Miracle-dz/UniFAR-FTUs huggingface dataset:
Decreased MARCKS Protein Expression in Kidney Cortex Membrane Fractions of Cathepsin B Knockout Mice Is Associated with Reduced Lysophosphatidylcholine and Protein Kinase C Activity

Cathpesin B is a multi-functional protease that plays numerous roles in physiology and pathophysiology. We hypothesized that actin cytoskeleton proteins that are substrates of cathepsin B, various lipids, and kinases that are regulated by lipids would be down-regulated in the kidney of cathepsin B knockout mice. Here, we show by Western blot and densitometric analysis that the expression and proteolysis of the actin cytoskeleton proteins myristoylated alanine-rich C-kinase substrate (MARCKS) and spectrin are significantly reduced in kidney cortex membrane fractions of cathepsin B knockout mice compared to C57B6 wild-type control mice. Lipidomic results show that specific lipids are increased while other lipids, including lysophosphatidylcholine (LPC) species LPC (16:0), LPC (18:0), LPC (18:1), and LPC (18:2), are significantly decreased in membrane fractions of the kidney cortex from Cathepsin B null mice. Protein Kinase C (PKC) activity is significantly lower in the kidney cortex of cathepsin B knockout mice compared to wild-type mice, while calcium/calmodulin-dependent protein kinase II (CaMKII) activity and phospholipase D (PLD) activity are comparable between the two groups. Together, these results provide the first evidence of altered actin cytoskeleton organization, membrane lipid composition, and PKC activity in the kidneys of mice lacking cathepsin B.
Analysis of Cache Memory Circuit for Compute In Memory Applications

The rapid development of Artificial Intelligence computations requires efficient SRAMs for Compute In Memory (CIM) implementations. SRAM cells are all that exists in cache memory because that is how cache memory is implemented. The major advantage of in-memory applications is that they can access data much speeder than conventional applications which rely on disk-based storage. Among the cells in SOC, only 15 –20% of cells are functional cells. The remaining cells are memory cells. As the need for frequently accessing the memory for the CIM emerges, the stability of accessing the data in SRAM is in demand. Static Noise margin analyses are commonly employed to access the stability of SRAM. Rechargeable applications require fewer voltages, endangering the stability of memory. The analysis of SRAM with respect to stability checking is performed for CIM applications. The applications of CIM precisely exist in the hardware implementation of deep learning structures.
Comparison of Different Parameters of Feedforward Backpropagation Neural Networks in DEM Height Estimation for Different Terrain Types and Point Distributions

Digital Elevation Models (DEMs) are commonly used for environment, engineering, and architecture-related studies. One of the most important factors for the accuracy of DEM generation is the process of spatial interpolation, which is used for estimating the height values of the grid cells. The use of machine learning methods, such as artificial neural networks for spatial interpolation, contributes to spatial interpolation with more accuracy. In this study, the performances of FBNN interpolation based on different parameters such as the number of hidden layers and neurons, epoch number, processing time, and training functions (gradient optimization algorithms) were compared, and the differences were evaluated statistically using an analysis of variance (ANOVA) test. This research offers significant insights into the optimization of neural network gradients, with a particular focus on spatial interpolation. The accuracy of the Levenberg–Marquardt training function was the best, whereas the most significantly different training functions, gradient descent backpropagation and gradient descent with momentum and adaptive learning rule backpropagation, were the worst. Thus, this study contributes to the investigation of parameter selection of ANN for spatial interpolation in DEM height estimation for different terrain types and point distributions.
Numerical schemes based on non-standard methods for initial value problems in ordinary differential equations

In this paper we present new methods of constructing Non-Standard schemes. Two new logical ways for constructing the denominator functions for the discrete derivatives were introduced and schemes were constructed based on Non Local Approximation. The schemes were applied on sampled IVPs in ordinary differential equations to verify whether the schemes are computationally reliable. The results obtained show that the schemes are suitable for problems for which they were proposed.
Criminal identification using face detection and recognition

- Face recognition and detection are unimaginable inventions in today’s world when it comes to experiencing every crime. Protection, surveillance, entertainment sites, and criminal identification are just a few of the amazing applications. This device is particularly useful for customer screening in banks, airports, and other businesses. convolutional neural networks need more data for deep learning because it is still a facial recognition system, which is particularly problematic in the case of applications such as criminal investigations (murder, robbery, etc.). As a result, this project incorporates a face-recognition system that makes it easier and more reliable to scan for offenders, thus assisting police officers effectively and organization. In this paper, we implement the Deep Metric Learning to construct the face embedding for recognition process using dib face recognition library
Increasing the penetration depth for ultrafast laser tissue ablation using glycerol based optical clearing

Background: Deep tissue ablation is the next challenge in ultrafast laser microsurgery. By focusing ultrafast pulses below the tissue surface one can create an ablation void confined to the focal volume. However, as the ablation depth increases in a scattering tissue, increase in the required power can trigger undesired nonlinear phenomena out of focus that restricts our ability to ablate beyond a maximum ablation depth of few scattering lengths. Optical clearing (OC) might reduce the intensity and increase the maximal ablation depth by lowering the refractive index mismatch, and therefore reducing scattering. Some efforts to ablate deeper showed out of focus damage, while others used brutal mechanical methods for clearing. Our clinical goal is to create voids in the scarred vocal folds and inject a biomaterial to bring back the tissue elasticity and restore phonation. Materials and methods: Fresh porcine vocal folds were excised and applied a biocompatible OC agent (75% glycerol). Collimated transmittance was monitored. The tissue was optically cleared and put under the microscope for ablation threshold measurements at different depths. Results: The time after which the tissue was optically cleared was roughly two hours. Fitting the threshold measurements to an exponential decay graph indicated that the scattering length of the tissue increased to 83±16 μm, which is more than doubling the known scattering length for normal tissue. Conclusion: Optical clearing with Glycerol increases the tissue scattering length and therefore reduces the energy for ablation and increases the maximal ablation depth. This technique can potentially improve clinical microsurgery.
Semi-Active Control of Horizontal Vibrations for Mine Hoisting Containers Using Fuzzy Skyhook Control Strategy

Mine hoisting systems have been widely used in coal mine industries. Due to a series of uncertain faults in the steel guide, serious vibrations will be excited during the operation of the machine, which will seriously influence the stability and safety of the system. In this paper, a semi-active vibration control strategy based on magnetorheological dampers was innovatively proposed. Firstly, a reasonable simplified dynamic model of mine hoisting system was established. Secondly, the simulation results from the virtual prototype model were used to verify the rationality of the established dynamic model. Then, a fuzzy skyhook damping semi-active control scheme based on magnetorheological dampers was designed. Finally, the availability of this vibration control scheme was verified by the Adams/Simulink co-simulation technique. The root mean square (RMS), mean and maximum values of the horizontal vibration acceleration of the container were reduced by 47.32%, 22.50% and 35.39%, respectively. The fuzzy skyhook damping semi-active control strategy of good stability can well suppress the horizontal vibrations of the mine hoisting system.
A survey on personalized health recommender systems for diverse healthcare applications

Health recommender systems are computer algorithms designed to suggest personalized health information to individuals based on their unique needs and preferences. These systems use data such as demographic information, lifestyle habits, and medical history to make tailored personal recommendations. This paper aims to critically assess the present state of the field and pinpoint the key trends, challenges, and opportunities for future development. Initially, the various types of health recommender systems, their applications and benefits are analysed. Next, the various techniques, features and challenges in the area of health recommender systems are surveyed. In addition, the application domains and evaluation metrics utilized for system assessment for each contribution are recorded. Finally, this survey delivers valuable perspectives and suggests potential avenues for future research around health recommender systems, aimed at improving the health and well-being of individuals. It highlights the current shortcomings and difficulties in the field, serving as a guide for researchers, professionals, and decision makers in creating a more effective health recommender system.
Refining the “Melting Pot” Genus Holosticha s. l. (Protozoa, Ciliophora, Hypotrichia) Based on Multigene Datasets with Establishment of a New Species Caudikeronopsis monilata sp. nov.

The genus Holosticha s. l. is a typical “melting pot” group with an intricate history, and so far, it has been divided into eleven genera. Both newly obtained taxonomic and molecular data provide the opportunity to gain more insights to outline the taxa in it and to understand their systematic and evolutionary relationship. Here, we describe Caudikeronopsis monilata sp. nov. from intertidal sediment on the China coast of the Yellow Sea and analyze the phylogenetic relationships of Holosticha s. l. by obtaining a total of 16 new sequences of seven isolates. The results demonstrate that (1) the morphological features of Holosticha s. str. are outlined very well, but its systematic relationship with Uncinata is still puzzling; (2) based on both morphological and molecular databases, the genera Adumbratosticha, Arcuseries, Caudikeronopsis, Extraholosticha, and “Holosticha + Uncinata” complex are separated clearly from each other in the phylogenetic analyses; and (3) the Anteholosticha isolates are dispersed among the urostylids in the phylogenetic analyses, even though its generic diagnostic features are described very clearly. In the present work, however, the secondary structure predictions do not provide better resolutions for understanding the systematic and evolutionary relationships among the holostichids. And the genus Anteholosticha becomes a new “melting pot” taxon.
Non-Intervisible Pose Estimation Based on Plannar Mirror

Pose estimation is a thoroughly studied problem in computer vision. But in some realistic scenarios, reference points cannot lie within camera’s field of view (non-intervisible). In this article, planar mirror is placed in front of body, allowing the camera to observe reflection of reference points which can characterize body coordinate. We can form an equation system related to transformation between camera coordinate and body coordinate. We propose an unattached and linear approach to solve and optimize transformation of camera-to-body without any prior information of planar mirror configuration. Additionally, we analyze the sensitivity of our algorithm. We present a number of simulations and experiments to prove that our formulation significantly improves accuracy and robust.
Economic Implications of the Climate Provisions of the Inflation Reduction Act

ABSTRACT: The Inflation Reduction Act (IRA) represents the largest US federal response to climate change to date. We highlight the key climate provisions and assess the act's potential economic impacts. Substantially higher investments in clean energy and electric vehicles imply that fiscal costs may be larger than projected. However, even at the high end, IRA provisions remain cost-effective. The IRA has large impacts on power sector investments and electricity prices, lowering retail electricity rates and resulting in negative prices in some wholesale markets. We find small quantitative macroeconomic effects, including a small decline in headline inflation, but macroeconomic conditions—particularly higher interest rates and materials costs—may have substantial negative effects on clean energy investment. We show that the subsidy approach in the IRA has expansionary supply-side effects relative to a carbon tax but, in a representative-agent dynamic model, is preferable to a carbon tax only in the presence of a strong learning-by-doing externality. We also discuss the economics of the industrial policy aspects of the act as well as the distributional impacts and the possible incidence of the different tax credits in the IRA.
Differential Detection of Alternaria alternata Haplotypes Isolated from Carya illinoinensis Using PCR-RFLP Analysis of Alt a1 Gene Region

Alternaria black spot disease on pecan is caused by the opportunistic pathogen Alternaria alternata and poses a serious threat to the local South African and global pecan industry. Several diagnostic molecular marker applications have been established and used in the screening of various fungal diseases worldwide. The present study investigated the potential for polymorphism within samples of A. alternata isolates obtained from eight different geographical locations in South Africa. Pecan (Carya illinoinensis) leaves, shoots, and nuts-in-shuck with Alternaria black spot disease were sampled, and 222 A. alternata isolates were retrieved. For rapid screening to identify Alternaria black spot pathogens, polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) analysis of the Alternaria major allergen (Alt a1) gene region was used, followed by the digestion of the amplicons with HaeIII and HinfI endonucleases. The assay resulted in five (HaeIII) and two (HinfI) band patterns. Unique banding patterns from the two endonucleases showed the best profile and isolates were grouped into six clusters using a UPGMA (unweighted pair group method with arithmetic averages) distance matrix (Euclidean) dendrogram method on R-Studio. The analysis confirmed that the genetic diversity of A. alternata does not depend on host tissues or the pecan cultivation region. The grouping of selected isolates was confirmed by DNA sequence analysis. The Alt a1 phylogeny corroborated no speciation within the dendrogram groups and showed 98–100% bootstrap similarity. This study reports the first documented rapid and reliable technique for routine screening identification of pathogens causing Alternaria black spot in South Africa.
Finite Element Bending Analysis of Reissner Plates

A finite element plate bending analysis which includes the effects of transverse shear is described. A complete displacement formulation based on the governing equations of the Reissner theory is developed for application to the bending of rectangular plates. The finite element employed is a rectangle with 20 degrees of freedom which include both bending and shear deformation states and their interaction. Of primary importance is the ability of the finite element method to overcome certain of the anomalies found in classical plate theory. The presence of “Kirchoff shear forces” and concentrated corner reactions can be eliminated by the specification of more realistic boundary conditions. Results of several numerical examples are in excellent agreement with those of the Reissner theory for maximum displacements and for the distribution of shear stress resultants along plate edges. Also, the added transverse shear degrees of freedom allow shear deformations along an edge parallel to the edge. Thus, the vanishing edge twisting moment condition can be satisfied approximately. The formulation described offers a convenient method for approximating the Reissner theory with a discrete element approach.
Fittings Detection Method Based on Multi-Scale Geometric Transformation and Attention-Masking Mechanism

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.
Beam geometry, alignment, and wavefront aberration effects on interferometric differential wavefront sensing

Heterodyne interferometry is a widely accepted methodology with high resolution in many metrology applications. As a functionality enhancement, differential wavefront sensing (DWS) enables simultaneous measurement of displacement, pitch, and yaw using a displacement interferometry system and a single beam incident on a plane mirror target. The angular change is measured using a weighted phase average between symmetrically adjacent quadrant photodiode pairs. In this paper, we present an analytical model to predict the scaling of differential phase signals based on fundamental Gaussian beams. Several numerical models are presented to discuss the effects of physical beam parameters, detector size, system alignment errors, and beam wavefront aberrations on the DWS technique. The results of our modeling predict rotational scaling factors and a usable linear range. Furthermore, experimental results show the analytically predicted scaling factor is in good agreement with empirical calibration. Our three degree-of-freedom interferometer can achieve a resolution of 0.4 nm in displacement and 0.2 μrad in pitch and yaw simultaneously.
Opportunities and Challenges for Implementation of Harmonized Competence-Based Curricula in Medicine and Nursing Programmes in Tanzania: Experiences of Biomedical Sciences’ Stakeholders

Background In 2012, the Muhimbili University of Health and Allied Sciences (MUHAS) embarked on structured competency-based curricula (CBC) for its programmes. Other health profession training institutions continued with their traditional way of teaching and thus causing variability in the competencies of the graduates. We aimed to analyze the experiences of different stakeholders on the implementation of CBC specifically on biomedical sciences by MUHAS to inform the development of harmonized competency-based curricula in three health professional training institutions in Tanzania. Methods We adopted an exploratory case study to analyse the implementation of CBC in programmes of Medicine and Nursing involving MUHAS graduates, immediate supervisors at the employment sites, faculty, and continuing students at MUHAS. Kiswahili guides were used to conduct the in-depth interviews (IDIs) and focus group discussions (FGDs). Qualitative content analysis was adopted for analysis. Results From the 38 IDIs and 15 FGDs, four categories of human resources teaching and learning environment; curriculum content; and support systems emerged. Human resources were attributed to the shortage of an adequate number of faculty and teaching skills variation. The curriculum content category was linked to the redundancy of courses or topics, poor sequencing of some topics or courses, and limited time for teaching some essential courses or topics. Training and practice area mismatch, accommodation to students, teaching space, and library were the sub-categories linked to teaching and learning environment. Lastly, support systems related to teaching methods and opportunities for improving teaching and learning were revealed. Conclusion The findings of this study highlight the challenges and opportunities for the implementation of CBC. The solutions to the revealed challenges are beyond the training institutions’ capacity. The latter call for multi-stakeholder engagement including those from the public and private sectors in health, higher education and finance for common and sustainable solutions.
Go Formative for Formative Assessment Feedback on the 10th Graders’ Material Comprehension and Learning Motivation

Feedback on formative assessment is a teacher's effort to help students with learning difficulties by responding to their evaluation results, especially on HOTS-based evaluations, which have a higher level of completion. Therefore, this study aims to determine the effect of immediate feedback on improving Material Comprehension (MC) and Learning Motivation (LM). Unfortunately, the teacher needs to provide feedback due to several factors, one of which is time constraints due to teachers’ activity. This study aims to investigate the effect of Go Formative in providing immediate feedback on formative physics assessments on material comprehension and learning motivation enhancement of 10th-grade high school students. This research is quasi-experimental with a non-equivalent control group design involving two classes, namely the control and experimental classes. The two classes were selected by employing a simple random sampling technique. This study's results show a different effect of using Go Formative in providing quick feedback in improving students' material comprehension. The existence of these differences makes the provision of quick and immediate feedback through the Go Formative website a solution to overcome the obstacles experienced by teachers in carrying out formative assessments.