Miracle-dz/UniFAR-FTUs huggingface dataset:
Provision of Frequency Stability of an Islanded Microgrid Using a Novel Virtual Inertia Control and a Fractional Order Cascade Controller

Nowadays, the renewable energy sources in microgrids (MGs) have high participation to supply the consumer’s demand. In such MGs, the problems such as the system frequency stability, inertia, and damping reduction are threatened. To overcome this challenge, employing the virtual inertia control (VIC) concept in the MG structure could be considered as a viable solution to improve the system frequency response. Hence, this work proposes a novel modeling for VIC in an islanded MG that provides simultaneous emulation of the primary frequency control, virtual inertia, and damping. To show the efficiency of the proposed technique, a comparison is made between the dynamic performance of the proposed VIC and conventional VIC under different scenarios. The results indicate that the proposed VIC presents superior frequency performance in comparison with conventional VIC. In addition to VIC modeling, a new cascade controller based on three-degrees of freedom and fractional-order controllers (FOCs) is proposed as an MG secondary controller. The effectiveness of the proposed controller is compared to tilt-integral-derivative and FO proportional-integral-derivative controllers. The Squirrel search algorithm is utilized to obtain the optimal coefficients of the controllers. The results demonstrate that the proposed controller improves the MG frequency performance over other controllers. Eventually, the sensitivity analysis is performed to investigate the robustness of the proposed controller in the face of the variations of the parameters.
Effectiveness of Double-hit Model (Post-weaning Social Isolation and NMDA Receptor Antagonist) in the Development of Schizophrenic Like Symptoms on Rodents: A Protocol for a Systematic Review.

BackgroundSchizophrenia is a heterogeneous neuropsychiatric disorder, categorized by positive, negative, and cognitive symptoms. In trying to improve diagnosis and treatment of schizophrenia researchers have turn to “dual hit” models of schizophrenia that are able to reproduce all symptoms of the disorder. The main objective of this protocol is to present a transparent process on how we plan to review the existing international literature on effectiveness of “dual hit” models used to induce schizophrenia on rodents. MethodsLiterature search strategies will be developed using medical search headings (MeSH). The MEDLINE (PubMed), EMBASE, and Google Scholar databases will be used to search for published studies electronically. We will search for studies involving inducing schizophrenic symptoms using “dual hit” rodent model (post-weaning social isolation and NMDA receptor antagonist). Studies will be screened by titles, abstracts, keywords, and synonyms followed by identifying the full-text articles. All studies that will pass the quality assessment will be included. Data will be extracted by two authors independently and in duplicate, from each eligible study in ensuring that there is consistency between reviews. If the design and comparator are sufficiently homogenous for all studies, meta-analysis will be conducted using a random-effect model.DiscussionThe results of this review will contribute in the development of new “dual hit” models that will be able to characterize schizophrenia symptoms better. It will also shed light to researchers on new developments that need to be made in improving animal models of schizophrenia.Systematic reviews registrationPROSPERO CRD42021247585
An exploration of how people living in Belem, a city in the Amazon region of Brazil, perceive their occupational lives: considerations to identify potential implications for occupational therapy services

ABSTRACT This study aimed to examine the occupational lives of persons served in the Basic Health Unit or employed in the National Institute of Social Security within communities in Belem, Brazil. A mixed-method design was performed with qualitative interviews and two quantitative measurements, the Canadian Occupational Performance Measure, and the Health Assessment Questionnaire. The intent was to provide occupational therapists a better understanding of the need for their service in both institutions. The two populations included were similar regarding age, gender, and race, however, although connected geopolitically, differences in educational level, income, and access to goods and services were noted. The results demonstrated that both groups experienced difficulties and unfulfilled wishes for their occupational performances and lack of occupational engagement, due to environmental circumstances, which may lead to a risk of occupational injustice. This study contributes valuable knowledge which may determine if occupational therapy services could benefit these populations.
Child and Adolescent Anxiety as a Result of the COVID-19 Pandemic

As the coronavirus disease 2019 (COVID-19) pandemic has spread, so has the psychological impact of the disease been felt worldwide. Among the various types of psychological problems that are caused by COVID-19, anxiety poses a great threat to the physical and mental health of children and adolescents. With an aim of advancing the current work of diagnosing and treating child and adolescent anxiety as a result of the COVID-19 pandemic, this chapter discusses this noticeable global health issue focusing on the following key parts: possible etiology, clinical characteristics, diagnosis and available therapeutic options.
Emotions and the body. Testing the subtraction argument

ABSTRACT Can we experience an emotion without the feeling of accelerated heartbeats, perspiration, or other changes in the body? In his paper “What is an emotion”, William James famously claimed that “if we fancy some strong emotion and then try to abstract from our consciousness of it all the feelings of its bodily symptoms, we find we have nothing left behind”. Thus, bodily changes are essential to emotion. This is known as the Subtraction Argument, and proponents of Embodied theories of emotion still use it to defend their claims. Proponents of Disembodied theories, however, have different intuitions regarding the role of bodily changes in the experience of emotion. In order to advance this debate, I conducted a series of studies testing people’s intuitions regarding the Subtraction Argument. Results show that, against James’ claims, most people consider that their hypothetical (Study 1) and actual (Study 2) emotions persist in the absence of bodily feelings. Furthermore, Study 1 found that participants’ responses were not related to individual differences in cognitive reflection or interoceptive awareness. These results pose a problem for Embodied theories of emotion and challenge extant explanations for the persistence of emotional experience in cases of impaired interoception.
Physics of Space Weather Phenomena: A Review

In the last few decades, solar activity has been diminishing, and so space weather studies need to be revisited with more attention. The physical processes involved in dealing with various space weather parameters have presented a challenge to the scientific community, with a threat of having a serious impact on modern society and humankind. In the present paper, we have reviewed various aspects of space weather and its present understanding. The Sun and the Earth are the two major elements of space weather, so the solar and the terrestrial perspectives are discussed in detail. A variety of space weather effects and their societal as well as anthropogenic aspects are discussed. The impact of space weather on the terrestrial climate is discussed briefly. A few tools (models) to explain the dynamical space environment and its effects, incorporating real-time data for forecasting space weather, are also summarized. The physical relation of the Earth’s changing climate with various long-term changes in the space environment have provided clues to the short-term/long-term changes. A summary and some unanswered questions are presented in the final section.
TURKISH UNIVERSITY STUDENTS’ CAUSAL ATTRIBUTIONS TO THEIR PERCEIVED ACHIEVEMENTS AND FAILURES IN LEARNING ENGLISH

This study aimed to look into Turkish learners’ examples of success and failure, and attributions to their perceived achievements and failures in learning English as a foreign language. Data were collected from 41 students majoring in physics, metallurgy engineering and paramedics at a public university in Turkey. An open-ended questionnaire was translated into Turkish and delivered to the students to obtain data. Research findings firstly showed that one-third of the participants did not recount an example of success or failure, which might point out learners’ lack of self-awareness and self-assessment. Furthermore, their examples of success and failure mainly were grade-oriented. This finding might display the influence of the Turkish educational system focusing on academic achievement which is primarily evaluated by exam results. Secondly, students were found to attribute both their achievements and failures to effort as an internal and controllable factor. Another important factor was found to be motivation; hence the importance of motivating university students who claimed their own responsibility for success and failure arises.
Factor structure of early numeracy: evaluation of a measurement model in greek-speaking children with intellectual disabilities

Exploring the individual differences of the longitudinal growth of early numeracy (EN) in young children with Intellectual disabilities (IDs) prerequires the critical stage of exploring and validating the potential factor structure. Despite the fact that Relational Skills (RS), Counting Skills (CS) and Operations (O) are expected to constitute distinct domains of EN, there is not sufficient evidence to support either the above position or the position that they are different means of assessing a general-informal numeracy skill construct. This study was designed to shed light in the field through the evaluation of a measurement model describing the structure of RS, CS and O domains of EN and their interrelation. The sample included N = 155 children with IDs, enrolled in special school classes, mentally aged between 5;02 (y;m) and 6;10 (M = 5.11, SD = 0.974). Confirmatory Factor Analysis indicated a “good fit” of the two-factor model (RS, CS + O) of EN in ID (RMSEA=.029 < 0.08, CFI = 0.98 ≥ 0.90 and SRMR = 0.000 < 0.08). No measurement invariance across gender was reported for the proposed two-factor model of EN. The nested EN models validated full measurement invariance across gender. Finally, educational implications are discussed.
Finite-Time Performance Guaranteed Event-Triggered Adaptive  Control for  Nonlinear Systems with Unknown Control Direction

This paper studies the issue of finite-time performance guaranteed event-triggered (ET) adaptive neural tracking control for strict-feedback nonlinear systems with unknown control direction. A novel finite-time performance function is first constructed to describe the prescribed tracking performance, and then a new lemma is given to show the differentiability and boundedness for the performance function, which is important for the verification of the closed-loop stability. Furthermore, with the help of the error transformation technique, the origin constrained tracking error is transformed into an equivalent unconstrained one. By utilizing the first-order sliding mode differentiator, the issue of ``explosion of complexity'' caused by the backstepping design is adequately addressed. Subsequently, an ingenious adaptive updated law is given to co-design the controller and the ET mechanism by the combination of the Nussbaum-type function, thus effectively handling the influences of the measurement error resulted from the ET mechanism and the challenge of the controller design caused by the unknown control direction. The presented event-triggered control scheme can not only guarantee the prescribed tracking performance, but also alleviate the communication burden simultaneously. Finally, numerical and practical examples are provided to demonstrate the validity of the proposed control strategy.
Cervical Cancer Metastasis and Recurrence Risk Prediction Based on Deep Convolutional Neural Network

Evaluating the risk of metastasis and recurrence of a cervical cancer patient is critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the testing of tens to thousands of genes from patients’ tissue samples, which is expensive and time-consuming. Therefore, computer-aided diagnosis and prognosis prediction based on hematoxylin and eosin (H&E) pathological images have received much attention recently. The prognosis of whether patients will have metastasis and recurrence can support accurate treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical cancer patient. To address this problem, we propose a hybrid method. Transfer learning is used to extract features, and it is combined with traditional machine learning in order to analyze and determine whether or not patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches using a color-based method from Hamp;E pathological images, which were then subjected to image preprocessing steps such as image normalization and color homogenization. Based on the labeled patched images, the Xception model with good classification performance was selected, and deep features of patched pathological images were automatically extracted with transfer learning. After that, the extracted features were combined to train a random forest model to predict the label of a new patched image. Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient based on the predictions of patched images from the whole-slide H&E image. In our experiment, the proposed model yielded an area under the receiver operating characteristic curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features extracted by the deep convolutional neural network from the whole-slide image could be used to predict the risk of recurrence and metastasis after surgical resection and help identify patients who might receive additional benefit from adjuvant therapy.
From Stationary to Remote: Employee Risks at Pandemic Migration of Workplaces

The first lockdown due to COVID-19 in the year 2020 created a particular scenario that forced a change to telework among diverse professions and social groups. This article presents the results of research carried out among samples of Polish, Lithuanian and Spanish remote workers concerning working conditions in organizations and at home, and the potential impact of some professional hazards from home-based telework. On the contrary to earlier published papers on pandemic-induced telework that focused on how the limitations at home of first-time remote workers impacted on their well-being and work–family balance, our research contributes to a more recent endeavor that focuses the analysis on the work design perspective. The results of the survey indicate that employees felt more stressed and in conflict at their remote workstations when they had to telework during the lockdown, and that this negative output was significantly related to the deterioration of some working dimensions like space, quality and design but not to the perception of professional hazards from home-based telework. According to our research, the forced situation seemed not to be a favorable factor for implementing changes in light of the insufficient technical and organizational preparation of employers as well as the employees’ mental preparation. It should be necessary to update sequentially the results of the epidemic-induced telework and conduct research for various stages of the pandemic and the subsequent economic recovery. This could help popularize remote work as one of the tools of the labor market in the future and as a tool for treating labor resources as an element of sustainable development.
Spatio-Temporal Pain Estimation Network With Measuring Pseudo Heart Rate Gain

Pain is a significant indicator that shows people are suffering from an unwell experience and its automatic estimation has attracted much interest in recent years. Of late, most estimation methods are designed to capture the dynamic pain information from visual signals while a few physiological-signal based methods can provide extra potential cues to analyze the pain more accurately. However, it is still challenging to capture the physiological data from patients as it requires contact devices and patients’ cooperation. In this paper, we propose to leverage the pseudo physiological information by generating new modal data from the original visual videos and jointly estimating the pain by an end-to-end network. To extract the representations from bi-modal data, we design a spatio-temporal pain estimation network, which employs a dual-branch framework for extracting pain-aware visual and pseudo physiological features separately and fuses the features in a probabilistic way. The inherent vital sign, i.e., heart rate gain (HRG), from pseudo physiological information can be utilized as an auxiliary signal and integrated with the visual pain estimation framework. Moreover, specially-designed 3D convolution filters and attention structures are employed to extract spatio-temporal features for both branches. To use the HRG as an auxiliary way for pain estimation, we propose a probabilistic inference model by jointly considering the visual branch and physiological branch, which makes our model estimate the pain comprehensively. Experiments on two publicly-available datasets show the effectiveness of introducing the pseudo modality, and the proposed method can outperform the state-of-the-art methods.
FORMULATION AND CHARACTERIZATION OF SUSTAINED RELEASE MATRIX TABLETS OF IVABRADINE USING 32 FULL FACTORIAL DESIGN

Objective: Ivabradine (IB) is anti-Ischemic drug and used for the symptomatic management of stable angina pectoris. IB acts by reducing the heart rate in a mechanism different from beta blockers and calcium channel blockers, two commonly prescribed anti-anginal drugs. IB has a short biological half-life and the dose of 5/7.5 mg twice a day. In this present study, an attempt has been made to prepare sustained release tablet of IB to achieve the desired drug release. Methods: The sustained release polymers, hydroxypropyl methylcellulose K100M (HPMC K100M), guar gum (GG) and xanthan gum (XG) were taken for the preliminary trail from which guar gum and xanthan gum had shown better drug release. Initially, drug-excipients compatibility studies were carried out by using Fourier transformed infrared spectroscopy (FTIR) and Differential Scanning Calorimetry (DSC) which showed no interaction between drug and excipients. Tablets were prepared by wet granulation technique and evaluated for pre-compression and post-compression parameters. Results: 3 2 full factorial design was applied to achieve controlled drug release up to 24 h. The concentration of GG (X 1 ) and XG (X 2 ) were selected as independent variables and the % CDR at 2 h. (Y 1 ) and 18 h. (Y 2 ) were taken as dependent variables . In vitro drug release study revealed that as the amount of polymers increased, % CDR decreased. Conclusion: Contour as well as response surface plots were constructed to show the effect of X 1 and X 2 on % CDR and predicted at the concentration of independent variables X 1 (10 mg) and X 2 (10 mg) for a maximized response. The optimized batch (O1) was kept for stability study at 40±2 °C/75±5 %RH for a period of 6mo according to ICH guidelines and found to be stable.
Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.
Cavity-Enhanced Near-Infrared Organic Photodetectors Based on a Conjugated Polymer Containing [1,2,5]Selenadiazolo[3,4-c]Pyridine

: Miniaturization of spectrometers can be achieved using an array of narrowband photodetectors responsive at speci ﬁ c wavelengths. In this work, sensitive organic narrowband photodetectors with tunable spectral response in the near-infrared (NIR) wavelength range are demonstrated. This is achieved using a resonant optical-cavity device architecture based on a thick ZnO spacer to increase the weak NIR absorption of a low-band gap conjugated polymer containing [1,2,5]- selenadiazolo[3,4-c ]pyridine. We realize spectral response full-width at half-maximum (FWHM) values down to 22 nm and achieve calculated peak speci ﬁ c detectivity (D * ) over 10 11 Jones in these cavity-enhanced photodetectors. More importantly, we achieve continuously tuned detection wavelengths over a wide wavelength range of 660 − 1510 nm. This allows for a proof-of-concept demonstration of a miniaturized spectrometer for the NIR wavelength range and can resolve the NIR characteristic absorption bands of water, ethanol, and acetone.
What drivers the decoupling of CO2 emissions in the Indonesian manufacturing industry? An analysis of firm level factors

ABSTRACT This study identifies determinants (firm characteristics) that drive the decoupling of CO2 emissions in the Indonesian manufacturing industry. The estimation method applied was Seemingly Unrelated Regression (SUR). The data were sourced from Indonesia’s large and medium industry statistics survey for 2010–2015, prepared by Statistics Indonesia. The estimation results show that a reduction in tax/price of gas fuel or will encourage decoupling. Medium-tech firms are more polluting, while foreign investors-owned firms and firms in the Java-Bali region are less polluting, and could encourage decoupling. The Indonesian government should implement technology transfer by following the energy conversion pattern from petroleum fuels to gas fuels, increase capitalisation to firms that tend to be heavy polluters, small firms, and firms owned by domestic investors and outside Java-Bali. Novelty this study is linking firm characteristics as a decoupling driving factor of CO2 emissions in the manufacturing industry.