Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Weight Determination in Model Predictive Control for Personalized Car-Following Control

Car-following control is a fundamental application of autonomous driving. This control has multiple objectives, including tracking a safe distance to a preceding vehicle and enhancing driving comfort. Model Predictive Control (MPC) is a powerful method due to its intuitiveness and capability to cover multiple objectives. MPC determines the relative importance of objectives through a set of weight factors, depending on which, the controller’s behavior changes even if the traffic situations are the same. However, determining the optimal weight is not a trivial problem because there is no benchmark to evaluate the performance of the weight, and searching for weight factors with repeated driving experiments is time-consuming. To solve this problem, we proposed an automatic tuning method to determine the weights of the MPC based on personal driving data. Personal driving data under naturalistic driving conditions provide car-following situations and driver’s behaviors. These data can generate a reference model to represent the driver’s driving style. Based on this model, the proposed method defined the automatic tuning problem as an optimization problem that minimizes the difference between the reference and the controller’s response using the optimal weight factors. This optimization problem was solved using the Particle Swarm Optimization algorithm. The proposed method was implemented with an embedded optimization coder in an offline fashion. Its performance was evaluated using personal driving data. From this, the proposed method can reduce the effort and time required for an engineer to find the optimal weight factors.
Efectos del control de Tensión y Frecuencia en una operación aislada por Micro-redes con Pequeñas Centrales Hidroeléctricas: Estabilidad Transitoria

A Microgrid is a part of an electrical distribution system that can operate autonomously either in parallel with the main grid or isolated from the main grid. The operational flexibility of a microgrid reduces the vulnerability of the power system and also helps to avoid costly blackouts. In Colombia there is great potential for the creation of microgrids that use power generated by small hydropower plants, which are considered as a low environmental impact technology. small hydropower plants are currently unable to operate in isolation as they do not have the mechanisms required to maintain stable frequency and voltage levels after disconnection from the interconnected system. In this paper, we propose the idea of planning a Microgrid using real data from a distribution system that has an small hydropower plant installed in order to evaluate its transitory behaviour when faced with disturbances. Different control strategies are implemented, with the aim of determining which system shows the best performance.
ESPAR Antenna Design and Performance for the BS MIMO System in Railway Wireless Communication

In this paper, we design and analyze a patch ESPAR antenna for beamspace MIMO system. The proposed ESPAR antenna consists of a single active element with RF-chain and 6 parasitic elements. The ESPAR antenna can radiate beam patterns by adjusting the reactance of the parasitic element. We design a 7-element patch ESPAR antenna and simulate it according to the change of reactance of the parasitic element. As simulation results show, we can get the proposed ESPAR antenna structure can support beamspace MIMO transmission.
The Lipid Droplet Knowledge Portal: A resource for systematic analyses of lipid droplet biology

Lipid droplets (LDs) are organelles of cellular lipid storage with fundamental roles in energy metabolism and cell membrane homeostasis. There has been an explosion of research into the biology of LDs, in part due to their relevance in diseases of lipid storage, such as atherosclerosis, obesity, type 2 diabetes mellitus, and hepatic steatosis. Consequently, there is an increasing need for a resource that combines large datasets from systematic analyses of LD biology. Here we integrate high-confidence, systematically generated data on studies of LDs in the framework of an online platform named the Lipid Droplet Knowledge Portal. This scalable and interactive portal includes comprehensive datasets, across a variety of cell types, for LD biology, including transcriptional profiles of induced lipid storage, organellar proteomics, genome-wide screen phenotypes, and ties to human genetics. This new resource is a powerful platform that can be utilized to uncover new determinants of lipid storage. HIGHLIGHTS ■ The LD-Portal is a resource combining datasets from systematic analyses in LD biology ■ The LD-Portal allows users to query genetic, proteomic, and phenotypic aspects of LD biology ■ The LD-Portal can be used to discover new facets of lipid storage and LD biology ■ A crucial function of MSRB3 is uncovered in cholesterol ester storage in LDs
A Study of Software Change Management Problem

Software change management is an essential discipline for enterprise IT organizations. In modern enterprises, software automates a wide variety of business processes. Changes made to software are, in effect, changes made to the business processes themselves. While the majority of enterprises have implemented basic software change management processes, these processes fall down in the face of the increased complexity of today’s business and IT environments. Truly effective and efficient software change management will require better coordination of change across geographic, organizational, functional, and technological arena of IT implemented organization. This paper presents a classification of the change management problems derived from the literature study done. This study explores software change management — the definition and implementation of defined processes to control changes made to software. It also addresses the specific problems in the change management of application software development.
Optimization of Carotenoids Production from Rhodotorula sp. Strain ATL72 for Enhancing Its Biotechnological Applications

Rhodotorula yeasts which are known as carotenogenic yeasts have a great industrial value due to their ability to produce carotenoids. In particular, the isolated yeast Rhodotorula sp. (strain ATL72) has been reported to be a promising producer of high concentrations of carotenoids. A combination of central composite design (CCD) and Plackett–Burman (PB) design was used to optimize carotenoids produced by this yeast. The optimum production of carotenoids was completed when the yeast was grown in a production medium composed of 3.7 g/L malt extract, 7.7 g/L fructose, 9 g/L urea, 35 g/L NaCl, and 1 g/L yeast extract at 27.5 °C, pH 6.7, and 180 rpm. Two batch runs in 1 L and 7 L bioreactors were conducted which increased the productivity of carotenoid concentration from 21.5 mg/L after 98 h of incubation at the level of the shake flask to 229.9 mg/L after 47 h of incubation at the level of 7 L bioreactor. The carotenoid pigment was extracted in dimethylsulfoxide (DMSO), acetone, petroleum ether, and sodium chloride, and subsequently identified and characterized using UV-visible scanning, thin layer chromatography, and gas chromatography/mass spectrometry.
Evaluation of Active, Beautiful, Clean Waters Design Features in Tropical Urban Cities: A Case Study in Singapore

In Singapore, active, beautiful, clean waters design features (ABCWDFs), such as rain gardens and vegetated swales, are used as a sustainable approach for stormwater management. Field monitoring studies characterising the performance of these design features in the tropical region are currently limited, hampering the widespread implementation of these systems. This study characterised the performance of individual ABCWDFs in the tropical climate context by monitoring a rain garden (FB7) and a vegetated swale (VS1) that were implemented in a 4-ha urban residential precinct for a period of 15 months. Results showed that total suspended solids (TSS), total phosphorus (TP) and total nitrogen (TN) concentrations were low in the new residential precinct runoff, leading to poor removal efficiency despite the effluent concentrations of individual ABCWDFs that were within the local stormwater treatment objectives. Average TSS, TP and TN EMCs of four sub-catchment outlets were lower (23.2 mg/L, 0.11 mg/L and 1.00 mg/L, respectively) when compared to the runoff quality of the major catchments in Singapore, potentially demonstrating that the ABCWDFs are effective in improving the catchment runoff quality. Findings from this study can help to better understand the performance of ABCWDFs receiving low influent concentrations and implications for further investigations to improve stormwater runoff management in the tropics.
HPV Vaccination after Primary Treatment of HPV-Related Disease across Different Organ Sites: A Multidisciplinary Comprehensive Review and Meta-Analysis

Objective: To assess evidence on the efficacy of adjuvant human papillomavirus (HPV) vaccination in patients treated for HPV-related disease across different susceptible organ sites. Methods: A systematic review was conducted to identify studies addressing the efficacy of adjuvant HPV vaccination on reducing the risk of recurrence of HPV-related preinvasive diseases. Results were reported as mean differences or pooled odds ratios (OR) with 95% confidence intervals (95% CI). Results: Sixteen studies were identified for the final analysis. Overall, 21,472 patients with cervical dysplasia were included: 4132 (19.2%) received the peri-operative HPV vaccine, while 17,340 (80.8%) underwent surgical treatment alone. The recurrences of CIN 1+ (OR 0.45, 95% CI 0.27 to 0.73; p = 0.001), CIN 2+ (OR 0.33, 95% CI 0.20 to 0.52; p < 0.0001), and CIN 3 (OR 0.28, 95% CI 0.13 to 0.59; p = 0.0009) were lower in the vaccinated than in unvaccinated group. Similarly, adjuvant vaccination reduced the risk of developing anal intraepithelial neoplasia (p = 0.005) and recurrent respiratory papillomatosis (p = 0.004). No differences in anogenital warts and vulvar intraepithelial neoplasia recurrence rate were observed comparing vaccinated and unvaccinated individuals. Conclusions: Adjuvant HPV vaccination is associated with a reduced risk of CIN recurrence, although there are limited data regarding its role in other HPV-related diseases. Further research is warranted to shed more light on the role of HPV vaccination as adjuvant therapy after primary treatment.
Predicting Positive ELISA Results in Dairy Herds with a Preferred Status in a Paratuberculosis Control Program

Simple Summary In many paratuberculosis control programs, test-negative herds are assigned a preferred herd status. This may induce the false belief that these herds are free of paratuberculosis. Hence, farmers may refrain from control measures that could prevent the spread of any undetected infections. The aim of the present study was to develop a predictive model to alert farmers with test-negative herds and a preferred status in the Dutch paratuberculosis control program if they are at an increased risk of positive ELISA results in a subsequent 30-month period. On the basis of the results of this study, we conclude that discrimination of herds with high (52%) and low (17%) risks of positive ELISA results is feasible. This might help farmers with the highest risks of future positive results to make better informed decisions regarding the need to take additional control measures to prevent the spread of any undetected Map infections. Abstract Dairy herds participating in the Dutch milk quality assurance program for paratuberculosis are assigned a herd status on the basis of herd examinations by ELISA of individual serum or milk samples, followed by an optional confirmatory fecal PCR. Test-negative herds are assigned Status A; the surveillance of these herds consists of biennial herd examinations. Farmers falsely believing that their Status A herds are Map-free may inadvertently refrain from preventive measures. Therefore, we aimed to develop a predictive model to alert Status A farmers at increased risk of future positive ELISA results. Using data of 8566 dairy herds with Status A in January 2016, two logistic regression models were built, with the probabilities of ≥1 or ≥2 positive samples from January 2017–June 2019 as dependent variables, and province, soil type, herd size, proportion of cattle born elsewhere, time since previous positive ELISA results, and the 95th percentile of the S/P ratios in 2015–2016, as explanatory variables. As internal validation, both models were applied to predict positive ELISA results from January 2019–June 2021, in 8026 herds with Status A in January 2019. The model predicting ≥1 positive sample had an area under the receiver operating characteristics curve of 0.76 (95% CI: 0.75, 0.77). At a cut-off predicted probability πc = 0.40, 25% of Status A herds would be alerted with positive and negative predictive values of 0.52 and 0.83, respectively. The model predicting ≥2 positive samples had lower positive, but higher negative, predictive values. This study indicates that discrimination of Status A herds with high and low risks of future positive ELISA results is feasible. This might stimulate farmers with the highest risks to take additional measures to control any undetected Map infections.
Engineering Characteristics of Cement Composites Containing a Chitosan-Based Polymer and Steel Slag Aggregates

Recently, sustainable development has attracted significant global attention. Toward this, several studies have been performed on the development of alternative aggregates for mortar or concrete to prevent environmental damage and rapid depletion of natural aggregates. In this study, we investigated the applicability of a chitosan-based polymer (CBP), a biomimetic polymer, to cement mortar using steel slag as a fine aggregate. The CBP was synthesized via an amide coupling reaction among chitosan, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride, and 3-(3,4-dihydroxyphenyl)propionic acid. Upon addition to cement mortar using natural sand or a blast furnace slag aggregate, the CBP contributed toward increasing the compressive strength and tensile strength. However, in mortar mixes using a ferronickel slag aggregate, the tensile strength decreased by ~5.7–25.4% upon CBP addition. Moreover, the CBP reduced the total charge passed through the mixes. In particular, in the mortar mix using the steel slag aggregate, the CBP showed improved chloride-ion penetration resistance. The results showed that the as-prepared CBP was a suitable improving agent and exhibited promising compatibility with cement composites containing steel slag aggregates.
Micro-expression recognition based on deep capsule adversarial domain adaptation network

Abstract. Micro-expression (ME), which reveals the genuine feelings and motives within human beings, attracts considerable attention in the field of automatic affective recognition. The main challenges for robust micro-expression recognition (MER) are from the short ME duration, low intensity of facial muscle movements, and insufficient samples. To meet these challenges, we propose an optical flow-based deep capsule adversarial domain adaptation network (DCADAN) for MER, which leverages a deep neural network stemming from these speculations. To alleviate the negative impact of the identity related features, optical flow preprocessing is applied to encode the subtle face motion information that is highly related to facial MEs. Then, a deep capsule network is developed to determine the part–whole relationships on optical flow features. To cope with the data deficiency and enhance the generalization capability via domain adaptation, an adversarial discriminator module that enriches the available samples from macro-expression data is integrated into the capsule network to train an expeditious end-to-end deep network. Finally, a simple and yet efficient attention module is embedded to the DCADAN to adaptively aggregate optical flow convolution maps into the primary capsule layers. We evaluate the performance of the entire network on the cross-database ME benchmark (3DB) using the leave-one-subject-out cross-validation. Unweighted F1-score (UF1) and unweighted average recall (UAR) are exploited as the evaluation metrics. The MER based on DCADAN achieves a UF1 score of 0.801 and a UAR score of 0.829 in comparison with a UF1 of 0.788 and a UAR of 0.782 for the updated approach. The comprehensive experimental results show that the incorporation of adversarial domain adaption into the capsule network is feasible and effective for representing discriminative features in ME and the proposed model outperforms state-of-the-art deep learning networks for MER.
The impact of new technologies “e-learning” on the productivity of companies in a period of crisis: Interviewing BPOs during Covid-19

New information and communication technologies have changed our economic and business functions. In this case, the training strategy has changed, disrupting the internal functioning of the organization. For this reason, information and communication technologies (ICT) must be applied in training, creating a new reality called “E-learning”. Indeed, the Covid-19 crisis has disrupted the economic system in all sectors and proved the limits of face-to-face training. As a result, the company is forced to change its habits and adapt more quickly to these turbulences to weather the financial crisis and ensure performance. This article aims to explore the contribution of e-learning to the commercial performance of three companies in the field of outsourcing and transaction management BPO’s in times of covid-19 crisis. A qualitative analysis was conducted: A series of semi-directional interviews were conducted with about 14 participants from the Human Resources Department. The companies studied have therefore embarked on this new practice, which is beginning to grow.
The Gender Gap in Political Participation: Evidence from the MENA Region

ABSTRACT This paper investigates gender differences in political participation across 10 countries in the Middle East and North Africa (MENA) region using data from the World Values Survey (2010-2014). A distinction is made between two different participation types, institutional and non-institutional. We use an ordered logit model to evaluate whether the gender gap in both forms is mediated by demographic and attitudinal controls and assess whether variables influencing participation affect men and women differently. We find that most socioeconomic resources and political attitudes are correlated with higher levels of participation. However, the analysis reveals a persistent gender gap that can be generalized to the entire spectrum of engagement in the MENA, with larger gaps for less institutionalized forms.
MemUnison: A Racetrack-ReRAM-Combined Pipeline Architecture for Energy-Efficient in-Memory CNNs

Though ReRAM has been greatly successful in reducing energy consumption of various neural networks, it still suffers write amplification in energy, which impedes ReRAM to provide efficient storage for the ubiquitous streaming data in CNNs, such as feature-maps. Racetrack memory, an emerging magnetic memory technique, is a proper candidate to hold streaming data since it enjoys fast sequential-access with ultra-low operating energy in read and write. In this work, we propose a hybrid processing-in-memory architecture, called MemUnison, that coordinates ReRAM and racetrack to overcome the expenditure storage of streaming data in ReRAM. By placing feature-maps in racetrack and leaving weights in ReRAM, a datapath is constructed between the two sides to form a fetch-process-writeback pipeline. As the invalid-shifts of the racetrack memory incurs a large amount of pipeline bubble, we propose a row-based access that can read and write a feature-map without any invalid-shifts. For the row-based operation, a cohesive controlling method is proposed to coordinate racetrack and ReRAM. In runtime, convolution kernels are scheduled in ReRAM banks for cross-channel calculations of one row, by which computing complexity of a convolutional layer can be reduced by 4 orders of magnitude, excessing the 2 order of reduction by traditional ReRAM.
Variable Selection in Frailty Models using FrailtyHL R Package: Breast Cancer Survival Data

Abstract Determining relevant variables for a regression model is important in regression analysis. Recently, a variableselection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) havebeen widely studied in simple statistical models such as linear models and generalized linear models. Theadvantage of these methods is that they select important variables and estimate regression coeﬃcients,simultaneously; therefore, they delete insigniﬁcant variables by estimating their coeﬃcients as zero. Westudy how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametricfrailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection wedevelop a new function in the “frailtyHL” R package. Our methods are illustrated with breast cancersurvival data from the Medical Center at Chonnam National University in Korea. We compare the resultsfrom three variable-selection methods and discuss advantages and disadvantages.Keywords: frailty models, H-likelihood, LASSO, SCAD, Variable selection
Contamination Levels and the Ecological and Human Health Risks of Potentially Toxic Elements (PTEs) in Soil of Baoshan Area, Southwest China

The primary goals of this study were to reveal the environmental status of potentially toxic elements (PTEs) and their ecological risks, as well as their associated health risks in the Baoshan area, southwest China, which has been surveyed with the scale of 1:250,000 geochemical mapping. Based on a comparison of the PTE concentrations with the soil environmental quality of China and the enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (Cf), and potential ecological risk indexes ( and PERI), as well as the potential non-carcinogenic hazard indices (HI and CHI) and carcinogenic risks indices (TCR and CTCR), the following conclusions were drawn: The PTE concentrations in the surface soil samples that were collected from the investigated area (1.65% sites) exceeded the risk intervention values (RIV) for soil contamination of agricultural land of China. Cadmium (Cd) and mercury (Hg) posed higher ecological risks than other PTEs (arsenic (As), chromium (Cr), lead (Pb), copper (Cu), nickel (Ni), and zinc (Zn)), which was highlighted by their toxic response factor. Arsenic was the main PTE with a non-carcinogenic risk (19.57% sites for children and 0.25% sites for adults) and the only PTE that carries a carcinogenic risk (2.67% sites for Children and 0.76% sites for adults) to humans in the research area. Children are more vulnerable to health risks when compared to adults because of their behavioral and physiological traits. Geological genesis was responsible for the high concentrations, ecological risk, and health risk distribution patterns of the examined PTEs. Even though the present research highlights several important aspects related to PTE pollution in the research area, further investigations are needed, especially in mining areas.