Miracle-dz/UniFAR-FTUs huggingface dataset:
Cookies enriched with anethole and secoisolariciresinol diglucoside from flaxseed and fennel seeds improve hypercholesterolemia, lipid profile, and liver functions: A pilot study

Abstract Over millennia, flaxseeds and fennel seeds have captured greater attention owing to the broad spectrum of bioactive compounds and their respective therapeutic potential. They are well‐known therapeutic plants, frequently used in home treatments for a variety of medical conditions. The novelty of this pilot study is to assess the beneficial health effects of secoisolariciresinol diglucoside (SDG) and anethole‐based enriched cookies among hyperlipidemic patients. The result of sensory evaluation revealed that cookies with anethole and SDG (500 + 500 mg/day) were significantly acceptable in terms of color, texture, taste, and overall acceptability same as that of control. This study was performed among 34 patients with hyperlipidemia in a university‐affiliated hospital, Lahore, Pakistan. In this study, patients received dietary supplementation with anethole and SDG (500 + 500 mg/day) administered in cookies for 8 weeks. Patients were assigned into two groups, intervention (receiving anethole + SDG‐enriched cookies; n = 16) and placebo (n = 18), for 8 weeks. Both groups maintained the same diet and lifestyle. Pre‐ and postintervention weight, lipid profile, and liver enzyme levels were measured. Analysis of covariance and paired sample t‐test were used for comparing the two groups. After 8 weeks, a significant mean weight loss was observed in the intervention group (4.26%) as compared to the placebo group (0.3%). A significant reduction of TC (177.02 ± 5.14 mg/dL; p = .024), TG (150.19 ± 7.94 mg/dL; p = .032), and LDL (87.38 ± 3.58 mg/dL; p = .001) were compared to the control group and HDL level (57.09 ± 3.90 mg/dL; p = .035) were increased in the intervention group as compared to the placebo. Meanwhile, it had a minor improvement in AST (30.97 ± 2.95 U/L; p = .01), ALT (33.05 ± 1.52 U/L; p = .025), and ALP (112.15 ± 4.03 U/L; p = .03) among the intervention group. Thus, based on the results from the study, it can be said that anethole + SDG‐enriched bakery products could be developed as a functional dietary option for hyperlipidemia in developing countries like Pakistan.
Exploring perceived occupational performance among individuals with Vertigo in Jordan: A cross-sectional study

Purpose: To explore the self-perceived occupational performance in daily life activities in individuals with vertigo in Jordan. Existing evidence is limited on how vertigo affects performing daily life activities like self-care. Materials and methods: This study utilized a cross-sectional design, and convenient sampling. Thirty individuals with vertigo (M = 47.6 years, SD = 15.93) agreed to participate in this study, filled-out the study demographic and condition-related questionnaire, and completed the Canadian Occupational Performance Measure via a semi-structured interview with study research personnel. Data analyses consisted of descriptive statistics, analysis of frequencies, and Analysis of Variance. Results and conclusions: Individuals with vertigo reported low to average self-perceived occupational performance (M = 4.13, SD = 1.64) and satisfaction (M = 3.46, SD = 1.98). The most challenging area of occupation was self-care (66% of the reported challenging activities), followed by productivity (18%) and leisure (16%). Personal care activities were the most challenging from the self-care area (31%), household management activities from the productivity area (13%), and active recreation activities from the leisure area (7%). In conclusion, individuals with vertigo experience low to average occupational performance and satisfaction in daily life activities. Targeted interventions for individuals with vertigo should increase occupational performance in daily life activities, especially self-care.
In Praise of Shadows: Exploring the hidden (responsibility) curriculum

We frame this special issue on the hidden responsibility curriculum through the lens of Jun’ichirō Tanizaki’s 1933 essay, In Praise of Shadows, which recognises the subtlety, modesty and dignity of shadows that are highly prized in Japanese culture. We do this to embody the themes both present and absent from the seven articles in this special issue. The articles share flecks and flickers, suggesting that (1) salient things happen in the shadows when it comes to responsibility learning – for better or worse, (2) students can play a role in illuminating and challenging the shadow sides of learning environments and (3) discernible symbols provide navigational possibilities in the shadows. Our tribute to Tanizaki reflects both the involuntary absence, in our Special Issue, of contributions beyond dominant White, Northern European perspectives and the lack of methodological apparatus that can effectively capture the implicit, shadow side of educational life – and life beyond – that evades conventional academic approaches. We also share reflections from the shadows of our own curation of this special issue, as an invite to shine a light on how curational ecosystems might be reimagined.
Reliability assessment of hafnia-based ferroelectric devices and arrays for memory and AI applications (Invited)

Ferroelectricity in doped HfO2 thin films was reported for the first time 12 years ago, generating strong interest in the non-volatile memory and logic community. Thanks to their CMOS compatibility and potential for scaling, hafnia-based Ferroelectric Random Access Memories (FeRAMs), Ferroelectric Tunnel Junctions (FTJ s) and Ferroelectric Field Effect Transistors (FeFETs) are not only a breakthrough with respect to conventional perovskite-based ferroelectric (FE) devices but also potentially a revolution from an application prospective, in particular considering the non-volatility and intrinsic energy efficiency of these devices. However, their maturity is currently too low to consider practical applications. In this paper, we therefore focus on the reliability assessment of Metal/FE/Metal (MFM) and Metal/FE/Dielectric/Metal (MFDM) stacks, either in the form of large area ferroelectric capacitors, or in the form of kbit arrays integrated in CMOS Back-End of Line.
Feature extraction based ensemble stacking for combating cyber threat in phishing URLs

Cyber threat is a process of exploiting sensitive information by losing the golden triad (Confidentiality, Integrity, and Availability). The threats may include Trojans, Phishing, and DDOS attacks. Phishing works as a social engineering technique where in the malicious attacker attempts in such a way that the appearance to the user would seem legitimate thereby causing the user to fall in a trap which may lead to the compromising of the user's sensitive information by means of illegitimate URLs or links etc. According to previous records approximately 18% of businesses lost their financial information due to phishing attack. Phishing can exploit data such as credentials, personal data, medical, etc. In this work we proposed a feature extraction-based stacking model where the URL (uniform resource locators) is identified as a legitimate or illegitimate URL. In the process of feature extraction, the features are considered based on hostname _length, path_length, fd _length, tld_length, count of -, count of @, count of? count of %, count of. , count of =, count of http, count of https, count of www, count of digits, count of letters, dir. , Ip (v6 or v4). We trained the model with different machine learning algorithms for chosen the best models for stacking the models. We considered the best four machine learning algorithms to be stacked and a meta-layer for the final prediction. We compare the metrics such as accuracy, recall, precision, and F1-score with other state of art models.
Algorithmic Resource Allocation for Spacecraft Operations

The operation of any spacecraft requires a constant trade-off between available resources onboard the spacecraft such as power, the correct thermal operating range, downlink capacity, and payload stakeholder interests. On a commercial spacecraft, cost-efficient operations pose an additional requirement with significant influence on the success of the mission. On hosted payload missions, the interface and contractual constraints between the spacecraft operator and payload operator add to the challenges. Economic success calls for automated scheduling of operations and must consider all of the above constraints. This paper presents the algorithm-based optimization of the operational schedule for the wildfire detection satellite mission FOREST-1, the concept of which can be transferred to the operation of any Low-Earth-Orbit Earth observation satellite. The state of the art of generally applicable algorithms is presented and a comparison for the adaptability to the underlying problem statement is made. Compared algorithms include sequential, forward-chronological, linear search, and evolutionary algorithms. For this application, the simplex algorithm was chosen due to its capabilities regarding depleting one pivotal resource to maximize a mathematically defined gain to the mission. The implementation of this algorithm, which now is used to build the schedules of FOREST-1 regularly is presented. It is compared against the manual scheduling approach used during the commissioning phase in terms of controllability, flex-ibility, transparency, and efficiency. When used for scheduling weekly operations, the automatic scheduler achieves a reliable resource allocation of at least 98%, with an average cloud coverage of 2.5% and the highest value at 13% compared to around 80% utilization, 16.5% and up to 79% respectively. The benchmarks for the manual scheduling approach required 90 minutes on average while one execution of the automated scheduler required around 20 minutes. The manually generated schedules consist of 96% of requested sequences and only three out of 73 targets where chosen from areas of interest whereas the scheduler allocated 81.25% to areas of interest and 18.75% to requests.
Metabolic study of trimetazidine using ultra-high performance liquid chromatography-tandem mass spectrometry

In the present study, the application of ultra-high performance liquid chromatography-tandem mass spectrometry allowed us to study of known—as well as hitherto unknown— trimetazidine (TMZ) metabolites in human urine and to propose their renal excretion profiles. Urine samples from a healthy volunteer were analyzed at baseline and at 0–4 h, 4–8 h, 8–12 h, and 12–24 h after a single dose of TMZ. A dilute-and-shoot procedure was used as sample treatment before separation. Full-scan spectra of possible metabolites were acquired. Additionally, product ion scan spectra of precursor ions of interest were also acquired at two collision energies. Intact TMZ was a major excretion product, with a maximum concentration at 4-8 h after administration. Moreover, five minor metabolites were observed, namely trimetazidine-N-oxide (M1), N-formyl trimetazidine (M2), desmethyl-trimetazidine O-sulfate (M3), desmethyl-trimetazidine O-glucuronide (M4), and desmethyl-trimetazidine-N-oxide-O-glucuronide (M5). Metabolite M5 has not previously been reported. Excretion curves were constructed based on the chromatographic peak areas of specific mass transitions (precursor ion > product ion) related to each of the detected metabolites.
An examination of kinematic parameters related to pole vault performance

ABSTRACT Although various studies have examined factors associated with pole vault performance, only a few have identified a linear relationship between these factors and performance. This study aimed to identify the kinematics variables correlating with the maximum centre of gravity (CG) height. This cross-sectional study included 16 male pole vaulters and recorded their performance using four high-speed cameras at 240 Hz. The vaulters cleared the bungee bars that were set at 90% of their personal best record. The variables during pole vaulting from the touchdown of the penultimate step on the approach to the peak height of the CG were calculated. Pearson’s correlation analysis and Spearman’s rank correlation were used to test the correlation between maximum CG height and the variables. Significant correlations were observed between the maximum CG height and the six variables (grip height, approach velocity, takeoff velocity, takeoff distance, push height, and maximum vertical velocity). Grip height showed the strongest relationship, followed by push height and takeoff velocity. Pole vaulters should aim to gain a high approach velocity and grip height to increase their mechanical energy.
Static, Dynamic, and Short-circuit Characteristics of Split-Gate 1.2 kV 4H-SiC MOSFETs

This paper reports static, dynamic, and short-circuit characteristics of split-gate (SG) 1.2 kV 4H-SiC MOSFETs. Conventional (C) MOSFETs and SG-MOSFETs were fabricated and evaluated. Identical conduction behaviors were achieved due to them having the same cell pitch. Although the maximum electric field in the gate oxide is higher in the SG-MOSFETs, this both device architectures obtained similar breakdown voltages with low leakage current. Due to the structure of the split-gate, the reverse capacitance $(\mathbf{C}_{\mathbf{rss}})$ was reduced by 32 % when compared to conventional MOSFETs. As a result, switching loss for turn-on and turn-off transients was reduced, and thus total switching loss was reduced by 25 % in the SG- M OSFE Ts. Finally, the short-circuit (SC) ruggedness of the MOSFETs were evaluated. Even though the maximum drain current is higher in the SG-MOSFETs, under SC condition, a similar short-circuit withstand time (SCWT) was obtained. In order to further investigate short-circuit characteristics, non-isothermal simulations were conducted. It was discovered that there is no issue with the exposed edge of the gate in SG- M OSFE Ts under SC conditions despite the high electric field in gate oxide. Significantly reduced energy loss was achieved in the SG-MOSFETs with no compromise in static and short-circuit characteristics compared to the conventional MOSFETs.
Automatic Identification of Crash-inducing Smart Contracts

Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain’s characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The existing approaches to identify crash-inducing smart contracts are either limited in finding vulnerability (e.g., pattern-based static analysis) or very expensive (e.g., program analysis), which is insufficient for Ethereum.To reduce runtime errors on Ethereum, we propose an efficient, generalizable, and machine learning-based crash-inducing smart contract detector, CRASHSCDET, to automatically identify crash-inducing smart contracts. To investigate the effectiveness of CRASHSCDET, we firstly propose 34 static source code metrics from four dimensions (i.e., complexity metrics, count metrics, object-oriented metrics, and Solidity-specific metrics) to characterize smart contracts. Then, we collect a large-scale dataset of verified smart contracts (i.e., 54,739) and label these smart contracts based on their execution traces on Etherscan. We make a comprehensive comparison with three state-of-the-art approaches and the results show that CRASHSCDET can achieve good performance (i.e., 0.937 of F1-measure and 0.980 of AUC on average) and statistically significantly improve the baselines by 0.5%-60.4% in terms of F1-measure and by 41.2%-44.3% in terms of AUC, which indicates the effectiveness of static source code metrics in identifying crash-inducing smart contracts. We further investigate the importance of different types of metrics and find that metrics in different dimensions have varying abilities to depict the characteristic of smart contracts. Especially, metrics belonging to the "Count" dimension are the most discriminative ones but combining all metrics can achieve better prediction performance.
Comparing the thermal performance and endurance of resistive and PIN silicon microheaters for phase-change photonic applications

: Optical phase-change materials have enabled nonvolatile programmability in integrated photonic circuits by leveraging a reversible phase transition between amorphous and crystalline states. To control these materials in a scalable manner on-chip, heating the waveguide itself via electrical currents is an attractive option which has been recently explored using various approaches. Here, we compare the heating efficiency, fabrication variability, and endurance of two promising heater designs which can be easily integrated into silicon waveguides—a resistive microheater using n-doped silicon and one using a silicon p-type/intrinsic/n-type (PIN) junction. Raman thermometry is used to characterize the heating efficiencies of these microheaters, showing that both devices can achieve similar peak temperatures but revealing damage in the PIN devices. Subsequent endurance testing and characterization of both device types provide further insights into the reliability and potential damage mechanisms that can arise in electrically programmable phase-change photonic devices
SegNet Architecture for Dermoscopic Image Segmentation

The lesion segmentation is a fundamental step in developing a Computer-Aided Diagnosis system for skin cancer detection. However, accurate segmentation is challenging due to the variability of lesion characteristics (e.g., shapes, sizes, colors, etc.) and artifacts in a dermoscopic image (e.g., hair, color charts, etc.). Therefore, most of the existing methods heavily depend on pre-processing procedures that often cause an information loss and, consequently, an inaccurate segmentation. This paper proposes a deep learning-based method using a SegNet architecture for pixel-wise segmentation. First, we trained the SegNet on the PH2 dataset. However, this dataset is small, and the images are almost clean of artifacts. Unlike the PH2 dataset, the ISIC 2018 dataset contains a more significant number of images with different types of artifacts. Therefore, we trained the proposed model on the ISIC2018 dataset in a second experiment. As a result, we achieved an IOU of 95.72% for the PH2 dataset and an IOU of 95.06% for the ISIC2018 dataset. A combination of quantitative and qualitative experimental evaluation has been conducted on two publicly available datasets. The achieved results of the empirical analysis demonstrate that the proposed method is highly accurate.
Methods of Treatment of Hemangiomas

The prevalence of hemangiomas in children reaches 45.7% of all benign childhood tumors, among newborns up to 1/4 of children are affected by the disease. The complexity of diagnosing and treating hemangiomas is exacerbated by the lack of a unified classification capable of providing mutual understanding between pediatricians, dermatologists, pediatric surgeons, and oncologists. One of the generally recognized problems in the treatment of hemangiomas is the definition of indications for the start of treatment. A serious problem is the treatment of ulcerated hemangiomas, accompanied by infection and bleeding. The tactics of managing a patient with hemangioma require an individual approach. For example, propranolol therapy and laser removal of hemangiomas are the most effective, safe, and cosmetically acceptable methods of removal. Surgical treatment and electrocoagulation show a persistent, relapse-free effect. As part of the scientific work, a statistical study of 40 case histories of children was carried out. The article presents data on the distribution of the disease by sex, and age, as well as by location and method of treatment.
Static and Fatigue Mechanical Performance of Abutments Materials for Dental Restorations

The choice of the proper restorative material is essential for the long-term success of implant-supported rehabilitations. This study aimed to analyze and compare the mechanical properties of four different types of commercial abutment materials for implant-supported restorations. These materials included: lithium disilicate (A), translucent zirconia (B), fiber-reinforced polymethyl methacrylate (PMMA) (C), and ceramic-reinforced polyether ether ketone (PEEK) (D). Tests were carried out under combined bending–compression conditions, which involved applying a compressive force tilted with respect to the abutment axis. Static and fatigue tests were performed on two different geometries for each material, and the results were analyzed according to ISO standard 14801:2016. Monotonic loads were applied to measure static strength, whereas alternating loads with a frequency of 10 Hz and a runout of 5 × 106 cycles were applied for fatigue life estimation, corresponding to five years of clinical service. Fatigue tests were carried out with a load ratio of 0.1 and at least four load levels for each material, and the peak value of the load levels was reduced accordingly in subsequent levels. The results showed that the static and fatigue strengths of Type A and Type B materials were better than those of Type C and Type D. Moreover, the fiber-reinforced polymer material, Type C, showed marked material–geometry coupling. The study revealed that the final properties of the restoration depended on manufacturing techniques and the operator’s experience. The findings of this study can be used to inform clinicians’ choice of restorative materials for implant-supported rehabilitation, considering factors such as esthetics, mechanical properties, and cost.
Mitigation of Thermal Instability for Electrical Properties in CaZrO3-Modified (Na, K, Li) NbO3 Lead-Free Piezoceramics

Lead-free ceramics 0.96(Na0.52K0.48)0.95Li0. 05NbO3-0.04CaZrO3 (NKLN-CZ) are prepared by using the solid-state procedure and two-step synthesis technique. The crystal structure and thermal stability of NKLN-CZ ceramics sintered at 1140–1180 °C are investigated. All the NKLN-CZ ceramics are ABO3-type perovskite phases without impure phases. With the increase in sintering temperature, a phase transition occurs in NKLN-CZ ceramics from the orthorhombic (O) phase to the concomitance of O-tetragonal (T) phases. Meanwhile, ceramics become dense because of the presence of liquid phases. In the vicinity of ambient temperature, an O-T phase boundary is obtained above 1160 °C, which triggers the improvement of electrical properties for the samples. The NKLN-CZ ceramics sintered at 1180 °C exhibit optimum electrical performances (d33 = 180 pC/N, kp = 0.31, dS/dE = 299 pm/V, εr = 920.03, tanδ = 0.0452, Pr = 18 μC/cm2, Tc = 384 °C, Ec = 14 kV/cm). The relaxor behavior of NKLN-CZ ceramics was induced by the introduction of CaZrO3, which may lead to A-site cation disorder and show diffuse phase transition characteristics. Hence, it broadens the temperature range of phase transformation and mitigates thermal instability for piezoelectric properties in NKLN-CZ ceramics. The value of kp for NKLN-CZ ceramics is held at 27.7–31% (variance of kp < 9%) in the range from −25 to 125 °C. The results indicate that lead-free ceramics NKLN-CZ is one of the hopeful temperature-stable piezoceramics for practical application in electronic devices.