and its harmonics after Fast Fourier transform algorithm. Furthermore, the fluctuation strength varies with the changes in operating conditions. Then, the pressure fluctuation is used to acquire the aero-acoustic source, and the compressor tonal noise and the compressor broadband noise are calculated by the boundary element method. The compressor tonal noise increases with operating conditions shift to high rotating speed and the surge line. The acoustic directivity of radiated tonal noise at inlet duct nozzle is obvious, which is particularly detectable at the blade passing frequency. The further analysis shows that the acoustic directivity is greatly influenced by the frequency, compressor rotating speed, and total-to-total pressure ratio.
Determining Semantic Similarity among Entity Classes from Different Ontologies

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.
Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful.
Zoledronate derivatives as potential inhibitors of uridine diphosphate‐galactose ceramide galactosyltransferase 8: A combined molecular docking and dynamic study

Krabbe's disease is a neurodegenerative disorder caused by deficiency of galactocerebrosidase activity that affects the myelin sheath of the nervous system, involving dysfunctional metabolism of sphingolipids. It has no cure. Because substrate inhibition therapy has been shown to be effective in some human lysosomal storage diseases, we hypothesize that a substrate inhibition therapeutic approach might be appropriate to allow correction of the imbalance between formation and breakdown of glycosphingolipids and to prevent pathological storage of psychosine. The enzyme responsible for the biosynthesis of galactosylceramide and psychosine is uridine diphosphate‐galactose ceramide galactosyltransferase (2‐hydroxyacylsphingosine 1‐β‐galactosyltransferase; UGT8; EC 2.4.1.45), which catalyzes the transferring of galactose from uridine diphosphate‐galactose to ceramide or sphingosine, an important step of the biosynthesis of galactosphingolipids. Because some bisphosphonates have been identified as selective galactosyltransferase inhibitors, we verify the binding affinity to a generated model of the enzyme UGT8 and investigate the molecular mechanisms of UGT8–ligand interactions of the bisphosphonate zoledronate by a multistep framework combining homology modeling, molecular docking, and molecular dynamics simulations. From structural information on UGTs’ active site stereochemistry, charge density, and access through the hydrophobic environment, the molecular docking procedure allowed us to identify zoledronate as a potential inhibitor of human ceramide galactosyltransferase. More importantly, zoledronate derivates were designed through computational modeling as putative new inhibitors. Experiments in vivo and in vitro have been planned to verify the possibility of using zoledronate and/or the newly identified inhibitors of UGT8 for a substrate inhibition therapy useful for treatment of Krabbe's disease and/or other lysosomal disorders. © 2016 Wiley Periodicals, Inc.
A Graph-Based Fuzzy Evolutionary Algorithm for Solving Two-Echelon Vehicle Routing Problems

Two-echelon vehicle routing problem (2E-VRP) is a challenging problem that involves both the strategic and tactical planning decisions on both echelons. The satellite locations and the customer distribution affect the cost of different components on the second echelon, thus the possibilities of satellite-to-customer assignment complicates the problem. In this paper, we propose a graph-based fuzzy evolutionary algorithm for solving 2E-VRP. The proposed method integrates a graph-based fuzzy assignment scheme into an iteratively evolutionary learning process to minimize the total cost. To resolve the possibilities of the satellite-to-customer assignment, graph-based fuzzy operator is used to take advantage of population evolution and avoid excessive fitness evaluations of unpromising moves in different satellites. Each offspring is produced via graph-based fuzzy assignment procedure out of an assignment graph from parent individuals, and fuzzy local search procedure is used to further improve the offspring. The experimental results on the public test sets demonstrate the competitiveness of the proposed method.
Efficient Plant Foods Processing Based on Infrared Heating

ABSTRACT Infrared (IR) heating is a potential energy-saving and high-efficiency technology, whose application in the food processing field is still in its infancy. IR heating has prominent advantages over conventional heating, namely, uniform heating in a shorter time with less quality degradation, but it has a salient shortcoming of low penetration depth, which makes it unsuitable for heating large or thick food materials. Efficient IR processing is to maximize the advantages of IR heating, and to overcome its shortcomings for achieving the goal of producing high-quality foods efficiently. In order to open up a broader path for IR processing in foods, this article reviews approaches to realizing efficient IR processing and introduces main applications as well as research progress of high-efficiency IR technology. Finally, some trends about the development of IR processing are also presented.
MSEPBurr Distribution: Properties and Parameter Estimation

MSNBurr and MSTBurr distribution have been developed as Neo-Normal distributions that represent a relaxation of normality. The difference between them is that the MSTBurr’s peak is below MSNBurr’s. In this paper, we propose a MSEPBurr distribution with its peak could be not only lower but also high-er than MSNBurr. Furthermore, we study several properties of MSEPBurr, such as mean, variance, skewness, kurtosis, and quantile. The MSEPBurr parameters are estimated by using the Bayesian approach with the BUGS language implementation for its computation. We employ simulation study and use existing data to illustrate the application of the regression model. In real data, we notice that MSEPBurr has similar performance with MSNBurr and MSTBurr that they outperform Normal and Student-t distribution in Australian athlete data because their skewness can accommodate long left tail excellently. However, their performance is less than the Student-t model in chemical reaction rate data because their skewness can not accommodate long right tail perfectly. Although in general their perfor-mance is the same, we observe that the MSEPBurr performs better than the MSNBurr and the MSTBurr in some simulated data.
A finite element contour approach to affine invariant shape representation

This paper1 presents a novel shape representation approach, Finite Element Contour (FEC), based on studies of shape analysis from the perspective of Finite Element Method (FEM). We assume that an edge of a contour can be modeled as a bendable beam element. Linking finite number of beam elements end to end along the contour, we obtain a closed-loop Finite Element Contour model as an approximate physical model of the original shape. By this model, we can calculate its natural frequency, which is one of mechanical properties that directly related to the geometric shape, and employed it as the shape representation. FEC shape feature possesses translation and rotation invariant properties naturally. We also realized scale and unique affine normalization in few simple steps based on intrinsic physical properties of shape from FEM viewpoint. Experimental results validated that the proposed FEC feature is capable of identifying shape in object recognition task. It also can describe shape deformation. In the well-known MPEG 7 shape retrieval task, the enhanced FEC approach obtains Bullseye score 87.11%.
Application of integrated magnetics in resonant converters

The physical size of inductors in resonant converters was examined in terms of inductor to transformer size ratio. Following the general discussion of the issue, the inductor size in a series resonant DC-DC converter is examined in detail. It is shown that the inductor to transformer size ratio is a function of the peak voltage of the inductor to the RMS voltage of the transformer and is independent of the inductance. The physical size of the inductor could be much larger than the transformer when the converter operates near resonance. Away from resonance, the inductor size becomes close to, and even smaller, than the transformer. Leakage inductance of the transformer can be used as the resonant inductance but if the leakage is associated with the secondary windings of the transformer, it will increase the peak reverse voltage of the rectifier diodes when a center-tapped rectifier topology is used. To solve this problem, an integrated transformer-inductor element with high primary leakage inductance and low secondary leakage is proposed and tested. The experimental unit was built for a power level of 1 kVA and switching frequency range 30-60 kHz.
Edge-to-Edge Quality-of-Service Domain

Researchers have proposed numerous approaches to providing Quality-of-Service (QoS) across the Internet. The IETF has proposed two reservation approaches: hop-by-hop bandwidth reservation (IntServ); and per-hop behaviour bandwidth reservation (DiffServ). An edge router generates traffic, accepts per-flow reservation and classifies them into predetermined service class; while a core router ensures different QoS guarantees for each service class. We propose an Edge-to-Edge Quality-of-Service Domain in which packet trains with the same service requirements aggregated using packet deadline at edge router. The properties of a packet train like Inter-Packet Departure Time, Inter-flow Departure Time and accumulated packet delay are embedded and used by our quantum-based scheduler and QoS packet forwarding scheme in core routers. Thus, we are able to extract per-queue and per-flow information. Each queue is reconstructed at core router with packets having an expected departure time that is relative to the ingress router. Useful functions like instantaneous service rate and fine granular dropping scheme can be derived with a combination of embedded information and relative virtual clock technique. The encapsulation of our packet train information converges mathematically. Through simulations, we show that our architecture can provide delay and rate guarantees and minimise jitter for QoS-sensitive flows that requires LR-coupled or LR-decoupled reservations.
‘Fractional Recovery’ Analysis of a Presynaptic Synaptotagmin 1-Anchored Endocytic Protein Complex

Background The integral synaptic vesicle protein and putative calcium sensor, synaptotagmin 1 (STG), has also been implicated in synaptic vesicle (SV) recovery. However, proteins with which STG interacts during SV endocytosis remain poorly understood. We have isolated an STG-associated endocytic complex (SAE) from presynaptic nerve terminals and have used a novel fractional recovery (FR) assay based on electrostatic dissociation to identify SAE components and map the complex structure. The location of SAE in the presynaptic terminal was determined by high-resolution quantitative immunocytochemistry at the chick ciliary ganglion giant calyx-type synapse. Methodology/Principle Findings The first step in FR analysis was to immunoprecipitate (IP) the complex with an antibody against one protein component (the IP-protein). The immobilized complex was then exposed to a high salt (1150 mM) stress-test that caused shedding of co-immunoprecipitated proteins (co-IP-proteins). A Fractional Recovery ratio (FR: recovery after high salt/recovery with control salt as assayed by Western blot) was calculated for each co-IP-protein. These FR values reflect complex structure since an easily dissociated protein, with a low FR value, cannot be intermediary between the IP-protein and a salt-resistant protein. The structure of the complex was mapped and a blueprint generated with a pair of FR analyses generated using two different IP-proteins. The blueprint of SAE contains an AP180/X/STG/stonin 2/intersectin/epsin core (X is unknown and epsin is hypothesized), and an AP2 adaptor, H-/L-clathrin coat and dynamin scission protein perimeter. Quantitative immunocytochemistry (ICA/ICQ method) at an isolated calyx-type presynaptic terminal indicates that this complex is associated with STG at the presynaptic transmitter release face but not with STG on intracellular synaptic vesicles. Conclusions/Significance We hypothesize that the SAE serves as a recognition site and also as a seed complex for clathrin-mediated synaptic vesicle recovery. The combination of FR analysis with quantitative immunocytochemistry provides a novel and effective strategy for the identification and characterization of biologically-relevant multi-molecular complexes.
Element-independent design technique for wide angle impedance matching material

This paper presents a design technique for a wide angle impedance matching network material which does not require the knowledge of the analytical form of the scan impedance of the radiating element. The design procedure is therefore applicable for any type of array element and it is based on a Floquet-mode expansion. The presented approach is applied to the design of a multilayer Wide Angle Impedance Matching (WAIM) material for a phased array of patch elements.
Caffeine in Floral Nectar Enhances a Pollinator's Memory of Reward

Bees Get That Caffeine "Buzz" Caffeine improves memory in humans, millions of whom find that their daily dose enhances clarity, focus, and alertness. The human relationship with caffeine is relatively recent, however, and thus its impact on our brains is likely a by-product of its true ecological role. Caffeine occurs naturally in the floral nectar of Coffea and Citrus plants. Wright et al. (p. 1202; see the Perspective by Chittka and Peng) found that caffeine presented at naturalistic levels significantly improved the ability of bees to remember and locate a learned floral scent and potentiated the responses of neurons involved in olfactory learning and memory. Caffeine at concentrations found in flowers helps individual honeybees remember where to find associated nectar. [Also see Perspective by Chittka and Peng] Plant defense compounds occur in floral nectar, but their ecological role is not well understood. We provide evidence that plant compounds pharmacologically alter pollinator behavior by enhancing their memory of reward. Honeybees rewarded with caffeine, which occurs naturally in nectar of Coffea and Citrus species, were three times as likely to remember a learned floral scent as were honeybees rewarded with sucrose alone. Caffeine potentiated responses of mushroom body neurons involved in olfactory learning and memory by acting as an adenosine receptor antagonist. Caffeine concentrations in nectar did not exceed the bees' bitter taste threshold, implying that pollinators impose selection for nectar that is pharmacologically active but not repellent. By using a drug to enhance memories of reward, plants secure pollinator fidelity and improve reproductive success.
Fast dual graph-based hotspot detection

As advanced technologies in wafer manufacturing push patterning processes toward lower-k1 subwavelength printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in generation of many hotspots, which are actual device patterns with relatively large CD and image errors with respect to on-wafer targets. Hotspots can be formed under a variety of conditions such as the original design being unfriendly to the RET that is applied, unanticipated pattern combinations in rule-based OPC, or inaccuracies in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of a device, device performance and parametric yield can be significantly degraded. Previous rule-based hotspot detection methods suffer from long runtimes for complicated patterns. Also, the model generation process that captures process variation within simulation-based approaches brings significant overheads in terms of validation, measurement and parameter calibration. In this paper, we first describe a novel detection algorithm for hotspots induced by lithographic uncertainty. Our goal is to rapidly detect all lithographic hotspots without significant accuracy degradation. In other words, we propose a filtering method: as long as there are no "false negatives", i.e., we successfully have a superset of actual hotspots, then our method can dramatically reduce the layout area for golden hotspot analysis. The first step of our hotspot detection algorithm is to build a layout graph which reflects pattern-related CD variation. Given a layout L, the layout graph G = (V, Ec union Ep) consists of nodes V, corner edges Ec and proximity edges Ep. A face in the layout graph includes several close features and the edges between them. Edge weight can be calculated from a traditional 2-D model or a lookup table. We then apply a three-level hotspot detection: (1) edge-level detection finds the hotspot caused by two close features or "L-shaped" features; (2) face-level detection finds the pattern-related hotspots which span several close features; and (3) merged-face-level detection finds hotspots with more complex patterns. To find the merged faces which capture the pattern-related hotspots, we propose to convert the layout into a planar graph G. We then construct its dual graph GD and sort the dual nodes according to their weights. We merge the sorted dual nodes (i.e., the faces in G) that share a given feature, in sequence. We have tested our flow on several industry testcases. The experimental results show that our method is promising: for a 90nm metal layer with 17 hotspots detected by commercial optical rule check (ORC) tools, our method can detect all of them while the overall runtime improvement is more than 287X.
Studies of Gene Variants Related to Inflammation, Oxidative Stress, Dyslipidemia, and Obesity: Implications for a Nutrigenetic Approach

Obesity is currently considered a serious public health issue due to its strong impact on health, economy, and quality of life. It is considered a chronic low-grade inflammation state and is directly involved in the genesis of metabolic disturbances, such as insulin resistance and dyslipidemia, which are well-known risk factors for cardiovascular disease. Furthermore, there is evidence that genetic variation that predisposes to inflammation and metabolic disturbances could interact with environmental factors, such as diet, modulating individual susceptibility to developing these conditions. This paper aims to review the possible interactions between diet and single-nucleotide polymorphisms (SNPs) in genes implicated on the inflammatory response, lipoprotein metabolism, and oxidative status. Therefore, the impact of genetic variants of the peroxisome proliferator-activated receptor-(PPAR-)gamma, tumor necrosis factor-(TNF-)alpha, interleukin (IL)-1, IL-6, apolipoprotein (Apo) A1, Apo A2, Apo A5, Apo E, glutathione peroxidases 1, 2, and 4, and selenoprotein P exposed to variations on diet composition is described.