Classical and alternative fuel mix optimization in cement production using mathematical programming

In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology.     

The EPH/Ephrin System in Bone and Soft Tissue Sarcomas’ Pathogenesis and Therapy: New Advancements and a Literature Review

Musculoskeletal sarcomas represent rare heterogenous malignancies of mesenchymal origin that can be divided in two distinct subtypes, bone and soft tissue sarcomas. Current treatment options combine the surgical excision of local tumors and multidrug chemotherapy to prevent metastatic widespread disease. Due to the grim prognosis that usually accompanies such tumors, researchers have attempted to shed light on the molecular pathways implicated in their pathogenesis in order to develop novel, innovative, personalized therapeutic strategies. Erythropoietin-producing human hepatocellular receptors (EPHs) are tyrosine-kinase transmembrane receptors that, along with their ligands, ephrins, participate in both tumor-suppressive or tumor-promoting signaling pathways in bone and soft tissue sarcomas. The EPH/ephrin axis orchestrates cancerous processes such as cell–cell and cell–substrate adhesion and enhances the remodeling of the intracellular cytoskeleton to stimulate the motility and invasiveness of sarcoma cells. The purpose of our study was to review published PubMed literature to extract results from in vitro, in vivo and clinical trials indicative of the role of EPH/ephrin signaling in bone and soft tissue sarcomas. Based on these reports, significant interactions between the EPH/ephrin signaling pathway and a plethora of normal and abnormal cascades contribute to molecular mechanisms enhancing malignancy during sarcoma progression. In addition, EPHs and ephrins are prospective candidates for diagnostic, monitoring and therapeutic purposes in the clinical setting against bone and soft tissue sarcomas.     

Trap‐State Suppression and Improved Charge Transport in PbS Quantum Dot Solar Cells with Synergistic Mixed‐Ligand Treatments

The power conversion efficiency of colloidal PbS‐quantum‐dot (QD)‐based solar cells is significantly hampered by lower‐than‐expected open circuit voltage (V_OC). The V_OC deficit is considerably higher in QD‐based solar cells compared to other types of existing solar cells due to in‐gap trap‐induced bulk recombination of photogenerated carriers. Here, this study reports a ligand exchange procedure based on a mixture of zinc iodide and 3‐mercaptopropyonic acid to reduce the V_OC deficit without compromising the high current density. This layer‐by‐layer solid state ligand exchange treatment enhances the photovoltaic performance from 6.62 to 9.92% with a significant improvement in V_OC from 0.58 to 0.66 V. This study further employs optoelectronic characterization, X‐ray photoelectron spectroscopy, and photoluminescence spectroscopy to understand the origin of V_OC improvement. The mixed‐ligand treatment reduces the sub‐bandgap traps and significantly reduces bulk recombination in the devices.     

Distributed filtering over sensor networks for autonomous navigation of UAVs

The paper studies the problem of localization and autonomous navigation of a multi-UAV system with the use of Distributed Filtering methods (DF). It is considered that m UAV (helicopter) models are monitored by n different ground stations. The overall concept is that at each monitoring station a filter is used to track each UAV by fusing measurements which are provided by various UAV sensors, while by fusing the state estimates from the distributed local filters an aggregate state estimate for each UAV is obtained. In particular, the paper proposes first the extended information filter (EIF) and the unscented information filter (UIF) as possible approaches for fusing the state estimates provided by the local monitoring stations, under the assumption of Gaussian noises. The EIF and UIF estimated state vector is in turn used by a flatness-based controller that makes the UAV follow the desirable trajectory. Moreover, the distributed particle filter (DPF) is proposed for fusing the state estimates provided by the local monitoring stations (local filters). The motivation for using DPF is that it is well-suited to accommodate non-Gaussian measurements. The DPF estimated state vector is again used by the flatness-based controller to make each UAV follow a desirable flight path. Finally, a derivative-free implementation of the extended information filter (DEIF) is introduced aiming at obtaining more accurate estimates of the UAV state vector in real-time. The performance of the EIF, of the UIF, of the DPF and of the DEIF is evaluated through simulation experiments in the case of a 2-UAV model monitored and remotely navigated by two local stations.     

Solid-State Thin-Film Broadband Short-Wave Infrared Light Emitters

Solid-state broadband light emitters in the visible have revolutionized today's lighting technology achieving compact footprints, flexible form factors, long lifetimes, and high energy saving, although their counterparts in the infrared are still in the development phase. To date, broadband emitters in the infrared have relied on phosphor-downconverted light emitters based on atomic optical transitions in transition metal or rare earth elements in the phosphor layer resulting in limited spectral bandwidths in the near-infrared and preventing their integration into electrically driven light-emitting diodes (LEDs). Herein, phosphor-converted LEDs based on engineered stacks of multi-bandgap colloidal quantum dots (CQDs) are reported as a novel class of broadband emitters covering a broad short-wave infrared (SWIR) spectrum from 1050–1650 nm with a full-width-half-maximum of 400 nm, delivering 14 mW of optical power with a quantum efficiency of 5.4% and power conversion efficiency of 13%. Leveraging the electrical conductivity of the CQD stacks, further, the first broadband SWIR-active LED is demonstrated, paving the way toward complementary metal–oxide–semiconductor integrated broadband emitters for on-chip spectrometers and low-cost volume manufacturing. SWIR spectroscopy is employed to illustrate the practical relevance of the emitters in food and material identification case studies.     

Ultrahigh Carrier Mobility Achieved in Photoresponsive Hybrid Perovskite Films via Coupling with Single‐Walled Carbon Nanotubes

Organolead trihalide perovskites have drawn substantial interest for photovoltaic and optoelectronic applications due to their remarkable physical properties and low processing cost. However, perovskite thin films suffer from low carrier mobility as a result of their structural imperfections such as grain boundaries and pinholes, limiting their device performance and application potential. Here we demonstrate a simple and straightforward synthetic strategy based on coupling perovskite films with embedded single‐walled carbon nanotubes. We are able to significantly enhance the hole and electron mobilities of the perovskite film to record‐high values of 595.3 and 108.7 cm² V^?1 s^?1, respectively. Such a synergistic effect can be harnessed to construct ambipolar phototransistors with an ultrahigh detectivity of 3.7 × 10¹⁴ Jones and a responsivity of 1 × 10⁴ A W^?1, on a par with the best devices available to date. The perovskite/carbon nanotube hybrids should provide a platform that is highly desirable for fields as diverse as optoelectronics, solar energy conversion, and molecular sensing.     

A Cascade Visual Front End for Speaker Independent Automatic Speechreading

We propose a three-stage pixel-based visual front end for automatic speechreading (lipreading) that results in significantly improved recognition performance of spoken words or phonemes. The proposed algorithm is a cascade of three transforms applied on a three-dimensional video region-of-interest that contains the speaker's mouth area. The first stage is a typical image compression transform that achieves a high-energy, reduced-dimensionality representation of the video data. The second stage is a linear discriminant analysis-based data projection, which is applied on a concatenation of a small amount of consecutive image transformed video data. The third stage is a data rotation by means of a maximum likelihood linear transform that optimizes the likelihood of the observed data under the assumption of their class-conditional multivariate normal distribution with diagonal covariance. We applied the algorithm to visual-only 52-class phonetic and 27-class visemic classification on a 162-subject, 8-hour long, large vocabulary, continuous speech audio-visual database. We demonstrated significant classification accuracy gains by each added stage of the proposed algorithm which, when combined, can achieve up to 27% improvement. Overall, we achieved a 60% (49%) visual-only frame-level visemic classification accuracy with (without) use of test set viseme boundaries. In addition, we report improved audio-visual phonetic classification over the use of a single-stage image transform visual front end. Finally, we discuss preliminary speech recognition results.     

A derivative-free distributed filtering approach for sensorless control of nonlinear systems

This article examines the problem of sensorless control for nonlinear dynamical systems with the use of derivative-free Extended Information Filtering (EIF). The system is first subject to a linearisation transformation and next state estimation is performed by applying the standard Kalman Filter to the linearised model. At a second level, the standard Information Filter is used to fuse the state estimates obtained from local derivative-free Kalman filters running at the local information processing nodes. This approach has significant advantages because unlike the EIF (i) is not based on local linearisation of the nonlinear dynamics (ii) does not assume truncation of higher order Taylor expansion terms thus preserving the accuracy and robustness of the performed estimation and (iii) does not require the computation of Jacobian matrices. As a case study a robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the EIF is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered sensorless control scheme is evaluated through simulation experiments.