Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Conversion Ratio Multi-Phase VRM Realized with Stacking of Generic Series-Capacitor-Buck Converter Cells

This paper introduces a new design approach for multiphase nonisolated DC-DC topology. The design approach is based on a new generic hybrid cell that consists of both capacitor and inductor. Using a stand-alone cell, the approach contributes to a high modularity of the resulting converters and enables high conversion ratios at higher efficiencies. The unique interaction between the capacitor and the inductor result in a soft charging operation, which curbs the losses of the converter, and contributes to higher efficiency. The method was used to create a multiphase voltage regulator module VRM. The new converter significantly extends the effective duty ratio and lowers the voltage stress of the transistors, while delivering high current to the output and has inherent current sharing to balance the load between the phases. Experimental results of a modular interleaved three phase prototype demonstrate an excellent proof of design methodology concept and agree well with the simulations and theoretical analyses developed in this study. Typical applications are point-of-load (PoL) and voltage regulator modules (VRM).
Congenital Atlanto-Occipital Fusion and its Effect on the Myodural Bridge: A Case Report Utilizing the P45 Plastination Technique

Congenital atlantooccipital fusion and its effect on the myodural bridge: A case report utilizing the P45 plastination technique. Int. J. Morphol. , 40(4) :796-800, 2022. SUMMARY: The atlanto-occipital joint is composed of the superior fossa of the lateral masses of the atlas (C1) and the occipital condyles. Congenital Atlanto-occipital fusion (AOF) involves the osseous union of the base of the occiput (C0) and the atlas (C1). AOF or atlas occipitalization/assimilation represents a craniovertebral junction malformation (CVJM) which can be accompanied by other cranial or spinal malformations. AOF may be asymptomatic or patients may experience symptoms from neural compression as well as limited neck movement. The myodural bridge (MDB) complex is a dense fibrous structure that connects the suboccipital muscular and its related facia to the cervical spinal dura mater, passing through both the posterior atlanto-occipital and atlanto-axial interspaces. It is not known if atlas occipitilization can induce structural changes in the MDB complex and its associated suboccipital musculature. The suboccipital region of a cadaveric head and neck specimen from an 87-year-old Chinese male having a congenital AOF malformation with resultant changes to the MDB complex was observed. After being treated with the P45 plastination method, multiple slices obtained from the cadaveric head and neck specimen were examined with special attention paid to the suboccipital region and the CVJM. Congenital atlanto-occipital fusion malformations are defined as partial or complete fusion of the base of the occiput (C0) with the atlas (C1). In the present case of CVJM, unilateral fusion of the left occipital condyle with the left lateral mass of C1 was observed, as well as posterior central fusion of the posterior margin of the foramen magnum with the posterior arch of C1. Also noted was a unilateral variation of the course of the vertebral artery due to the narrowed posterior atlanto-occipital interspace. Surprisingly, complete agenesis of the rectus capitis posterior minor (RCPmi) and the obliques capitis superior (OCS) muscles was also observed in the plastinated slices. Interestingly, the MDB, which normally originates in part from the RCPmi muscle, was observed to originate from a superior bifurcation within an aspect of the nuchal ligament. Therefore, the observed changes involving the MDB complex appear to be an effective compensation to the suboccipital malformations.
Promoting a culture of inclusion: impact of professional development on teachers’ assistive technology practices to support early literacy

ABSTRACT Despite the efficacy of assistive technology (AT), many children with disabilities do not have access to AT and are not fully benefiting from inclusive preschool education. The current mixed-methods study examined a multi-faceted professional development (PD) intervention aligned with the CEC’s Early Intervention/Early Childhood Special Education (EI/ECSE) Standards to increase general early childhood education (ECE) teachers’ use of AT to promote early literacy in young children with disabilities. The 24-week PD intervention included: (a) online modules, (b) coaching; and (c) a classroom kit of AT devices. Participants were six lead teachers, 10 teacher aides, and 34 children from six inclusive classrooms in two preschools. Quantitative methods included pre-and post- surveys, early literacy assessments, and classroom observations. Qualitative analysis explored data from focus groups and input from teacher adherence forms. Results revealed that (a) teachers’ AT knowledge, positive beliefs, and use; and (b) children’s use of AT and early literacy skills significantly increased pre- to post- intervention. Qualitative findings revealed themes relating to how teachers benefitted from the PD and the impact of AT on teacher instruction and child learning. Findings support innovative PD practices to build capacity of practitioners implementing AT as a responsive instructional strategy in inclusive classrooms.
REQUIREMENTS FOR A META-MODEL TO REPRESENT PRODUCT-SERVICE SYSTEMS (PSS) THAT INCORPORATE INTERNET OF THINGS (IoT) SOLUTIONS

The Internet of Things (IoT) infrastructure of networked objects presents potential to enable new value proposition for PSS offering, due to its ability to intensify and aggregate different levels of intelligence; interaction; and perception of real world events. Furthermore, these levels can be represented by a combination of attributes of design objects. Moreover, the described IoT’s "abilities" have the potential to emulate human actor’s activities that support PSS Operations. However, the behavioral perspective of activities performed by IoT’s design objects, while emulating human behavior must be better understood. Therefore, the objective of this work is to identify requirements in the literature to define a meta-model to represent both PSS ́s and IOT ́s design objects. This work is based on the Design Research Methodology (DRM), which is used to help to improve the design research process. The partial result so far is represented by the identification of five models in the literature, with different behavioral perspectives and attributes, able to represent the initial requirements to compose a meta-model of a PSS that comprises IoT solutions. Next steps will be to analyze, merge and consolidate the final set of behavioral perspectives and attributes.
Body Processing in Children and Adolescents with Traumatic Brain Injury: An Exploratory Study

Dysfunctions in body processing have been documented in adults with brain damage, while limited information is available for children. This study aimed to investigate body processing in children and adolescents with traumatic brain injury (TBI) (N = 33), compared to peers with typical development. Two well-known computerized body-representation paradigms, namely Visual Body Recognition and Visuo-spatial Imagery, were administered. Through the first paradigm, the body inversion and composite illusion effects were tested with a matching to sample task as measures of configural and holistic processing of others’ bodies, respectively. The second paradigm investigated with a laterality judgement task the ability to perform first-person and object-based mental spatial transformations of own body and external objects, respectively. Body stimuli did not convey any emotional contents or symbolic meanings. Patients with TBI had difficulties with mental transformations of both body and object stimuli, displaying deficits in motor and visual imagery abilities, not limited to body processing. Therefore, cognitive rehabilitation of body processing in TBI might benefit from the inclusion of both general training on visuo-spatial abilities and specific exercises aimed at boosting visual body perception and motor imagery.
Parkinson’s Disease Etiology: Insights and Associations with Phosphate Toxicity

The present paper investigated the association of Parkinson’s disease etiology with phosphate toxicity, a pathophysiological condition in which dysregulated phosphate metabolism causes excessive inorganic phosphate sequestration in body tissue that damages organ systems. Excessive phosphate is proposed to reduce Complex I function of the mitochondrial electron transport chain in Parkinson’s disease and is linked to opening of the mitochondrial permeability transition pore, resulting in increased reactive oxygen species, inflammation, DNA damage, mitochondrial membrane depolarization, and ATP depletion causing cell death. Parkinson’s disease is associated with α-synuclein and Lewy body dementia, a secondary tauopathy related to hyperphosphorylation of tau protein, and tauopathy is among several pathophysiological pathways shared between Parkinson’s disease and diabetes. Excessive phosphate is also associated with ectopic calcification, bone mineral disorders, and low levels of serum vitamin D in patients with Parkinson’s disease. Sarcopenia and cancer in Parkinson’s disease patients are also associated with phosphate toxicity. Additionally, Parkinson’s disease benefits are related to low dietary phosphate intake. More studies are needed to investigate the potential mediating role of phosphate toxicity in the etiology of Parkinson’s disease.
A 44.3mW 62.4fps Hyperspectral Image Processor for MAV Remote Sensing

This work presents the first dedicated processor that supports the entire hyperspectral image (HSI) processing work-flow. Folding and data interleaving are applied to the processing elements (PEs), reducing the area by 57%. The reconfigurable PE datapath enables 92.7% utilization with 38.1% less area. Data sparsity is leveraged for clock gating, saving 32.2% power. The chip dissipates 44.3mW at 175MHz from a 0.68V supply. It delivers a 62.4fps throughput for 256×256×64 HSIs. Compared to a high-end CPU, this work achieves a 544× higher throughput and 82,352× higher energy efficiency.
High-Throughput Remote Sensing of Vertical Green Living Walls (VGWs) in Workplaces

Vertical green living walls (VGWs)—growing plants on vertical walls inside or outside buildings—have been suggested as a nature-based solution to improve air quality and comfort in modern cities. However, as with other greenery systems (e.g., agriculture), managing VGW systems requires adequate temporal and spatial monitoring of the plants as well as the surrounding environment. Remote sensing cameras and small, low-cost sensors have become increasingly valuable for conventional vegetation monitoring; nevertheless, they have rarely been used in VGWs. In this descriptive paper, we present a first-of-its-kind remote sensing high-throughput monitoring system in a VGW workplace. The system includes low- and high-cost sensors, thermal and hyperspectral remote sensing cameras, and in situ gas-exchange measurements. In addition, air temperature, relative humidity, and carbon dioxide concentrations are constantly monitored in the operating workplace room (scientific computer lab) where the VGW is established, while data are continuously streamed online to an analytical and visualization web application. Artificial Intelligence is used to automatically monitor changes across the living wall. Preliminary results of our unique monitoring system are presented under actual working room conditions while discussing future directions and potential applications of such a high-throughput remote sensing VGW system.
A Longitudinal Characterization of the Third-Party Authentication Landscape

Many websites offer users to authenticate using third-party identity providers (IDPs) such as Facebook or Google. As part of the signup process, these websites often ask the user to give them additional permissions with the IDP (e.g., some data sharing or authorize some actions) that can have significant privacy implications. Motivated by the increased scrutiny of Facebook and other popular IDPs (e.g., due to the 2018 Cambridge Analytica scandal), we present a longitudinal analysis of the IDP usage and permissions changes over the past nine years (2012–2021) as well as a large-scale characterization of the current state. Our longitudinal analysis identifies trends and characterizes changes in both the IDP usage and permission agreements of different subsets of websites. For our large-scale analysis, we develop and share a Selenium-based measurement framework that we use to collect datasets. Using this data, we study the IDP usage across popularity ranges, the permissions used in the wild, and highlight differences between websites using different IDPs and those that do not. Our analysis shows increased IDP usage, especially among the most popular websites, and that the permission requests on average are becoming more modest but also brings forward significant exceptions that may need further scrutiny.
A 22nm 128-kb MRAM Row/Column-Parallel In-Memory Computing Macro with Memory-Resistance Boosting and Multi-Column ADC Readout

This paper presents a 128-kb in-memory computing (IMC) macro for fully row/column-parallel matrix-vector multiplication (MVM), implemented using a foundry MRAM in 22nm FD-SOI. Previous IMC in eNVM relied on RRAM with significantly higher resistance and resistance-state contrast than typical in foundry processes [1]–[3] or where parallelism was substantially reduced [4]. MRAM addresses distinct application requirements (e.g., temperature, radiation). This work advances previous MRAM IMC by improving area-normalized EDP by 60× over [5] and by employing a standard high-density bit cell without additional devices, as in [6]. This is achieved via a readout architecture that performs column-resistance boosting, with integrated auto-zeroing, and conductance-to-current sampling, to simultaneously feed four IMC columns to a single ADC for conversion to 6-b outputs (highest ADC precision among eNVM IMC designs).
Sex and slaughter weight on carcass traits and non-carcass components of horses

ABSTRACT: The study evaluated the effect of sex and slaughter weight on carcass traits and non-carcass components of 1,489 horses (739 geldings and 750 females, classified into eight groups according to their weight). Data were analyzed under a completely randomized design. Geldings had a higher (P < 0.05) hot carcass yield (HCY) and cold carcass yield (CCY) than females; however, fat yellowness values (b*) were greater (P < 0.05) in females. Slaughter weight (SW) affected the carcass characteristics. Hot carcass weight (HCW), cold carcass weight (CCW), HCY, and CCY increased (P < 0.05) in accordance with slaughter weight, while cool loss percentage (CL%) was lower (P < 0.05) in horses weighing more than 300 kg. In addition, the non-carcass components decreased (P < 0.05) as the weight of the horses increased. In conclusion, sex and body weight are factors to consider in the production of equine meat since they affect carcass characteristics and non-carcass components.
TEACHING ENGLISH LANGUAGE AND CULTURE THROUGH PBL AT THE TERTIARY LEVEL

"Research has shown that language and culture are closely related (e.g., Brown, 2007; Kramsch, 1998; Kuang, 2007; Savignon & Sysoyev, 2005) and are best acquired together (Schulz, 2007). This paper aims to demonstrate how project-based learning (PBL), one of the main innovative approaches in the current educational landscape, can be useful for teaching culture in the English language classroom at the tertiary educational level. PBL is a teaching approach in which students gain knowledge and skills by working for an extended period of time to investigate and respond to an authentic, engaging, and complex question, problem, or challenge (Kingston, 2018). According to different studies in the field (Kavlu, 2017), this approach can be more engaging and motivating for English language learners than traditional methods. First, the language is being used for an authentic purpose, which gives the language relevance. Further, learners develop content knowledge as well as critical thinking, creative thinking and communication skills. This study will focus on the implementation of PBL in the English language classroom in different degree programs: Primary Education, Infant Education, Hispanic Philology and Humanities. Students created an authentic final product that explored cultural aspects of English-speaking countries in combination with different subjects such as geography, history, literature and economy. The findings are based on a questionnaire that measures tertiary students’ motivation levels and expectations in relation to this pedagogical framework. "
Distributed Beamforming for Joint Radar-Communications

Distributed co-phasing has received much attention recently to achieve distributed beamforming because of its simplicity and support of numerous power transmission schemes. In this context, we study distributed beamforming for a joint distributed radar-communications (JDRC) system. We employ a distributed multiple-input multiple-output (MIMO) radar that operates within the same frequency spectrum as a full-duplex (FD) wireless sensor network of multiple FD transceiver, receive-only and transmit-only nodes. We formulate a joint beamformer and MIMO radar code co-design non-convex optimization problem based on the co-phasing technique and solve it using the block coordinate descent method. Numerical experiments demonstrate performance improvement in JDRC over the conventional beamforming strategies.
ECG Heartbeat Classification Using CONVXGB Model

ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification using a convolutional neural network (CNN) with eXtreme Gradient Boosting (XBoost), ConvXGB. This model was established by stacking two convolutional layers for automatic feature extraction from ECG signals, followed by XGBoost as the last layer, which is used for classification. This technique simplified ECG classification in comparison to other methods by minimizing the number of required parameters and eliminating the need for weight readjustment throughout the backpropagation phase. Furthermore, experiments on two famous ECG datasets–the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets–demonstrated that this technique handled the ECG signal classification issue better than either CNN or XGBoost alone. In addition, a comparison showed that this model outperformed state-of-the-art models, with scores of 0.9938, 0.9839, 0.9836, 0.9837, and 0.9911 for accuracy, precision, recall, F1-score, and specificity, respectively.
Examination of Trace Metals and Their Potential Transplacental Transfer in Pregnancy

With the ever-growing concern for human health and wellbeing, the prenatal period of development requires special attention since fetuses can be exposed to various metals through the mother. Therefore, this study explored the status of selected toxic (Pb, Cd, Ni, As, Pt, Ce, Rb, Sr, U) and essential trace metals (Mn, Co, Cu, Zn, Se) in the umbilical cord (UC) sera, maternal sera, and placental tissue samples of 92 healthy women with normal pregnancies. A further aim focuses on the potential transplacental transfer of these trace metals. Based on the obtained levels of investigated elements in clinical samples, it was observed that all of the trace metals cross the placental barrier and reach the fetus. Furthermore, statistical analysis revealed significant differences in levels of toxic Ni, As, Cd, U, Sr, Rb, and essential Mn, Cu, and Zn between all three types of analyzed clinical samples. Correlation analysis highlighted As to be an element with levels that differed significantly between all tested samples. Principal component analysis (PCA) was used to enhance these findings. PCA demonstrated that Cd, Mn, Zn, Rb, Ce, U, and Sr were the most influential trace metals in distinguishing placenta from maternal and UC serum samples. As, Co, and Cu were responsible for the clustering of maternal serum samples, and PCA demonstrated that the Pt level in UC sera was responsible for the clustering of these samples. Overall, the findings of this study could contribute to a better understanding of transplacental transfer of these trace metals, and shed a light on overall levels of metal exposure in the population of healthy pregnant women and their fetuses.
Epigenetic Peripheral Biomarkers for Early Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and represents the leading cause of cognitive impairment and dementia in older individuals throughout the world. The main hallmarks of AD include brain atrophy, extracellular deposition of insoluble amyloid-β (Aβ) plaques, and the intracellular aggregation of protein tau in neurofibrillary tangles. These pathological modifications start many years prior to clinical manifestations of disease and the spectrum of AD progresses along a continuum from preclinical to clinical phases. Therefore, identifying specific biomarkers for detecting AD at early stages greatly improves clinical management. However, stable and non-invasive biomarkers are not currently available for the early detection of the disease. In the search for more reliable biomarkers, epigenetic mechanisms, able to mediate the interaction between the genome and the environment, are emerging as important players in AD pathogenesis. Herein, we discuss altered epigenetic signatures in blood as potential peripheral biomarkers for the early detection of AD in order to help diagnosis and improve therapy.
Microbial and Planktonic Community Characteristics of Eriocheir sinensis Culture Ponds Experiencing Harmful Algal Blooms

The Chinese mitten crab, Eriocheir sinensis (H. Milne Edwards, 1853), is an economically important aquaculture species in China. It is a significantly desirable species by Chinese consumers that causes a high demand for environmentally friendly culture farming. In aiming to break through bottlenecks, i.e., “pond moss” and cyanobacteria, we investigated the microbial community and plankton composition of ponds with filamentous algae and cyanobacterial blooms. As results, we found Actinobacteria, Proteobacteria, and Bacteroidetes were dominant bacterial phyla, while Chlorophyta and Bacillariophyta were dominant phytoplankton phyla in E. sinensis ponds. Nitrospira sp. , Flectobacillus sp. BAB-3569, Staphylococcus warneri, Fusarium oxysporum, Gromochytrium mamkaevae, and Rhizophydium sp. JEL317 were screened as bioindicators for harmful algal blooms. We found a close relationship between water quality parameters and the species composition of bacteria and zooplankton in the present study. Specifically, total nitrogen and total ammonia nitrogen significantly affected the bacterial community composition, while total phosphorus contributed to the phytoplankton community composition. We further indicated the potential competitive inhibition of Chlamydomonadales on the direct regulation of the control of harmful algal blooms. Finally, we suggested a combination of probiotics and microalgae, e.g., C. vulgaris, to prevent and control potential risks in the culture of E. sinensis. In conclusion, the present study deepened our understanding of harmful algal blooms in aquaculture ponds and suggested the baseline indications for the prevention and control of algal blooms.