Miracle-dz/UniFAR-FTUs huggingface dataset:
First Detection of Proteus cibarius sp. of clinical significance

The rapidly changing resistance pattern of multidrug resistant Gram negative bacilli over the last decade is challenging. Their ability to develop resistance to β-lactam antibiotics and produce extended spectrum β-lactamases is most current clinical issue in antibiotic resistance, globally. This study was carried out to identify Gram negative bacterial responsible for β-lactam antibiotics treatment failure in an 88-year-old patient not responding to cephalosporin treatment. Wound sample was obtained from patient with long term infected diabetic wound and cultured on MacConkey agar. Antimicrobial susceptibility and double disk synergistic test were carried out to ascertain antibiotics resistance profile of isolate and production of extended spectrum beta-lactamases, respectively. Isolate was presumptively identified using standard biochemical tests. Genomic DNA extraction, amplification of the 16S rRNA gene with 27F (f5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (r5′-GGTTACCCTTGTTACGACTT-3′) primers and sequencing analysis were carried out to confirm presumptive identification. Nucleotides sequences were aligned with bioedit 7.2. 5 and aligned sequences were compared with similar sequences on Gene bank. Construction of phylogenetic tree and evolutionary analysis of related sequences was conducted in MEGA7, using the Neighbor-Joining method. Isolate was identified to be Proteus cibarius strain ADE 1 with ascension number MH037129. Proteus cibarius strain ADE 1 was resistant to all β-lactam antibiotics and positive for ESBL production. This is the first report on Proteus cibarius associated with human pathogenicity. It is also the first detection of antibiotics resistance and ESBL production in Proteus cibarius. Proteus cibarius has a pathogenic potential and is an emerging opportunistic pathogen capable of spreading multiple antibiotics resistance.
Efficient approach for EEG‐based emotion recognition

Identi ﬁ cation of human emotion involving electroencephalogram (EEG) signals has become an emerging ﬁ eld in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an ef ﬁ cient framework based on deep learning concept for automatic identi ﬁ cation of human emotion from EEG signals. In the proposed framework, the signals are pre-processing for removing noises by low-pass ﬁ ltering and then delta rhythm is extracted. After that, the extracted rhythm signals are converted into the EEG rhythm images by employing the continuous wavelet transform and then deep features are discovered by using a pre-trained convolutional neural networks model. Afterwards, MobileNetv2 is used for deep feature selection to obtain the most ef ﬁ cient features and ﬁ nally, long short term memory method is employed for classi ﬁ cation of selected features. The proposed methodology is tested on ‘ DEAP EEG data set ’ (publicly available). This study considers two emotions namely ‘ Valence ’ and ‘ Arousal ’ for classi ﬁ cation. The experimental results demonstrate that the proposed approach produced accuracies of 96.1% for low/ high valence and 99.6% for low/high arousal classi ﬁ cation. A further comparison of the proposed method is also carried out and it is seen that the proposed method outperforms other compared methods.
Unsupervised spectral–spatial multiscale feature learning framework for hyperspectral image classification based on multiple kernel self-organizing maps

Abstract. Hyperspectral image (HSI) analysis is a growing area in the community of remote sensing, particularly with images exhibiting high spatial and spectral resolutions. Multiple kernel learning (MKL) has been proposed and found to classify HSIs efficiently owing to its capability for handling diverse feature fusion. However, constructing base kernels, selecting key kernels, and adjusting their contributions to the final kernel remain major challenges for MKL. We propose a scheme to generate effective base kernels and optimize their weights, which represent their contribution to the final kernel. In addition, both spatial and spectral information are utilized to improve the classification accuracy. In the proposed scheme, the spatial features of HSIs are introduced through multiscale feature representations that preserve the relationship between the classification process and the pixel context. MKL and self-organizing maps (SOMs) are integrated and used for the unsupervised classification of HSIs. The weights of both the base kernels and neural networks are simultaneously optimized in an unsupervised manner. The results indicate that the proposed MKL-SOM scheme outperforms state-of-the-art algorithms, particularly when applied to large HSIs. Moreover, its ability to fuse multiscale features, especially in large HSIs, is useful for various analysis tasks.
PCAOB Inspections and Audit Fees: An Analysis of Inspection Rounds of Small Audit Firms

This study investigates the impact on audit pricing of PCAOB inspection outcomes on the quality control systems of small audit firms. Quality control deficiencies (QCDs) provide a strong signal about audit quality, which is expected to lead to price changing. We first document that audit fees in the pre-inspection period were higher for small audit firms without QCDs, implying that quality differences were already partially known and priced in the market. Next, we find evidence that in the post-inspection period audit fees further increase for firms without QCDs, while there is limited evidence for fee increases for firms with remediated QCDs, suggesting changes in audit firm reputation. Interestingly, we do not find support for fee changes for audit firms with disclosed quality control deficiencies. Finally, the magnitude of change in audit fees decreases over time. Collectively, these findings contribute to our understanding of audit quality differences amongst small audit firms and the functioning of the small audit firm market.
Multi-Hop Dynamic Map Data Propagation Algorithm for Clustered Vehicular Networks

To ensure the driving safety in vehicular network, it is necessary to construct a local dynamic map (LDM) for an extended range. Using the standard vehicular communication protocols, however, vehicles can construct the LDM for only one-hop range. Constructing large-scale LDM is highly challenging because vehicles randomly change their position. This paper proposes a dynamic map propagation (DMP) method, which builds a large aggregated LDM data using a multi-hop communication. To reduce the data overhead, we introduce an efficient clustering method based on a half-circle of the forwarder’s wireless range. The DMP elects one forwarder per cluster, which constructs LDM and forwards it to a neighbor cluster. The inter-cluster interference is minimized by allocating a different transmit window to each cluster. DMP copes with a dynamic environment by frequently re-electing the forwarders and their associated transmission windows. Simulation results reveal that DMP enhances the forwarders’ reception ratio by 20%, while extending LDM dissemination range by 29% over a previous work.
PENGEMBANGAN ANALISIS SENTIMEN PADA TWITTER UNTUK LAYANAN OJEK ONLINE MENGGUNAKAN RULE BASED

Ojek online terus berupaya memberikan pelayanan terbaik untuk memuaskan pelaggan mereka seperti memberikan sejumlah diskon di layanan yang ada ataupun memberikan jenis pelayanan yang baru. Dengan perkembangan di setiap layanan, membuat minat masyarakat terhadap Gojek dan Grab semakin tinggi. Apresiasi juga sering diberikan terhadap dua perusahaan ojek online tersebut. Media social merupakan wadah masyarakat untuk mengaspresiasi Gojek dan Grab, baik berupa pujian, saran maupun kritik. Salah satu media social yang menjadi wadah apresiasi adalah twitter. Opini yang disampaikan pelanggan melalui akun resmi twitter Gojek yakni @gojekindonesia dan Grab yakni @GrabId bisa dijadikan alat ukur kepuasan pelanggan terhadap layanan ojek online di kedua penyedia layanan ojek online tersebut dengan menganalisis twitter mereka. Opini yang berupa komentar pelanggan akan di Analisa, apakah opini tesebut bisa di klasifikasikan menjadi positif, negative ataupun netral. Analisis sentiment ini menggunakan rule based untuk mengukur klasifikasi opini pelanggan. Data yang digunakan yakni 1000 tweet. Pengujian dilakukan menggunakan k-fold cross validation dengan hasil akurasi klasifikasi rule based ini mendapatkan akurasi terbaik 79%,Precision terbaik 100%, Recall terbaik 80% dan f-measure terbaik 79%
An occupant-centric method for window and shading design optimization in office buildings

Building performance optimization is a powerful technique that assists designers in identifying optimal design solutions. However, occupant-related uncertainty is considered a major challenge that can influence the credibility of the selected optimal design. This paper proposes an occupant-centric method for window and shading design optimization in office buildings that assists designers in evaluating and handling the impact of occupant-related assumptions. The method consists of three major steps: generating occupant scenarios, conducting occupant-centric mathematical optimization, and deriving rules for selecting optimal design parameters using decision trees. An office building model with numerous possible window-related parameters was used to demonstrate the proposed optimization process. Sixty-four different occupant scenarios were considered. The results of the optimization process demonstrated that occupant-related assumptions are crucial in determining optimal window and shading design, as different occupant scenarios led to different optimal solutions. The study findings also provided insights to building designers regarding classifying and selecting window and shading design parameters under varying occupant scenarios.
Barrakuda: A Hybrid Evolutionary Algorithm for Minimum Capacitated Dominating Set Problem

The minimum capacitated dominating set problem is an NP-hard variant of the well-known minimum dominating set problem in undirected graphs. This problem finds applications in the context of clustering and routing in wireless networks. Two algorithms are presented in this work. The first one is an extended version of construct, merge, solve and adapt, while the main contribution is a hybrid between a biased random key genetic algorithm and an exact approach which we labeled Barrakuda. Both algorithms are evaluated on a large set of benchmark instances from the literature. In addition, they are tested on a new, more challenging benchmark set of larger problem instances. In the context of the problem instances from the literature, the performance of our algorithms is very similar. Moreover, both algorithms clearly outperform the best approach from the literature. In contrast, Barrakuda is clearly the best-performing algorithm for the new, more challenging problem instances.
Global Estimation of the Elasticity of “International Tourist Arrivals/Income from Tourism”

The prevailing discourse relates the widespread belief that an increase in the volume of tourist arrivals is a clear sign of success for tourism destination managers. According to a logarithmic regression function that estimates the inverse demand-income elasticity, it has been found that tourism flows are not the best measure to use in assessing the contribution of tourism to economic growth. Rather, the volume of income that is generated from these tourists should be considered. Furthermore, based on an analysis of correlations, it was found that the territories having the greatest efficiency in generating income from tourism are more competitive in terms of tourism. The results obtained may contribute to changing the approach used by both private and public bodies, and they may also help them to re-focus their business planning and policymaking so as to emphasize an increase in income level and not in volume of tourists.
Magnitud, distribución y tendencia espacial de homicidios registrados en el Área Metropolitana de Guadalajara: análisis comparativo entre 2013 y 2018

Los homicidios son un problema prioritario de seguridad pública en México, dada la magnitud e impacto que los caracteriza; sin embargo, raramente se estudian como causa de muerte y hecho violento desde una perspectiva geográfica y de salud pública. A partir de fuentes secundarias (prensa escrita y medios electrónicos) se llevó a cabo un análisis comparativo de homicidios ocurridos en 2013 y 2018 por colonias y municipios del Área Metropolitana de Guadalajara (AMG), Jalisco; los homicidios fueron georreferenciadas considerando el lugar donde ocurrió la agresión y analizados mediante medidas de tendencia central y dispersión con el objetivo de determinar la magnitud, distribución y tendencia espacial. Como resultado, de 2013 (n= 465) a 2018 (n= 988) la cantidad de homicidios ocurridos en el AMG aumentó, salvo en el municipio de Zapopan. En consecuencia, en 2018 la tasa de homicidios osciló entre 6.2 (Tonalá) y 50.7 por 100 mil (Tlajomulco de Zúñiga), un incremento hasta 4 veces mayor respecto al 2013. Además, el número de homicidios analizado se concentró territorialmente en el 16% (2013) y 24% (2018) del total de colonias del AMG, desplazándose del noroeste-sureste hacia el norte-sur, respectivamente. En este sentido, los resultados implican la identificación de las colonias de mayor riesgo de homicidio. Por lo tanto, en estas zonas es donde se requiere (re) dirigir y mejorar políticas públicas para modificar factores de riesgo, actividades o comportamientos ilícitos que generalmente preceden la ocurrencia de homicidios.
Effect of kinematic interaction on seismic response of offshore wind turbines on monopiles

Interest in renewable energy over the past decade has motivated a remarkable research on offshore wind energy. Due to the environmental loading conditions in the early developments of offshore wind farms, the focus of the research and engineering has been on aerodynamic and hydrodynamic subjects. However, earthquake has turned out to be a major design concern in seismic areas such as East Asia, Southern Europe, and United States. The topics include, among others, nonlinear response of foundations, soil liquefaction, and their impacts on foundation performance. An important topic in seismic soil‐structure interaction (SSI) analysis is the kinematic seismic response of foundations. This information is crucial for analyses based on the substructuring approach where both kinematic response and foundation impedances are key parameters. While considerable research has been spent on the latter topic, very little has been reported on the kinematic interaction. Large monopoles with low aspect ratios tend to rotate much more than regular piles in pile group foundations due to seismic waves. The rotation leads to additional load on the tower and the turbine. This article uses a rigorous numerical model for monopole‐soil interaction in layered soil and computes both rotational and horizontal responses at the pile head (seabed) as functions of frequency. A suite of generic homogeneous and heterogeneous soil profiles with shear moduli representative of clay (generally linear) and sand (generally parabolic) are considered in these analyses. Through representative analyses, both in frequency domain and time domain, it is illustrated how the kinematic interaction influences the earthquake loads imparted to an offshore wind turbine (OWT).
Transistor‐level optimization methodology for the complete design of switched‐capacitor filter circuits

This paper presents an automatic procedure for the design and optimization of switched‐capacitor (SC) filters, including the automatic sizing of transistors in the amplifiers and switches. The optimization procedure is based on genetic algorithms (GAs) where the circuit's fitness is first computed using equations describing the filter's transfer function and then using transient simulations. These equations are obtained using a fast numerical methodology that takes into consideration the electrical behavior of all components in the circuit. The poles and zeros of the SC filter's transfer function are computed using a system of differential equations, obtained from the inspection of the circuit. This system describes the filter's behavior for all switch combinations, including the non‐ideal effects of the transistors in the switches and amplifiers. Due to the low computational effort and accuracy of this methodology, it is possible to use a large population in the GA. After finding a solution through equations, the more computationally intensive SPICE transient simulations are used to fine‐tune the solution, with a much smaller population in the GA. Taking advantage of the equations' low computational load and accuracy, process, voltage, temperature (PVT) corners and mismatch errors optimizations are also performed, allowing the chromosomes fitness to be calculated taking into consideration multiple cases, thus resulting in a low sensitivity design.
Complex conductivity signatures of microbial induced calcite precipitation, field and laboratory scales

Soil stabilization processes aim at enhancing soil's engineering properties. Although the concept is straightforward, it involves physical and chemical changes to the subsurface that could result in local environmental changes. Compared to conventional soil stabilization methods (such as cement grouting), bio-mediated soil stabilization, such as microbial-induced calcite precipitation (MICP), offers the opportunity to minimize environmental impact, but the underlying processes need to be well understood for proper applications. Accurate characterization and long-term monitoring are paramount for the success of soil improvement, especially MICP treatments. Spectral induced polarization (SIP), an established geophysical method, has shown to be sensitive to MICP processes and products (e.g. calcite). In this work, we performed a two-phase study to explore SIP's suitability as a monitoring tool. Phase 1 involved a laboratory scale MICP study under controlled conditions and phase 2 a pilot field scale study. In the laboratory, MICP was induced through the introduction of ureolytic microorganisms, while in the field, indigenous soil microbes were stimulated to promote ureolysis. In both cases, traditional geochemical monitoring, along with spatiotemporally dense SIP monitoring, were performed. Over the course of the laboratory study, SIP successfully tracked the MICP progress as well as the calcite precipitation behaviour. Similarly, the SIP results of the field scale study showed to be sensitive to the subsurface changes in response to MICP. SIP offered spatiotemporally rich information on the MICP progress and process status. The similarity between observed signal trends in the laboratory and field in this study clearly proved that SIP signals from MICP in controlled laboratory environments can be successfully used to study field MICP applications despite scale and complexity differences.
Phase-space behavior of physical nonlinear coherent states

In this paper, we construct the generalized su(1, 1) nonlinear coherent states for particular physical systems. We study the behavior of the Wigner function for coherent states built from differential operators that satisfy either a generalized Heisenberg algebra or a generalized su(1, 1) nonlinear algebra. The systems studied are the harmonic oscillator and those ones associated with Pöschl-Teller potentials. The results show that the Wigner function for the associated nonlinear coherent states is positive for small values of their convergence radius. Furthermore, the Wigner function for these coherent states depends on the corresponding algebraic structure.
ALAT PENGERING KERUPUK BERBASIS ARDUINO UNO MENGGUNAKAN METODE FUZZY

Abstrak— Pengeringan kerupuk merupakan suatu proses untuk pembuatan kerupuk. Namun proses pengeringan kerupuk yang biasanya memakai sinar matahari sering terhambat dikarenakan musim yang tidak menentu, sehingga produksi kerupuk akan berhenti dan mengakibatkan pemilik usaha kerupuk merugi. Maka dari itu dibutuhkan sistem untuk pengeringan kerupuk berbasis internet of things (IoT). Penelitian ini menggunakan sensor suhu dan kelembaban DHT11 sebagai input data. Perangkat Arduino Uno sebagai pembaca data dan kontrol power dari lampu halogen sebagai sumber panas pada prototipe pengering kerupuk. Dashboard pengering kerupuk digunakan untuk menerima data yang dikirim arduino uno dan mengolah data tersebut menggunakan metode. Perangkat alat pengering kerupuk dan dashboard pengering kerupuk berkomunikasi melewati thingspeak sebagai penerima data dan penyalur data. Metode yang digunakan untuk mengolah data adalah metode Fuzzy Sugeno dengan dua parameter utama yaitu suhu dan kelembaban. Pada 9 kali pengujian menunjukkan tingkat keberhasilan perhitungan metode Fuzzy Sugeno adalah 100%. Sistem ini berhasil melakukan monitoring dan controlling pada prototipe pengering kerupuk berbasis IoT yang bisa memantau dari jarak jauh dan secara otomatis mengelola suhu pada prototipe pengering kerupuk agar tetap stabil, sehingga mempermudah produsen kerupuk untuk memantau kondisi pengering kerupuk meskipun berada jauh dari tempat pengeringan kerupuk selama alat dan produsen kerupuk terhubung ke jaringan internet.
Anti-tumor effect of trametinib in bladder cancer organoid and the underlying mechanism

ABSTRACT Bladder cancer (BC), a main neoplasm of urinary tract, is usually inoperable and unresponsive to chemotherapy. As a novel experimental model for muscle-invasive BC, we previously established a culture method of dog BC organoids. In the present study, the detailed in vitro and in vivo anti‐tumor effects of trametinib were investigated by using this model. In each BC organoid strain, epidermal growth factor receptor (EGFR)/ERK signaling was upregulated compared with normal bladder cells. Trametinib even at a low concentration inhibited the cell viability of BC organoids and the activation of ERK through decreasing expression of c-Myc, ELK1, SIK1, and PLA2G4A. Trametinib arrested cell cycle of BC with few apoptosis. Dual treatment of BC organoids with trametinib and YAP inhibitor, verteporfin extremely inhibited the cell viability with apoptosis induction. Moreover, trametinib induced basal to luminal differentiation of BC organoids by upregulating luminal markers and downregulating basal ones. In vivo, trametinib decreased the tumor growth of BC organoids in mice and the xenograft-derived organoids from trametinib-administered mice showed enhanced sensitivity to carboplatin due to MSH2 upregulation. Our data suggested a new strategy of trametinib-YAP inhibitor or trametinib-carboplatin combination as a promising treatment of BC.
Earnings management in V4 countries: the evidence of earnings smoothing and inflating

Abstract Earnings management is one of the most challenging, debated and controversial topics in finance and financial management. Organisational, legislative, and social norms regarding the ethics of earnings management may vary significantly, with the views of top management and economic environment playing significant roles in shaping the organisational social norm. Thus, the aim of this research is to assess if earnings management is the common practice of enterprises within V4 countries. A novel approach of the selection process to assess the ability of selected earnings management detection models was applied. To highlight discrepancies and similarities among the countries, the non-parametric alternative of analysis of variance was applied. The research confirmed that enterprises do manipulate earnings, typical is upward manipulation. Furthermore, the research unveiled the extent of manipulation with earnings in unique country samples and thus emphasised the importance of both corporate and national ethical principles and managerial decisions, which affect corporate financial reporting quality. However, it is increasingly challenging to identify various aspects and incentives which forced enterprises operating at the global level to smooth and inflate earnings to improve earnings presented in the financial statements and to misinterpret financial results.