Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Relaxation and Slow Breathing Technique (RSBT) as A Non-Pharmacological Intervention on Blood Pressure Level Among Stage 1 Hypertensive Older Adults

The prevalence of hypertension is relatively high, and the number still increases by year. Various studies have demonstrated that non-pharmacological methods have been shown capable of lowering blood pressure such as relaxation techniques, and also slow breathing technique. This study aims to determine the effects of relaxation and slow breathing technique (RSBT) on blood pressure level among stage 1 hypertensive older adults. This study utilized quantitative research method and the research design employed in this study is the one group interrupted time series pre-test post-test quasi-experimental design. Two types of instruments were used in this study to gather the pertinent data and information. The researcher utilized the checklist form to record the result of observation. The researcher also utilized biophysiologic instrument to measure to measure the blood pressure level of the participants. The participants of the study were ten (10) male and ten (10) female older adults with stage 1 hypertension who were selected through purposive sampling technique. Repeated-Measures ANOVA and the Post Hoc test was utilizing series of t-test. The result shows that there is a significant difference in the systolic and diastolic blood pressure level among participants before and after the implementation of the RSBT in both initial phase (first week) and reinstituted phase (third week). Prolonging of implementation of RBST would be more powerful to reduce blood pressure level among older adult with stage I hypertension.
Multi-target threat assessment for autonomous emergency braking

While automotive manufacturers are trying their best to decrease the number and severity of road fatalities, the demand for Active safety features increases. Today, Active safety is the way ahead to decrease road fatalities even more and Autonomous Emergency Braking (AEB) and Forward Collision Warning (FCW) play a key role. The foundation of AEB and FCW is Threat Assessment (TA). This thesis aims to provide an approach to improve the AEB and decrease the risk of road fatalities further than Single-target Threat Assessment is capable of. This is accomplished by evaluating multiple target vehicles and barriers, i.e. Multi-target Threat Assessment. The proposed approach, based on a particle filter, will evaluate the possible escape paths that the driver is expected to manage independently of the traffic scenario. The particle filter approach solves the Multi-target Threat Assessment (MTTA) with only a few assumptions, making it versatile. However, the method requires more computational power compared to Spline Based Search Tree (SBST). On the other hand, the SBST requires a spline design that may only work in certain scenarios. While the versatility of particle filter presents a possible avoidance path close to a real avoidance maneuver, it also decreases the risk of getting killed in traffic when compared to Single-target Threat Assessment (STTA). Multi-target Threat Assessment, Active safety, AEB, Automotive, Particle filter, Path planning.
Practical Dynamic Extension for Sampling Indexes

The execution of analytical queries on massive datasets presents challenges due to long response times and high computational costs. As a result, the analysis of representative samples of data has emerged as an attractive alternative; this avoids the cost of processing queries against the entire dataset, while still producing statistically valid results. Unfortunately, the sampling techniques in common use sacrifice either sample quality or performance, and so are poorly suited for this task. However, it is possible to build high quality sample sets efficiently with the assistance of indexes. This introduces a new challenge: real-world data is subject to continuous update, and so the indexes must be kept up to date. This is difficult, because existing sampling indexes present a dichotomy; efficient sampling indexes are difficult to update, while easily updatable indexes have poor sampling performance. This paper seeks to address this gap by proposing a general and practical framework for extending most sampling indexes with efficient update support, based on splitting indexes into smaller shards, combined with a systematic approach to the periodic reconstruction. The framework's design space is examined, with an eye towards exploring trade-offs between update performance, sampling performance, and memory usage. Three existing static sampling indexes are extended using this framework to support updates, and the generalization of the framework to concurrent operations and larger-than-memory data is discussed. Through a comprehensive suite of benchmarks, the extended indexes are shown to match or exceed the update throughput of state-of-the-art dynamic baselines, while presenting significant improvements in sampling latency.
Landslide manual and automated inventories, and susceptibility mapping using LIDAR in the forested mountains of Guerrero, Mexico

ABSTRACT Landslides are a pervasive natural disaster, resulting in severe social, environmental and economic impacts worldwide. The tropical, mountainous landscape in South-West Mexico is predisposed to landslides because of frequent hurricanes and earthquakes. The main goal of this study is to compare landslide susceptibility maps in Guerrero derived using high-resolution LIDAR (light detection and ranging) data from both a manual landslide event inventory and an automated landslide inventorying algorithm. The paper also highlights the importance of applying LIDAR data in landslide inventorying and susceptibility mapping. We mapped landslides based on two approaches: (1) manual mapping using satellite images and (2) automatic identification of landslide morphology employing the Contour Connection Method (CCM). We produced a landslide susceptibility map by computing the probability of landslide occurrence from statistical relationships of inventoried landslides detected with LIDAR digital terrain models (DTMs) and derived landslide-causing factors using the logistic regression method. Our results suggest that the automated inventory derived through the CCM algorithm with LIDAR DTMs effectively minimizes the time-consuming and subjective manual inventorying process. The high overall prediction accuracy (up to 0.83) from logistic regression demonstrates the validity and applicability deriving reliable landslide susceptibility maps from an automated inventory; however, LIDAR data are required.
STUDIES WITH TRIAZOLES TO ALLEVIATE DROUGHT STRESS IN GREENHOUSE-GROWN MAIZE (Zea mays) SEEDLINGS

In semi-arid environments, dry-land farming often exposes crops to drought stress. Although some plant species are well adapted to drought, most crops are not. Drought can reduce plant populations and limit growth and development in ways that have serious yield consequences. Planting at the beginning of the wet season, when rainfalls are often sporadic and unreliable, can expose young maize seedlings to severe drought. Through the use of plant growth regulators (PGR), maize seedlings can perhaps be altered to elicit responses that mimic drought adaptation mechanisms. A series of studies conducted in the laboratory and greenhouse looked at the response of maize seedlings (two hybrids that differed in their reported drought sensitivity) to severe drought and to PGR applications with or without drought. Results showed that drought stress altered plant morphology and key physiological parameters. Applications of three triazoles (paclobutrazol, uniconazole and tetraconazole) altered morphology and physiology in ways that might impart drought resistance. Paclobutrazol and uniconazole increased root:shoot ratio in laboratory studies and in the greenhouse. When compared to non-triazole-treated controls, uniconazole and paclobutrazol treatments caused water conservation in earlier stages of drought stress, and therefore afforded increased transpiration (and presumably less stress) at later stages. Uniconazole and tetraconazole increased photosynthesis of well-watered plants. Proline content was increased to a greater degree by these same two triazoles under drought stress conditions. It is hoped that knowledge obtained from these studies can be extended to drought-prone areas where maize dry-land farming is practiced. Dedication To my parents, Mr. and Mrs. Modikwa, and the rest of my family members; Oaitse, Gaone, Puso, Boago, Opelo, Mosupi, Maipelo, Moakanyi, Ontlametse and Ikanyeng. To the loving memory of my brother and sister, Nonofo and Latelang.
Master Computer Science Investigating De-identification Methodologies in Dutch Medical Texts: A Replication Study of Deduce and Deidentify

De-identifying sensitive information in electronic health record (EHR) systems is becoming increasingly important as legal obligations to data privacy evolve and the need to protect patient and institutional confidentiality. We conduct research on the task of de-identifying clinical records stored in hospital EHR systems in accordance with Dutch and EU legislations. This research employs two distinct datasets: the ’Annotation-based Dataset’ from the Utrecht University Medical Center, containing a wide array of manually annotated patient records spanning 1987 to 2021, and the ’Synthetic Dataset,’ generated using a two-step process involving OpenAI’s GPT-4 model. These diverse datasets, although disparate in their origin, real-world and synthetic, provide a comprehensive basis to evaluate de-identification techniques in the context of Dutch medical texts. The thesis focuses on a comparative analysis of two de-identification techniques, Deduce and Deidentify, considering their performance in de-identifying the Dutch medical texts. Utilizing precision, recall, and F1 scores as evaluation metrics, the research uncovered the relative strengths and limitations of the two methods. Our findings indicate that both techniques show variable performance across different entities of de-identifying text information. Deduce out-performs Deidentify in overall accuracy by a margin of 0.42 on the synthetic datasets, and on the real-world annotation-based dataset, the generalization ability of Deidentify is lower than Deduce by 0.2. However, the performance of both techniques is affected by the limitations of the dataset.
A Harmonic Suppression Strategy Based on Adaptive Synchronous Rotating Frame Transformation for Improving the Estimation Accuracy of Sensorless Drivers With Small Capacitors

The small capacitor drivers use film capacitors instead of electrolytic capacitors in the dc-link, which will improve the life and reliability of the drivers. However, the dc-link voltage of the small capacitor drivers fluctuates seriously, and the ac component in it causes |(ω+6kωg)/ω|th and |(ω−6kωg)/ω|th harmonics in the extended back electromotive force. These harmonics will increase the estimated position error in the sensorless driver. A harmonic suppression method based on adaptive synchronous rotating frame transformation (ASRFT) is presented in this article to improve the control accuracy of sensorless drivers with small capacitors. First, the motor current harmonics caused by harmonics in the dc-link voltage are analyzed, and the relationship between these harmonics and the estimated sensorless position error was derived. Second, ASRFT and fixed synchronous rotation frame transformations can be used to extract current harmonics in corresponding harmonic coordinate systems. Then, the voltage compensation component generated by the harmonic compensation link will be injected into the driver to suppress the current harmonics extracted in the corresponding coordinate system. Finally, the controller parameters of the proposed method are designed. Using a 2 kW permanent magnet synchronous motor sensorless driver with a small capacitor, the proposed method is demonstrated to be effective.
Integrating fractional amplitude of low-frequency fluctuation and functional connectivity to investigate the mechanism and prognosis of severe traumatic brain injury

Objective Functional magnetic resonance imaging (fMRI) has been used for evaluating residual brain function and predicting the prognosis of patients with severe traumatic brain injury (sTBI). This study aimed to integrate the fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to investigate the mechanism and prognosis of patients with sTBI. Methods Sixty-five patients with sTBI were included and underwent fMRI scanning within 14 days after brain injury. The patient’s outcome was assessed using the Glasgow Outcome Scale—Extended (GOSE) at 6 months post-injury. Of the 63 patients who met fMRI data analysis standards, the prognosis of 18 patients was good (GOSE scores ≥ 5), and the prognosis of 45 patients was poor (GOSE scores ≤ 4). First, we apply fALFF to identify residual brain functional differences in patients who present different prognoses and conjoined it in regions of interest (ROI)-based FC analysis to investigate the residual brain function of sTBI at the acute phase of sTBI. Then, the area under the curve (AUC) was used to evaluate the predictive ability of the brain regions with the difference of fALFF and FC values. Results Patients who present good outcomes at 6 months post-injury have increased fALFF values in the Brodmann area (7, 18, 31, 13, 39 40, 42, 19, 23) and decreased FC values in the Brodmann area (28, 34, 35, 36, 20, 28, 34, 35, 36, 38, 1, 2, 3, 4, 6, 13, 40, 41, 43, 44, 20, 28 35, 36, 38) at the acute phase of sTBI. The parameters of these alterations can be used for predicting the long-term outcomes of patients with sTBI, of which the fALFF increase in the temporal lobe, occipital lobe, precuneus, and middle temporal gyrus showed the highest predictive ability (AUC = 0.883). Conclusion We provide a compensatory mechanism that several regions of the brain can be spontaneously activated at the acute phase of sTBI in those who present with a good prognosis in the 6-month follow-up, that is, a destructive mode that increases its fALFF in the local regions and weakens its FC to the whole brain. These findings provide a theoretical basis for developing early intervention targets for sTBI patients.
Master Computer Science A research platform for autonomous navigation of pedestrian spaces.

In this paper we study the problem of autonomous driving in pedestrian environments. We focus on End-to-End models trained using RGB monocular images for ego-position estimation and waypoint driving. A mobile platform was designed and implemented that was able to capture RGB monocular images as well as precise RTK-GNSS location data while driving in a pedestrian environment. This platform was used to construct a dataset with applications for research on autonomous driving in pedestrian spaces. The dataset is used for two experiments on waypoint driving as well as ego-position estimation. For each of these two tasks we trained and validated a baseline neural network. The resulting experiments show interesting preliminary results for both tasks in pedestrian environments.
Engineering a New IFN-ApoA-I Fusion Protein with Low Toxicity and Prolonged Action

Recombinant human interferon alpha-2b (rIFN) is widely used in antiviral and anticancer immunotherapy. However, the high efficiency of interferon therapy is accompanied by a number of side effects; this problem requires the design of a new class of interferon molecules with reduced cytotoxicity. In this work, IFN was modified via genetic engineering methods by merging it with the blood plasma protein apolipoprotein A-I in order to reduce acute toxicity and improve the pharmacokinetics of IFN. The chimeric protein was obtained via biosynthesis in the yeast P. pastoris. The yield of ryIFN-ApoA-I protein when cultivated on a shaker in flasks was 30 mg/L; protein purification was carried out using reverse-phase chromatography to a purity of 95–97%. The chimeric protein demonstrated complete preservation of the biological activity of IFN in the model of vesicular stomatitis virus and SARS-CoV-2. In addition, the chimeric form had reduced cytotoxicity towards Vero cells and increased cell viability under viral load conditions compared with commercial IFN-a2b preparations. Analysis of the pharmacokinetic profile of ryIFN-ApoA-I after a single subcutaneous injection in mice showed a 1.8-fold increased half-life of the chimeric protein compared with ryIFN.
Bursting dynamics in a spiking neuron with a memristive voltage-gated channel

We introduce a voltage-gated conductance model for an artificial neuron that exhibits tonic, fast, and two types of intrinsic burst spiking. The spike generation is achieved with a single voltage-gated channel that exploits the conductance commutation properties of a two-terminal memristive device. Our circuit implementation is of unprecedented simplicity, using just four electronic components, all conventional, cheap and out-of-the-shelf. Our bursting neuron is a two-compartment model, similar to the Pinsky–Rinzel model. We obtain the full phase diagram and discuss the origin of the different regions. We find that the spike traces of the model bare striking similarity to experimental biological neuronal recordings. Our work may open a new way to investigate neural pathologies, such as epilepsy and Parkinson’s disease, from the study of the phase diagram and the transitions between spiking states of physical neuron models.
Isolation and Morphological Identification of Cellulolytic Fungi from Domestic Waste in South Toapaya Village, Bintan Regency

Domestic waste is one of the unresolved issues. The main challenge is the difficulty in degrading domestic waste due to its lignocellulosic content, which is hard to break down. One of the uses of cellulolytic fungi is identified as organisms capable of breaking down cellulose components in domestic waste and assisting in speeding up the degradation process. The aim of this research is to discover and identify the types of cellulolytic fungi present in household domestic waste in the South Toapaya Village. This research was conducted by collecting samples in South Toapaya Village and analyzing them at the Provincial Marine Service Laboratory of Riau Province from February to August 2023. The research methods included field exploration, laboratory analysis, identification with reference to the literature, as well as microscopic and macroscopic observations. The research results show that out of 14 fungal isolates isolated from domestic waste, 8 of them have significant cellulolytic activity. Among them, the isolate with code UMRCC 03 has the highest cellulolytic index of 3.24 and is identified as the Aspergillus sp. fungus, while the isolate with code UMRCC 05 has a cellulolytic index of 2.14 and is identified as the Trichoderma sp. Fungus.
Natural resource management planning: can more cost effective outcomes be achieved

The decline in health of the Great Barrier Reef and the pressure on allocating funds efficiently has resulted in the catchments adjacent to the reef revising their Water Quality Improvement Plans. The Fitzroy basin and coastal catchments is 152,000km2 and geographically diverse, past work has identified the need to prioritise funds to achieve cost effective outcomes. For this paper we aim to present an alternative approach to effective prioritisation of sediment reductions. The approach integrates spatial information regarding the sediment source and process, levels of adoption, bare ground cover, and cost into a function to rank neighbourhood catchments. The results identify particular areas of the catchment and also demonstrate the complexity of the issue and the challenge the Fitzroy Basin Association faces when allocating funds. It does however demonstrate that there are effective opportunities in particular areas within the catchment, proving it to be a useful approach in understanding where in the catchment to focus efforts for different sediment reductions.
Factors influencing the coexistence of bromeliad-dwelling chironomids on Ilha do Cardoso, Brazil

Species interactions can influence the spatial distribution of organisms and the composition of local communities. To investigate how interactions influence the coexistence of invertebrates living in bromeliad phytotelmata, I combined methodological development and empirical exploration with the aim of understanding: 1) Which species in a community show signs of strong interactions, 2) Whether predators influence the outcome of competitive interactions and 3) Whether equalizing or stabilizing interactions between species change depending on context. To detect interactions between species given observational field data, I designed a method of finding negative co-occurrence patterns (using checkerboard units) between species based on their abundances in nature. Using this method, I found that three chironomid (Diptera: Chironomidae) species showed very strong negative co-occurrence patterns, suggesting that they experience net negative interactions (e.g. competition) or habitat filtering. Next, I performed a predator-addition experiment to assess the importance of predators in mediating the coexistence of the three chironomid species. Three predator species were added to bromeliads containing the three chironomid species. Although field observations suggested that at least one chironomid species should improve performance in the absence of predators, there was only a slight differential response to predators. Furthermore, one species of chironomid was competitively superior to the others in both the presence and absence of predators. We suspect that differing habitat preferences and the presence of other prey may be more important to coexistence than the presence or absence of predators alone. Finally, I performed an experiment to assess how habitat and ontogeny affect the outcome of competition between
Graph Neural Network for Smartphone Recommendation System: A Sentiment Analysis Approach for Smartphone Rating

The increasing demand for mobile phones has resulted in abundant online reviews, making it challenging for consumers to make informed purchasing decisions. In this study, we propose Graph Neural Network (GNN) models to classify mobile phone ratings using Term Frequency-Inverse Document Frequency (TF-IDF) features. We collected a dataset of over 13,000 mobile phone evaluations from the Flipkart website. The proposed method includes data purification, balancing, feature extraction from the TF-IDF, and model prediction using deep learning models. The proposed approach utilized other models such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM to compare other classifiers. The experiments’ outcomes demonstrate that the suggested model performs better than conventional deep learning methods regarding accuracy and efficiency. The GNN model achieved the best 99.0% accuracy rate. The proposed approach can help consumers make informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.