Miracle-dz/UniFAR-FTUs huggingface dataset:
Closed loop system for controlling insulin infusion

(57) Abstract: closed loop infusion system for controlling the velocity of the fluid injected into the body of a user. Closed loop infusion system includes a sensor system, and a control unit, and a delivery system. The sensor system includes a sensor for monitoring the condition of the user. Sensor emits a sensor signal representative of the condition of the user. Controller input using the sensor signal is issued. The controller generates a command for operating the delivery system using the control device input. Delivery system for injecting a liquid to a user at a rate dictated by a command from the control unit. Preferably, the sensor system monitors the glucose concentration in the body of the user, the liquid to be injected into the body of the user by the delivery system includes insulin. The sensor system generates a message to be sent to the delivery system using the sensor signal. The message includes information used to generate the controller input. The sensor may be a subcutaneous sensor in contact with interstitial fluid. The sensor system may use two or more sensors.
Historical development of the BFGS secant method and its characterization properties

Historical Development of the BFGS Secant Method and Its Characterization Properties by Joanna Maria Papakonstantinou The BFGS secant method is the preferred secant method for finite-dimensional unconstrained optimization. The first part of this research consists of recounting the historical development of secant methods in general and the BFGS secant method in particular. Many people believe that the secant method arose from Newton’s method using finite difference approximations to the derivative. We compile historical evidence revealing that a special case of the secant method predated Newton’s method by more than 3000 years. We trace the evolution of secant methods from 18th-century B.C. Babylonian clay tablets and the Egyptian Rhind Papyrus. Modifications to Newton’s method yielding secant methods are discussed and methods we believe influenced and led to the construction of the BFGS secant method are explored. In the second part of our research, we examine the construction of several rank-two secant update classes that had not received much recognition in the literature. Our study of the underlying mathematical principles and characterizations inherent in the updates classes led to theorems and their proofs concerning secant updates. One class of symmetric rank-two updates that we investigate is the Dennis class. We demonstrate how it can be derived from the general rank-one update formula in a purely algebraic manner not utilizing Powell’s method of iterated projections as Dennis did it. The literature abounds with update classes; we show how some are related and show containment when possible. We derive the general formula that could be used to represent all symmetric rank-two secant updates. From this, particular parameter choices yielding well-known updates and update classes are presented. We include two derivations of the Davidon class and prove that it is a maximal class. We detail known characterization properties of the BFGS secant method and describe new characterizations of several secant update classes known to contain the BFGS update. Included is a formal proof of the conjecture made by Schnabel in his 1977 Ph.D. thesis that the BFGS update is in some asymptotic sense the average of the DFP update and the Greenstadt update.
Adaptive Grid Impedance Shaping Approach Applied for Grid-Forming Power Converters

This paper proposes an adaptive grid impedance shaping approach, implemented through a variable structure virtual impedance control strategy (VS-VIS), to improve the power flow control of grid-forming power converters (GF-PC). In this strategy, an equivalent grid impedance estimator (EGIE) evaluates the feeder X/R ratio to determine the required virtual impedance to reach the grid shaping objective. Besides, the virtual impedance definition also considers the converter power rating to ensure the system’s feasibility. A virtual negative resistance series interconnected with a positive virtual reactance implements the virtual impedance. Moreover, the required virtual impedance is only updated when the X/R ratio extrapolates deviation limits determined by allowed grid voltages boundaries specified by the standards. Besides, the virtual reactance is split into two parts that use the conventional and sliding mode (SMC) approaches. The first implements the main part of required virtual reactance, while the second provides a fine-tuning action matching the X/R control objectives and compensating grid impedance uncertainties. This grid-shaping method provides a smooth compensation increasing the GF-PC resilience under grid impedance variation. These features constitute a novelty compared to the shaping methods presented in the literature. Experimental tests obtained from a GF-PC laboratory setup demonstrate the effectiveness and robustness of the proposed grid impedance shaping method.
On degrees and genera of curves on smooth quartic surfaces in P3

Our result is motivated by the results [GP] of Gruson and Peskin on characterization of the pair of degree d and genus g of a non-singular curve in P3. In the last step, they construct the required curve C on a singular quartic surface when Here we consider curves on smooth quartic surfaces.
A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multiple-input multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.
FORMATION OF STUDENTS' COMMUNICATIVE COMPETENCE WITH THE HELP OF NONFICTION TEXTS

The importance of forming communicative competence through the lens of nonfiction texts is considered in the article. Several directions are offered - exercises on forming the culture of publicistic speech of students in the process of learning German, and the effectiveness of using the basic communicative method of teaching a foreign language in higher pedagogical education. Communication is defined as communication, combination, transmission, and perception of information in interpersonal relationships that implies a communication process. The norm of communication depends on adherence to certain moral qualities and the culture of speech of the interlocutors, in our case students. Today, as ever, foreign language is becoming an effective factor in the socio-economic, scientific, technical, and cultural progress of society. In the article, nonfiction texts are also considered as one of the factors in the formation of nonfiction speech in student youth. The culture of nonfiction, after all, is the meaning of language and its use in the whole set of functional styles inherent in it. After all, according to the authors, the texts of newspaper and journalistic journalism most strongly encourage students to exchange ideas and opportunities for creative self-realization, formation of critical thinking, associative thinking, and spontaneous foreign language.
Cadmium and cobalt pollution and their effect on behavior in children and adolescents and its social dimensions

Karbala provides, theoretical assistance for Cadmium and Cobalt pollution prevention, by determining blood, levels and identifying relevant risk, factors among adolescents. Blood Cd and Co levels determine by Graphite Furnace . Atomic Absorb, piton Spectrometry (GFAAS) ) result. Methods blood samples were taken from both males and females. A total of 100 subjects (average age of 13-17 years, 8 females, and 92 males) were included. The arithmetic means Cadmium concentration were patients their number 50 and control also 50 Mean ± SE( 25.38± 0.92 ),( 11.88± 0.89) resp. while Samples Patients and control (T=10.55)In addition to Cobalt concentration were patients their number patients and control Mean ±SE(64.00 ± 3.1),( 30.71± 2.27) resp. and value(T=8.69) Conclusions”. The levels of Cadmium and Cobalt in the” blood of adolescents 13 to 17 years in Karbala governorate were higher than in other areas due to leaving schools” and working in different” professions at an early “age and living in unhealthy families.
The effectiveness of Mindfulness Based on Cognitive Therapy (MBCT) on quality of life and optimism of adolescent pregnant women

Background: Pregnancy during adolescence causes a person to face many challenges, so that its contradiction with the adolescent's identity structure can have negative effects on her psychological dimension, especially on optimism and quality of life. Since the health of the pregnant mother is one of the health priorities of any country, the present study aimed to investigate the effectiveness of mindfulness (MBCT) on the quality of life and the optimism of adolescent pregnant mothers. Methods: The present study was performed as an interventional study with a pre-test-post-test design on 88 adolescent pregnant women referring to health centers in Ahvaz. Participants were divided into 44 groups of intervention and control. For the intervention group, 8 sessions of 90-minute mindfulness counseling were held weekly. The control group also received routine pregnancy care. Participants completed the LOT-R and SF-36 quality of life questionnaires before and after the intervention. Data were analyzed by SPSS26 software. Findings: There was a significant difference in the overall quality of life between the control and experimental groups before and after the intervention (p <0.001).
Physical custody arrangements and fathers’ post-separation well-being

ABSTRACT Joint physical custody (JPC) is an emerging post-separation care arrangement in which children spend considerable periods of time with both of their parents. Although there is some research on JPC that has focused on children’s well-being, little is known about how this physical custody arrangement affects fathers’ well-being. Therefore, this study investigated the well-being of fathers with symmetric JPC and of non-resident fathers whose children live either mostly or exclusively with their mother. The statistical analysis draws on longitudinal data from waves 4–12 of the German Family Panel (pairfam). Random- and fixed-effects regression models were estimated for 1,372 observations of 389 fathers. The results of the bivariate random-effects models showed that fathers with symmetric JPC reported higher life satisfaction than other fathers. However, the fixed-effects model indicated that these differences were due to positive selection into symmetric JPC. Moreover, the results of the multivariate random- and fixed-effects models showed no statistically significant relationship between physical custody arrangements and fathers’ stress, depressiveness, life satisfaction, and general health. Taken together, this study suggests that physical custody arrangements are unrelated to fathers’ post-separation well-being while hinting at the relevance of selection processes among post-separation families.
Staggere d oriente d airfoil shape d pin-ﬁn heat sink: Investigating the eﬃcacy of novel water based ferric oxide-silica hybrid nanoﬂuid

Nowadays, electronic components are one of the essential parts of almost every smart device. To eﬃ- ciently transfer the desired amount of heat, recent studies have focused on investigating the potential of advanced thermal coolants and heat sink conﬁgurations. Current study reveals the potential of novel water-based hybrid nanoﬂuid of silica (SiO 2 ) and ferric oxide (Fe 2 O 3 ) for cooling high-heat-generating electronic devices. The experimental work was conducted to inspect the heat transfer characteristics of a uniquely designed staggered oriented airfoil shaped pin-ﬁn heat sink employing Fe 2 O 3 -SiO 2 hybrid nanoﬂuid with different mixture ratios (25%:75%), (50%:50%), (75%:25%). Airfoil shaped pin-ﬁns offer less resistance to ﬂuid ﬂow and maximum effective area due to their unique shape and delayed separation of ﬂuid at the rear end. All the mixture ratios were tested at three different heating powers (75, 100, 125 W) with varying Reynolds number in laminar ﬂow regime. Experimental results revealed that the ﬂuid having a mixture ratio of 50:50 showed the least thermal resistance followed by 25:75 and 75:25. Maxi- mum enhancement of 17.65% in average Nusselt number was observed against the heating power of 75W. Pumping power was found to increase with the supplementation of nanoparticles in base ﬂuid, while a little variation was observed among different mixture ratios. Finally, the results were compared with recently published studies, which revealed that the airfoil shaped ﬁns have better thermal characteristics and offer less resistance to ﬂuid ﬂow.
Deep learning-based alzheimer disease detection techniques

Alzheimer disease (AD) is one of the most common degenerative illnesses of the elderly worldwide. It is a progressive neurological condition that impairs cognitive memory. As a result, Alzheimer's sufferers struggle to recall daily activities, recollect family members, and solve logical problems. Medication which reduces the creation of proteins, block data communication between brain neurons and it can also delay the course of Alzheimer's disease. Mild Cognitive Impairment (MCI) seems to be a common disorder that does not usually progress to Alzheimer's. It is difficult to find patients with modest cognitive decline who may acquire Alzheimer's. As a result, creating deep learning-based disease detection techniques to assist clinicians in detecting prospective Alzheimer's patients is crucial. The performance comparison of the Imaging, Electronic Health Record (EHR), and Single nucleotide polymorphisms (SNP) datasets is evaluated using the metrics Accuracy, Sensitivity, Specificity, and Multi Area. Different mistakes are added under the curves for gradient calculation.
Machine‐Learning Estimation of Snow Depth in 2021 Texas Statewide Winter Storm Using SAR Imagery

The frequency of extreme climate events has escalated since 1980. In February 2021, an unprecedented winter storm dumped the snow record in Texas. It claimed hundreds of lives and evolved into a national major disaster. However, we still lack a systematic approach to quantify large‐scale snow depth. Here, we use the differential coherence from Sentinel‐1 synthetic aperture radar (SAR) imagery to characterize the surface disturbance due to this winter storm. We further rely on machine‐learning algorithms to quantify Texas statewide snow depth using surface disturbance map, SAR amplitude, precipitation, temperature, surface topography, land cover, and population. Our approach can provide an independent snow depth estimation. Approximately 89% of Texas accumulated over 30‐mm snow depth. The SAR and machine‐learning integrated methods can also be applied to quantify other forms of surface disturbance and to ultimately help natural hazard mitigation.
Laparoscopic versus open liver resection for hepatocellular carcinoma in elderly patients: A systematic review and meta-analysis of propensity score-matched studies

Purpose Laparoscopic liver resection (LLR) is a widely practiced therapeutic method and holds several advantages over open liver resection (OLR) including less postoperative pain, lower morbidity, and faster recovery. However, the effect of LLR for the treatment of hepatocellular carcinoma (HCC) in elderly patients remains controversial. Therefore, we aimed to perform the first meta-analysis of propensity score-matched (PSM) studies to compare the short- and long-term outcomes of LLR versus OLR for elderly patients with HCC. Methods Databases including PubMed, Embase, Scopus, and Cochrane Library were systematically searched until April 2022 for eligible studies that compared LLR and OLR for the treatment of HCC in elderly patients. Short-term outcomes include postoperative complications, blood loss, surgical time, and length of hospital stay. Long-term outcomes include overall survival (OS) rate and disease-free survival (DFS) rate at 1, 3, and 5 years. Results A total of 12 trials involving 1,861 patients (907 in the LLR group, 954 in the OLR group) were included. Compared with OLR, LLR was associated with lower postoperative complications (OR 0.49, 95% CI 0.39 to 0.62, P < 0.00001, I 2 = 0%), less blood loss (MD −285.69, 95% CI −481.72 to −89.65, P = 0.004, I 2 = 96%), and shorter hospital stay (MD −7.88, 95% CI −11.38 to −4.37, P < 0.0001, I 2 = 96%), whereas operation time (MD 17.33, 95% CI −6.17 to 40.83, P = 0.15, I 2 = 92%) was insignificantly different. Furthermore, there were no significant differences for the OS and DFS rates at 1, 3, and 5 years. Conclusions For elderly patients with HCC, LLR offers better short-term outcomes including a lower incidence of postoperative complications and shorter hospital stays, with comparable long-term outcomes when compared with the open approach. Our results support the implementation of LLR for the treatment of HCC in elderly patients. Systematic review registration https://inplasy.com/inplasy-2022-4-0156/, identifier INPLASY202240156.
Reliability- Based Stability Analysis of Slope

In Geotechnical engineering, the design and construction is done based on the Factor of Safety obtained from the deterministic approach. This Factor of safety doesn’t take into account the source and amount of uncertainty associated with the soil properties. Therefore, reliability based approach for the stability analysis has to be done to consider these uncertainties. In the present study, reliability-based stability analysis of slope has been made for using Finite Element Method, Upper bound Limit Analysis and Analytical method given by Low (1989). The commercially available software PLAXIS 2D-V9.02 is used for Finite Element Method and LimitState:GEO for Limit Analysis. The limit state function is developed using response surface methods. Full factorial design is used for development of response surface models. In this study, reliability analysis is done using first order reliability method. The need for reliability analysis and the corresponding reliability index and factor of safety is discussed. The study is validated by analyzing a case study of James Bay dykes. Parametric study has been done varying the soil and slope properties and modification has been made in the equation given by Low’s equation of Factor of Safety.
Informal Entrepreneurship: An Integrative Review and Future Research Agenda

Informal entrepreneurship (IE) has received increased recognition because of its theoretical distinctiveness and practical relevance. However, the burgeoning literature on IE is difficult to navigate, due to its rapid growth across different disciplines. Through an integrative review, we introduce a novel typology of informal entrepreneurs that captures their heterogeneity across various contexts. We point out a dynamic perspective of IE, consisting of three pathways—the reactive formalizing, the proactive formalizing, and the informalizing pathways—along which informal entrepreneurs move, acquiring or foregoing regulative legitimacy. Our review extends the theory on IE, outlines promising research avenues, and suggests relevant practical implications.
Investigation of human-centered transportation (walking and biking) for low-income workers in Kabul, Afghanistan

Human-centered transportation (walking and biking) has been the cheapest, healthiest, and most convenient mode of transportation throughout history. In the new global economy, walking and biking have become common modes of transportation for low-income groups of people. Kabul is the biggest city in Afghanistan with scattered space organizations and currently is unfavorable for walking and biking due to insufficient attention to pedestrian and bicycle routes in city planning and poor road network and sidewalk conditions, which are among the issues that affect this 4-5 million population city. The purpose of this research is to analyze the current traffic situation in Kabul and identify the role and share of citizens' use of human-centered transportation (walking and biking) for transportation. This research also aims to investigate the relationship between the economic scope of low-income workers and the use of walking and biking for transportation. The statistical population of the current study was selected from three municipal districts as travel zones. Using cluster sampling, a sample participant of 929 people was obtained. It was observed that in the broad context, due to increasing cost and insufficient public transportation, low-income workers use bicycles and walking as a reliable mode of transportation. Finally, it is suggested that the spatial organization of Kabul is redefined and designed based on the new space organization, and the local organization and formulation of urban transportation strategies in urban strategic plans for pedestrian and bicycle transportation systems are strengthened, especially for roads leading to employment locations. Furthermore, in planning, priority is to be shifted to human-centered transportation (walking and biking).
Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets

Morphological assessment of blastocyst quality is one of the most significant challenges in the IVF process because the current assessment is based on evaluation by an embryologist; thus, it is still manual and subjective and lacks precision. Artificial intelligence (AI) plays a role in overcoming the limitations of the manual assessment system, and its use is expected to increase implantation rates in IVF. This study aims to optimize the convolutional neural network (CNN) model using the grid search method and to evaluate the effectiveness of different machine learning models in classifying the blastocyst quality in a small dataset. The reliability of the proposed model will be compared with that of other machine learning methods, such as logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), the boosting algorithm, and with the addition of the Canny operator as a segmentation process and principal component analysis (PCA) as a feature extraction approach. We evaluated the results using various performance measures, such as the precision, recall, F1-measure, accuracy, and area under the curve of the receiver operating characteristic curve (AUC-ROC). The final results showed that our proposed CNN model achieves a validation accuracy of 84.00%, a test accuracy of 83.33%, and an AUC of 0.844. McNemar’s statistical test results support that our CNN model outperforms the other classifiers.
The spectrum of a single photoionized cloud

The emission-line spectrum of a quasar is most likely emitted by an ensemble of photoionized clouds moving with a variety of velocities, with a range of densities and distances from the central object. The state of the art in this field is to first compute the emission from a single cloud, and then prescribe a mix of clouds to reproduce line intensities, profiles, and reverberation lags. Here I review the parameters that de-fine emission from a single cloud and the outstanding problems in qua-sar emission-line analysis.