Miracle-dz/UniFAR-FTUs huggingface dataset:
ASIC Framework Simplified and Operationalised – An Operational Matrix for Optimising the Use of Technologies and Innovations in Medical Education

Abstract The ASIC [adaptation, standardisation, integration and compliance] framework was developed to set the standard for the use of innovations and technologies in medical education. There is a need to develop frameworks and reference guides for educational technologies [EdTechs] and innovations, noting that EdTechs are becoming increasingly important to the delivery of medical education. The ASIC framework as previously published presents four major tenets including: adaptation, standardisation, integration and compliance. The operational matrix is now developed and presented in this article. Each tenet of the ASIC framework has key requirements or questions that the user of an EdTech or educational innovation should address. Each question represents a key requirement to be satisfied by the user of the EdTech to satisfy the ASIC tenets. Each question is also equally weighted as every other one. The matrix has 12 key questions, representing 12 key requirements. The results measurement can either be a fraction of 12 or a percentage. As a requirement, the minimum score under each category is 2 out of 3; ideally, optimisation of an educational innovation or technology requires that all boxes are checked in the affirmative. However, a minimum of 2 out of 3 in every category would indicate a minimum score or threshold. This effort is in line with previous and ongoing efforts to ensure that educational technologies and innovation that are deployed to deliver medical education are adapted for optimal performance, standardised for the use of training, integrated into the medical education delivery system, and compliant with professional, institutional, and regulatory standards.
Proof-of-Concept Preclinical Use of Drosophila melanogaster in the Initial Screening of Immunomodulators

Drug discovery is a complex process, and the use of a comprehensive approach is deemed necessary to discover new chemical entities with novel mechanisms of action. This research was carried out to determine whether Drosophila melanogaster can serve as an appropriate model organism in the initial screening of drug candidates with immunomodulatory activities. To test this, we performed phenotypic assay and molecular analysis to investigate the immunomodulatory activities of aspirin, dexamethasone, curcumin, and epigallocatechin gallate (EGCG), that have been reported to yield such effects in the mammalian model system. In vivo survival analysis demonstrated that all drugs/compounds were relatively safe at the tested concentrations. In the infection assay, curcumin and EGCG showed a protective signature to bacterial infection in flies lacking Toll-mediated immune responses. Furthermore, dexamethasone and aspirin, drugs with immunosuppressive activity, could improve the survival of PGRP-LBΔ mutant flies with hyperactivated immune system. These phenotypes were supported by RT-qPCR-based molecular analysis, revealing that drugs/compounds used in this study could modulate the expression level of genes related to the immune system. In conclusion, while curcumin and EGCG could promote the improvement of fly survival against infection, aspirin and dexamethasone were able to suppress overactivation of immune responses in D. melanogaster. These results are in line with the ones observed in the mammalian model system, further emphasizing the notion that flies would serve as a prospective model organism in the initial screening of drug candidates for their immunomodulatory activities prior to further checking in the mammalian animal models. In the end, this will reduce the use of mammalian animal models for preliminary experiments in an effort to discover/repurpose drugs with immunomodulatory activity.
Transvaginal Sonoelastography on Benign Masses of Cervix

Cervix is the lowermost portion of the uterus. Benign pathologies of the uterus mostly occur in women falling under the conceptive age profile. Frequently exhibited symptoms are excessive bleeding, menorrhagia and pain during menstruation or coitus. Sonoelastography in comparison to the sonography alone is a rather recent development and is easily affordable and available. This makes it ideal for use as a first – look diagnostic tool to determine the nature of the cervical mass without unnecessary invasive or expensive examinations. There is an urgent need for early differentiation of benign masses from malignant ones as it impacts the type of treatment administered, its efficacy, prognosis, and lifestyle post-treatment. This review aims to evaluate the accuracy of transvaginal sonography in correctly diagnosing benign masses of cervix from malignancy, and thus gives an overview of the evolution of its application in routine clinical practice. As seen in the review, only studies performing strain elastography have been taken into consideration with strain ratio being a focal point as this is a quantifiable unit, used for measurement of tissue elasticity. This gives us comparable results regarding the diagnostic precision of transvaginal elastography in cervical masses. Gold standard is used is histopathological diagnosis.
The Impact of the Neophyte Tree Fraxinus pennsylvanica [Marshall] on Beetle Diversity under Climate Change

We studied the impact of the neophyte tree Fraxinus pennsylvanica on the diversity of beetles in floodplain forests along the river Elbe in Germany in 2016, 2017 and in 2020, where 80% of all Fraxinus excelsior trees had died following severe droughts. Beetles were collected by insecticidal knock-down from 121 trees (64 F. excelsior and 57 F. pennsylvanica) and identified to 547 species in 15,214 specimens. The trees sampled in 2016 and 2017 showed no signs of drought stress or ash dieback and serve as a reference for the comparison with the 2020 fauna. The data proved that F. excelsior harbours the most diverse beetle community, which differed also significantly in guild composition from F. pennsylvanica. Triggered by extremely dry and long summer seasons, the 2020 ash dieback had profound and forest-wide impacts. Several endangered, red-listed beetle species of Saxonia Anhalt had increased in numbers and became secondary pests on F. excelsior. Diversity decreased whilst numbers of xylobionts increased on all trees, reaching 78% on F. excelsior. Proportions of xylobionts remained constant on F. pennsylvanica. Phytophages were almost absent from all trees, but mycetophages increased on F. pennsylvanica. Our data suggest that as a result of the dieback of F. excelsior the neophyte F. pennsylvanica might become a rescue species for the European Ash fauna, as it provides the second-best habitat. We show how difficult it is to assess the dynamics and the ecological impact of neophytes, especially under conditions similar to those projected by climate change models. The diversity and abundance of canopy arthropods demonstrates their importance in understanding forest functions and maintenance of ecosystem services, illustrating that their consideration is essential for forest adaptation to climate change.
Identifying educational needs by rationalising TVET industry clusters in Brazil

Technical Vocational Educational and Training (TVET) institutions need to reinvent themselves to keep up with market trends, which is why they should assume a more proactive strategy instead of a reactive one towards their Catalogue of Courses. Assuming that TVET institutions are also revenue-maximising institutions, they must provide all of the information that employers and workers need in their materials in a common language and should avoid multiple marketing actions. Therefore, the aim of this research is to present an empirical study to group industrial segments with similar occupational structures and R&D investments to help TVET institutions identify their training needs and tailor their descriptive materials of their marketing and sales teams. Data mining and cluster analysis were used to identify how regular courses at the Brazilian National Service for Industrial Training (SENAI) could be offered within industrial segments in 2011.
Literature Review of Optimization Techniques for Chatter Suppression In Machining

Chatter produces a poor surface finish, high tool wear, and can even damage machine tools because of the regenerative effect, the loss of contact effect, and the mode coupling effect. Various research works have investigated the suppression of chatter by either passive or active methods, such as by applying absorbers, damping, varied speeds and other alternatives. In this paper, it can be observed that for chatter suppression, optimization focuses on spindle design, tool path, cutting process, and variable pitch. Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.
Effect of elastic boundary supports and stiffening treatments on vibroacoustic response of plate-like structures

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Day-ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days’ Load Profiles

Short-term load forecasting (STLF) is necessary for system operators; however, its difficulty has been increasing since distributed resources, particularly behind-the-meter (BTM) PV resources, have been introduced to power systems. This study proposes a framework for STLF for holidays considering the four major factors that affect the net load profiles —calendar, trend, weather, and BTM PV. The target holiday is first paired with historical holidays following its calendar factor, which are defined as “similar days. ” Subsequently, in terms of the remaining three factors, the differences between the historical holidays and target holidays are calculated, and their effects on load differences (factor-induced load differences) are quantified and reflected. Finally, for each pair, the modified load profiles are generated and combined to obtain a daily load profile of the target holiday. The proposed framework was implemented on a case study of Korean national holidays, and its forecasting accuracy was compared with conventional forecasting methods. The accuracy metrics show that the proposed framework outperforms conventional methods. The results suggest that the proposed framework can be applied to STLF for holidays to improve forecasting accuracy.
Review of Oil-Filled Transformer Monitoring Technology for New User

Distribution and power transformer is the essential part of electrical power transmission system. Abnormalities occur at the transformer can be interpreted and analysed to get the cause of faults, thus it can be further studied for monitoring ageing and condition of the transformer. A lot of studies have been done to detect as many abnormalities that can possibly arise in a transformer. For inexperienced user, it can be a bit of overwhelming to choose which studies that they can refer to. Numerous techniques and methods has been used for transformer monitoring till date and still being improved which includes: Common Electrical Tests, Power and Dissipation Factor Measurement, Partial Discharge (PD) Analysis, Frequency Response Analysis (FRA), Dielectric Response Analysis (DRA), Dissolved Gas Analysis (DGA), Dielectric Breakdown Voltage, Oil Resistivity, and Karl Fischer Titration. These techniques and methods are developed to monitor and analyse the common part of transformer which are winding, core, bushing, insulation oil, and insulation paper. This paper reviews the current improved techniques and methods in transformer monitoring according to the technology, accuracy, complexity, and cost for the past five years. A few methods and techniques can be appointed to each of classified user as a beginner, intermediate and competent. Finally, this paper suggests more improvement to be studied on the existing established methods. Some of the techniques can be combined to create a better accuracy and efficiency for fault identification, ageing estimation and condition monitoring.
Computational study of nanoscale mechanical properties of Fe–Cr–Ni alloy

ABSTRACT Mechanical properties of Fe–Cr–Ni alloy nanowires have been investigated using molecular dynamics simulation with embedded atom method and first principles approach. Various cases of uniaxial tension, compression and shear deformations have been performed and studied in this work. From the first principles calculations, the higher magnitudes of uniaxial and shear deformations resulted in higher probability of martensitic transformations. Before the first yielding, nanowires preserved the elastic stage and then the mechanical deformation proceeded in alternating quasi-elastic and yielding stages. The plastic behaviour was not observed in compression while both tensile and shear deformations showed apparent plastic behaviour. In shear deformation, due to the martensitic phase transformation, the plastic behaviour persisted for total strain of 0.6 which was much larger than that during tensile and compression. This validated the previous experimental observations. In the studied Fe–Cr–Ni nanowires, deformations were controlled by dislocations. Dislocation-mediated twinnings were captured by common neighbour analysis. Twin quantification showed that the twin activity increased with increasing strain rate. Twinnings originated from stacking faults led by 1/6 <112> Shockley partial dislocations. At elevated temperature (beyond 500 K), the materials softening happened, and 316L nanowire became more plastic under a lower stress.
System Failure Detection Using Deep Learning Models Integrating Timestamps with Nonuniform Intervals

System logs play an important role in software development and system maintenance. Many system software programs continuously generate system logs during software runtimes for failure detection and diagnosis purposes. Currently, the analysis of system log data is mainly a manual process that highly depends on human knowledge and experience. This time-consuming task has become a problem because of the ever-increasing volume of log data. Existing studies have investigated machine learning and deep learning techniques to automate the failure detection task. This paper takes the deep learning approach and proposes two detection structures based on recurrent and convolutional neural networks. More importantly, this paper takes a step further by closely examining the timestamps of log data which existing studies have generally ignored. This study found that time information can be a distinguishing factor between regular and abnormal log sequences. Inspired by this observation, a novel method is proposed to integrate log timestamps in deep learning models using interpolation techniques. The evaluation results show that the log timestamps can significantly improve the performance of failure detection. Cross-comparison of the different models demonstrates that the proposed network structure can successfully utilize the timestamp information. The code is available on GitHub: https://github.com/hfyxin/Ts-models-log-data-analysis.git.
What do Functional Synchronization Networks Indicate About Underlying Structure and System Dynamics? A network theory study in the islet

Network theory provides tools for quantifying communication within heterogeneous biological systems. Calcium-based imaging is a common technique used to analyze cellular dynamics and create functional networks. However, the use of functional networks in understanding the underlying biological structure, formed by physical connections, has not been well investigated. The islet of Langerhans is a biological system with relatively simple architecture that affords the opportunity for high-fidelity experimental and computation analysis, making it an ideal system to answer biological network theory-based questions. Here, we analyze both experimental and computational models of the islet to quantify the relationship between the gap junction structural network and functional synchronization networks. We show that functional networks poorly reflect gap junction structure. Instead, they indicate intrinsic factors driving excitability, such as cellular metabolism. This surprising finding provides a new interpretation of functional networks within biological systems.
Predictive Efficacy of Blood-Based Tumor Mutation Burden Assay for Immune Checkpoint Inhibitors Therapy in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis

Background In non-small cell lung cancer (NSCLC) patients treated by immune checkpoint inhibitors (ICIs), tumor mutation burden (TMB) has been found to have predictive potential for survival. When compared to TMB detection in tissue (tTMB), detecting TMB in the blood (bTMB) has practical advantages; yet, the results of various studies are conflicting. The question of whether bTMB can be utilized as a predictive biomarker is becoming increasingly contentious. To confirm the predictive efficacy of bTMB, researchers did a systematic review and meta-analysis to look into the relationship between ICIs and bTMB. Method From the inception to March 2021, Cochrane Library, PubMed, EMBASE and other databases were systematically searched. The predictive value of bTMB in ICIs, or the efficacy of ICIs against chemotherapy, was studied. The results were presented as pooled ratio rate (RR) and hazard ratio (HR) with 95% confidence intervals for the Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Subgroup analysis, heterogeneity analyses, and sensitivity analysis were also performed. Results A total of 2,610 NSCLC patients were studied in seven trials. There were no significant differences in OS (HR = 1.09; 95% CI: 0.62–1.91, P = 0.774) or PFS (HR = 0.73; 95% CI: 0.20–2.65, P = 0.629) between high and low bTMB groups in the ICIs cohort. When ICIs were compared to chemotherapy, ICIs were found to enhance OS (HR = 0.74; 95% CI: 0.59–0.92, P = 0.006), but the improvement in PFS and ORR was only a numerical trend (PFS: HR = 0.83; 95% CI: 0.63–1.09, P = 0.173; ORR: RR = 0.92, 95% CI: 0.77–1.10, P = 0.372). NSCLC patients treated with ICIs in the high bTMB group had better survival benefits than chemotherapy patients in terms of OS (HR = 0.63; 95% CI: 0.51–0.76, P <0.001), PFS (HR = 0.63; 95% CI: 0.52–0.76, P <0.001), and ORR (RR = 1.86; 95% CI: 1.32–2.62, P <0.001), while in the low TMB group, the results were no different or even reversed (OS: HR = 0.89; 95% CI: 0.64–1.24, P = 0.485; PFS: HR = 1.21, 95% CI: 0.93–1.58, P = 0.154; ORR: RR = 0.68, 95% CI: 0.54–0.85, P = 0.001). Conclusions TMB could predict the enhanced survival benefit of NSCLC patients treated with ICIs; however the role of bTMB is limited at this stage. For NSCLC patients with high TMB, ICIc may be a better option than chemotherapy.
Capacity and tradeoffs in neural encoding of concurrent speech during Selective and Distributed Attention

Speech comprehension is severely compromised when several people talk at once, due to limited perceptual and cognitive resources. Under some circumstances listeners can employ top-down attention to prioritize the processing of task-relevant speech. However, whether the system can effectively represent more than one speech input remains highly debated. Here we studied how task-relevance affects the neural representation of concurrent speakers under two extreme conditions: when only one speaker was task-relevant (Selective Attention), vs. when two speakers were equally relevant (Distributed Attention). Neural activity was measured using magnetoencephalography (MEG) and we analysed the speech-tracking responses to both speakers. Crucially, we explored different hypotheses as to how the brain may have represented the two speech streams, without making a-priori assumptions regarding participants’ internal allocation of attention. Results indicate that neural tracking of concurrent speech did not fully mirror their instructed task-relevance. When Distributed Attention was required, we observed a tradeoff between the two speakers despite their equal task-relevance, akin to the top-down modulation observed during Selective Attention. This points to the system’s inherent limitation to fully process two speech streams, and highlights the complex nature of attention, particularly for continuous speech.
Experimental research on damage and formation limits on porous silicon materials by electrochemical etching method

Efficient silicon electrochemical etching (anodization) is essential to produce porous silicon (PSi) for a wide variety of applications, but the damage that occurs to the porous layer of the silicon during the anodization is wasteful and inefficient. The porous formation mechanism of highly doped p-type silicon was studied by considering the applicable range of the anodizing parameters and their effects on the pore formation and distribution. The results show that most of the PSi damage attributed to the severe potential drop in the developed oxide film, which promoted the reaction toward the passivation region of the I–V curve. There is indirect dissolution, which produces a delocalized distribution of the electrochemical reaction over the etched area; meanwhile, the internal residual stress inside the porous layer causes the pore damage. It provides a guide map for PSi formation and damage control in order to promote more effective production of PSi-based devices. Porous silicon formation in p-type silicon was investigated comprehensively. Particular emphasis is put on the limitations and sensitivity of etching parameters. A neat and substantiated classification of the porous layer’s damage is set out. The formation damage is always ahead of the volumetric damage. Most of the damage is due to the high potential drop in the oxide film.
BCOR-CCNB3 sarcoma arising in the proximal tibia: A case report

BCL6 corepressor-cyclin B3 (BCOR-CCNB3) fusion sarcoma was classified as an emerging subgroup of undifferentiated small round cell sarcoma in 2020. The incidence of BCOR-CCNB3 fusion sarcoma is reportedly 1.5-14% among undifferentiated unclassified sarcomas, representing a rare entity among primary malignant bone tumors. The present study reports a case of BCOR-CCNB3 fusion sarcoma in the proximal tibia of a boy. A 12-year-old boy presented with a 6-month history of knee pain and a slowly growing mass in the anteromedial aspect of the left proximal tibia. Plain radiography and computed tomography of the knee demonstrated a lytic lesion with cortical destruction of the proximal tibia. Magnetic resonance imaging showed the bone tumor expanding into soft tissue with almost homogeneous hypointensity on T1-weighted imaging and slightly hyperintensity on T2-weighted imaging. On histopathological evaluation, the tumor comprised a proliferation of small, round to ovoid-shaped mesenchymal cells without osteoid formation. Histopathologically, BCOR-CCNB3 sarcoma of bone was finally diagnosed based on immunohistochemical staining and additional molecular analyses. The patient underwent bone tumor resection followed by pre- and post-operative chemotherapy according to a Ewing sarcoma protocol. The patient showed no evidence of local recurrence or distant metastasis at 12 months after completion of adjuvant chemotherapy. We present herein an additional case of BCOR-CCNB3 sarcoma of the proximal tibia, and review the relevant literature on BCOR-CCNB3 sarcoma of bone.