Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Preoperative Prognostic Nutritional Index with Risk of Postoperative Acute Kidney Injury: A Meta-Analysis of Observational Studies

This meta-analysis aimed to assess the clinical association of the preoperative prognostic nutritional index (pre-PNI) with the risk of postoperative acute kidney injury. Four databases (e.g., Medline) were searched from inception to December 2022 to investigate the association between pre-PNI (i.e., low vs. high) and PO-PNI as well as the correlation between pre-PNI and other postoperative prognostic indices. Overall, 13 observational studies, including 9185 patients, were eligible for analysis. A low PNI was related to increased risks of PO-AKI [odd ratio (OR) = 1.65, p = 0.001, 3811 patients], postoperative infection (OR = 2.1, p < 0.00001, 2291 patients), and mortality (OR = 1.93, p < 0.0001, 2159 patients). Albeit statistically nonsignificant, a trend was noted, linking a low PNI to higher risks of postoperative bleeding (OR = 2.5, p = 0.12, 1157 patients) and stroke (OR = 1.62, p = 0.07, 2036 patients). Pooled results revealed a prolonged intensive care unit (ICU) stay in patients with low PNIs compared to those with high PNIs (MD: 0.98 days, p = 0.02, 2209 patients) without a difference in hospital stay between the two groups (MD: 1.58 days, p = 0.35, 2249 patients). This meta-analysis demonstrated an inverse correlation between PNI and the risks of PO-AKI, postoperative infection, and mortality, as well as the length of ICU stay, which warrants further investigations for verification.
Prevalence and Perception of Herbal Medicine Use among Patients with Chronic Diseases in a Nigerian Teaching Hospital

Background: Man's quest to manage illness over many years has led to the development of complementary and alternative medicine (CAM). There are many types of CAM, and the most utilized is herbal medicine. The relevance of herbal medicine has increased over the years due to the surge in health demands and other factors that influence the management of diseases. Hence, this study was conducted to ascertain the frequency and pattern of herbal medicine use among patients diagnosed with chronic diseases in the University of Nigeria Teaching Hospital, its predictors, magnitude, and perception. Methodology: This was a cross-sectional descriptive study conducted among adult patients (n = 319) diagnosed with various chronic diseases, such as Diabetes Mellitus, Hypertension, Glaucoma, cancer, etc., from October 2022 to November 2022, at the University of Nigeria Teaching Hospital, Enugu, Nigeria. Questionnaires were administered through face-to-face interviews with patients. Results: A total of 319 patients were included in this study. The gender distribution is 139 males and 180 females. The rate of herbal medicine use was 65.2%. Herbal medicine use among the female gender was higher, but there was no relationship between gender and herbal medicine use (P = 0.842). Of participants using herbal medicine, 46.1% used it for treating their health condition, 30.4% used herbal medicine to improve well-being, and 10.3% for preventing diseases. Their perception of herbal medicine uses as compared to conventional synthetic medicine based on modifications of some issues found from literature reviews that militate against the use of herbal medicine such as dose (15% strongly agreed, 20.1% agreed, 23.2% neutral, 18.8% disagreed and 22.9% strongly disagreed) etc. Short Conclusion: The study discovered that patients in Nigeria with chronic conditions frequently use herbal medicines. Age, gender, marital status, occupation, education, monthly income, religion, health insurance, the timing of sickness diagnosis, the number of medications prescribed, and multiple chronic conditions were all linked to the usage of herbal medicines.
Poles and zeros of alpha-beta and alpha-beta-gamma tracking filters

Discrete transfer functions for the alpha-beta and alpha-beta-gamma Kalman tracking filters are presented. Filters for the update, as well as the prediction, estimates are considered. Root loci for the poles and zeros are presented, parameterised in the manoeuvring index. Closed-form solutions for the alpha-beta-gamma gains are given as functions of the manoeuvring index.
Feasibility and Performance of Free-Hand Single-Photon Computed Tomography/Ultrasonography for Preoperative Parathyroid Adenoma Localization: A Pilot Study

The aim of this prospective pilot study was to evaluate the feasibility of a new hybrid imaging modality, free-hand single-photon computed tomography/ultrasonography (fhSPECT/US), for preoperative localization of parathyroid adenomas and to compare its performance with conventional ultrasonography and SPECT/CT. Twelve patients diagnosed with primary hyperparathyroidism underwent sequentially US and parathyroid scintigraphy, including SPECT/CT, followed by fhSPECT/US, allowing for real-time fusion between US and freehand-generated gamma-camera images. The fhSPECT/US detection rates were correlated with histopathology, when available, or with the imaging modality showing the most lesions. Based on a per patient analysis, the detection rate was significantly different when comparing SPECT/CT to fhSPECT/US (p = 0.03), and not significantly different when comparing SPECT/CT to US (p = 0.16) and US to fhSPECT/US (p = 0.08). Based on a per-lesion analysis, the detection rate of SPECT/CT was significantly higher than that of US (p = 0.01) and fhSEPCT/US (p = 0.003), and there was no significant difference in detection rate when comparing US to fhSPECT/US (p = 0.08). The main perceived limitations of fhSPECT/US in lesion detection were: (i) lesions localized at a depth ≥4.5 cm; (ii) imperfect image fusion due to tissue compression; (iii) limited spatial manipulation ability of the SPECT mobile camera handheld probe; and (iv) a wide spread of detected activity. In conclusion, clinical use of fhSPECT/US for localization of parathyroid adenomas is feasible, but shows lower sensitivity than conventional modalities and requires technical improvements.
Symbolic interactionism, role-identities, and delinquency: examining the moderating role of social rewards

ABSTRACT Criminological research consistently supports that reflected appraisals in various domains predict delinquency, yet, extant work has not fully explored theoretical nuances outlined in traditional symbolic interactionism, specifically concerning how conditioning factors are involved in role-taking among peers. This study assesses whether social rewards moderate the association between reflected appraisals and delinquency in order to elucidate the role-taking process and explore a possible mechanism of peer influence on offending. Using data from the National Youth Survey, analyses estimate the relationship among adolescents’ ‘rule violator’ and ‘sociable’ identities from the perspective of peers, perceptions of social rewards, and their count number of delinquent acts. The findings demonstrate mixed support for moderation hypotheses. Perceptions of greater social rewards for delinquency increase the positive association between the sociable appraisal and delinquency, while this interaction occurs in the opposite direction for the association between the rule violator appraisal and delinquency. These results support that social rewards influence behavior differently based on the content of reflected appraisals. This implies a process in which adolescents consider different factors to define delinquency as consistent with the identity they enact among peers; furthermore, the results identify directions for research incorporating the symbolic interactionist perspective in criminology.
Degree Restrictions in SpanishExclamatives

The semantics of wh-exclamatives has been traditionally derived from the semantics of whquestions (Zanuttini & Portner’s 2003, Gutierrez-Rexach 1996). Nevertheless, several authors (Villalba 2003, Castroviejo 2006, Rett 2008) have emphasized certain important properties of exclamative constructions relating them to degree expressions. This would amount to making exclamatives equivalent to expressions of high degree. In this paper we present evidence from Spanish with two goals: (i) to characterize a more refined and complex classification of exclamatives and, (ii) to defend a unified approach that would explain the restrictions on exclamative constructions.
Forward and inverse calculation methods for Lorentz force evaluation applied to laminated composites

The classiﬁcation of material deﬁciencies is a key feature in quality assurance. In this framework, laminated composite materials are of special interest, because they increasingly replace monolithic materials. The Lorentz force evaluation (LFE) is an evaluation technique to reconstruct the geometry of ﬂaws in electrically conducting composites using inverse calculations. These calculations are based on perturbations that occur in the measured Lorentz force signals and are caused by the ﬂaws. The force signals are obtained using the nondestructive testing method Lorentz force eddy current testing (LET). In this electromagnetic technique, a permanent magnet and the material under investigation move relative to each other. As a consequence eddy currents are induced in the conductor. The eddy currents in turn interact with the magnetic ﬁeld and cause a Lorentz force. Inverse calculations in LFE require a forward solution of the measured force signals, which incorporates a model of the LET setup. The objective of this thesis is the development and evaluation of forward and inverse calculation methods for LFE. The proposed methods are assessed using Lorentz force data obtained from laminated composites. In order to model the permanent magnet in the forward solution for LFE the magnetic dipoles model (MDM) is introduced. In the MDM, a permanent magnet is represented by an assembly of magnetic dipoles. An optimization procedure is used to determine optimal dipole positions. Contrary to analytic models the MDM can be applied to permanent magnets of arbitrary geometry, and forward calculations can be performed with analytic mathematics. For defect reconstruction three inverse methods are introduced in this thesis. In the ﬁrst method, conductivity reconstructions are performed using a stochastic optimization algorithm, the Differential Evolution (DE). Prior to inverse calculations, the intrinsic control parameters of the DE are determined based on parameter studies. As the second inverse strategy, current density reconstructions (CDR) calculated with minimum norm estimates (MNE) are employed. This approach is based on interpreting a defect in the forward solution for LFE as a distributed current source. In the third method, a goal function scan is performed to reconstruct the geometry parameters of the defect. All three inverse methods are suitable for reconstructing defects, whereas the ﬁrst and third method provide more accurate results than the second. Further, measured Lorentz force signals obtained from glass laminate aluminum reinforced epoxy (GLARE) composite are investigated. GLARE is widely used in the aircraft industry. The ﬂaw detectability of LET and LFE for GLARE is proved.
Ultralow-Power and High-Speed In-Memory Computing Unit Based on Field-Accelerated Spin-Orbit Torque MRAM Utilizing Voltage-Controlled Magnetic Anisotropy

Spintronic-based in-memory computing (IMC) architectures have gained intensive attention as a potential solution to address the von Neumann bottleneck. In particular, the write-like in-memory logic paradigm via memory cells has been explored as an alternative to traditional computing architectures with complex sensing circuits and independent logic computing units. However, this approach suffers from the drawback of high write energy consumption compared to the conventional read energy consumption. In this article, we propose an ultralow-power, high-speed IMC unit based on field-accelerated spin-orbit torque (SOT) magnetic random access memory (MRAM) using voltage-controlled magnetic anisotropy (VCMA). We investigate the magnetization dynamics of the device by solving the modified Landau–Lifshitz–Gilbert equation using a physics-based compact model. The proposed MRAM is evaluated for Boolean-logic operations and nonvolatile full-adder (NVFA) under 28 nm process technology node using hybrid CMOS/MTJ simulations. Our results show that the proposed MRAM achieves a low write latency of 0.8 ns and low power dissipation of 2.804 fJ/bit. Additionally, it supports 1-bit NVFA operations of 13.0 ns and 25.2 fJ/bit. These findings suggest that the proposed MRAM can serve as a flexible and scalable IMC platform.
Texture Image Retrieval Using Fourier Transform

 Abstract— Texture is an important visual property that characterizes a wide range of natural and artificial images which makes it a useful feature for retrieving images. During the last decades, several approaches have been proposed to describe the texture contents of an image. In early research works, texture descriptors were mainly extracted from the pixel space itself, edge histograms and co-occurrence-based features, are examples of such descriptors. Later on, dual spaces (transform of pixel space) such as frequency space or spaces resulting from Gabor and wavelet transforms were explored for texture characterization. This paper describes a Fourier- based technique for characterizing image textures. The performance of the technique is compared with several Fourier- and wavelet-based methods described in literature. The two main criteria that were used in this comparison are the accuracy and execution time of the techniques. simpler and faster. As a consequence, wavelet-based approaches gained much more popularity among the computer vision community. Fourier transform has also been widely used in characterizing textures. One of the reasons for that is its suitability for describing periodic functions such as texture images which usually contain quasi-repetitive patterns. Concentrations of Fourier power spectrum values capture dominant orientations of the patterns in the image and their distribution in the frequency space is closely related to coarseness of the texture (5, 6). These two features (directionality and coarseness of a texture) are of importance in texture analysis (17). The main drawback of using Fourier transform is the poor spatial localization it provides. Windowed Fourier transform has been introduced to overcome this problem at the cost of a significant increase in the computations (18). In this paper we describe a Fourier-based method for characterizing texture images and show that it performs better than many of the recently proposed techniques for texture characterization. We have included in our comparative study several Fourier- and wavelet-based texture characterization techniques. The two main criteria that were used in this comparison are the accuracy and running time of the techniques.
Psychiatry for all: enhancing medical practice and patient outcomes through psychiatric education for all doctors

This article emphasizes the importance of studying psychiatry for all medical professionals. Psychiatry deals with mental, emotional, and behavioural disorders, and a strong understanding of the field leads to more holistic, effective, and empathetic patient care. Key reasons for doctors to study psychiatry include adapting a biopsychosocial model of care, early identification and intervention for psychiatric disorders, improved health outcomes, better communication skills, combating stigma surrounding mental health, multidisciplinary collaboration, personal growth and self-care, and a more comprehensive approach to chronic illness management. Integrating psychiatry into medical education prepares doctors for the challenges they will face in their careers, ultimately improving the quality of patient care. The practice of offering internships in psychiatry, as seen in countries like UK and Australia, should be encouraged globally to create a cohort of doctors knowledgeable in the basics of psychiatry, ultimately reducing the impact of a shortage of psychiatrists and improving overall healthcare.
Development of an Evaluation System for Transfer Care Skills Using Embroidered Body Pressure and Proximity Sensor

Objective: It is important to improve caregiving skills to help reduce the strain on inexperienced caregivers. Previous studies on quantifying caregiving skills have predominantly relied on expensive equipment, such as motion-capture systems with multiple infrared cameras or acceleration sensors. To overcome the cost and space limitations of existing systems, we developed a simple evaluation system for transfer care skills that uses capacitive sensors composed of conductive embroidery fibers. The proposed system can be developed with a few thousand US dollars. Method: The developed evaluation system was used to compare the seating position and velocity of a care recipient during transfers from a nursing-care bed to a wheelchair between groups of inexperienced and expert caregivers. To validate the proposed system, we compare the motion data measured by our system and the data obtained from a conventional three-dimensional motion-capture system and force plate. Results: We analyze the relationship between changes in the center of pressure (CoP) recorded by the force plate and the center of gravity (CoG) obtained by the developed system. Evidently, the changes in CoP have a relation with the CoG. We show that the actual seating speed ( $v_{\mathrm {z}}) $ measured by the motion-capture system is related to the speed coefficient calculated from our sensor output. A significant difference exists in $v_{\mathrm {z}}$ between the inexperienced group and the physical therapists/occupational therapists’ group. Conclusions: The proposed system can effectively estimate a caregiver’s skill level in transferring patients from a bed to a wheelchair in terms of the seating position and velocity.
The Current Progress of Tetrahedral DNA Nanostructure for Antibacterial Application and Bone Tissue Regeneration

Abstract Recently, programmable assembly technologies have enabled the application of DNA in the creation of new nanomaterials with unprecedented functionality. One of the most common DNA nanostructures is the tetrahedral DNA nanostructure (TDN), which has attracted great interest worldwide due to its high stability, simple assembly procedure, high predictability, perfect programmability, and excellent biocompatibility. The unique spatial structure of TDN allows it to penetrate cell membranes in abundance and regulate cellular biological properties as a natural genetic material. Previous studies have demonstrated that TDNs can regulate various cellular biological properties, including promoting cells proliferation, migration and differentiation, inhibiting cells apoptosis, as well as possessing anti-inflammation and immunomodulatory capabilities. Furthermore, functional molecules can be easily modified at the vertices of DNA tetrahedron, DNA double helix structure, DNA tetrahedral arms or DNA tetrahedral cage structure, enabling TDN to be used as a nanocarrier for a variety of biological applications, including targeted therapies, molecular diagnosis, biosensing, antibacterial treatment, antitumor strategies, and tissue regeneration. In this review, we mainly focus on the current progress of TDN-based nanomaterials for antimicrobial applications, bone and cartilage tissue repair and regeneration. The synthesis and characterization of TDN, as well as the biological merits are introduced. In addition, the challenges and prospects of TDN-based nanomaterials are also discussed.
Self-efficacy and academic performance of vocational high school students

: The aim of this research is to examine the role of self-efficacy on academic performance of Vocational High School students. This research uses a quantitative approach with a correlational research design. The participants of this research are at 115 students of the Vocational High School, Department of Electrical and Industrial Engineering who were obtained based on convenience sampling technique. The data analysis is used by regression analysis with Jeffrey’s Amazing Statistics Program (JASP). The result of this research indicated that self-efficacy has a significant role on academic performance.
Evaluation of four degree-day estimation methods in eight Colombian coffee-growing areas

Methods to estimate the accumulation of degree-days based on maximum and minimum temperaturesare are commonly used to determine relationships or to adjust phenological models based on the "physiological time". Degree-days are obtained indirectly by these methods, this information is not generally available on hourly or shorter time scales due to the type of equipment used to record data or a data loss in historical time series. To compare the performance of such methods, degree-days were estimated with four indirect methods in eight Colombian locations during 1 year. Each indirect method was evaluated in comparison to the numerical integration method by the trapezoidal rule (reference method) using temperatures recorded every 5 min. Based on the percent bias error, the methods proposed by Arnold, Ometto and Snyder tend to overestimate thermal time, whereas the Villa-Nova method underestimates this time, but with a lower performance as regards to the previous ones.
Designing formulations of bio-based, multicomponent epoxy resin systems via machine learning

Abstract Petroleum-based epoxy resins are commonly used as a matrix in fiber-reinforced polymer composites. Bio-based epoxy resin systems could be a more environmentally friendly alternative to conventional epoxy resins. In this work, novel formulations of multicomponent, amino acid-based resin systems exhibiting high or low glass-transition temperatures ( $$T_{{\text{g}}}$$ T g ) were designed via Bayesian optimization and active learning techniques. After only five high- $$T_{{\text{g}}}$$ T g experiments, thermosets with $$T_{{\text{g}}}$$ T g already higher than those of the individual components were obtained, pointing out the existence of synergistic effects among the amino acids used and confirming the efficiency of the theoretical design. Linear and nonlinear machine learning (ML) models successfully predicted $$T_{{\text{g}}}$$ T g with a mean absolute error of 3.98 $$^{\circ }{\text{C}}$$ ∘ C and $$R^2$$ R 2 score of 0.91. A price reduction of up to 13.7% was achieved while maintaining the $$T_{{\text{g}}}$$ T g of 130 $$^{\circ }{\text{C}}$$ ∘ C using an optimized formulation. The LASSO model provided information about the dependence of $$T_{{\text{g}}}$$ T g on the number of active hydrogen atoms and aromaticity. This study highlights the importance of Bayesian optimization and ML to achieve a more sustainable development of epoxy resin materials. Impact statement This article shows how the sustainability of epoxy resin systems (ERSs) can be significantly improved by combining experimental and theoretical strategies. First, amino acids are used as curing agents in multicomponent formulations to produce bio-based ERSs. Second, the number of trial-and-error experiments required to obtain formulations with high or low glass-transition temperatures ( T _g) is greatly reduced using machine learning (ML) strategies to design all experiments. Not only is it shown how T _g can be maximized in only five new theoretically designed formulations, but the economic advantages of the proposed approach are also discussed. The trends between T _g and the type of optimized biocomponents are discussed based on the unambiguous interpretation of the best-trained ML model. The results presented in this study pave the way for the theoretical design of more sustainable polymeric materials. Graphical abstract