Miracle-dz/UniFAR-FTUs huggingface dataset:
B/LV Laboratory Accessibility Technology Adapted for Neurodiverse Chemistry Students

Text-to-speech technology is a common accommodation available for students with disabilities. Despite the ubiquitous nature of text-to-speech, this technology has not been explored in laboratory settings for neurodiverse college students. This study explores the adaptability of laboratory accessible text-to-speech technology (originally developed for blind/low vision (B/LV) students) for neurodiverse students. Students were asked to provide general feedback about the usability and effectiveness of the technology using Likert surveys. The students also answered open-ended questions about how the technology could be adapted to be more neurodiverse friendly. Overall, more than 50% of the students found the technology useful but had specific feedback about adaptations that could make it even more universal.
A Deep Learning-Based Indoor Radio Estimation Method Driven by 2.4 GHz Ray-Tracing Data

This paper presents a novel method for estimating received signal strength (RSS) in indoor radio propagation using a deep learning approach. The proposed method utilizes a training dataset comprised of imitated real-world indoor environments and radio-map images generated through 2.4 GHz ray-tracing. Additionally, we introduce a convolutional neural network (CNN) named Radio Residual UNet (RadioResUNet) to facilitate the training and prediction of indoor radio propagation. To assess the feasibility and effectiveness of this deep learning network for indoor radio estimation, we compare the RSS obtained from practical wireless equipment with that obtained by RadioResUNet in two indoor environments: an anechoic chamber and an office floor. Furthermore, we explore the prediction outcomes achieved using different loss functions, including mean squared error (MSE), binary cross-entropy (BCE), and dice binary cross-entropy (Dice_BCE), across varying dataset sizes. The results reveal that the proposed deep learning-based radio estimation method exhibits estimation discrepancies of 4.25 dB and 5.4 dB compared to practical measurements in real-world environments of the anechoic chamber and the office floor, respectively. These results indicate a performance that is comparable to the indoor propagation model of ITU-R P.1238. Additionally, we introduce an indoor radio estimation tool that utilizes the deep learning network of RadioResUNet to predict radio propagation in a target area with minimal input.
Establishing mRNA and microRNA interactions driving disease heterogeneity in amyotrophic lateral sclerosis patient survival

Abstract Amyotrophic lateral sclerosis is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3–4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of amyotrophic lateral sclerosis pathophysiology and could be used to monitor a patient’s response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding microRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as amyotrophic lateral sclerosis. The present study aimed to investigate the transcriptomic profile (mRNA and microRNA) of lymphoblastoid cell lines from amyotrophic lateral sclerosis patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (<12 months) (n = 22) compared with those that had a longer disease duration (>6 years) (n = 20). Transcriptional profiling of microRNA–mRNA interactions from lymphoblastoid cell lines in amyotrophic lateral sclerosis patients revealed differential expression of genes involved in cell cycle, DNA damage and RNA processing in patients with longer survival from disease onset compared with those with short survival. Understanding these particular microRNA–mRNA interactions and the pathways in which they are involved may help to distinguish potential therapeutic targets that could exert neuroprotective effects to prolong the life expectancy of amyotrophic lateral sclerosis patients.
A global reconstruction of climate‐driven subdecadal water storage variability

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of global mass redistribution caused by hydrological processes. However, there are still few sources on pre‐2002 global terrestrial water storage (TWS). Classical approaches to retrieve past TWS rely on either land surface models (LSMs) or basin‐scale water balance calculations. Here we propose a new approach which statistically relates anomalies in atmospheric drivers to monthly GRACE anomalies. Gridded subdecadal TWS changes and time‐dependent uncertainty intervals are reconstructed for the period 1985–2015. Comparisons with model results demonstrate the performance and robustness of the derived data set, which represents a new and valuable source for studying subdecadal TWS variability, closing the ocean/land water budgets and assessing GRACE uncertainties. At midpoint between GRACE observations and LSM simulations, the statistical approach provides TWS estimates (doi:10.5905/ethz-1007-85) that are essentially derived from observations and are based on a limited number of transparent model assumptions.
Structural, electronic, optical and elastic properties of Mg3TH7 (T=Mn, Tc and Re) complex hydrides: First-principles calculations

The structural, electronic, optical and elastic properties of Mg3TH7 (T=Mn, Tc, Re) complex hydrides have been investigated by using the density functional theory ‘DFT’ within the generalized gradient approximation ‘GGA’ parameterized by the Perdew, Burke and Ernzerhof ‘PBE’. The optimized lattice constants, atomic positions and interatomic distance are in good agreement with both theoretical results and experimental data available in the literature. Band structure, densities of states ‘DOS’, optical properties, and elastic constants have been calculated. The obtained results of elastic properties were compared with those of the simplest Mg-based hydride MgH2. More importantly, it is found that, the energy band gap is highly augmented when Mn atom is replaced by Re, but it is less pronounced when Re atom is replaced by Tc. From elastic constants calculations, it is noted that MgH2, Mg3MnH7, Mg3TcH7 and Mg3ReH7 compounds are mechanically stable and are brittle. In addition, the polycrystalline elastic properties such as bulk modulus, shear modulus, Young modulus and Poisson’s ratio were also determined by the Voigt-Reuss-Hill, ‘VRH’ approximation. It was found that the bulk modulus of complex hydrides Mg3TH7 (T=Mn, Tc and Re) are higher than that of MgH2 and increases with increasing atomic number of element (Mn, Tc, Re) in the sequence (B) Mg3MnH7 < (B) Mg3TcH7 < (B) Mg3ReH7. The shear anisotropy, linear bulk modulus and percentage anisotropy in compressibility and shear were also determined and discussed in details. Finally, Debye temperatures were calculated and discussed.
Global Fuzzy Adaptive Consensus Control of Unknown Nonlinear Multiagent Systems

This paper investigates the global consensus problems for the first-order and second-order unknown nonlinear multiagent systems (MASs) with uncertain input disturbance. Fuzzy logic systems are applied to solve the global consensus problem for unknown nonlinear MASs. A fully distributed adaptive fuzzy control is designed to enable followers asymptotically to track the leader without using any dynamics of the leader. The global consensus conditions are also derived for the first-order and second-order unknown MASs, which overcomes the drawback of the semiglobal consensus in existing literature. It is worth mentioning that the proposed approach can greatly alleviate the computation burden because it only needs to update a few parameters. An efficient framework is also given to achieve the global formation control of the second-order unknown nonlinear MAS with an undirected connected graph. Finally, four simulated examples are given to illustrate the effectiveness of the proposed control protocols.
Vibration Energy Harvesting for Aerospace Applications

The aerospace industry is actively seeking to employ wireless sensors for various data collection and structural health monitoring applications. While continued development of wireless sensors and sensor networks holds promise in the aerospace industry, the challenge of powering these sensors must still be addressed. Vibration energy harvesting is one possible approach in this regard, and is particularly promising for aerospace applications due to the ubiquitous presence of vibration energy. In this review paper the generic design considerations and challenges involved in harvesting energy from vibration energy are described. The paper also highlights recent progress addressing current challenges in adapting a vibration energy harvesting approach in a practical application, including resonance frequency tuning and design considerations for optimal energy harvesting performance. Recent progress in adapting this approach to the microscale using MEMS technology is also highlighted.
Subsidence monitoring of the Seattle viaduct tunnelling project with Homogeneous Distributed Scatterer InSAR

Subsurface excavations may cause subsidence due to redistribution of stress in the overburden or changes in the groundwater state. In the case of tunnelling under urban areas, this may result in damage to existing high value infrastructure within the surface zone of influence. Synthetic aperture radar interferometry (InSAR) is a useful tool for urban subsidence monitoring because it provides measurements that are widescale and ongoing at regular time intervals. However, many existing InSAR methods such as persistent scatterer InSAR and differential InSAR do not provide sufficient spatial resolution to adequately measure short-scale spatial subsidence gradients which represent the greatest risk to infrastructure. MDA Systems Ltd. (MDA) has developed an InSAR method, Homogeneous Distributed Scatterer (HDS-InSAR), which generates higher spatial densities of measured coherent targets compared to many current methods. This is achieved through a novel combination of spatial phase filtering and coherence based target selection. This method exploits both persistent point and coherent distributed scatterers by using adaptive multilooking over statistically homogenous pixel neighbourhoods to improve the spatial resolution of the resulting subsidence maps. Furthermore, a matched parametric temporal model is fit to the deformation signal in order to enhance the detection of small amplitude subsidence. These techniques in combination provide a means for measuring small scale deformations within an urban environment and correlating their occurrence with known deformation drivers. Results are presented for the ongoing Seattle viaduct tunnelling project which consists of a 3.2 km long bored road tunnel under the city. Two satellite datasets (RADARSAT-2 Spotlight mode stacks from opposing look directions) are analysed to derive 2D (vertical + east–west) deformation maps over the area of interest spanning a time period from 2012-2015 and these results are correlated with known groundwater extraction events.
Retrieval Method of Electronic Medical Records Based on Rules and Knowledge Graph

Every day, the hospital has accumulated a large number of electronic medical records, which record the doctor's description of various patients' conditions, including a large number of medical knowledge related to the patient's health status. So intelligent retrieval research of EMR is an important extension of the medical field. Its accurate retrieval plays an important guiding role in research in the medical field. In this paper, we come up with the combination of rules and knowledge graph, and experimental results demonstrate that the electronic medical record retrieval method based on rules and knowledge map technology can significantly improve the retrieval effect.
Linking climate change adaptation, social capital and natural resource conflict prevention. The case of disaster risk reduction interventions in the Uganda-Kenya border region

In recent years drought-related crises and emergencies have increasingly plagued various regions across the globe. Recurrent droughts particularly in disaster-prone areas affect groups whose livelihoods depend solely on the availability of renewable natural resources, and in some cases, they have led to natural resource-based conflicts among communities. This is the case of pastoralist communities living in arid and semi-arid rangelands that are increasingly engaging in violent competition for scarce resources such as water and grasslands to feed their livestock. Responses from aid agencies and international humanitarian organisations to drought crises have thus shifted from a relief aid perspective that aims to simply save lives to a more systematic approach to saving lives by protecting livelihoods that are vulnerable to climate change impacts thereby ensuring food security. In practice, climate change adaptation programmes that aim to prevent natural resource conflicts have adopted the sustainable resource management approach. However, experiences with this approach in post-conflict settings show that some of these interventions have had a positive impact on peacebuilding while others did not. Emerging views within the climate change adaptation field stress that building the adaptive capacity and resilience of vulnerable livelihood groups to climatic shocks can prevent natural resource conflicts. This thesis thus explores the ways in which climate change adaptation programmes sustainably build community resilience to natural resource-based conflicts. Drawing upon the linkages between theoretical concepts such as adaptive capacity or resilience, and conflict transformation, this paper attempts to explain how conflicts triggered by deprivation and grievances can be prevented. The paper conducts a qualitative case study analysis of two Disaster Risk Reduction (DRR) projects implemented along the Uganda-Kenya border and funded within the framework the European Union Drought Risk Reduction Action Plan (DRRAP) for the Horn of Africa from 2010 to 2013. Research findings show that forms of climate change adaptation interventions matter for natural resource conflict prevention. Climate adaptation strategies that aim to build resilience to climate change risks by developing social capital and thereby mediating access to other livelihood resources create avenues for conflict prevention. This paper also suggests that it is necessary to change the structures and attitudes that drive conflict as a means to resolve protracted social conflicts.
Development of Metal and Machinery Small and Medium Enterprises (SMEs) in West Lombok Regency with the Blue Ocean Strategy Approach

Blue Ocean Strategy aims to open new market share to minimize competition with similar competitors. After receiving a touch from the West Lombok Regency Industry and Trade Service (Prindag Lobar), the Metal and Machinery Community Activity Unit in West Lombok initially produced gates, trellises and other similar works, creating quite a lot of competition. So that with the guidance of the Service which offers solutions with the Blue Ocean Strategy, the metal and machine industry (SMEs) in West Lombok Regency can compete. This study used a qualitative method, data obtained from in-depth interviews with section heads of the metal, machine, electronics and transportation equipment (ILMEA) industry, descriptive analysis and source triangulation. This article reveals that Blue Ocean Strategy manufactures metal products and machinery in different segments. The Blue Ocean Strategy segment is offered in the form of changing the mindset and technical skills of metal and machining (SME) players. Metal and machinery SMEs are trained to manufacture machine products needed by the food and beverage industry. One of the concrete steps is the manufacture of machines for making palm sugar machines, coffee roasting machines and other processing machines. This has an impact on the shift of the metal and machine SME segment from household products to processed machine products.
Article in press-uncorrected proof Letter to the Editor Clinically needed sensitivity for very low levels of Factor XIII : not yet proven for a new functional assay

Recently, Kappel et al. (1) published a new functional method for the quantification of Factor XIII. One of their goals was to develop a method with improved trueness of measurement in the lower measuring range for Factor XIII activity. The clinical importance of a laboratory assay capable of measuring Factor XIII activities below 0.05 IU/mL is evident (2, 3). Surveys of the ECAT external quality assessment programme show clearly that there are problems in the detection of Factor XIII around the clinically important regions of detection. In samples with a Factor XIII activity of ;0.07 IU/mL (Technochrom FXIII assay, Technoclone, Vienna, Austria) the observed average activity for the current Siemens Berichrom Factor XIII assay was 0.13 and 0.14 IU/mL in two different surveys (ns25–39; CV: 35%–37%). For a sample with a Factor XIII activity of ;0.02 IU/mL the observed average activity for Berichrom users was 0.07 and 0.08 IU/mL (ns20–25; CV: 62%–68%). This indicates that the levels at these clinically important levels of Factor XIII quantification differ according to the method, with the Siemens Berichrom method over-estimating the Factor XIII levels. Unfortunately, the conclusion of the authors that the new assay exhibited improved recovery in the low measuring range because of the application of FXIII-depleted plasma as diluent for the standard wfigure 4, Ref. (1)x was not supported by the validation of the method using clinical samples. In Figure 3 of their publication, where 173 clinical samples were used for the comparison with the current Berichrom assay, none of the samples used had an FXIII activity of -0.20 IU/mL. Samples in the clinically important range below 0.05 IU/mL were not included. A second comment about the method comparison is that agreement has not been formally evaluated. The authors have shown in their publication only a good correlation of the newly developed assay with the current Berichrom assay wrs0.95; figure 3, Ref. (1)x. However, this is only a confir-
Bioinformatics-led discovery of liver-specific genes and macrophage infiltration in acute liver injury

Background Acute liver injury (ALI) is an important global health concern, primarily caused by widespread hepatocyte cell death, coupled with a complex immune response and a lack of effective remedies. This study explores the underlying mechanisms, immune infiltration patterns, and potential targets for intervention and treatment ALI. Methods The datasets of acetaminophen (APAP), carbon tetrachloride (CCl4), and lipopolysaccharide (LPS)-induced ALI were obtained from the GEO database. Differentially expressed genes (DEGs) were individually identified using the limma packages. Functional enrichment analysis was performed using KEGG, GO, and GSEA methods. The overlapping genes were extracted from the three datasets, and hub genes were identified using MCODE and CytoHubba algorithms. Additionally, PPI networks were constructed based on the String database. Immune cell infiltration analysis was conducted using ImmuCellAI, and the correlation between hub genes and immune cells was determined using the Spearman method. The relationship between hub genes, immune cells, and biochemical indicators of liver function (ALT, AST) was validated using APAP and triptolide (TP) -induced ALI mouse models. Results Functional enrichment analysis indicated that all three ALI models were enriched in pathways linked to fatty acid metabolism, drug metabolism, inflammatory response, and immune regulation. Immune analysis revealed a significant rise in macrophage infiltration. A total of 79 overlapping genes were obtained, and 10 hub genes were identified that were consistent with the results of the biological information analysis after screening and validation. Among them, Clec4n, Ms4a6d, and Lilrb4 exhibited strong associations with macrophage infiltration and ALI.
Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions

Understanding the complex mechanisms of mycobacterial pathophysiology and adaptive responses presents challenges that can hinder drug development. However, employing physiologically relevant conditions, such as those found in human macrophages or simulating physiological growth conditions, holds promise for more effective drug screening. A valuable tool in this pursuit is proteomics, which allows for a comprehensive analysis of adaptive responses. In our study, we focused on Mycobacterium smegmatis, a model organism closely related to the pathogenic Mycobacterium tuberculosis, to investigate the impact of various carbon sources on mycobacterial growth. To facilitate this research, we developed a cost-effective, straightforward, and high-quality pipeline for proteome analysis and compared six different carbon source conditions. Additionally, we have created an online tool to present and analyze our data, making it easily accessible to the community. This user-friendly platform allows researchers and interested parties to explore and interpret the results effectively. Our findings shed light on mycobacterial adaptive physiology and present potential targets for drug development, contributing to the fight against tuberculosis.
Knowledge Management in Sustainable Competitive Destinations (SCD) of National Cultural Tourism Event Bakar Tongkang Bagansiapiapi

Purpose: This study aims to discuss the concept of sustainable competitive destination (SCD) in cultural tourism based on knowledge management to ensure that the collaboration in a balanced way contributes to the economy through increasing tourist visits, providing benefits to local communities, and preserving existing culture. Methodology: This article conducts an in-depth literature review related to the development of cultural tourism through ecosystem synergy and applies Nonaka’s knowledge conversion framework in explaining the role of knowledge management in the knowledge collaboration of cultural tourism ecosystems to create SCDs. Finding: Research activities by academics have not been followed up in real terms. Academics are less motivated to synergize the ecosystem. The participation of the people of Rokan Hilir Regency remains lacking. Originality: This paper discusses how to apply knowledge management in cultural tourism, and ecosystem synergies in creating an SCD for the national cultural event Bakar Tongkang Bagansiapiapi. Research implications: Implementation of knowledge management concepts in the cultural tourism ecosystem is a necessity. Practical implications: The synergy of Penta Helix is built through an understanding of the capacity of stakeholders to ensure that it can be a guide for each in creating the SCD for the Bakar Tongkang cultural event in Bagansiapiapi. Social implications: Tourism management and marketing strategies have reached local and foreign tourists who are related to the Tionghoa descendants of Bagansiapiapi.