Miracle-dz/UniFAR-FTUs huggingface dataset:
Advantages, limitations, and solutions in the use of ERP systems: a case study in the hospitality industry

Enterprise Resource Planning (ERP) systems have emerged as solutions oriented to manage organiza-tions’ resources in an integrated way. They allow the automation of department activities, make information available to users at the right time, and support more accurately their decision-making needs. However, although the implementation of these systems has brought considerable benefits to users, they do not cover all processes from all industries. Many organizations have recognized this limitation and consequently felt the need to implement specific solutions to their industry, sector, or line of business. From the collected case study business drivers and objectives, the authors analyze the advantages and limitations of ERP Systems in the hospitality industry in order to understand how this industry uses ERP Systems and solves the challenge of integrating information spread through several heterogeneous information systems.
Optimal planning and operation of energy storage systems in radial networks for wind power integration with reserve support

Though energy storage system (ESS) is a promising approach to alleviate the variability of non-dispatchable wind power and other forms of renewable energy sources, its high investment cost has impeded its wide deployment. Aiming at exploiting the arbitrage benefit of ESS in reserve market and raising revenue of shareholders, this study explores the optimal planning and operation of ESS in radial networks. Besides load balancing, ESS is used to provide three kinds of operating reserve services in presence of high wind power penetration, including spinning reserve, upward and downward regulation reserves. In light of the capacity limitation of ESS, the time duration of reserve provision has been taken into account. In the proposed model, unit commitment and AC optimal power flow (AC-OPF) are combined together over sequential time series to find the optimal location and size of ESSs. In order to reduce the computational complexity, the extended DistFlow model of AC-OPF is adopted to convert the problem into a mixed-integer second-order cone programming. Numerical studies on the IEEE 34-bus distribution test feeder are used to investigate the effects of ESS with respect to various penetration levels of wind power and load scales.
Preliminary screening and microbiological evaluation on the environmental
hygiene for galley equipment, safety equipment and cabin common facilities of a
local airline in Malaysia

Over the last two decades, only a handful of research have been conducted pertaining to food safety in the aviation industry. The gap of knowledge in in-flight food safety literature has long been silenced. Therefore, it was the objective of this study to conduct preliminary screenings to evaluate the environmental quality of the service equipment (GE), safety equipment (SE) and common facilities (CF) within the confinement of commercial aeroplanes in Malaysia. A total of 112 swab samples (n = 112) were analyzed to detect the prevalence of Escherichia coli, Vibrio, Salmonella and coliforms using conventional microbiological methods. The qualitative aerobic mesophilic plate count revealed that 99 (88.39%) and 13 (11.61%) were reported as positive and negative samples, respectively. It was reported that all 17 samples taken from the long-haul flight were positive, with 8 (9.14%), 4 (3.57%) and 5 (4.46%) samples belonging to the GE, SE, and CF, respectively. Forty-five positive swab samples taken from medium-haul flight sectors showed that 30 (26.79%), 8 (9.14%), 7 (6.25%) samples were that of the GE, SE, and CF, respectively. Meanwhile, 19 (16.96%), 8 (9.14%), and 10 (8.93%) of the shorthaul flights samples were that of the GE, SE, and CF, respectively. It was therefore concluded that GE, SE and CF were reported at 57 (50.89%), 20 (17.86%) and 22 (19.64%), respectively. In view of the large numbers and high percentages of positive sample results, it is our opinion that the cleaning, sanitizing and disinfecting procedures of the galley equipment, safety equipment and common facilities are revisited. The assurance in conformance to the hazard analysis and critical control points (HACCP) management system may enhance the safety and reliability of all stakeholders especially the flight attendants who are the final custodians of the environmental hygiene and that of themselves.
DEVELOPMENT OF COUNTERMEASURES FOR LATERAL FLOW OF LIQUEFIED GROUND

我が国の大都市圏の湾岸部には造成年代の古い埋立地が広く存在し，それらの多くは護岸，地盤とも液状化対策が施工されておらず，将来，これらの地域を襲う大規模地震により，液状化と側方流動を生じる可能性が極めて高い．既存の埋立地，特に危険物施設や高圧ガス施設が建設されている埋立地の安全を図ることは，近傍の大都市圏市街地の安全性にとっても重要な課題である．本研究では，液状化地盤の側方流動を防止するための効果的，経済的な工法の開発を目的として，鋼矢板による地中壁，セメント系材料による地盤改良および抑止杭など各種の対策工法の変位抑制効果を遠心力載荷場での模型実験で比較検証した．さらに，実験結果をもとに対策工に作用する外力評価を行い，対策工の設計と施工のための基礎的な知見を得た．
Average Based-FTS Markov Chain With Modifications to the Frequency Density Partition to Predict COVID-19 in Central Java

Every day, new Covid-19 positive cases are discovered in Central Java. Many research has used various methodologies to try to forecast new positive instances. The fuzzy time series (FTS) approach is one of them. Many FTS are now in development, including the FTS Markov Chain. The duration of the gap in the FTS must be determined carefully because it will affect the FLR, which will be used to estimate the forecast value. The average-based method can be used to determine the optimum interval length; however, other research use frequency density partitioning to determine the optimal interval length in order to produce superior predicting values. The goal of this research is to improve the accuracy of forecasting values by modifying the frequency density partition on the average based-FTS Markov Chain. The approach utilized is average-based, with the length of the interval determined by the average, the forecast value determined by the FTS Markov Chain, and the frequency density partition modified to provide the ideal interval. The average-based FTS Markov Chain approach with adjustments to the frequency density partition achieves an accuracy rate of 89.3 percent, according to the findings of this study. Because changes to the frequency density partition can produce a good level of accuracy in forecasting new positive cases of Covid-19 in Central Java, it is hoped that this modification of the frequency density partition on the average-based FTS Markov chain can be used as a model for forecasting in fields other than new positive cases. Covid-19.
Influence of pH of co-cultivation medium, claforan® concentration, co-cultivation period, pre-selection duration and selection regime on tomato transformation

Tomato fruit is enriched with lycopene and β-carotene which is an initiator of vitamin A, whereas lycopene has a shielding effect against prostate cancer. The tomato crop is very sensitive to cold stress which is a great threat to the yield of cultivated tomato. Genetic transformation offers the basis of foreign genes integration in the plant for the improvement of crop. Therefore, the present study was carried out to optimize different parameters of gene transformation in three tomato genotypes. These three tomato genotypes were transformed for cold tolerance via DREB1A gene with inducible promoter (Lip9). Optimal conditions to achieve 14.16-16.45% transformation efficiency (TE) was found as pH ranged between 5.65. 7 for co-cultivation media, 500 mg/l claforan®, 6-d pre-selection period, 48-h co-cultivation duration and 35 mg/l hygromycin. Specific gene primers were used for screening and real time semi quantitative PCR assay that proved the insertion of DREB1A gene and its expression in T0 and T1 transformed lines. During the cold stress period, the increase in expression pattern was confirmed by the results of semi-quantitative PCR. Under normal growth settings, there were no considerable differences among transformants and non transformants based on morphological characteristics. Convincingly, DREB1A gene over expression in transgenic tomato plants increased the protection and delivered cold tolerance.
Intragenerational Mobility of the Top Earners in Israel before and after the 2008 Global Financial Crisis

In this study we explore how macroeconomic shocks affect the intragenerational mobility of the top income earners. We analyze a panel data on the income levels of 22,601 individuals in Israel for the period between 1999-2013, and use the Global Financial Crisis of 2008 as a case study, as it occurred in the middle of our analyzed period. We use the common intragenerational mobility measurements used in the literature. However, since these are usually descriptive, we improve them by using different statistical tests, which enable us to estimate the statistical significance of these measurements. Our results show that the Global Financial Crisis had a minor and transitory effect, if any, on the intragenerational mobility of the top income earners in Israel. The implications of the expected global economic crisis due to the coronavirus pandemic, for the top income earners is discussed.
Effect of compound fertilizers as Foliar Spray with Application Bio-Fertilizers on Growth, Yield and quality of spinach Plants

Two field experiments were completed at particular plantation in Qalyobia Governorate, Egypt through the two sequential winter seasons of 2016/2017 and 2017/2018. The research aimed to research the influence of inoculation soil by bio-fertilizer (Biogein) rates (0, 1 and 2 kg/fad.) with foliar sprinkle with nutrient complex (Aquacool) on growth, yield and leaves specialty of spinach plants cv Balady grown in clay soil conditions. The acquired results specified that, the interaction amidst inoculation soil by high level of N-fixing bacteria (2 kg/fed.) with spray with higher concentration (2 cm/L) of nutrient complex (Aquacool) gave the biggest value of plant height, number of leaves/ plant, leaves fresh and dry weight, total chlorophyll as well as total yield and big contents of the percentage of N, P, K, and protein on leaves tissue.
Advanced phase change materials applications in thermal management field: A topical review

Due to their high latent heat stowing ability and steady thermal phase change behavior, phase change materials (PCMs) are studied for energy storage materials in the thermal management field. Pure PCMs have innately low thermal conductivity, which is improved by using different techniques, including the use of metal foams, nanoparticles, a mixture of different PCMs, encapsulation of PCM, and the finned structures. These modified PCMs are researched for various applications such as thermal control of electronic devices, thermoelectric generators (TEGs), smart textiles, heat exchangers, building thermal management and solar photovoltaic modules. It is observed that the utilization of PCMs is a promising option for performance enhancement of these applications. The utilization of PCMs have proven to overcome the decrement that was in terms of thermal and electrical effectiveness and indicated better results.
GraphTrack: A Graph-based Cross-Device Tracking Framework

Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications and applications for user profiling, personalized services, etc. One particular, wide-used type of cross-device tracking is to leverage browsing histories of user devices, e.g., characterized by a list of IP addresses used by the devices and domains visited by the devices. However, existing browsing history based methods have three drawbacks. First, they cannot capture latent correlations among IPs and domains. Second, their performance degrades significantly when labeled device pairs are unavailable. Lastly, they are not robust to uncertainties in linking browsing histories to devices. We propose GraphTrack, a graph-based cross-device tracking framework, to track users across different devices by correlating their browsing histories. Specifically, we propose to model the complex interplays among IPs, domains, and devices as graphs and capture the latent correlations between IPs and between domains. We construct graphs that are robust to uncertainties in linking browsing histories to devices. Moreover, we adapt random walk with restart to compute similarity scores between devices based on the graphs. GraphTrack leverages the similarity scores to perform cross-device tracking. GraphTrack does not require labeled device pairs and can incorporate them if available. We evaluate GraphTrack on two real-world datasets, i.e., a publicly available mobile-desktop tracking dataset (around 100 users) and a multiple-device tracking dataset (154K users) we collected. Our results show that GraphTrack substantially outperforms the state-of-the-art on both datasets.
Randomized algorithms for Tikhonov regularization in linear least squares

We describe two algorithms to efficiently solve regularized linear least squares systems based on sketching. The algorithms compute preconditioners for $\min \|Ax-b\|^2_2 + \lambda \|x\|^2_2$, where $A\in\mathbb{R}^{m\times n}$ and $\lambda>0$ is a regularization parameter, such that LSQR converges in $\mathcal{O}(\log(1/\epsilon))$ iterations for $\epsilon$ accuracy. We focus on the context where the optimal regularization parameter is unknown, and the system must be solved for a number of parameters $\lambda$. Our algorithms are applicable in both the underdetermined $m\ll n$ and the overdetermined $m\gg n$ setting. Firstly, we propose a Cholesky-based sketch-to-precondition algorithm that uses a `partly exact' sketch, and only requires one sketch for a set of $N$ regularization parameters $\lambda$. The complexity of solving for $N$ parameters is $\mathcal{O}(mn\log(\max(m,n)) +N(\min(m,n)^3 + mn\log(1/\epsilon)))$. Secondly, we introduce an algorithm that uses a sketch of size $\mathcal{O}(\text{sd}_{\lambda}(A))$ for the case where the statistical dimension $\text{sd}_{\lambda}(A)\ll\min(m,n)$. The scheme we propose does not require the computation of the Gram matrix, resulting in a more stable scheme than existing algorithms in this context. We can solve for $N$ values of $\lambda_i$ in $\mathcal{O}(mn\log(\max(m,n)) + \min(m,n)\,\text{sd}_{\min\lambda_i}(A)^2 + Nmn\log(1/\epsilon))$ operations.
Quasi-Semilattices on Networks

This paper introduces a representation of subnetworks of a network Γ consisting of a set of vertices and a set of relations, where relations are the primitive structures of a network. It is proven that all connected subnetworks of a network Γ form a quasi-semilattice L(Γ), namely a network quasi-semilattice.Two equivalences σ and δ are defined on L(Γ). Each δ class forms a semilattice and also has an order structure with the maximum element and minimum elements. Here, the minimum elements correspond to spanning trees in graph theory. Finally, we show how graph inverse semigroups, Leavitt path algebras and Cuntz–Krieger graph C*-algebras are constructed in terms of relations.
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings

ASR Error Detection (AED) models aim to post-process the output of Automatic Speech Recognition (ASR) systems, in order to detect transcription errors. Modern approaches usually use text-based input, comprised solely of the ASR transcription hypothesis, disregarding additional signals from the ASR model. Instead, we utilize the ASR system’s word-level confidence scores for improving AED performance. Specifically, we add an ASR Confidence Embedding (ACE) layer to the AED model’s encoder, allowing us to jointly encode the confidence scores and the transcribed text into a contextualized representation. Our experiments show the benefits of ASR confidence scores for AED, their complementary effect over the textual signal, as well as the effectiveness and robustness of ACE for combining these signals. To foster further research, we publish a novel AED dataset consisting of ASR outputs on the LibriSpeech corpus with annotated transcription errors.
Deterministic Conjunction Tracking in Long-term Space Debris Simulations

Numerical simulations are at the center of predicting the space debris environment of the upcoming decades. In light of debris generating events, such as continued antisatellite weapon tests and planned mega-constellations, accurate predictions of the space debris environment are critical to ensure the long-term sustainability of critical satellite orbits. Given the computational complexity of accurate long-term trajectory propagation for a large number of particles, numerical models usually rely on Monte-Carlo approaches for stochastic conjunction assessment. On the other hand, deterministic methods bear the promise of higher accuracy and can serve to validate stochastic approaches. However, they pose a substantial challenge to computational feasibility. In this work, we present the architecture and proof-of-concept results for a numerical simulation capable of modeling the long-term debris evolution over decades with a deterministic conjunction tracking model. For the simulation, we developed an efficient propagator in modern C++ accounting for Earth’s gravitational anomalies, solar radiation pressure, and atmospheric drag. We utilized AutoPas, a sophisticated particle container, which automatically selects the most efficient data structures and algorithms. We present results from a simulation of 16 024 particles in low-Earth orbit over 20 years. Overall, conjunctions are tracked for predicted collisions and close encounters to allow a detailed study of both. We analyze the runtime and computational cost of the simulation in detail. In summary, the obtained results show that modern computational tools finally enable deterministic conjunction tracking and can serve to validate prior results and build higher-fidelity numerical simulations of the long-term debris environment.
Relative State Quantum Logic

A projective quantum logic in terms of relative states is developed, emphasizing the importance of information transfer between a system under study and its environment. The need for accounting for the historical evolution of system is highlighted and it is found that the conjunction of observations involving conjugate variables can be consistently defined but is found to be non-commutative. It is shown that the Birkhoff and von Neumann approach to quantum logic is unable to deal with such conjunctions. It is found that whilst the proposed scheme is still not distributive in general, the discrepancy is directly related to interference effects that may disappear when information is transferred from the system to its environment. It is argued that the probabilities associated with projections be mapped to an orthocomplemented ternary logic, in which it is shown that the law of the excluded middle still holds.
Web Mining to Inform Locations of Charging Stations for Electric Vehicles

The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so that they reach a large utilization. Here, we hypothesize that the utilization of EV charging stations is driven by the proximity to points-of-interest (POIs), as EV owners have a certain limited willingness to walk between charging stations and POIs. To address our research question, we propose the use of web mining: we characterize the influence of different POIs from OpenStreetMap on the utilization of charging stations. For this, we present a tailored interpretable model that takes into account the full spatial distributions of both the POIs and the charging stations. This allows us then to estimate the distance and magnitude of the influence of different POI types. We evaluate our model with data from approx. 300 charging stations and 4,000 POIs in Amsterdam, Netherlands. Our model achieves a superior performance over state-of-the-art baselines and, on top of that, is able to offer an unmatched level of interpretability. To the best of our knowledge, no previous paper has quantified the POI influence on charging station utilization from real-world usage data by estimating the spatial proximity in which POIs are relevant. As such, our findings help city planners in identifying effective locations for charging stations.
Snowmass 2021: Quantum Sensors for HEP Science -- Interferometers, Mechanics, Traps, and Clocks

A wide range of quantum sensing technologies are rapidly being integrated into the experimental portfolio of the high energy physics community. Here we focus on sensing with atomic interferometers; mechanical devices read out with optical or microwave fields; precision spectroscopic methods with atomic, nuclear, and molecular systems; and trapped atoms and ions. We give a variety of detection targets relevant to particle physics for which these systems are uniquely poised to contribute. This includes experiments at the precision frontier like measurements of the electron dipole moment and electromagnetic fine structure constant and searches for fifth forces and modifications of Newton's law of gravity at micron-to-millimeter scales. It also includes experiments relevant to the cosmic frontier, especially searches for gravitional waves and a wide variety of dark matter candidates spanning heavy, WIMP-scale, light, and ultra-light mass ranges. We emphasize here the need for more developments both in sensor technology and integration into the broader particle physics community.
Gradient Methods with Memory for Minimizing Composite Functions

The recently introduced Gradient Methods with Memory use a subset of the past oracle information to create an accurate model of the objective function that enables them to surpass the Gradient Method in practical performance. The model introduces an overhead that is substantial on all problems but the smooth unconstrained ones. In this work, we introduce several Gradient Methods with Memory that can solve composite problems efficiently, including unconstrained problems with non-smooth objectives. The auxiliary problem at each iteration still cannot be solved exactly but we show how to alter the model and how to initialize the auxiliary problem solver to ensure that this inexactness does not degrade the convergence guarantees. Moreover, we dynamically increase the convergence guarantees as to provably surpass those of their memory-less counterparts. These properties are preserved when applying acceleration and the containment of inexactness further prevents error accumulation. Our methods are able to estimate key geometry parameters to attain state-of-the-art worst-case rates on many important subclasses of composite problems, where the objective smooth part satisfies a strong convexity condition or a relaxation thereof. In particular, we formulate a near-optimal restart strategy applicable to optimization methods with sublinear convergence guarantees of any order. We support the theoretical results with simulations.