Miracle-dz/UniFAR-FTUs huggingface dataset:
Educational intervention for the prevention of internet addiction disorder among 15-19-year-old adolescents in Colombo district, Sri Lanka.

Introduction Internet addiction disorder is growing as a potentially problematic condition, especially among adolescents. Nonetheless, the condition is widespread and problematic, limited scientific evidence is available on the prevention, and efficacy of the treatment globally as well as locally. Objectives: The objective of this study was to determine the effectiveness of the developed educational package for the prevention of Internet addiction disorder among 15-19-year-old adolescents in Colombo district. Methods The study was a quasi-experimental study to determine the effectiveness of an educational intervention for the prevention of Internet addiction disorder. The educational package was developed on Social Cognitive Theory. The contents were designed following a literature review, expertise from a multidisciplinary panel, and using the preliminary results of the descriptive cross-sectional study. Adolescents from two educational zones in Colombo district were purposefully selected for the Intervention group (n=280) and control group (n=290). A self-administered questionnaire including a validated Internet Addiction Test(IAT) Sinhala version was used for the data collection. Following the implementation of the educational package, primary and secondary outcomes were compared between study groups by chi-square test and paired t-test using the SPSS-21 version. Results Following the intervention, the proportion of adolescents with Internet addiction disorder in the intervention group was less in comparison with the control group and the difference was statistically significant (χ2=9.026, df=1, p=0.003). There was a statistically significant difference between the mean differences of pretest and post-test IAT scores (t=-0.412, df=279, p=0.001) with the reduction of mean IAT scores among the intervention group. The proportion of study participants engaged in social media (χ2=10.6,df=1, p=0.001) and excessive engagement in internet gaming (χ2=5.514, df=1, p=0.019) among the intervention and the control groups revealed a statistically significant difference in favor of the intervention group. Conclusions The developed educational package was found effective for the prevention of Internet Addiction Disorder and recommended to use in future prevention programs and to aid the policymakers and administrators in the prevention, diagnosis, and management of Internet addiction disorder among this age group.
Decoupling Between Two Closely Spaced Asymmetric Microstrip Patch Antenna by Mode Cancellation Method

In this paper, a pair of asymmetrically coupled microstrip patch antennas working at 2.45 GHz are proposed. The proposed antenna has been designed using the mode cancellation theory for both common mode (CM) and differential mode (DM). The proposed antenna is self-decoupled by simply inserting a lumped inductance between the two antennas to adjust the differential mode impedance, and no additional decoupled structure is required. The simulation results show that the -10 dB band-width of the proposed antenna is 2.42 GHz ~2.49 GHz, and the isolation is improved greatly from 8.8 dB to 19.5 dB. Compared with the existing research, this design studies the self-decoupling of asymmetric antenna, and has a simple structure, which has a possesses promising potential in MIMO antenna array.
Evaluating a multicast backhaul protocol for reliable low latency communication over mmWave links

Millimeter wave (mmWave) wireless links are an important part of next generation cellular networks because of the abundant available bandwidth in this frequency band. However, these frequencies are highly susceptible to blockages due to obstructions in the signal path, making it difficult to reliably offer high-data-rate, low-latency service over mmWave links. In this work, we demonstrate a prototype of a previously proposed multicast backhaul architecture, which takes advantage of the excess capacity on mmWave backhaul links and multi-connectivity support at user devices to mitigate the latency associated with frequent blockages on the mmWave access network. The testbed implementation of this protocol overcomes some limitations of a previous mininet- and P4-based prototype, allowing us to run real-time experiments with live application traffic and evaluate the delay benefits of the proposed architecture.
Comparative Analysis of Anticancer and Antibacterial Activities among Seven Trametes Species

Abstract Species in the genus Trametes (Basidiomycota, Polyporales) have been used in natural medicine for a long time. Many studies reported that mycelia or fruiting bodies of Trametes spp. exhibited effects of antioxidant, anti-inflammatory, anticancer, and antimicrobial activities. However, comparative analysis in this genus is scarce due to limitation of morphological identification and the sample number. In this study, the 19 strains of seven Trametes species were chosen to generate a five-gene-based phylogeny with the 31 global references. In addition, 39 culture extracts were prepared for 13 strains to test for anticancer and antibacterial activities. Strong anticancer activities were found in several extracts from T. hirsuta and T. suaveolens. Anticancer activities of T. suaveolens, T. cf. junipericola and T. trogii were first described here. The antibacterial ability of T. versicolor and T. hirsuta extracts has been confirmed. The antibacterial activities of T. suaveolens have been reported at the first time in this study. These results suggest an efficient application of the genus Trametes as the drug resources especially for anticancer agents.
A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure

The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect in-focused smooth objects from the out-of-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest ${ \mathcal {F} _{\alpha }}$ -score of the proposed approach on Zhao’s dataset is 0.7940 whereas on Shi’s dataset is 0.9178.
Robustness Testing of an Industrial Road Object Detection System

As AI-based critical systems are expected to operate in dynamic environments, it is crucial to ensure their reliability under various operational conditions. In computer vision, one way to achieve this is by testing the system’s robustness to input image perturbations. However, while many methods have been proposed and evaluated in academic settings, their effectiveness and applicability in practice remain largely unknown. In this paper, we report the results of testing the robustness of an industrial case of an AI-based road object detection system, in a black-box setting. By defining relevant perturbations and metrics, we analyse the system’s response to changes in its hardware and software environment, and identify areas for improvement through retraining with data augmentation. We address the key challenges encountered during this evaluation and provide insights that may help practitioners in performing similar tests and guide future research on robustness testing of AI-based object detection systems.
Detection distribution of CNVs of SNX29 in three goat breeds and their associations with growth traits

As a member of the SNX family, the goat sorting nexin 29 (SNX29) is initially identified as a myogenesis gene. Therefore, this study aimed to examine the polymorphism in the SNX29 gene and its association with growth traits. In this study, we used an online platform to predict the structures of the SNX29 protein and used quantitative real-time PCR to detect potential copy number variation (CNV) in Shaanbei white cashmere (SBWC) goats (n = 541), Guizhou black (GB) goats (n = 48), and Nubian (NB) goats (n = 39). The results showed that goat SNX29 protein belonged to non-secretory protein. Then, five CNVs were detected, and their association with growth traits was analyzed. In SBWC goats, CNV1, CNV3, CNV4, and CNV5 were associated with chest width and body length (P < 0.05). Among them, the CNV1 individuals with gain and loss genotypes were superior to those individuals with a median genotype, but CNV4 and CNV5 of individuals with the median genotype were superior to those with the loss and gain genotypes. In addition, individuals with the gain genotype had superior growth traits in CNV3. In brief, this study suggests that the CNV of SNX29 can be used as a molecular marker in goat breeding.
Advanced hitchhiking nanomaterials for biomedical applications

Hitchhiking, a recently developed bio-inspired cargo delivery system, has been harnessed for diverse applications. By leveraging the interactions between nanoparticles and circulatory cells or proteins, hitchhiking enables efficient navigation through the vasculature while evading immune system clearance. Moreover, it allows for targeted delivery of nutrients to tissues, surveillance of the immune system, and pathogen elimination. Various synthetic nanomaterials have been developed to facilitate hitchhiking with circulatory cells or proteins. By combining the advantages of synthetic nanomaterials and circulatory cells or proteins, hitchhiking nanomaterials demonstrate several advantages over conventional vectors, including enhanced circulatory stability and optimized therapeutic efficacy. This review provides an overview of general strategies for hitchhiking, choices of cells and proteins, and recent advances of hitchhiking nanomaterials for biomedical applications.
Robust Scuba Diver Tracking and Recovery in Open Water Using YOLOv7, SORT, and Spiral Search

Target tracking is a classic problem in computer vision, with numerous applications in robotics. However, tracking targets underwater presents additional complications due to the six degrees of freedom nature of the problem and the challenging visual environment. In this paper, we address the problem of robotic underwater tracking of scuba divers by partitioning it into two parts: vision and control. We propose a new approach that exploits a highly-maneuverable underwater robot to perform experiments in open water, coupling sensing and control for improved performance. To evaluate the temporal stability of different tracking paradigms, we introduce a new metric, frame-to-frame vari-ance, which is better suited to assess the smoothness of detections from the vision side. We implement PID controllers for control and a spiral search algorithm for target recovery in case of a tracking failure. Our approach only uses observations in the image plane, eliminating the need for robot localization or camera calibration. Using a tracking-by-detection paradigm that combines YOLOv7 for target detection, a tuned filtering technique for temporal stability, and a spiral search algorithm for target recovery, we demonstrate promising performance for long-term tracking. We evaluate our proposed paradigm on the VDD-C dataset and deploy it on an underwater robot for several experiments in open water. Our outcomes show consistency with the ones in the initial studies, and the spiral search algorithm demonstrates promising performance for recapturing a target after a tracking failure. Our approach delivers promising performance for robust underwater tracking, achieving successful open-water tracking scenarios in the presence of strong water currents.
Analysis and Evaluation of Modular Multilevel Converters for DC–DC Solid-State Transformer Based on Different Operating Conditions

In modular multilevel converter (MMC)-based solid-state transformer (SST), the MMC dominates total volume and power loss. The loss and volume of MMC are influenced by various factors, such as operating frequencies, waveforms of ac-link voltage, numbers of submodules, as well as modulation methods. Their impacts on loss and volume of MMC have not been comprehensively analyzed and evaluated. To investigate and evaluate the loss and volume of MMC based on various operating conditions, the computational model is developed for MMC-SST according to its operational principles. The developed model considered the parameter design of MMC to determine the capacitance and arm inductance based on various operating conditions, which are essential parameters to solve state variables. Then, with the developed computational model, the methodology is developed to comprehensively evaluate the losses and volume of MMC-SST under various operating conditions, and to investigate the impacts of the abovementioned factors. The proposed analysis and methodology serve as a guidance and basis for the future optimal design of MMC-SST by providing insights into its performance. The proposed model and analysis are verified by experimental results.
Evaluation of Zero and Negative Sequence Currents Influence of Asymmetric Load on the Power Losses and Quality in Distribution Networks

Researchers and system engineers have given distribution system power quality issues a lot of attention recently. Due to the rising use of sensitive loads in the distribution system, difficulties with unbalanced voltage coefficient and losses are given the utmost attention among the many power quality challenges. This paper studies the effect of the various compensation devices of zero, negative and reactive current components on both the unbalanced distribution system losses and the unbalanced voltage coefficient. The effect of the conventional zero current compensators such as zigzag transformer is compared with modern unified power control (UPC) which can compensate not only zero current component but also the negative and reactive current components. This paper develops a method to evaluate the performance of various compensators on the losses and unbalanced voltage coefficient if these devices are applied in IEEE 13 bus unbalanced system loaded by standard daily loading. This paper provides a general platform to evaluate the unbalanced distribution system's performance under various proposed compensators' effect.
HeartPrint: Passive Heart Sounds Authentication Exploiting In-Ear Microphones

Biometrics has been increasingly integrated into wearable devices to enhance data privacy and security in recent years. Meanwhile, the popularity of wearables in turn creates a unique opportunity for capturing novel biometrics leveraging various embedded sensing modalities. In this paper, we study a new intracorporal biometrics combining the uniqueness of i) heart motion, ii) bone conduction, and iii) body asymmetry. Specifically, we design HeartPrint as a passive yet secure user authentication system: it exploits the bone-conducted heart sounds captured by (widely available) dual in-ear microphones (IEMs) to authenticate users, while neatly leveraging IEMs renders itself transparent to users without impairing the normal functions of earphones. To suppress the interference from other body sounds and audio produced by the earphones, we develop a novel interference elimination method using modified non-negative matrix factorization to separate clean heart sounds from background interference. We further explore the uniqueness of IEM-recorded heart sounds in three aspects to extract a novel biometric representation, based on which HeartPrint leverages a convolutional neural model equipped with a continual learning method to achieve accurate authentication under drifting body conditions. Extensive experiments with 18 pairs of commercial earphones on 45 participants confirm that HeartPrint can achieve 1.6% FAR and 1.8% FRR, while effectively coping with major attacks, complicated interference, and hardware diversity.
Influence of Block-Copolymers’ Composition as Compatibilizers for Epoxy/Silicone Blends

The objective of this study was to prepare crosslinked epoxy networks containing liquid silicone particles in order to improve their mechanical properties and obtain less brittle materials. Different copolymers were used as compatibilizers. These copolymers vary in their chemical composition and structure. All of the copolymers contain hydrophobic (PDMS sequences) and hydrophilic groups. The effect of their chemical structure and architecture on the morphology of the dispersed phase, and on the final physico-chemical and flexural characteristics of epoxy/silicone blends, was explored. The morphology of crosslinked formulations was studied by scanning electron microscopy (SEM), and the thermal characteristics (glass transition temperature, Tg, and curing exothermic peak) were determined by differential scanning calorimetry (DSC). The experimental results have shown that the average diameter and particle size distribution of silicone particles depend on the chemical structure and architecture of the compatibilizers. One copolymer has been identified as the best compatibilizer, allowing a lower mean diameter and particle size distribution in addition to the best mechanical properties of the final network (less brittle character). This study has consequently evidenced the possibility of creating in situ silicone capsules inside an epoxy network by adding tailored compatibilizers to epoxy/silicone formulations.
The Impact of Core Complex Training on Some Basketball-Related Aspects of Physical Strength and Shooting Performance

Exercises for the core can be categorized as promoting core-stability, core-strength, or functionality, as these are crucial aspects of most sports activities. This study aimed to examine the effects of using core complex training (CCT), complex training (CT), and core exercise (CE) on some aspects of muscle strength and shooting performance in basketball players. The 36 participants were divided into three groups of 12 each, and then the experimental approach was applied to each group. The groups were labeled as follows: the core complex training group (N = 12; age, 18.58 ± 0.67 years; height, 178.08 ± 0.79 cm; weight, 76.42 ± 1.38 kg; training age, 7.42 ± 0.51 years); the complex training group (N = 12; age, 18.50 ± 0.52 years; height, 177.92 ± 1.31 cm; weight, 76.67 ± 1.78 kg; training age, 7.33 ± 0.49 years); and the core exercise group (n = 12; age, 18.42 ± 0.52 years; height, 177.75 ± 1.29 cm; weight, 76.58 ± 1.38 kg; training age, 7.42 ± 0.67 years). For ten weeks, each of the three groups participated in three training sessions every week. This study investigates the impact of core complex training on basketball shooting ability and muscle strength. The eight-week program, consisting of weight training, plyometric exercises, and core exercises, yielded improvements in muscle strength and shooting accuracy. In tests of muscular strength and basketball shooting ability, the CCT group outperformed the CE and CT groups. The F value varied from 3.75 to 58.77, which are function values with a p < 0.05 significance level. The core complex training group exhibited superior muscle strength to that of both the complex training group and the core exercise group, in some areas. This is shown in the results of the javelin quadrathlon medicine ball test, the core muscle strength and stability test, the sit-up abdomen test, the sit-up back test, the standing long jump test, the Sargent jump test, and the shooting test (p < 0.005). Due to the effect of the core complex training program on improving performance efficiency and muscle strength, which affects the results of matches, we have recommended using the proven basic strength training program at other age stages, with the objective of including the concept, importance, and design of compound basic strength training in training programs used by basketball coaches.
A Compact Metamaterial Dual-band Branch-line Coupler for 5G Applications

This study introduces a compact branch-line coupler (BLC) based on metamaterial (MTM) technology that can operate in sub-6 GHz 5G frequency bands. Conventional vertical and horizontal sections of the BLC were replaced with T-shaped TLs and interdigital capacitor (IDC) unit cells incorporated into the BLC sections to improve their performance and reduce their dimensions. The BLC is designed on a Rogers RT5880 substrate material, and it operates at dual frequencies of 0.7 GHz and 3.5 GHz. The design was simulated using CST Microwave Studio software and achieved enhanced frequency band ratio.
Tamil Document Summarization Using Semantic Graph Method

Document summarization refers to the task of producing shorter version of the original document by selecting important sentences from the text. Tamil Document Summarization using sub graph presents a method for extracting sentences from an individual document to serve as a document summary or a pre-cursor to creating a generic document abstract. Language-Neutral Syntax (LNS), a system of representation for natural language sentences has been used for considering the semantics of the documents. Syntactic analysis of the text that produces a logical form analysis has been applied for each sentence. Subject-Object-Predicate (SOP) triples are extracted from individual sentences to create a semantic graph [2] of the original document and the corresponding human extracted summary. Semantic Normalization is applied to SOP triples to reduce the number of nodes in the semantic graph of the original document. Using the Support Vector Machine (SVM) learning algorithm, a classifier has been trained to identify SOP triples from the document semantic graph that belong to the summary. The classifier is then used for automatic extraction of summaries from the test documents.
A GAF and CNN based Wi-Fi Network Intrusion Detection System

Wi-Fi networks have become ubiquitous nowadays in enterprise and home networks creating opportunities for attackers to target them. These attackers exploit various vulnerabilities in Wi-Fi networks to gain unauthorized access to networks or extract data from end users' devices. A network intrusion detection system (NIDS) is deployed to detect these attacks before they can cause any significant damages to the network's functionalities or security. In this work, we propose a deep learning based NIDS using a 2D convolutional neural network (CNN) to detect intrusions inside a Wi-Fi network. Wi-Fi frames are transformed into images using Gramian Angular Field (GAF) technique. These images are then fed to the proposed deep learning based NIDS for intrusion detection. We used a benchmark Wi-Fi intrusion datasets, AWID3, for our model development. Our proposed model is able to achieve an accuracy and f-measure of 99.77% and 99.66%, respectively.