Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensorless Control for SynRM Drives Using a Pseudo-Random High-Frequency Triangular-Wave Current Signal Injection Scheme

This article proposes a pseudo-random high-frequency triangular-wave current signal injection (PR-HFTCI) based sensorless control scheme for synchronous reluctance motor (SynRM) drives at zero-low speed domain to suppress the high-frequency (HF) loss and the HF audible noise which are introduced by the significant nonlinearities of SynRMs and the traditional constant amplitude HF square-wave voltage injection method. The pseudo-random HF triangular-wave current signal with random frequency and random phase is injected to eliminate the discrete spectrum of the current power spectra density (PSD) and the corresponding current PSD analysis is performed to evaluate it. In addition, the HF component amplitude of the d-axis voltage reference can be adjusted adaptively with the variation of load to maintain the HF current amplitude constant, it is easy to implement and does not require any extra regulator and signal processing. Hence, the HF loss and the HF torque ripple can be effectively reduced under the heavy load. Finally, the effectiveness and the correctness of the proposed PR-HFTCI based sensorless control scheme are validated with experiments on a 1.5-kW SynRM drive platform.
†Antephilorhizus zerovae sp. nov. (Carabidae: Lebiini), the second ground beetle species from Rovno amber

ABSTRACT †Antephilorhizus zerovae Kirichenko-Babko & Perkovsky sp. nov., the second species of this genus, is described from the Rovno amber (Ukraine). The new species is rather similar to the A. pripiatiensis Kirichenko-Babko & Perkovsky, 2021. The new taxon is compared with lebiines from Baltic amber. Based on a new data, the diagnosis of the genus †Antephilorhizus Kirichenko-Babko, 2021 is clarified and a key to both species of genus is given.
Deep Learning Models for Strawberry Yield and Price Forecasting Using Satellite Images

Forecasting crop yields and prices is crucial for both global food security and providing farmers with valuable information to avoid a price crash. This work proposes a hybrid deep learning model that uses satellite images to forecast strawberry yield along with farmers’ prices, applied in three counties in California. For tractability, a dimensionality reduction technique is applied by converting the images to histograms representing the pixel frequency. The models tested are Convolutional Neural Network (CNN), Variational AutoEncoder (VAE), CNN-Long Short-Term Memory (CNN-LSTM), Stacked AutoEncoder (SAE), and a voting ensemble of CNN-LSTM and SAE. It is found that the proposed voting ensemble of CNN-LSTM and SAE is the best at forecasting the daily strawberry yields and prices in all three counties. Based on an aggregated performance measure (AGM), the voting ensemble model outperforms the models suggested in literature with up to 70% forecasting improvement compared to the CNN model and up to 22% improvement over the CNN-LSTM model.
Understanding Peak Average Power Ratio in VFFT-OFDM Systems

This paper describes an impairment commonly encountered in an OFDM system that must be considered in the design that is peak average power ratio (PAPR). In these studies, a statistical approach to analysing PAPR is suggested. The PAPR statistics of OFDM and VFFT-OFDM are studied by simulation of the statistical distribution of the quantity that is the Complementary Cumulative Distribution Function (CCDF) of the PAPR. The simulation results show that the simulated CCDF of PAPR, the 1% PAPR of OFDM is about 11.40 dB, whereas for VFFT-OFDM this rises to less than 3% of the time. Simulations show that by employing VFFT in OFDM system results in a 0.55 dB deterioration in the PAPR 1% of time.
An Empirical Analysis of Income Elasticity of Out-of-Pocket Healthcare Expenditure in Mauritius

A free universal healthcare provision exists in Mauritius. Yet the share of out-of-pocket healthcare expenditure out of total household expenditure has been growing over time. This study estimates income elasticity of out-of-pocket healthcare expenditure using Mauritian household data within an Engel curve framework. In the absence of longitudinal data on out-of-pocket healthcare expenditure patterns, the study proposes the application of the pseudo-panel approach using cross-sectional Household Budget Survey waves from 1996/97 to 2017. Income elasticity of out-of-pocket healthcare expenditure is estimated to be 0.938, which is just below unity. This implies that out-of-pocket healthcare demand is not considered to be a luxury, but a necessity in Mauritius. In order to see the differences in income elasticities by income groups, separate regressions are estimated for each income quartile over different years. The results indicate that income elasticities of out-of-pocket healthcare expenditure vary non-monotonically.
INTEREST RATE SPREADS IN THE BALTICS AND THE REST OF THE EURO AREA: UNDERSTANDING THE FACTORS BEHIND THE DIFFERENCES

This paper analyzes the determinants of interest rate spreads during the period 2014–2020 in the euro area, with a focus on the Baltic countries. Against the background of accommodative monetary policy, interest rates on loans in the euro area have declined markedly, except in a few countries. In Latvia, Lithuania and Estonia, interest rates on new loans to non-financial corporations in 2020 were about the same as in 2014, and at the same time they were among the highest in the euro area. In this study, we apply the Ho and Saunders (1981) theoretical framework to identify explanatory factors of spreads and use the obtained econometric estimates to calculate the so-called pure spread by subtracting the influence of the bank funding structure and other bank-specific factors from the interest rate spread. Our study shows that even after accounting for the conventional determinants of interest rate spread, differences in the pure spread between euro area countries, especially between the Baltic countries and the rest of the euro area, persist. In part, these differences can be explained by varying degrees of financial sector market concentration. However, the bulk of the gap in spreads remains unexplained. The findings of this paper suggest that properly designed policy measures are needed to reduce spreads in the Baltic countries and lessen the fragmentation in the euro area. This would allow for more effective monetary policy transmission and stimulate lending and post-Covid economic recovery in the Baltics.
Designing a Combined World and Story Procedural Content Generation Engine

Procedural content generation (PCG) has seen relative widespread adoption in games and game development. While PCG methods are gaining traction in commercial games, there are comparatively fewer cases where PCG has been used to generate the game world, characters, and the narrative for a single game. We present our research, collaborating with a small independent game studio, on the design of a game engine designed to generate a world, characters, supporting quests and overarching narrative content for their upcoming game. We discuss our choice of game artiﬁcial intelligence algorithms, decisions made in designing our system, and our progress and implementation under the constraints and requirements given to us. We posit our system will generate a rich game world with an explainable world history for players to explore and quests to complete. We believe that our engine will meet their requirements increasing the replayability of the game, reducing the authorship burden, while providing the authors with signiﬁcant control over quest and narrative structure.
Evaluation in the computational calculus is non-conﬂuent

In Moggi’s computational calculus, reduction is the contextual closure of the rules obtained by orienting three monadic laws. In the literature, evaluation is usually deﬁned as the closure under weak contexts (no reduction under binders): E = (cid:104)(cid:105) | let x : = E in M . We show that, when considering all the monadic rules, weak reduction is non-deterministic, non-conﬂuent
SEPRES: Sepsis prediction via a clinical data integration system and real-world studies in the intensive care unit

Hospital for the real-world study of sepsis early warning on ICU management. The trial was registered with ClinicalTrials.gov (NCT05088850). Findings: Our best early warning model achieved an area under the receiver operating characteristic curve (AUC) of 0·9833 in the task of detecting sepsis in 4-h preceding on the open-source database, while our ensemble model achieved an AUC of 0·9065  0·9436 in the retrospective research from 1  5-h preceding on the private database, and 0·8636  0·8992 in real-time real-world studies using the data integration system in the ICU of the Ruijin Hospital. In the continuous early warning process of patients admitted to the ICU, 22 patients who met the diagnostic criteria for sepsis during hospitalization were predicted as positive cases; 29 patients without sepsis were predicted as negative cases. Additionally, 17 patients were predicted as false-positive cases; in six patients with sepsis during ICU stay, the predicted probabilities at different time nodes were all less than the warning threshold 0·7 and predicted as false-negative cases. Interpretation: Machine learning models could allow accurate and real-time inference to detect sepsis onset within 5-h preceding at most with the help of the data integration system. We identified the features such as age, antibiotics, ventilation, and net balance to be important for the sepsis prediction inference. We argue that this system has promising potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.
Agent-Based Computational Economics: Overview and Brief History 1

: Scientists and engineers seek to understand how real-world systems work and could work better. Any modeling method devised for such purposes must simplify reality. Ideally, however, the modeling method should be ﬂexible as well as logically rigorous; it should permit model simpliﬁcations to be appropriately tailored for the speciﬁc purpose at hand. Flexibility and logical rigor have been the two key goals motivating the development of Agent-based Computational Economics (ACE) , a completely agent-based modeling method characterized by seven speciﬁc modeling principles. This perspective provides an overview of ACE, a brief history of its development, and its role within a broader spectrum of experiment-based modeling methods.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Jun 20, 2021 Revised Dec 15, 2021 Accepted Dec 27, 2021 Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.
Frequency Detectors for PLL Acquisition in Timing and Carrier Recovery

A significant problem in phase-locked loop (PLL) timing and carrier extraction is the initial acquisition. Very narrow loop bandwidths are generally required to control phase jitter, and acquisition may depend on an extremely accurate initial veo frequency (VCXO) or sweeping. We describe two simply implemented frequency detectors which, when added to the traditional phase detector, can effect acquisition even with very small loop bandwidths and large initial frequency offsets. The first is the quadricorrelator, previously applied to timing recovery by Bellisio, while the second is new, and called a rotational frequency detector. The latter, while limited to lower frequencies and higher signal-to-noise ratios, is suitable for many applications and can be implemented with simpler circuitry.
The Effect of Corporate Water Disclosure on Financial Performance

Natural resources crises specifically the water crises are listed in the top ten global risk in 2021. Water demand and withdrawal increase as the population rise. To mitigate water scarcity, companies disclose more on water related information as an engagement to achieve the Sustainable Development Goal (SDG). Motivated towards exploring the impact of corporate water disclosures, this study aims to examine effect corporate water disclosures for five years to financial performance of the sample companies. The sample companies are the electric utilities companies that listed as the top 50 market capitalisation in the sector. The water related information disclosure in this study include resource reduction policy, policy of water efficiency policy of environmental supply chain, target water efficiency and environmental management team. This study found that corporate water information disclosures including resource reduction policy and policy of water efficiency) have positive significant relationship on earnings per share (EPS). However, the target water efficiency is negatively significant with EPS which explain the behaviour of electric utilities companies all this while.
Low-cost laser diode pulse generator for quantum information applications

A simple short-pulse generator circuit based on electronic gates is designed for short electric pulse of about 12 ns at Full Width at Half Maximum (FWHM) and 3.6 Volt amplitude for driving a laser diode. Using our circuit with a 780 nm laser diode designed and fabricated for producing short light pulses. The circuit utilizes an AND gate, a XOR gate, and a common function generator, provides a repetition rate from DC up to 1 MHz. The laser pulses were generated and then detected via an avalanche photodiodes (APD). This finding can benefit the field of light-based quantum information including single photon experiments.
Estimationof Biomass and Carbon Sequestration by Non-Destructive Method in Dry Deciduous Forest of Shivpuri, Madhya Pradesh, India

The study aims to assess the plant biomass and carbon storage potential of the forests of Shivpuri, Madhya Pradesh, India. The study was carried out in Satanwada Range in Shivpuri Forest Division. A total of 96 quadrats of 10×10 m2 were laid. The non-destructive algometric method was used to estimate carbon sequestration. The results show that Acacia catechu (202 individuals/ha) dominated the forest, followed by Anogeissus pendula (90 individuals/ha). The total biomass of the forest was estimated to be 34.72±0.41t/ha, with Boswellia serrata (7.943t) recording the highest. The minimum biomass was recorded for Grewia sp (0.06t). The highest carbon content was found in B. serrata (3.97t; 25 individuals) followed by A. Catechu (2.92t; 195 individuals). Although A. catechu was dominant in the area, due to its lower girth class and young age, the net carbon storage was less than that of B. serrata. It was found that plots with higher carbon stock had higher species richness than plots composed of single species. Therefore, plantations of mixed native species should be preferred for future restoration activities as they are more efficient in sequestrating carbon than monoculture plantations.
Indie Rocks! Mapping Independent Video Game Design

Though many video games scholars and journalists tend to train their sights on ‘big gaming’, there is a vibrant and varied sector of independent game design, production and distribution. Indie gaming is not a unitary field and, as well as producing a diverse range of games, indie designers occupy a range of positions vis-à-vis mainstream video gaming. Therefore, while this article gives examples of this diversity, it is by no means an exhaustive account. Industry watchers and events are together suggesting that low-cost, independent modes of production will become increasingly important and prevalent in the immediate future. Scholars and practitioners alike will do well to understand the historical trajectories of indie design, and to keep pace with its present and future diversity.
OPTIMIZATION OF PHENOLIC WASTES TREATMENT IN ARTIFICIAL PRODUCED WATER BY PSEUDOMONAS AERUGINOSA OPTIMALISASI PENGOLAHAN LIMBAH FENOL DALAM AIR TERPRODUKSI ARTIFISIAL OLEH BAKTERI

The purpose of this research is to obtain the optimum condition of phenol biodegradation in arti cially produced water using Pseudomonas aeruginosa. The screening of medium in the highest phenol degradation at 99.85% in medium M5, followed by M1, M4, M3, and M2, at 99.83%, 28.47%, 27.64%, and 0.09%, respectively, during the 24 hours incubation time. Medium M5 and M1 are relatively expensive if applied in the eld, thus optimization of incubation time, temperature, pH, and the adding of oil is needed in the treatment parameters affecting the phenol biodegradation process using an easy and cheap medium, M4. This research resulted in phenol biodegradation percentage at optimum condition for 2 days incubation, pH 7, and temperature 30°C, was 99.98%. The adding of oil at optimum condition: 100 mg/L could degrade phenol waste to 28.01%.
Minimizing Congestion in TCP Networks Using Active Queue Management: A Study on the Effectiveness by using PID-Chemical Reaction Optimization Algorithm

This study presents the Chemical Reaction Optimization (CRO) algorithm for making sense of most dependable parameters of the PID controller as an improvement invention for the end-to-end congestion control using the technique of Active Queue Management (AQM) which can safeguard a minute amount of queuing delay. By the use of a set of CRO rules, even though the PI (Proportional-Integral) controller for AQM design improves the solidness, the transitory exhibition of the PI controller isn't at the best, which incorporates the managing time that is excessively long. Therefore, as to win over this drawback, this paper, the PID (Proportional-Integral-Differential) controller is proposed to accelerate the responsiveness of AQM machine. Because of this, lately, researchers have carried out a random search approach which includes Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) to discover the choicest parameters for PID controller. Among modern-day heuristics algorithms, Chemical Reaction Optimization (CRO) turned and it combines functions of both GA and Simulated Annealing (SA) to find worldwide minimal in seeking space. The test model reproduction results effectively show the adequacy of the proposed calculation and its preferences over other current calculations.
Characteristics of Surface Temperature and Its Relationship with Meteorological Elements in China in the Last 73 Years

Climate, as the natural environment on which human life depends, is intricately linked to human society. This paper focuses on the characteristics of temperature and its relationship with meteorological elements in China in the last 73 years. The data of this research is from NCEP/NCAR Reanalysis Monthly Means. This study adopts the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) methods to study the surface temperature characteristics within China, and the synergistic variation between surface temperature and precipitable water content, wind field, and relative humidity in China. The results show that 1980s is a turning point for changes in surface temperature, precipitable water content, wind field, and relative humidity in China. Before 1980s, the temperature in China is low, while after this period, the temperature in China is high and China’s exposure to global warming has increased. Temperature is dominated by negative potential-phase oscillations with relative humidity and wind fields. In the north, temperature and precipitable water content have negative potential-phase oscillations, while temperature and precipitable water content have positive potential-phase oscillations in the south. In the central region of Xinjiang, temperature and precipitable water content have weak negative potential-phase oscillations, while temperature and wind field have positive potential-phase oscillations.
Identification of antioxidant components of Gayo Arabica Coffee Cascara using the GC-MS method

Approximately half of the coffee will become by-product in coffee processing. This by-product needs a special focus because of its content can be pollutant for nature. One of the coffee by product that underutilized is cascara, composed by pulp and husk. The major by-product of dry process is husk whereas pulp is the major by-product from wet process. Nowadays, there are various ways to utilize coffee by-product. Previous studies have shown that coffee has a potent antioxidant activity which can be a solution in dermatology problem. This research aims to identify the antioxidant component of coffee arabica husk and pulp of Gayo, Aceh Tengah, Aceh using GC-MS method. One of the compounds that identified in both coffee husk and pulp is caffeine. Caffeine is a potential natural antioxidant and it also has anticellulite effect to skin. The other compounds found in coffee husk and pulp also have benefit in dermatology. We conclude that coffee arabica husk and pulp can be a potential natural source to eradicate dermatologic problem.