Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategic facilities management of Suntec Singapore International Convention and Exhibition Centre: A case study

Purpose – This paper aims to highlight how facilities can enhance the strategic competitive position of a business organisation.Design/methodology/approach – The methods of investigation used in this study include observation, in‐depth interviews and secondary data.Findings – This case study has shown that both hardware, i.e. facilities and software, i.e. business philosophy of the organisation are important to enhance its competitive position.Research limitations/implications – This study has looked at only one case. Future research may use the same five‐force model to assess and determine the contribution of facilities to the competitive position of a business organisation.Practical implications – This study hopes to create more awareness among senior management of the strategic importance of facilities to a business's bottom‐line.Originality/value – Using a strategic management model, this paper illustrates the contribution of facilities management to the broader corporate goals.
A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.
Characteristics of a low-profile circular disk DR antenna with very high permittivity

The fundamental broadside TM 110 mode of an aperture-coupled circular dielectric resonator antenna is studied. By using high-dielectric-constant material, the DR antenna can be made low-profile with a relatively low resonant frequency. The return loss, radiation characteristics, antenna gain and beamwidth of the configuration are measured and discussed.
Driver Drowsiness Detection with Peripheral Cardiac Signals

Annually around 1.35 million people die worldwide as a result of road accidents. Of these, 90% occur because of human fault. Such faults have been continuously reduced by the development of safer road architectures and legislation that intends to guarantee the ideal conditions for driving. However, errors made by human drivers when driving while feeling drowsy result in a constancy of people involved in road accidents, raising the need for a drowsiness detection system. A physiological signal capable of early identifying such state is the heart rate variability, which can be obtained by analysis of the consecutive time intervals between heart beats. Using peripheral cardiac signals, signals containing cardiac rhythm information and obtained through non-intrusive ways, it is possible to integrate such detection on a vehicle without affecting the driving task. This work builds the pipeline to use any of three wearable devices: wrist worn PPG band, ECG chest strap and off-the-person ECG collection through a steering wheel, to collect the inter beat intervals, calculate HRV features and detect the drowsiness state of a driver. A filter was developed to compensate ambient light sensitivity of PPG based devices and the intervals detected from all signals were corrected by an algorithm created to possible wearable contact losses. SVM models with linear kernel and C=0.3 and a selected group of HRV features had good performances , reaching an average 0.62 Matthews correlation coefficient across 12 individuals. Simulator experiments showed good indication that peripheral cardiac signals can be used for drowsiness detection.
Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis

The main challenge of automatic license plate recognition (ALPR) systems is that the overall performance is highly dependent upon the results of each component in the system’s pipeline. This paper proposes an improved ALPR system for the Jordanian license plates. Ceiling analysis is carried out to identify potential enhancements in each processing stage of a previously reported ALPR system. Based on the obtained ceiling analysis results, several enhancements are then suggested to improve the overall performance of the system under study. These improvements are (i) vertical-edge histogram analysis and size estimation of the candidate regions in the detection stage and (ii) de-rotation of the misaligned license plate images in the segmentation unit. These enhancements have resulted in significant improvements in the overall system performance despite a <1% increase in the execution time. The performance of the developed ALPR is assessed experimentally using a dataset of 500 images for parked and moving vehicles. The obtained results are found to be superior to those reported in equivalent systems, with a plate detection accuracy of 94.4%, character segmentation accuracy of 91.9%, and character recognition accuracy of 91.5%.
A novel synthetic protocol for the synthesis of pulvinones, and naturally occurring Aspulvinone E, molecules of medicinal interest

Abstract A novel two step methodology for readily accessible natural “pulvinone” derivatives in excellent yields has been developed starting from activated precursors, bearing a functionalized 1,3-dioxolane-2,4-diones (OCA’s), as dually protected-activated synthons of α-hydroxy acids. The present procedure is based on a tandem C-acylation-cyclization process under mild conditions with good yields. Additionally, pulvinones show an important medicinal profile with the tetronate heterocycle exhibiting favorable pharmacokinetic (PK) and ADME-Tox properties that can be considered unexploited so far due to synthetic limitations. Graphical Abstract
A Multidimensional Scaling Approach to Technology Categorization

The increasing reliance on and advancement of technology requires an in-depth exploration into how users interact with mechanical and virtual devices. To explore how individuals categorize different technologies, we conducted a multidimensional scaling (MDS) study to analyze the perceived similarity or dissimilarity between technologies. In the exploratory study on 23 devices, we found items rated similarly based on two dimensions – technological sophistication and size. Technology categorization, specifically with the use of MDS, is an under investigated approach in human factors that, through further exploration, may yield a better understanding of users’ mental models and acceptance, with utility for both designers and researchers.
Forecasting petrol demand and assessing the impact of selective strategies to reduce fuel consumption

Abstract The use of fossil fuels in transportation is an important topic as a result of growing concerns over global warming. Automobile petrol demand has been of particular interest to researchers and policy-makers, given that the automobile is a major contributor to the enhanced greenhouse effect. This paper forecasts Australia's automobile petrol demand up to the year 2020 based on the best performing forecasting model selected out of eight models. In order to establish ways to reduce the demand for petrol, and the consequent by-product of reducing the amount of greenhouse gas emissions, we have estimated the impact on CO2 for several potential policy instruments, using Transportation and Environment Strategy Impact Simulator (an integrated transport, land use and environmental strategy impact simulation programme). We find that a carbon tax of AU$0.50/kg can reduce automobile kilometres by 5.9%, resulting in reduced demand for petrol and a reduction in CO2 of 1.5%.
A Fast Response Robust Deadbeat Predictive Current Control for Permanent Magnet Synchronous Motor

Deadbeat predictive current control (DBPCC) has the characteristic of fast current response, but it is sensitive to motor parameters. Observer-based DBPCC can eliminate the steady state current tracking error when parameter mismatch exists. However, the actual current will deviate from the reference current during transient state in the case of inductance mismatch. In this paper, a fast response robust deadbeat predictive current control (FRRDBPCC) method is proposed for surface mounted permanent magnet synchronous motor (SPMSM). Firstly, the current tracking error caused by inductance mismatch during transient state is analyzed in detail. Then, an extended state observer (ESO) is proposed to estimate the lumped disturbance caused by parameter mismatch. Based on discrete time ESO, the predicted currents are used to replace the sampled currents to compensate for one-step delay caused by calculation and sampling. Furthermore, an online inductance identification algorithm and a modified prediction model are proposed. The dq-axis currents can be completely decoupled by updating the inductance in the modified prediction model online, ensuring that the current can track the reference value in two control periods. The proposed method improves robustness against parameter mismatch and guarantees dynamic response performance simultaneously. The experimental results verify the effectiveness of the proposed method.
Let Them Eat Fish!—Exploring the Possibility of Utilising Unwanted Catch in Food Bank Parcels in The Netherlands

The Common Fisheries Policy of the European Union was reformed in 2013 with the aim of improving the sustainability of the fishing sector. The Landing Obligation, a cornerstone of this reform, requires fishers to land their unwanted catch instead of discarding it at sea. Existing literature pays little attention to what becomes of this unwanted catch once it is landed. To further the discourse on the sustainable valorisation of unwanted catch, this study explores whether unwanted catch that is safe for human consumption could be used for improving food security. The paper focuses on Dutch food banks, which deliver critical food aid to over 160,000 individuals yearly but struggle to provide all dependant recipients with nutritionally balanced food parcels. The research question is addressed in two ways. The food bank recipients’ willingness to consume UWC is evaluated quantitatively through a survey. Next to this, data from interviews with relevant stakeholders are analysed qualitatively. Results indicate that the Food Bank Foundation and its recipients are willing to receive this fish if it is safe to consume and accessible. However, various factors such as existing infrastructure, lack of economic incentive to donate, competition from non-food and black markets, and the fishing industry’s conflict with the landing obligation might pose barriers to this kind of valorisation. The dissonance between fisheries, food, and sustainability policies is discussed and identified as a key limiting factor. To bridge the differences between these policy areas, we propose public-private partnerships and voluntary agreements among involved stakeholders.
Fine‐grained semantic ethnic costume high‐resolution image colorization with conditional GAN

Grayscale image colorization, especially for ethnic costume images, is highly challenging due to its rich and complex color features. The existing image colorization methods usually take the costume image as a whole in practical applications that lead to the ignorance of the semantic information of different parts of the costume. It is known that each part's color distribution of the ethnic costume is different. So, the color mapping of other parts is also diverse, which is determined by distinctive ethnic characteristics. This study introduces fine‐grained level semantic information and proposes a high‐resolution image colorization model for ethnic costumes targeting enhancement, inspired by semantic‐level colorization. The semantic information of different regions of ethnic costumes has a significant impact on the performance of the coloring task. Using Pix2PixHD as the backbone network, we create a new network architecture that maintains color distribution correspondence and spatial consistency of costume images using fine‐grained semantic information. In our network, we take the splice result of fine‐grained semantic for ethnic costume and grayscale image as the conditions and then feed them into the generative adversarial networks. We also discuss and analyze the influences of the grayscale channel and fine‐grained semantics on discriminator. Extensive experiments demonstrate that our method performs well compared with other state‐of‐the‐art automatic colorization methods.
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures

Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a single-objective Bayesian optimization. This allows us to determine the robustness of a topic model for several evaluation metrics. We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other.
Female Farmers’ Agricultural Information Needs and Food Production: A Case Study of Imbulpe ds Division in Sri Lanka

The study was conducted to find out female farmers’ agricultural information needs and their impact on food production, in the Imbulpe DS Division in Sri Lanka. Of the female farmers in the area, 238 were taken as the sample for the study from seven selected Grama Niladhari (GN) divisions in the area. Female farmers were selected by using a simple random sampling method from these purposively selected GN divisions. A pre-tested, interviewer-administered questionnaire survey was used as the primary data collection method from March to July 2019. Data analysis was done by using descriptive statistics and chi-square analysis. The result revealed that the majority (62.6%) of respondents were middle aged (40-59 years), married, and belonged to families with 4-5 members. Most respondents (64.3 %) had studied up to junior secondary education level. Their average farm land size is 0.84 acres and they have farming experience of about 15 years. Most of the respondents mentioned that they had obtained higher levels of information needs about improved crop varieties. In addition, female farmers reported that they moderately need information on application of agrochemicals, improved market systems and modern farming technologies. Extension agents and other female farmers act as their major sources of agricultural information and ICT equipment acts as the least important agricultural information source in this area. Moreover, there is a significant positive association between the agricultural information needs and food production. Therefore, providing necessary agricultural information and enhanced utilization of ICT tools for agricultural information sources, and encouraging female farmers to participate in farming societies will lead to enhanced food production in this area.
Cell cycle inhibitor Whi5 records environmental information to coordinate growth and division in yeast

Proliferating cells need to evaluate the environment to determine the optimal timing for cell cycle entry, which is essential for coordinating cell division and growth. In the budding yeast Saccharomyces cerevisiae, the commitment to the next round of division is made in G1 at the Start, triggered by the inactivation of the inhibitor Whi5 through multiple mechanisms. However, how a cell reads environmental condition and uses this information to regulate Start is poorly understood. Here, we show that Whi5 is a key environmental indicator and plays a crucial role in coordinating cell growth and division. We found that under a variety of nutrient and stress conditions, the concentration of Whi5 in G1 is proportional to the doubling time in the environment. Thus, under a poorer condition a longer doubling time results in a higher Whi5 concentration, which in turn delays the next cell cycle entry to ensure sufficient cell growth. In addition, the coordination between division and the environment is further fine-tuned in G1 by environmentally dependent G1 cyclin-Cdk1 contribution and Whi5 threshold at Start. Our results show that Whi5 serves as an environmental ‘memory’ and that the cell adopts a simple and elegant mechanism to achieve an adaptive cellular decision making.
Simulation Study on the Effect of Doping Concentrations on the Photodetection Properties of Mg2Si/Si Heterojunction Photodetector

To develop and design an environmentally friendly, low-cost shortwave infrared (SWIR) photodetector (PD) material and extend the optical response cutoff wavelengths of existing silicon photodetectors beyond 1100 nm, high-performance silicon-compatible Mg2Si/Si PDs are required. First, the structural model of the Mg2Si/Si heterojunction was established using the Silvaco Atlas module. Second, the effects of the doping concentrations of Mg2Si and Si on the photoelectric properties of the Mg2Si/Si heterojunction PD, including the energy band, breakdown voltage, dark current, forward conduction voltage, external quantum efficiency (EQE), responsivity, noise equivalent power (NEP), detectivity, on/off ratio, response time, and recovery time, were simulated. At different doping concentrations, the heterojunction energy band shifted, and a peak barrier appeared at the conduction band of the Mg2Si/Si heterojunction interface. When the doping concentrations of Si and Mg2Si layer were 1017, and 1016 cm−3, respectively, the Mg2Si/Si heterojunction PD could obtain optimal photoelectric properties. Under these conditions, the maximum EQE was 70.68% at 800 nm, the maximum responsivity was 0.51 A/W at 1000 nm, the minimum NEP was 7.07 × 10−11 WHz–1/2 at 1000 nm, the maximum detectivity was 1.4 × 1010 Jones at 1000 nm, and the maximum on/off ratio was 141.45 at 1000 nm. The simulation and optimization result also showed that the Mg2Si/Si heterojunction PD could be used for visible and SWIR photodetection in the wavelength range from 400 to 1500 nm. The results also provide technical support for the future preparation of eco-friendly heterojunction photodetectors.
Effects of Occupational Hazards on Pulmonary Health among Lathe Machine Workers: A Cross-Sectional Study in Tangail, Bangladesh

Background: Occupational exposure to metal dust particles leads to irritation and initiation of inflammatory responses in the respiratory system that subsequently onsets lung diseases. Lathe machine workers have a greater risk of having lung diseases due to continuously conducting metal dust particles. This study aimed to explore the pulmonary health status of lathe machine workers. Methods: In this cross-sectional study, 80 individuals (lathe workers) were adopted from a different area of the Tangail district in Bangladesh. A questionnaire’s survey was accomplished along with oximetry and spirometry. After that, blood was drawn for measuring Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP), and Immunoglobulin E (IgE) test to the lung function abnormalities. Results: Among 80 lathe machine workers, 12% had breathing problems, 13.8% had coughing, 16.2% had to sneeze and 8.7% had a chest. In addition, spirometry results including, FVC, FEV1, FEV1%, PEF, PEF%, FEF25, FEF75, and FEF2575 were significantly lower in lathe workers compared to control. Moreover, higher levels of IgE (264.93±141) and ESR (11.5±4.77) were reported in lathe machine workers than in the control group. Not using protective masks and smoking were considered as risk factors for having respiratory diseases in lathe machine workers. Conclusion: This study concludes that exposure to metal dust particles can cause respiratory disease symptoms and lower pulmonary function in lathe machine workers. Furthermore, metal dust might also augment the blood IgE and ESR level.
Nodes Afford Connection: A Pilot Study Examining the Design and Use of a Graphical Modeling Language

External representations such as diagrams facilitate reasoning. Many diagramming systems and notations are amenable to manipulation by actual or imagined intervention (e.g., transposing terms in an equation). Such manipulation is constrained by user-enforced constraints, including rules of syntax and semantics which help preserve the representation’s validity. We argue that the concepts of affordances and signifiers can be applied to understand such representations, and we suggest the term graphical affordance to refer to rule-constrained syntactic manipulation of an external representation. Following this argument, we examine a graphical modeling language in terms of these graphical affordances, and we present a pilot study examining how participants interact with the modeling language. Preliminary results suggest that using the modeling language, as opposed to prose representation, influences user behavior in a manner aligned with the graphical affordances and signifiers of the modeling language.