Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending the POLCA production control system with centralized job release

Porto, Porto, Portugal *scarmo@dps.uminho. pt Abstract Paper aims: POLCA is a production control system that forwards to gateway manufacturing cells jobs that arrive to a manufacturing system. However, Workload Control literature shows benefits from using a centralized release pre-shop pool. Thus, the paper aims at investigating the performance impact of incorporating centralized job release into POLCA, in high-variety make-to-order production. Originality : This has not been investigated before. Research method: Simulation was used for experimentation. Main findings: In the case of the general flow shop, a manufacturing system configuration aligned with many real-world production systems, the investigation shows that adding centralized job release to the POLCA system, together with job release based on job workload, leads to a markedly improvement of job delivery performance. Implications for theory and practice: The results show theoretical and practical relevance of applying central job release to POLCA system, in high-variety production, due to its markedly positive
Ship Classification in SAR Images With Geometric Transfer Metric Learning

There are still many challenges to be resolved in the task of ship classification in synthetic aperture radar (SAR) images, such as limited number of labeled samples in SAR domain, large variance in the same subcategory, small variance among different subcategories, etc. Transfer metric learning (TML) has the potential to mitigate those issues in the domain of interest (target domain, TD) by leveraging knowledge/information from other related domains (source domain, SD). In this article, we proposed a novel TML method, termed as geometric transfer metric learning (GTML), which achieves discriminative information preservation (DIP), geometric structure preservation (GSP), and handles the domain shift (DS) simultaneously by integrating pairwise constraints (PC), joint distribution adaptation (JDA), and manifold regularization (MR) into a unified optimization function, aiming to make full use of their complementarity to improve SAR ship classification performance. In practice, we proposed two simple but effective optimization strategies, termed as GTML-A and GTML-R, to construct optimization function. We also proposed two solutions for two typical real-world application scenarios, that is, the task of: 1) zero-labeled sample (ZLS) and 2) scarce-labeled samples (SLS) in SAR domain. The experiments conducted on both tasks show that the proposed GTML outperforms most of state-of-the-art methods. Code is available at https://github.com/sky-Yongjie-Xu/geometric-transfer-metric-learning.
Functionally Essential Tubular Proteins Are Lost to Urine-Excreted, Large Extracellular Vesicles during Chronic Renal Insufficiency.

Background The 5/6 nephrectomy (5/6Nx) rat model recapitulates many elements of human CKD. Within weeks of surgery, 5/6Nx rats spontaneously exhibit proximal tubular damage, including the production of very large extracellular vesicles and brush border shedding. We hypothesized that production and elimination of these structures, termed large renal tubular extracellular vesicles (LRT-EVs), into the urine represents a pathologic mechanism by which essential tubule proteins are lost. Methods LRT-EVs were isolated from 5/6Nx rat urine 10 weeks after surgery. LRT-EV diameters were measured. LRT-EV proteomic analysis was performed by tandem mass spectrometry. Data are available via the ProteomeXchange Consortium with identifier PXD019207. Kidney tissue pathology was evaluated by trichrome staining, TUNEL staining, and immunohistochemistry. Results LRT-EV size and a lack of TUNEL staining in 5/6Nx rats suggest LRT-EVs to be distinct from exosomes, microvesicles, and apoptotic bodies. LRT-EVs contained many proximal tubule proteins that, upon disruption, are known to contribute to CKD pathologic hallmarks. Select proteins included aquaporin 1, 16 members of the solute carrier family, basolateral Na+/K+-ATPase subunit ATP1A1, megalin, cubilin, and sodium-glucose cotransporters (SLC5A1 and SLC5A2). Histologic analysis confirmed the presence of apical membrane proteins in LRT-EVs and brush border loss in 5/6Nx rats. Conclusions This study provides comprehensive proteomic analysis of a previously unreported category of extracellular vesicles associated with chronic renal stress. Because LRT-EVs contain proteins responsible for essential renal functions known to be compromised in CKD, their formation and excretion may represent an underappreciated pathogenic mechanism.
Conservation characteristics of grass and dry sugar beet pulp co-ensiled after different degrees of mixing

the lower 12.5 kg grass (LOWQ), (5) 0.625 kg BP mixed through the top 25 kg grass and 1.875 kg SBP mixed through the lower 25 kg grass (25/75), (6) BP placed in 0.5 kg layers beneath each 10 kg grass (LAYR), and (7) BP placed in a single layer under all of the grass (BOTM). Laboratory silos were filled and sealed, and stored at 15 ? C for 163 days. Effluent was collected and weighed from each silo throughout the ensilage period. At opening, silage composition and aerobic stability measurements were made. Total outflow of effluent was reduced (P< 0.001) by the addition of BP; LAYR had a greater effect (P< 0.001) than any of the other treatments. Effluent dry matter (DM) concentra tion was highest (P<0.05) for BOTM and lowest (P<0.01) for NONE. All treatments underwent similar lactic-acid dominant fermentations. Incorporation of BP with grass increased silage DM concentration (P<0.001), in vitro DM digestibility (P<0.05) and water soluble carbohydrate (P< 0.001) concentration and reduced acid detergent fibre (P< 0.001) concentration. Aerobic stability was similar across treatments and aerobic deterioration at 192 h was higher (P<0.05) for LOWQ, 25/75, LAYR and BOTM than
Probabilistic-Constrained Distributed Filtering for a Class of Nonlinear Stochastic Systems Subject to Periodic DoS Attacks

This paper investigates the probabilistic-constrained distributed filtering problem for a class of nonlinear stochastic systems with state constraints and cyber attacks. The considered cyber attacks are periodic denial-of-service (DoS) attacks which are modeled by a kind of periodic pulse-width-modulated (PWM) jamming signals. Different from some existing works, by using the proposed filter design method, the probability of the filtering error exceeding a threshold can be guaranteed below a certain level quantitatively. Furthermore, in order to economize the limited bandwidth resources, an event-triggered communication scheme (ETS) is designed for the data transmission. The aim of the problem addressed is to design a time-varying distributed filters such that: 1) the probability of the filtering error restricted to a given ellipsoid is larger than a specified value; and 2) the obtained ellipsoid threshold is minimized in the sense of matrix norm at each time point. To achieve this purpose, a recursive linear matrix inequality method is utilized and sufficient conditions are derived. Moreover, the filter parameters are explicitly determined in terms of the solution to certain matrix inequalities. Finally, the reliability and applicability of the proposed distributed filtering strategy are demonstrated by an illustrative example.
A Self-Calibrated 2-bit Time-Period Comparator-Based Synthesized Fractional-N MDLL in 22-nm FinFET CMOS

This article describes a synthesized fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> multiplying delay-locked loop (MDLL) implemented in Intel 22-nm FFL FinFET technology. A 2-bit time-period comparator (TPC) is proposed to adjust the ring oscillator frequency to suppress the spurs without introducing errors due to its inherent phase offset. A programmable delay is used to compare the periods of the MDLL output in time domain, reducing power consumption and making the design synthesizable. TPC 2-bit output achieves fast and robust locking without any initial calibration. The fractional multiplication ratio is achieved by using a digital-to-time converter (DTC) in the reference path with a replica of the digitally controlled oscillator (DCO) to automatically achieve a range of one DCO period. Any variation in DTC delay due to gain mismatch is corrected by a digital loop operating in the background. A detailed analysis of the small-signal model and noise of the proposed MDLL has also been presented. The core area of the synthesized design is 0.0052 mm<sup>2</sup> while operating over the frequency range from 1.2 to 3.8 GHz. The integrated rms jitter is 2.74 ps, and the spurs are below −47 dBc measured at 3.6175 GHz. Fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> MDLL power consumption is 3.19 mW with the figure of merit (FOM) of −226.3 dB.
Technical Performance Evaluation of Small-Scale Irrigation Scheme at Wondo Genet, Ethiopia

Expanding efficient irrigation development on various scales is one of the best alternatives to provide reliable and sustainable food security. However, many irrigation schemes in developing countries in general and particularly in Ethiopia are performing below capacity. This study was conducted to evaluate the performance of wosha soyama irrigation scheme at Wondo Genet SNNPRS, Ethiopia. The evaluation carried out using standard technical performance indicators including conveyance, application, water storage, water distribution uniformity efficiency and deep percolation ratio were used at the head, middle and tail reach of the scheme. The study shows that application efficiency was very which is 48.2% due to higher water loss through deep percolation. Conveyance efficiency, water storage, water distribution uniformity efficiency, and deep percolation ratio were revealed 55.6 89.8, 91.7, and 51.8%, respectively. The overall efficiency was also below the desired level which is 26%. Based on the above observation, adoption of water-saving practices such as deficit irrigation, surge and cutoff application to improving the application, frequent maintains of water conveyance system can enhance the conveyance efficiency of the scheme. Keywords : Irrigation scheme, Performance, evaluation, and technical indicators DOI: 10.7176/JRDM/68-02 Publication date: August 31 st 2020
Modeling of a Free Piston Energy Converter

ABSTRACT The Free Piston Energy Converter (FPEC) is a new type of automotive prime mover, offering high efficiency in conjunction with low emissions. The FPEC essentially consists of two opposing cylinders with a linear alternator incorporated in-between that produces electric power directly. As indicated by the name, there is no mechanical linkage that controls the motion of the piston in the FPEC. Instead the piston moves as a response to the forces acting upon it, which implies that the piston motion will depend on operating conditions. Furthermore, it is possible to vary the compression ratio in simple manners, without any hardware modifications. However, by eliminating the crank mechanism a simple and effective way of control is removed, and control issues are the major drawback of the FPEC. Modeling was performed in order to investigate the multi-fuel capability of the FPEC, which is an inherent benefit of its variable compression. A dynamic model predicting the movement, velocity, acceleration and frequency of the translator was used in an iterative manner, together with the commercial 1-D gas dynamic code BOOST, and detailed chemistry calculations in the SENKIN code. Reduced mechanisms of Diesel, gasoline, natural gas and hydrogen were used. Furthermore, detailed chemistry simulations employing a partially stirred reactor (PaSR) approach together with a detailed combustion mechanism (70 species participating in 306 reactions) of a Diesel oil surrogate, represented by a blend of n-heptane and toluene, were performed using the 3-D CFD code KIVA-3V. The effects of two consecutive cycles were modeled using a mesh representing the complete cylinder, whilst the effects of different injection schedules were studied using a sector mesh. Moreover, the effects of changing the scavenge port layout on the scavenging characteristics of a loop-scavenged engine were examined. Keywords: Free piston engine, combustion, variable compression, computational fluid dynamics (CFD), detailed chemistry
Influence of Asymmetrical Stator Axes on the Performance and Multi-Physical Field of Canned Permanent Magnet Machine for Vacuum Dry Pump With Vector Converter Supply

Limitation in manufacturing tolerances and assembly imperfections are the source of asymmetrical stator axes (ASA) defect, in particular, in a vacuum dry pumps (VDP) where the canned permanent magnet machines (CPMMs) are with fractional slot concentrated winding (FSCW). The performance and multi-physical field of CPMM in the vacuum environment is vulnerable to ASA. This work provides comprehensive research on the influence of ASA on the performance and multi-physical field of CPMM for VDP. This article establishes a mathematical model and Park transformation of a 1.6kW 6-pole/9-slot CPMM under the ASA condition. With the universality of ASA and not so strictly symmetrical stator axes as commonly expected, the experimental results demonstrated the significant impacts of ASA on the drive performance and multi-physical field, especially the inductance characteristics, the symmetry degree of stator current and flux linkage, rotor electromagnetic force and stator electromagnetic vibration of CPMM. The obtained conclusions can act as a significant reference for robust design and analysis of electric machines and drives.
Image Captioning using Deep Learning: A Systematic Literature Review

Auto Image captioning is defined as the process of generating captions or textual descriptions for images based on the contents of the image. It is a machine learning task that involves both natural language processing (for text generation) and computer vision (for understanding image contents). Auto image captioning is a very recent and growing research problem nowadays. Day by day various new methods are being introduced to achieve satisfactory results in this field. However, there are still lots of attention required to achieve results as good as a human. This study aims to find out in a systematic way that what different and recent methods and models are used for image captioning using deep learning? What methods are implemented to use those models? And what methods are more likely to give good results. For doing so we have performed a systematic literature review on recent studies from 2017 to 2019 from well-known databases (Scopus, Web of Sciences, IEEEXplore). We found a total of 61 prime studies relevant to the objective of this research. We found that CNN is used to understand image contents and find out objects in an image while RNN or LSTM is used for language generation. The most commonly used datasets are MS COCO used in all studies and flicker 8k and flicker 30k. The most commonly used evaluation matrix is BLEU (1 to 4) used in all studies. It is also found that LSTM with CNN has outperformed RNN with CNN. We found that the two most promising methods for implementing this model are Encoder Decoder, and attention mechanism and a combination of them can help in improving results to a good scale. This research provides a guideline and recommendation to researchers who want to contribute to auto image captioning.
Research on a Novel High-Efficiency Three-Phase Resonant Pole Soft-Switching Inverter

A high-efficiency three-phase resonant inverter is presented to optimize the performance of the inverter. When the auxiliary resonant circuit on each phase bridge arm works in the commutation process of the inverter, the voltage across resonant capacitors in parallel with main switches will form zero state periodically. In this case, main switches can achieve zero-voltage soft-switching. Meanwhile, auxiliary switches can achieve zero-current turn on and zero-voltage turn off during the operation of the auxiliary resonant circuit. Soft-switching can reduce switching loss and make the inverter work efficiently. The novelty of the inverter mainly reflects in the simple structure of the auxiliary circuit and the simple control method of auxiliary switches. This article analyzes the working mode of the circuit and the realization condition of soft-switching. Meanwhile, it discusses theoretical calculation of the power loss of the auxiliary circuit. The experimental results on the 3-kW prototype show that switching devices work in the soft-switching state. The efficiency of the prototype under the rated output power reaches 98.7%, which is higher than that of the same type of other soft-switching inverter. Therefore, the topology is valuable for the research and development of the high-performance three-phase inverter.
High-Density 3-D Stackable Crossbar 2D2R nvTCAM With Low-Power Intelligent Search for Fast Packet Forwarding in 5G Applications

By virtue of hardware parallelism, ternary content addressable memory (TCAM) is attractive for low-latency search for packet forwarding in routers for network communications in the upcoming fifth-generation (5G) era. However, the demand for high-density TCAM encounters remarkable costs in silicon area and power consumption. In this work, a 16-kb nonvolatile ternary content addressable memory (nvTCAM) test chip based on resistive memory (ReRAM) is demonstrated in 28-nm process with two techniques to deal with the aforementioned issues. First, the crossbar array with 2-diode-2-ReRAM (2D2R) nvTCAM cell is proposed to realize $>3\times $ improvement in storage density. The back-end-of-line integration of both diode and ReRAM resistor also allows for further 3-D stacking to realize larger storage density. Second, the machine learning concept is exploited to realize intelligent search operation. ${K}$ -means clustering is employed to allocate the entry storage and then the search of destination IP address can be targeted to a specific bank for low power. The evaluation shows >70% reduction in search energy with 2% overhead in silicon area for bank count of four. The test chip also achieves a match delay of 2 ns under nominal operating conditions.
The Semantic Prosody of Natural Phenomena in the Qur’an: A Corpus-Based Study

This thesis explores the Semantic Prosody (SP) of natural phenomena in the Qur’an and five of its prominent English translations [Pickthall (1930), Yusuf Ali (1939/ revised edition 1987), Arberry (1957), Saheeh International (1997), and Abdel Haleem (2004)]. SP, scarcely explored in Qur’anic research, is defined as ‘a form of meaning established through the proximity of a consistent series of collocates’ (Louw 2000, p.50). Theoretically, it is both an evaluative prosody (i.e., lexical items collocating with semantic word classes that are positive, negative, or neutral) and a discourse prosody (i.e., having a communicative purpose). Given the stylistic uniqueness of the Qur’an and considering that SP can be examined empirically via corpora, the present study explores the SP of 154 words associated with nature referenced throughout the Qur’an using Corpus Linguistics techniques. Firstly, the Python-based Natural Language Toolkit was used for the following: to define nature terms via WordNet; to disambiguate their variant forms with Stemmers, and to compute their frequencies. Once frequencies were found, a quantitative analysis using Evert’s (2008) five-step statistical analysis was implemented on the 30 most frequent terms to investigate their collocations and SPs. Following this, a qualitative analysis was conducted as per the Extended Lexical Unit via concordance to analyse collocations and the Lexical-Functional Grammar to find the variation of meanings produced by lexico-grammatical patterns. Finally, the resulting datasets were aligned to evaluate their congruency with the Qur’an. Findings of this research confirm that words referring to nature in the Qur’an do have semantic prosody. For example, astronomical bodies are primed to occur in predominantly positive collocations referring to glorifying God, while weather phenomena in negative ones refer to Day of Judgment calamities. In addition, results show that Abdel-Haleem’s translation can be considered the most congruent. This research develops an approach to explore themes (e.g., nature) via SP analysis in texts and their translations and provides several linguistic resources that can be used for future corpus-based studies on the language of the Qur’an.
Symmetric Displaced Coprime Array Configurations for Mixed Near- and Far-Field Source Localization

Conventionally, mixed near-field (NF) and far-field (FF) source localization is performed using a symmetric uniform linear array (ULA). Recently, two symmetric nonuniform linear arrays (NLAs), symmetric nested array (SNA) and compressed SNA (CSNA), have been developed to enhance the positioning performance by their advantages in the degrees of freedom and the array aperture over symmetric ULAs. In this article, we devise a new symmetric NLA, termed symmetric displaced coprime array (SDCA), to locate the NF and FF sources simultaneously. The SDCA configuration consists of three sparse ULAs with a certain displacement, implying there are two displaced coprime arrays with one common subarray. For a given number of sensors, the SDCA configuration is solely determined by a closed-form expression and its consecutive coarray ranges can also be analytically computed. In addition, we derive two optimum SDCA configurations by maximizing the number of the unique and consecutive lags in the difference coarray. Compared with the SNA and the CSNA, the SDCA provides more unique and consecutive virtual sensors, as well as a larger physical array aperture. Numerical experiments are presented to verify the superiorities of the SDCA configuration over the existing symmetric NLAs.
Prévision de l’activité économique au Québec et au Canada à l’aide des méthodes Machine Learning

Dans ce rapport nous appliquons de nombreuses techniques d’apprentissage automatique (Machine Learning) au probleme de prevision de l’activite economique au Quebec et au Canada. Six groupes de modeles sont consideres : les modeles a facteurs, regressions penalisees, regressions regularisees par sous-ensembles complets, regressions a vecteurs de support, forets d’arbres aleatoires et les reseaux de neurones. Tous ces modeles apportent differentes facons de gerer les grands ensembles de donnees et de generer les formes fonc-tionnelles hautement complexes. La prediction de 16 variables macroeconomiques quebecoises et canadiennes est evaluee dans un exercice de prevision hors echantillon. Les grands ensembles de donnees canadiennes et americaines sont consideres. Les resultats indiquent que les methodes machine learning, combinees avec les grands ensembles de donnees, ont un bon pouvoir predictif pour plusieurs variables d’activite reelle comme le PIB, la formation brute de capital fixe et la production industrielle. Les forets d’arbres aleatoires sont particulierement resiliantes, suivies des reseaux de neurones. La prevision des variables du marche d’emploi est amelioree par l’utilisation des regressions penalisees, simples ou par sous-ensembles complets. Les taux d’inflation sont previsibles avec les forets aleatoires et les regressions penalisees. Quant aux mises en chantier et le taux de change USD/CAD, les methodes machine learning n’arrivent pas a ameliorer la prevision ponctuelle, mais affichent des resultats interessants au niveau de la prevision de la direction future de ces variables.