Miracle-dz/UniFAR-FTUs huggingface dataset:
ENSEMBLE MACHINE LEARNING APPROACH FOR IOT INTRUSION DETECTION SYSTEMS

The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset.
Exploring Metabolic and Gut Microbiome Responses to Paraquat Administration in Male Wistar Rats: Implications for Oxidative Stress

In this study, we examined the metabolic and gut microbiome responses to paraquat (PQ) in male Wistar rats, focusing on oxidative stress effects. Rats received a single intraperitoneal injection of PQ at 15 and 30 mg/kg, and various oxidative stress parameters (i.e., MDA, SOD, ROS, 8-isoprostanes) were assessed after three days. To explore the omic profile, GC-qTOF and UHPLC-qTOF were performed to assess the plasma metabolome; 1H-NMR was used to assess the urine metabolome; and shotgun metagenomics sequencing was performed to study the gut microbiome. Our results revealed reductions in body weight and tissue changes, particularly in the liver, were observed, suggesting a systemic effect of PQ. Elevated lipid peroxidation and reactive oxygen species levels in the liver and plasma indicated the induction of oxidative stress. Metabolic profiling revealed changes in the tricarboxylic acid cycle, accumulation of ketone body, and altered levels of key metabolites, such as 3-hydroxybutyric acid and serine, suggesting intricate links between energy metabolism and redox reactions. Plasma metabolomic analysis revealed alterations in mitochondrial metabolism, nicotinamide metabolism, and tryptophan degradation. The gut microbiome showed shifts, with higher PQ doses influencing microbial populations (e.g., Escherichia coli and Akkermansia muciniphila) and metagenomic functions (pyruvate metabolism, fermentation, nucleotide and amino acid biosynthesis). Overall, this study provides comprehensive insights into the complex interplay between PQ exposure, metabolic responses, and gut microbiome dynamics. These findings enhance our understanding of the mechanisms behind oxidative stress-induced metabolic alterations and underscore the connections between xenobiotic exposure, gut microbiota, and host metabolism.
Subnanometer Resolution Displacement Sensor Based on Vernier Effect

A cascaded Fabry-Perot interferometer (FPI) for displacement sensing with the Vernier effect is proposed and experimentally demonstrated. The sensor consists of a single mode fiber (SMF) fusion spliced with a section of capillary and a hollow microsphere with a hole (HMH). Two kinds of Vernier effects are generated through three beams reflected from the three reflecting surfaces, including the SMF endface, erbium-doped fiber(EDF) endface, and inner surface of the HMH. We explore the specific relationship between the generation conditions of the Vernier effect and the enlargement factor of the envelope period. Consequently, we find the critical value of the Vernier effect generation by simulating the superimposed spectra of different distances between the SMF endface and the EDF endface. Moreover, the experimental results indicate the proposed sensor has a displacement sensitivity of 94.6 pm/nm with a subnanometer resolution of 0.21 nm.
Convolutional Neural Network (CNN) Based Identification of Crop Diseases Using ResNet-50

Agriculture is vital to the success of India's economy. Loss of crops and future disease propagation can be avoided if plant diseases are detected early. For the sake of human health worldwide, early identification of plant diseases is essential. In the past, a pathologist would have to physically inspect each plant to make a diagnosis. However, due to lower precision and limited availability of human resources, manual examination for agricultural diseases is restricted. Automated methods for spotting and labelling plant diseases are in high demand as a solution to this kind of problem. The recent achievements of DL in computer vision for plant disease categorization have served as inspiration for several publications. Unfortunately, the deficit translucence in these end-to-end deep classifiers can limit their practical application. We presented convolutional neural networks using the Resnet50 network to address the issues mentioned earlier. In this study, we use DL for the problem of disease classification in plants. Due to its immense success in image-based classification, the convolutional neural network (CNN) model is used. We can look at ResNet50 with Adam Optimizer to improve the system's accuracy. In this case, crop leaf images are used as input, and the system will “stumble on” the presence of disorder after processing the image. If a disease is found, the results will specify its kind and may suggest solutions like insecticides or other chemicals. The dataset is used to train the CNN model and previously trained models like VGG, ResNet, and DenseNet. The Resnet50 Network model is the most accurate of these. The results demonstrate that the proposed model outperformed state-of-the-art approaches by producing a 99.02% accuracy rate.
Demonstration of 420GHz highly scaled InAlN/GaN HEMTs by Electron Beam Lithography

GaN-based high electron mobility transistors (HEMTs) are promising candidates for millimeter wave amplifiers above 100 GHz for 6G communications. To satisfy those requirements, a maximum oscillation frequency $(\mathbf{f}_{\max})$ surpassing 400 GHz is essential, necessitating the deployment of advanced down-scaling and patterning technologies. Through the utilization of electron beam lithography, ultra-scaled InAlN/GaN HEMTs on sapphire with T-gates with gate length of~60 nm and source-drain distance of~300 nm were fabricated, reaching $\mathrm{f}_{\max}$ over 420 GHz with a corresponding fTexceeding 150 GHz. To the best of the author's knowledge, these scaled HEMTs exhibit the highest fmax among reported HEMTs on sapphire.
From Water for Water: PEDOT:PSS-Chitosan Beads for Sustainable Dyes Adsorption

This study investigates the viability of developing chitosan-based hydrogels derived from waste shrimp shells for the removal of methylene blue and methyl orange, thereby transforming food waste into advanced materials for environmental remediation. Despite chitosan-based adsorbents being conventionally considered ideal for the removal of negative pollutants, through targeted functionalization with poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) at varying concentrations, we successfully enhance the hydrogels’ efficacy in also adsorbing positively charged adsorbates. Specifically, the incorporation of PEDOT:PSS at a concentration of 10% v/v emerges as a critical factor in facilitating the robust adsorption of dyes. In the case of the anionic dye methyl orange (MO, 10−5 M), the percentage of removed dye passed from 47% (for beads made of only chitosan) to 66% (for beads made of chitosan-PEDOT:PSS 10%), while, in the case of the cationic dye methylene blue (MB, 10−5 M), the percentage of removed dye passed from 52 to 100%. At the basis of this enhancement, there is an adsorption mechanism resulting from the interplay between electrostatic forces and π–π interactions. Furthermore, the synthesized functionalized hydrogels exhibit remarkable stability and reusability (at least five consecutive cycles) in the case of MB, paving the way for the development of cost-effective and sustainable adsorbents. This study highlights the potential of repurposing waste materials for environmental benefits, introducing an innovative approach to address the challenges regarding water pollution.
Data Augmentation for Cardiovascular Time Series Data Using WaveNet

In this work we present a novel approach for generating cardiovascular data using a modified WaveNet architecture. This can enable further research in areas where data is scarce and hard to obtain. By generating additional time series data in a set of animal tests performance of existing models could be improved and more difficult approaches, that require substantial amounts of data, attempted. We validate our approach on a classification task and compare it to similar methods of data augmentation.
Regulators of G-Protein Signaling (RGS) in Sporadic and Colitis-Associated Colorectal Cancer

Colorectal cancer (CRC) is one of the most common neoplasms worldwide. Among the risk factors of CRC, inflammatory bowel disease (IBD) is one of the most important ones leading to the development of colitis-associated CRC (CAC). G-protein coupled receptors (GPCR) are transmembrane receptors that orchestrate a multitude of signaling cascades in response to external stimuli. Because of their functionality, they are promising targets in research on new strategies for CRC diagnostics and treatment. Recently, regulators of G-proteins (RGS) have been attracting attention in the field of oncology. Typically, they serve as negative regulators of GPCR responses to both physiological stimuli and medications. RGS activity can lead to both beneficial and harmful effects depending on the nature of the stimulus. However, the atypical RGS—AXIN uses its RGS domain to antagonize key signaling pathways in CRC development through the stabilization of the β-catenin destruction complex. Since AXIN does not limit the efficiency of medications, it seems to be an even more promising pharmacological target in CRC treatment. In this review, we discuss the current state of knowledge on RGS significance in sporadic CRC and CAC with particular emphasis on the regulation of GPCR involved in IBD-related inflammation comprising opioid, cannabinoid and serotonin receptors.
Plasmablastic Lymphoma. A State-of-the-Art Review: Part 1-Epidemiology, Pathogenesis, Clinicopathologic Characteristics, Differential Diagnosis, Prognostic Factors, and Special Populations

This two-part review aims to present a current and comprehensive understanding of the diagnosis and management of plasmablastic lymphoma. The first section, as presented in this paper, reviews epidemiology, etiology, clinicopathological characteristics, differential diagnosis, prognostic variables, and the impact of plasmablastic lymphoma on specific populations. Plasmablastic lymphoma (PBL) is a rare and aggressive form of lymphoma. Previous and modern studies have demonstrated a significant association between the human immunodeficiency virus (HIV) and the development of the disease. The limited occurrence of PBL contributes to a need for a more comprehensive understanding of the molecular mechanisms involved in its etiology. Consequently, the diagnostic procedure for PBL poses a significant difficulty. Among the group of CD20-negative large B-cell lymphomas, PBL can be correctly diagnosed by identifying its exact clinical characteristics, anatomical location, and morphological characteristics. PBL cells do not express CD20 or PAX5 but possess plasmacytic differentiation markers such as CD38, CD138, MUM1/IRF4, Blimp1, and XBP1. PBL must be distinguished from other B-cell malignancies that lack the CD20 marker, including primary effusion lymphoma, anaplastic lymphoma kinase-positive large B-cell lymphoma, and large B-cell lymphoma (LBCL). This condition is frequently associated with infections caused by the Epstein-Barr virus and genetic alterations involving the MYC gene. Despite advances in our comprehension of this disease, the prognosis remains dismal, resulting in a low overall survival rate, although recent reports suggest an apparent tendency towards substantial improvement.
Guidelines for Greening Healthcare Spaces

Biophilic Design is a concept used in the interior design sector to increase the connection of users to the natural environment. Used at both the building and city level, this technique is claimed to have health, environmental, and economic benefits for space occupants and urban environments. The biophilic design focuses on human well-being to improve physical and mental health, performance, and wellness. All this opens the way to an understanding of the solutions that the use of biophilic design in healthcare spaces could bring. Indeed, through this research and based on the analysis of health spaces whose use of biophilic design has been ubiquitous, we will develop the notion of biophilic health care design and how this new way of space design will affect positively mental health, and subsequently, physical health of the person concerned by the health care in these health centers, hence its impact on the acceleration of healing. Upon concluding our comprehensive investigation into the prevalent application of biophilic design within healthcare settings, this study endeavors to provide a comprehensive set of directives tailored for designers. These guidelines are formulated to serve as a lucid roadmap, facilitating the seamless integration of biophilic principles into the architectural blueprint of hospitals. This undertaking is driven by the overarching objective of augmenting the favorable impacts on both mental and physical well-being, ultimately speeding up recuperation and instigating a revolutionary transformation in the domain of healthcare environments.
MEMBANGUN GERAKAN MUTU DI LEMBAGA PENDIDIKAN ISLAM

The quality of education will not succeed without an appropriate strategy for improving the quality of all components. The problems that occur include teacher professionalism, graduate competency standards, effective learning, and programs that do not support quality achievement. One of the efforts to improve quality is through the implementation of strategic management so as to be able to determine the right strategy to improve the quality of education. Improving the quality of education is a top priority in all educational institutions. Likewise in Islamic educational institutions which are currently in the process of becoming educational institutions that have the same quality as other educational institutions and even become educational institutions of high quality. To achieve this, efforts continue to be made by Islamic educational institutions. One of the efforts is to apply various theories and concepts of quality management so that the quality of education can be maintained and recognized as an educational institution that carries out processes well and produces good output. There are several concepts of quality/quality improvement put forward by experts such as those relating to quality planning, quality control and improvement. This study will discuss several concepts of quality and their relevance to Islamic educational institutions. While the quality of education will not succeed without the right strategy for improving the quality of all components, the problems that occur include teacher professionalism, graduate competency standards, effective learning, and programs that do not support quality achievement. One of the efforts to improve quality is through the implementation of strategic management to be able to determine the right strategy to improve the quality of education.
Optimisation of seed germination and seedling emergence of sesbania cannabina (retz.) Poir.

This study aims to optimise the condition for seed germination and seedling emergence of Sesbania cannabina (Retz.) Poir. for a phytoremediation study. In this experiment, seed germination was carried out using two growth media: Murashige and Skoog basal medium (MS); and Whatman Grad 1 filter paper (FP), using the top of media or top of the paper method under three different photoperiods. Seeds were pre-treated for different lengths of time with various concentrations of hydrogen peroxide (H2O2) (v/v) multiple hot water treatments. Results showed that seeds pre-treated with H2O2 (6% v/v) for 5 minutes and primed with 65°C water for 5 minutes were considered as the ideal pre-treatment condition. Different photoperiods and media used in germination in this experiment do not significantly affect seed germination. The optimum condition for seed germination at 27.5 °C ± 2.5°C and relative humidity of ~ 75% for 5 days. Seed emergence in soil and compost was significantly affected by the burial depth and bulk density of the media, with the highest (98%±1) seed emergence observed at 1 cm depth for soil and compost, and decreasing with increased burial depth. This ideal condition will help in further studies related to plant growth and phytoremediation of S. cannabina. Bangladesh J. Bot. 52(4): 1015-1023, 2023 (December)
Intelligent Mode-Locking Enabled by Real-Time Reinforcement Learning

A real-time embedded approach to realize intelligent mode-locked fiber lasers based on reinforcement learning is proposed. The reinforcement learning is deployed in a Field programmable gate array (FPGA) to complete the inference process and real-time control. Benefited from the dedicated quantization and deployment strategy, the FPGA finishes a single forward inference in 6.4 us, which is about 32-times faster than an Nvidia RTX-3050 GPU, and the proposed solution manifests ultrahigh power efficiency.
Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

The digitalisation of visual tasks through imaging techniques and Computer Vision has the potential to disrupt the manner in which Advanced Manufacturing processes are deployed. In this study we collaborated with the manufacturing industry to investigate the effective usage of end-to-end convolutional neural networks (CNNs) to enable advanced manufacturing processes by inspecting the seal integrity of sterile barrier packaging in highly regulated products, such as Medical Devices. For this purpose, a novel ‘DS1’ dataset of labelled images representative of production samples was acquired in an industrial-like environment which is an open source for future research work. The core focus of this research is to address the common challenges associated with performing quality inspections in advanced manufacturing environments with the aim of detecting defects with very high impact but very low occurrence rates, by incorporating a set of pre-trained deep learning architectures. The performance of state-of-the-art CNNs is validated on unseen test data when trained in small and imbalanced datasets with low image variation and low pixel complexity. The study indicated that while CNN performance drops when datasets are imbalanced, some architectures are more resilient and capable of successfully classifying defects in small datasets in the order of a few hundred samples wherein as little as 5% of the samples are defective. Furthermore, this study also discusses the marginal impact of training with basic data augmentations and the tendency for models to overfit when trained with manufacturing datasets such as “DS1. ”
Astrocytic Regulation of Endocannabinoid-Dependent Synaptic Plasticity in the Dorsolateral Striatum

Astrocytes are pivotal for synaptic transmission and may also play a role in the induction and expression of synaptic plasticity, including endocannabinoid-mediated long-term depression (eCB-LTD). In the dorsolateral striatum (DLS), eCB signaling plays a major role in balancing excitation and inhibition and promoting habitual learning. The aim of this study was to outline the role of astrocytes in regulating eCB signaling in the DLS. To this end, we employed electrophysiological slice recordings combined with metabolic, chemogenetic and pharmacological approaches in an attempt to selectively suppress astrocyte function. High-frequency stimulation induced eCB-mediated LTD (HFS-LTD) in brain slices from both male and female rats. The metabolic uncoupler fluorocitrate (FC) reduced the probability of transmitter release and depressed synaptic output in a manner that was independent on cannabinoid 1 receptor (CB1R) activation. Fluorocitrate did not affect the LTD induced by the CB1R agonist WIN55,212-2, but enhanced CB1R-dependent HFS-LTD. Reduced neurotransmission and facilitated HFS-LTD were also observed during chemogenetic manipulation using Gi-coupled DREADDs targeting glial fibrillary acidic protein (GFAP)-expressing cells, during the pharmacological inhibition of connexins using carbenoxolone disodium, or during astrocytic glutamate uptake using TFB-TBOA. While pretreatment with the N-methyl-D-aspartate (NMDA) receptor antagonist 2-amino-5-phosphonopentanoic acid (APV) failed to prevent synaptic depression induced by FC, it blocked the facilitation of HFS-LTD. While the lack of tools to disentangle astrocytes from neurons is a major limitation of this study, our data collectively support a role for astrocytes in modulating basal neurotransmission and eCB-mediated synaptic plasticity.
Prevalence of tooth rotation in identical twins

The twins are a multidisciplinary phenomenon, the most enduring and closest biological connection. The aim was to determine the prevalence of tooth rotation of the intercanine sector in identical twins. The expected frequency is about 20%. Materials and Methods: The study included 40 pairs of identical twins, chronologically aging 18-65. The sample was formed on the basis of data from the registers of births in the municipalities of Serbia and Montenegro. The twins from Tutin, Novi Pazar Sjenica, Raska, Rozaje, Bijelo Polje, Podgorica, Cetinje, Pljevlja, Cacak and Belgrade were examined. Clinical examination recorded the rotation of individual teeth. Results: The most frequent abnormality in 40 pairs of identical twins is rotation lateral incisors - 73, then central incisors - 22 and canines - 14. As regards lateral incisors, the upper ones lead - left: right=25:20. The relation of the lower is 14:14. Among the central incisors, most-often rotated is the upper left incisor - 9, then the upper right - 8, and lower left central incisor - 5 rotations. On average, each twin has 1.36 (11%) of tooth rotation. Conclusion: Identical twins have twice lower occurrence of expected prevalence of teeth rotation.
River flow forecast by means of selected black box models

Performance of two different types of approaches were investigated with respect to the daily river flow predictions. The first approach is Multi-Layer Perceptron Artificial Neural Network, the second one, based on the deterministic chaos concept, is so called Phase-Space Reconstruction (PSR) model. Both models were applied to daily river flow data collected from several gauges, located in river reaches in western Canada. Each data set consists of more than 10000 consecutive daily measurements. The method based on the phase-space reconstruction theory has been applied despite the fact that the authors doubt in the existence of deterministic chaos in the river system. Classical phase-space method may be applied only to single time se- ries data. In the present study an extension was proposed, in which time series from additional gauge stations added to the system. Comparison of models' performance was made for this extended and classical ap- proaches for both phase-space reconstruction and Artificial Neural Networks models.