Miracle-dz/UniFAR-FTUs huggingface dataset:
Gratitude and Hope as Conditions for the Psychological Well-Being of Social Media Users

Entering the digital era, there has been a lot of research on the impact of social media technology on humans. Various studies have shown that there are negative impacts from using social media, such as increasing upward social comparisons and decreasing psychological well-being, even though social media itself has many benefits for its users. In order for social media technology to bring more benefits to its users, it is necessary to develop positive attitudes and behavior. In this research, it is assumed that developing gratitude and hope is one way to avoid the negative impacts of using social media. This research aims to determine the role of gratitude and hope on the mental health of social media users. Participants in this study were early adult social media users, aged 18 to 40. This study used quantitative methods with survey design. The measuring tools used in this study were Ryff's Psychological Well-Being Scale for psychological well-being, The Gratitude Questionnaire-Six Item Form (GQ-6) for gratitude, and State Hope Scale for hope. This study used multiple linear regression as a data analysis method. The results showed that there was a significant role (F = 91.915, p < 0.001) of gratitude and hope in predicting psychological well-being. In addition, it was found that there is a positive role of gratitude and hope in predicting psychological well-being. Thus, the higher the level of gratitude and hope of early adult social media users, the higher their level of psychological well-being, and vice versa with a low level.
Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks

Stride determines the distance between adjacent filter positions as the filter moves across the input. A fixed stride causes important information contained in the image can not be captured, so that important information is not classified. Therefore, in previous research, the DiffStride Method was applied, namely the Strided Convolution Method with which it can learn its own stride value. Severe Quantization and a constraining lower bound on preserved information are arises with Max Pooling Downsampling Method. Spectral Pooling reduce the constraint lower bound on preserved information by cutting off the representation in the frequency domain. In this research a CNN Model is proposed with the Downsampling Learnable Stride Technique performed by Backpropagation combined with the Spectral Pooling Technique. Diffstride and Spectral Pooling techniques are expected to maintain most of the information contained in the image. In this study, we compare the Hybrid Method, which is a combined implementation of Spectral Pooling and DiffStride against the Baseline Method, which is the DiffStride implementation on ResNet 18. The accuracy result of the DiffStride combination with Spectral Pooling improves over DiffStride which is baseline method by 0.0094. This shows that the Hybrid Method can maintain most of the information by cutting of the representation in the frequency domain and determine the stride of the learning result through Backpropagation.
Gaussian Model Approach for Identifying Car Engine Malfunctions Based on Embedded Sound System

One important aspect for car owners is to maintain their vehicles in optimal and well-maintained condition. This includes tasks such as regular oil changes and system inspections. It is crucial for car owners to periodically maintain their vehicles through recommended manufacturer services. By relying solely on scheduled maintenance, it is possible that cars can provide indications of malfunction through engine sounds, which, if ignored without the owner's knowledge, can have negative consequences. Therefore, the identification of car engine issues using deep learning is essential for owners to receive early warnings of malfunctioning vehicles. Hence, the focus is on identifying car malfunctions based on engine sounds, utilizing MFCC and GMM for classifying normal and abnormal engine sounds. Consequently, a practical system is developed that can be implemented using the Raspberry Pi 3 B+ minicomputer.
The Development of Hands-On Lab Platform Using Container-Based Virtualization Technology

Practicum allows students to develop practical skills relevant to their field of study, gain real-world experience, and enhance their understanding of the topics studied. However, the resources required for practicum often require significant cost. Limited resources in the laboratory resulted in a limited number of students accessing it as well. Virtual lab with container-based virtualization technology is one solution to reduce resource usage and simplify the laboratory environment while still providing a real system for the students’ experience. This research uses container-based virtualization technology with Docker to provide the hands-on lab. The hands-on lab requires two main components: Open edX and the hands-on lab server. Students can carry out learning activities through hands-on labs in the XBlock plugin from LMS (Learning Management System) Open edX. The results of this research are based on the functional test aspect that shows the hands-on lab can run according to its function and coordination among system components works correctly. Non-functional test will also quantify the hands-on lab's time behavior and resource utilization. The non-functional test results show that the time behavior is quite responsive, and resource utilization is quite good, with a CPU usage of 0.51% and RAM usage of 1.12%.
The Relationship Level Between Students' Learning Interests, Learning Motivation, and Career Planning Awareness on the Indonesian Vocational High School Majoring in Information Technology

This study aims to determine the correlation between students’ learning interest (LI) and learning motivation (LM), learning interest (LI) and career planning awareness (CPA), and learning motivation (LM) and career planning awareness (CPA). The research was conducted at a Public Indonesian Vocational High School majoring in information technology. The research design used was a survey research design. LI, LM, and CPA data were collected using different questionnaires. The sample size used in this study was 97 students, which exceeded the minimum sample size in correlation research, with a limit of 30 respondents. The data obtained were analyzed using non-parametric statistical methods, namely Spearman's Rho. Based on the analysis of this research known that (1) the significance value is < 0.01 and the correlation coefficient is 0.510 for the correlation between LI and LM; (2) the significance value is < 0.01 and the correlation coefficient is 0.374 for the correlation between LI and CPA; (3) the significance value is < 0.01 and the correlation coefficient is 0.432 for the correlation between LM and CPA. The conclusion can be drawn is that there is a high level of relationship between LI towards LM, a moderate level of relationship between LI towards CPA, and a moderate level of relationship between LM towards CPA.
Detection of Oil Palm Trees Using Deep Learning Method with High-Resolution Aerial Image Data

Oil palm is a plant with high economic value, and Indonesia is the second-largest producer and exporter of palm oil in the world, following Malaysia. The expansive plantation area and production output are predominantly managed by 1,569 private plantation companies and 162 state-owned plantation companies, spread across various provinces in Indonesia. Given the vast plantation land and the multitude of companies, information regarding the number of oil palm trees within the plantation areas becomes crucial for both the government and private sector. This is aimed at facilitating yield predictions, monitoring plant growth, and optimizing fertilization to maximize palm oil fruit productivity. In this research, the deep learning method known as YOLOv8 is employed to detect oil palm trees using high-resolution aerial images. The objective of this study is to assess the accuracy of the deep learning method in detecting oil palm trees compared to manual counts performed by experts. The research results demonstrate that the accuracy level of oil palm tree detection using the YOLOv8m-based deep learning method reaches 98.5%. This deep learning-based detection of oil palm trees also outperforms manual counting by experts in terms of time. Specifically, it's observed that while manually counting oil palm trees in one hectare of plantation land takes 23 minutes, utilizing deep learning only requires 16.1 milliseconds. Furthermore, using high-resolution aerial image data, the deep learning method has achieved a higher level of accuracy compared to previous research. This method is capable of detecting oil palm trees of various sizes, even in densely canopied and overlapping plantation areas.
The Effect of Social Media Overload, Exhaustion, and Technostress on Student’s Learning Productivity Using Stressor-Strain-Outcome Model

The widespread use of social media among Indonesians is evidenced by the increasing use of social media every year. Social media is a resource that can be actively utilized by students for better education and learning. Through social media, students can interact and socialize with other friends, improve learning achievement, get solutions to problems from finding information on social media, or even send questions and comments to other students. With the many benefits and conveniences of social media to help students’ daily lives, there are also negative impacts caused by the use of social media if used continuously by students. The purpose of this study is to determine the influence of social media overload, exhaustion, and technostress among university students and its consequences on student learning productivity. This research is quantitative, using the survey method as the main approach. The survey was conducted among undergraduate students. Based on the data distribution, 100 respondents were obtained and analyzed using the Generalized Linear Model. This study used the Stressor-Strain-Outcome model to conceptualize the framework model. The result suggested that social overload and communication overload affect technostress. Technostress, communication overload, and information overload affect exhaustion, which influences students’ learning productivity.
Low-Level Quantification of Cefixime and Cefdinir in Human Plasma Using Ultrasound-and Magnetic-Assisted Dispersive Micro-Solid-Phase Extraction (MSPE) Based upon Carbon Quantum Dots (CQDs) Functionalized with Magnetite/Zeolitic Imidazolate Framework 71/Polypyrrole Combined with High-Performance Liq

Abstract A promising and reusable nanohybrid based on carbon quantum dots immobilized with Fe3O4/zeolitic imidazolate framework-71/polypyrrole (CQD/Fe3O4/ZIF-71/PPy) was fabricated as a sorbent for ultrasound-and magnetic-assisted dispersive micro-solid-phase extraction (US-M-A-DMSPE) followed by high-performance liquid chromatography with ultraviolet detection (HPLC-UV) for simultaneous trace determination of cefixime (CFX) and cefdinir (CFD) in human plasma. In order to improve the contact between the analyte and the sorbent, the surface of the CQDs was modified by individual materials involving zeolitic imidazolate framework and polypyrrole via an in-situ synthesis of ZIF-71 followed by oxidative polymerization of pyrrole. The structure of the prepared sorbent was characterized by x-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). Under the optimized conditions, the nanosorbent provided high adsorption and selectivity toward the analytes. The limits of detection for CFX and CFD were 0.15 and 0.04 ng mL−1, respectively. The coefficients of determination (r2) were between 0.990 and 0.993, with dynamic ranges of 0.5–1750.0 and 0.2–1500.0 ng mL−1. The method was used for quantifying CFX and CFD in plasma samples to evaluate the pharmacokinetic aspects, including the half-life (T½), the time to reach the maximum concentration (Tmax), the maximum plasma concentration (Cmax), area under the curve (AUC0-24) and area under the curve at infinite time (AUC0-∞). Reliable reproducibility as the intra- assay (≤ 6.3%) and inter-assay (≤ 8.7%) together with reasonable accuracy (≤ 8.8%) were obtained.
Brood care in shell-dwelling cichlids is timed by independent maternal and larval clocks

Successful brood care requires a finely tuned interplay between caregiving parent and offspring. Cichlids are a diverse group of teleost fish known for their extensive parental care of altricial young. Female Lamprologus ocellatus lay their eggs in the protective confines of an abandoned snail shell, where they raise them to free-swimming larvae. Here we investigated whether the transition to larval independence is driven by the behavior of the mother, the offspring, or an interaction between the two. Using 3D printed shells with a window, we were able to observe the behavior of both mother and offspring inside the nest using automated object detection and tracking. We found that, once hatched, the larvae remain in the deep chambers of the shell, and at 9 dpf, actively emerge from the shell during the day. An inversion of larval phototactic behavior from dark- to light-seeking leads up to this emergence. Removal of the mother causes premature emergence of the larvae by a few days. The natural schedule (emergence at 9 dpf) is restored if the larvae are supplied with fresh water in the mother’s absence. In cross-fostering experiments, we found that swapping out the mother’s own clutch for older larvae forces a delay in emergence, as the foster mother prevents the larvae from leaving at 9 dpf. We conclude that larval and maternal behavior are each controlled by independent internal clocks, which, although normally synchronized, can be experimentally manipulated and brought into conflict. This study has thus revealed an innate sequence of behavioral adaptations that orchestrate brood care in shell-dwelling cichlids.
Efficacy of Drpitor1a, a Dynamin-Related Protein 1 inhibitor, in Pulmonary Arterial Hypertension

Rationale Dynamin-related protein 1 (Drp1), a large GTPase, mediates mitochondrial fission. Increased Drp1-mediated fission permits accelerated mitosis, contributing to hyperproliferation of pulmonary artery smooth muscle cells (PASMC), which characterizes pulmonary arterial hypertension (PAH). We developed a Drp1 inhibitor, Drpitor1a, and tested its ability to regress PAH. Objectives Assess Drpitor1a’s efficacy and toxicity in: a)normal and PAH human PASMC (hPASMC); b)normal rats versus rats with established monocrotaline (MCT)-induced PAH. Methods Drpitor1a’s effects on recombinant and endogenous Drp1-GTPase activity, mitochondrial fission, and cell proliferation were studied in hPASMCs (normal=3; PAH=5). Drpitor1a’s pharmacokinetics and tissue concentrations were measured (n=3 rats/sex). In a pilot study (n=3-4/sex/dose), Drpitor1a (1mg/kg/48-hours, intravenous) reduced adverse PA remodeling only in females. Consequently, we compared Drpitor1a to vehicle in normal (n=6 versus 8) and MCT-PAH (n=9 and 11) females, respectively. Drpitor1a treatment began 17-days post-MCT with echocardiography and cardiac catheterization performed 28-29 days post-MCT. Results Drpitor1a inhibited recombinant and endogenous Drp1 GTPase activity, which was increased in PAH hPASMC. Drpitor1a inhibited mitochondrial fission and proliferation and induced apoptosis, in PAH hPASMC but not normal hPASMC. Drpitor1a tissue levels were higher in female versus male RVs. In MCT-PAH females, Drpitor1a regressed PA obstruction, lowered pulmonary vascular resistance, and improved RV function, without hematologic, renal, or hepatic toxicity. Conclusions Drpitor1a inhibits Drp1 GTPase, reduces mitochondrial fission, and inhibits cell proliferation in PAH hPASMC. Drpitor1a caused no toxicity in MCT-PAH and had no significant effect on normal rats or hPASMCs. Drpitor1a is a potential PAH therapeutic which displays an interesting therapeutic sexual dimorphism.
Genetic evidence of a hybrid swarm between Alpine ibex and domestic goat

Improving the understanding of the causes and effects of anthropogenic hybridization is fundamental to ensure the conservation of wild species, particularly in the case of hybridization between wild species and their domestic relatives. Knowledge is missing for many species also because of a lack of appropriate tools for hybrid identification. Here, coupling genotype and phenotype analysis, we carried out an extensive investigation of ongoing hybridization in Alpine ibex Capra ibex, a mountain ungulate of conservation concern from a genetic perspective. By genotyping at 63 diagnostic and 465 neutral SNPs 20 suspected hybrids and 126 Alpine ibex without suspicious phenotype, representing eight populations across a major part of the species distribution, we found evidence for ongoing hybridization between Alpine ibex and domestic goat. We identified different levels of hybridization including back crosses into both Alpine ibex and domestic goat. Our results suggest a lack of reproductive barriers between the two species and good survival and reproductive success of the hybrids. Hybridization was locally intense, alike a hybrid swarm, but not spread across the rest of the species distribution. Most of the hybrids were discovered in two locations in the North-West of Italy, while random sampling of individuals from different areas did not provide evidence of recent hybridization. Our method, based on Amplicon sequencing of 63 diagnostic SNPs specifically developed for this purpose, allowed us to identify hybrids and back crosses up to the 4th-5th generation and was suitable for genetic samples of different quality, although with varying levels of certainty regarding the exact number of generations passed since hybridization. Based on the paired analysis of genotype and phenotype we provide guidelines for a first identification of hybrids in the field and suggest a procedure for the reliable identification of hybrids.
Environmental DNA reveals fine scale spatial and temporal variation of prey species for marine mammals in a Scottish marine protected area

Marine mammal foraging grounds are popular focal points for marine protected area (MPA) implementation, but may be temporally dynamic, requiring continuous monitoring to infer prey availability and abundance. Marine mammal distributions are assumed to be driven by their prey in foraging areas, but limited understanding of prey distributions often prevents us from exploring how shifting prey availability impacts both seasonal and long-term marine mammal distributions. Environmental DNA (eDNA) metabarcoding could enhance understanding of marine mammal habitat use in relation to their prey through simultaneous monitoring of both. However, eDNA applications focused on marine mammals or predator-prey dynamics have been limited to date. In this study, we assess spatiotemporal changes in the availability and abundance of minke whale (Balaenoptera acutorostrata) prey species in a newly established MPA, employing eDNA metabarcoding. We recovered 105 molecular operational taxonomic units (OTUs) from marine vertebrates using two primer sets targeting 12S and 16S genes, along with 112 OTUs from a broader eukaryotic primer set targeting 18S rRNA. Overall, key forage fish prey species, sandeels and clupeids, were the most abundant teleost fishes detected, although their availability varied temporally and with distance from shore. We also found clear spatial partitioning between coastal bottlenose dolphins and the more pelagic minke whales and harbour porpoises, paralleling availability of their main prey species. Other species of conservation interest were also detected including the critically endangered European eel (Anguilla anguilla), blue fin tuna (Thunnus thynnus), and the invasive pink salmon (Oncorhynchus gorbuscha). This study demonstrates the application of eDNA to detect spatiotemporal trends in the occurrence and abundance of cetacean predators and their prey, furthering our understanding of fine-scale habitat use within MPAs. Future, long-term monitoring of predator-prey dynamics with eDNA could improve our ability to predict climate-induced shifts in foraging grounds and enhance rapid responses with appropriate management actions.
Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI

In graph theory, “multilayer networks” represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or “lamina”. This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only (“layer-by-layer matrices”) to those considering all possible connections between regions and cortical depths (“multilayer matrix”). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.