Miracle-dz/UniFAR-FTUs huggingface dataset:
Antipsoriatic and Anti-inflammatory Studies of Berberis aristata Extract Loaded Nanovesicular Gels

Objective: Novel nanovesicular gel of Berberis aristata extract was developed and evaluated for its anti-inflammatory and antipsoriatic activity. Materials and Methods: Transferosomes were prepared using soya phosphatidylcholine and edge activators (Tween 80, Span 80, and sodium deoxycholate) by a modified lipid film hydration technique using rotary evaporator and evaluated for various parameters. The quantification and standardization of extract have been carried out using its alkaloid content as berberine as biomarker. Topical application of imiquimod (IMQ) (immune modifier) on the shaved back of mice developed psoriasis-like inflammation followed by histopathological study of inflamed skin. Results: The size of transferosomes was in the range of 265–345 nm whereas polydispersity index ranges from 0.10 to 0.63, and for zeta potential, it was from −19.3 to −43.3 mV. Transferosomes were further added to Carbopol 934P for gel formation and subsequently evaluated for their physicochemical properties. Their efficacy against inflammation, IMQ-induced psoriasis, and skin sensitivity was compared with conventional formulation (commercial formulation-Angle Gloss, Phytolab Pvt. Ltd.). Percent inhibition of edema by transferosomal gel (55.76%) was more as compared to conventional gel of extract (33.5%) found out by Carrageenan-induced paw edema method. Primary irritation index was found to be <0.4 inferring its safe use for topical formulation. Conclusion: Histopathological report showed that, in psoriasis-induced animal treated with topical application of extract loaded transferosomal gel showed a marked reduction in thickness of epidermis, length of rete ridges as compared to conventional gel formulation. It can be inferred that B. aristata extract loaded transferosomal gel can function as potential anti-inflammatory and antipsoriatic formulation. Abbreviations used: SPC: Soyaphosphatidylcholine, PDI: Polydispersity index, IMQ: Imiquimod, EA: Edge activator, BE: Berberine, TEM: Transmission electron microscopy, PBS: Phosphate buffered saline, H and E: Hematoxylin and eosin, ZP: Zeta potential, EE: Entrapment efficiency.
A network meta-analysis of atomoxetine and osmotic release oral system methylphenidate in the treatment of attention-deficit/hyperactivity disorder in adult patients

The lack of head-to-head clinical studies powered to compare atomoxetine and osmotic release oral system (OROS) methylphenidate necessitates treatment comparison by methods that include indirect evidence such as network meta-analysis (NMA). A NMA assessing the relative treatment effects of atomoxetine and OROS methylphenidate in adults with attention-deficit/hyperactivity disorder (ADHD) was conducted. Studies were identified by systematic literature review. Analyses summarised improvements in efficacy, measured by ADHD-specific scales, using Cohen’s d to calculate the standardised mean difference (SMD), and all cause discontinuations. Results showed effect sizes (SMD, 95% credible interval (CrI)) relative to placebo that did not differ significantly between atomoxetine (0.46, 0.36–0.56) and OROS methylphenidate (0.51, 0.40–0.63) in clinical studies of up to 12 weeks’ duration (SMD, 95% CrI for atomoxetine versus OROS methylphenidate: –0.05, –0.18–0.08). Patients treated with these medications responded better than those given placebo across all analyses. There was also no significant difference in discontinuation rates between atomoxetine and OROS methylphenidate (odds ratio, 95% CrI: 0.85, 0.53–1.35). Between-study heterogeneity was low overall. Results of this NMA suggest that the efficacy of atomoxetine and OROS methylphenidate in adults does not differ significantly. Clinical guidelines may require amendment to reflect these recent data.
Improving Transparency and Explainability of Deep Learning Based IoT Botnet Detection Using Explainable Artificial Intelligence (XAI)

Ensuring the utmost security of loT systems is imperative, and robust botnet detection plays a pivotal role in achieving this goal. Deep learning-based approaches have been widely employed for botnet detection. However, the lack of interpretability and transparency in these models can limit these models' effectiveness. In this research, we present a Deep Neural Network (DNN) model specifically designed for the detection of loT botnet attack types. Our model performs exceptionally, demonstrating outstanding performance of classification metrics with 99% accuracy, F1 score, recall, and precision. To gain deeper insights into our DNN model's behaviour, we employ seven different post hoc explanation techniques to provide local expla-nations. We evaluate the quality of Explainable AI (XAI) methods using metrics such as high faithfulness, monotonicity, complexity, and sensitivity. Our findings highlight the effectiveness of XAI techniques in enhancing the interpretability and transparency of the DNN model for loT botnet detection. Specifically, our results indicate that DeepLIFT yields high faithfulness, high consistency, low complexity, and low sensitivity among all the explainers.
Operational and performance issues of a CBQ router

The use of scheduling mechanisms like Class Based Queueing (CBQ) is expected to play a key role in next generation multiservice IP networks. In this paper we attempt an experimental evaluation of ALTQ/CBQ demonstrating its sensitivity to a wide range of parameters and link layer driver design issues. We pay attention to several CBQ internal parameters that affect performance drastically and particularly to "borrowing", a key feature for flexible and efficient link sharing. We are also investigating cases where the link sharing rules are violated, explaining and correcting these effects wheneverpossible. Finally we evaluate CBQ performance and make suggestions for effective deployment in real networks.
Fabrication and Characterization of Quad-Component Bioinspired Hydrogels to Model Elevated Fibrin Levels in Central Nervous Tissue Scaffolds

Multicomponent interpenetrating polymer network (mIPN) hydrogels are promising tissue-engineering scaffolds that could closely resemble key characteristics of native tissues. The mechanical and biochemical properties of mIPNs can be finely controlled to mimic key features of target cellular microenvironments, regulating cell-matrix interactions. In this work, we fabricated hydrogels made of collagen type I (Col I), fibrin, hyaluronic acid (HA), and poly (ethylene glycol) diacrylate (PEGDA) using a network-by-network fabrication approach. With these mIPNs, we aimed to develop a biomaterial platform that supports the in vitro culture of human astrocytes and potentially serves to assess the effects of the abnormal deposition of fibrin in cortex tissue and simulate key aspects in the progression of neuroinflammation typically found in human pathologies such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and tissue trauma. Our resulting hydrogels closely resembled the complex modulus of AD human brain cortex tissue (~7.35 kPa), promoting cell spreading while allowing for the modulation of fibrin and hyaluronic acid levels. The individual networks and their microarchitecture were evaluated using confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). Human astrocytes were encapsulated in mIPNs, and negligible cytotoxicity was observed 24 h after the cell encapsulation.
Effective and Precise Detection of Hazardous Fires from CCTV Images Using YOLOv7 Algorithm in Comparison with CNN

To improve the ability to recognize potentially hazardous fires, this research analyzed and contrasted the performance of two different types of deep learning methods: Novel YOLOv7 and Convolutional Neural Networks (CNN). The researchers amassed a dataset consisting of 68 samples, which were divided equally into two groups, each including 34 samples. Group 2 made use of CNN, whereas Group 1 used the Novel YOLOv7 method for their data. The dataset was imported into the Roboflow programme for analysis. The Novel YOLOv7 algorithm was trained on the dataset with the assistance of Google Collab. The number of participants in the experiment was determined with the use of an online statistical programme known as clincalc.com. The pre-test power was set at 80%, and the alpha value was set at 0.05. The size of the sample was established by using the findings of much earlier research. After the investigation was completed, the researchers estimated the mean average accuracy (mAP) for both methods. The mean absolute performance (mAP) of the CNN algorithm was 0.3291, whereas the mean absolute performance (mAP) of the Novel YOLOv7 method was 0.4506. According to these data, there was a statistically significant difference between the two algorithms in terms of accuracy, with a significance value of 0.001 (p0.05). This difference was found to be statistically significant. In conclusion, the Novel YOLOv7 algorithm showed more accurate hazardous fire detection compared to Convolutional Neural Networks (CNN) for the dataset that was presented.
Sequential Inductions of the ZEB1 Transcription Factor Caused by Mutation of Rb and Then Ras Proteins Are Required for Tumor Initiation and Progression*

Background: Ras mutation drives tumor initiation as well as invasion. Results: The ZEB1 transcription factor is sequentially induced with mutation of Rb1 and Ras, and these inductions are required for Ras-mediated tumor initiation and then invasion. Conclusion: ZEB1 plays a critical role in initiation and progression of Ras-mediated tumors. Significance: Induction ZEB1 is important for tumor initiation and invasion in a model of Ras-initiated cancer. Rb1 restricts cell cycle progression, and it imposes cell contact inhibition to suppress tumor outgrowth. It also triggers oncogene-induced senescence to block Ras mutation. Loss of the Rb1 pathway, which is a hallmark of cancer cells, then provides a permissive environment for Ras mutation, and Ras is sufficient for invasive tumor formation in Rb1 family mutant mouse embryo fibroblasts (MEFs). These results demonstrate that sequential mutation of the Rb1 and Ras pathways comprises a tumor initiation axis. Both Rb1 and Ras regulate expression of the transcription factor ZEB1, thereby linking tumor initiation to the subsequent invasion and metastasis, which is induced by ZEB1. ZEB1 acts in a negative feedback loop to block expression of miR-200, which is thought to facilitate tumor invasion and metastasis. However, ZEB1 also represses cyclin-dependent kinase (cdk) inhibitors to control the cell cycle; its mutation in MEFs leads to induction of these inhibitors and premature senescence. Here, we provide evidence for two sequential inductions of ZEB1 during Ras transformation of MEFs. Rb1 constitutively represses cdk inhibitors, and induction of ZEB1 when the Rb1 pathway is lost is required to maintain this repression, allowing for the classic immortalization and loss of cell contact inhibition seen when the Rb1 pathway is lost. In vivo, we show that this induction of ZEB1 is required for Ras-initiated tumor formation. ZEB1 is then further induced by Ras, beyond the level seen with Rb1 mutation, and this Ras superinduction is required to reach a threshold of ZEB1 sufficient for repression of miR-200 and tumor invasion.
Machine Translation Aid to the Test : A study on the benefits of post-editing for translation into the weaker language

Post-editing is today a potential job assignment for both freelance and in-house translators. Therefore, it is important for linguists to test their suitability for this procedure. An action research study has been undertaken to shed light on whether machine translation output post-editing is more efficient than translation from the source text, considering time, quality and effort. This study also aims to find out if said procedure is effective for translation into the weaker language. The author, a certified English>Spanish translator, tested her translation and MT output postediting performances with texts from three different domains: medical, legal and IT. The time and effort needed to elaborate the target texts, and the quality of the translations were analysed. Results indicate that, in the researcher’s particular case, post-editing is more time-efficient than translation from the source text in both language directions and in most domains. In fact, a greater time difference was observed in post-editing into the weaker language (English). With regard to quality, MT output post-editing is efficient only for translation into the mother tongue. Contrary to expectations, target texts into the second language have better quality when translation from the source text is used. Similarly, as regards effort, post-editing efficiency is more noticeable in translation into the mother tongue (Spanish). Effort reductions when post-editing into the weaker language were only registered in the IT and Legal domains.
Effect of Deposition Working Power on Physical Properties of RF-Sputtered CdTe Thin Films for Photovoltaic Applications

The main objective of this study was to determine the variation in the properties of cadmium telluride (CdTe) thin films deposited on a p-type Si substrate by the radio frequency magnetron sputtering technique at four different working powers (70 W, 80 W, 90 W, and 100 W). The substrate temperature, working pressure, and deposition time during the deposition process were kept constant at 220 °C, 0.46 Pa, and 30 min, respectively. To study the structural, morphological, and optical properties of the CdTe films grown under the mentioned experimental conditions, X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and optical spectroscopy were used. For a better analysis of the films’ structural and optical properties, a group of films were deposited onto optical glass substrates under similar deposition conditions. The electrical characterisation of Ag/CdTe/Al “sandwich” structures was also performed using current–voltage characteristics in the dark at different temperatures. The electrical measurements allowed the identification of charge transport mechanisms through the structure. New relevant information released by the present study points towards 90 W RF power as the optimum for obtaining a high crystallinity of ~1 μm nanostructured thin films deposited onto p-Si and optical glass substrates with optical and electrical properties that are suitable for use as absorber layers. The obtained high-quality CdTe nanostructured thin films are perfectly suitable for use as absorbers in CdTe thin-film photovoltaic cells.
A Study of AI-Based In-Vehicle Intrusion Detection Systems

We analyzed ML-based In-Vehicle Intrusion Detection Systems (IV-IDS). Our review of recent automotive forensics studies highlights constraints relevant to in-vehicle networks and the associated security/safety requirements, revealing gaps in the existing literature. This paper contributes to the existing research corpus by addressing AI's limitations in IV-IDS. It defines pertinent baseline metrics for in-vehicle networked systems, enabling an assessment of the viability of AI-based intrusion detection systems.
Life span variation in 13 Drosophila species: a comparative study on life span, environmental variables and stress resistance

Recently, heterogeneity of the environment has been suggested as an important player in the evolution of life span variation. Established ageing theories propose that life span variation is the result of coevolution with other traits, such as stress resistance. This study aimed to compare these alternative hypotheses by examining the relationship between four environmental variables and different types of stress resistance traits with life span in 13 Drosophila species originating from tropical, subtropical and temperate environments (ecotypes). Average life span was found to differ significantly both between species and sexes, but only male life span correlated with the environment and cold resistance. While controlling for phylogeny, the environmental variable precipitation seasonality and resistance against cold‐induced stress explained most variation in male life span. Furthermore, male life span varied between species in a manner represented by environmental variables linked to the different ecotypes, such that tropical species lived longer and were less cold resistant. The current results suggest that general mechanisms underlying stress resistance and life span are unlikely. In addition, our results point to the environment independently shaping variation in life span and cold resistance rather than genetic interactions.
Deep-Unfolding-Based Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Wireless Communication Systems

Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formulate a joint activity and data detection optimization problem, which we solve approximately using forward-backward splitting (FBS). We then apply deep unfolding to FBS to optimize algorithm parameters using machine learning. In order to improve data detection (DD) performance, reduce algorithm complexity, and enhance active user detection (AUD), we employ a momentum strategy, an approximate posterior mean estimator, and a novel soft-output AUD module, respectively. Simulation results confirm the efficacy of DU-JAD for AUD and DD.
An Obfuscation-Based Approach for Protecting Location Privacy

The pervasive diffusion of mobile communication devices and the technical improvements of location techniques are fostering the development of new applications that use the physical position of users to offer location-based services for business, social, or informational purposes. In such a context, privacy concerns are increasing and call for sophisticated solutions able to guarantee different levels of location privacy to the users. In this paper, we address this problem and present a solution based on different obfuscation operators that, when used individually or in combination, protect the privacy of the location information of users. We also introduce an adversary model and provide an analysis of the proposed obfuscation operators to evaluate their robustness against adversaries aiming to reverse the obfuscation effects to retrieve a location that better approximates the location of the users. Finally, we present some experimental results that validate our solution.
Indoor Air Quality(IAQ) assessment using Wireless Sensor Network (WSN) for Multi-Storey Building Residents

This paper introduces a groundbreaking embedded platform designed for the comprehensive assessment of indoor air quality within multi-story buildings. The key innovation lies in the integration of an array of cost-effective sensors in every room, forming a novel hybrid sensing node for data collection. The wireless sensor network employs ZigBee protocol to establish a mesh network throughout the entire building, ensuring seamless coverage of all relevant areas without relying on a local area network. This novel wireless sensor network comprises a diverse set of sensors, encompassing CO2, CO, VOC, temperature, and humidity sensors, complemented by routers for long-range network propagation across multiple floors, as well as a coordinator for seamless computer communication. The uniqueness of this work extends further as it presents a fully functional prototype that has been rigorously tested, generating real-time air quality monitoring data through the innovative use of the Node-red application. To push the boundaries even further, the captured data is subjected to analysis using a sophisticated multilayer perceptron model. This model enables precise air quality assessment and prediction, contributing significantly to the field of air quality monitoring.
Current Sensorless Online Load Estimation for Induction Heating Cooker

This paper proposes a current sensorless online load estimation method for induction heating cooker (IHC) to detect the cookware status, such as cookware material or position. First, the operating principle of half-bridge series converter (HBSRC) is analyzed, and then using the signals during natural resonant period to estimate cookware equivalent electrical parameters. Furthermore, to derive the equations of equivalent inductance and resistance based on resonant capacitor voltage, and verify the proposed method in simulation software. This method only needs to sample the key signals of resonant capacitor voltage to reduce the calculation loading and time in software. Finally, a DSP-based HBSRC is built to verify the proposed current sensorless load estimation method. The effectiveness of the proposed method is verified by experimental results with different cookware material and equivalent heating coverage ratio (EHCR). The maximum error of estimated inductance and resistance are 8.4% and 9.1%, respectively, indicating that the proposed estimation method is effective and can distinguish whether to execute heating power control.
Simulation of extreme Indian summer monsoon years in Coupled Model Intercomparison Project Phase 5 models: Role of cloud processes

The Indian summer monsoon (ISM) is a climate coupled system where cloud processes play a significant role in modulating the thermodynamics and dynamics of the atmosphere. The realistic representation of cloud by coupled model becomes indispensable for the simulation of the rainfall variability dictating the extreme ISM rainfall years. This study pinpoints the importance of correct representation of cloud processes and different types of rainfall in coupled climate model for the realistic simulation of monsoon extreme rainfall years. The study has used historical runs of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. It provided best opportunity to investigate the ISM rainfall characteristics in case of anomalous monsoon and associated physical processes responsible for the modulation of large‐scale circulation. The study has identified the mechanism behind the better simulation of anomalous years. Relatively more cloud condensate at upper level and middle level is present in excess monsoon years. Pertaining to phase changes and thermodynamical processes, there will be relatively more tropospheric temperature gradient and it will be conducive for strong monsoon. Proper representation of convective and stratiform rain is seen during extreme years. Precipitable water is also relatively more during excess monsoon years. As a result, total rainfall also enhances and it leads to excess monsoon. In contrast, during deficient monsoon relatively less cloud condensate at upper level and middle level is present. Therefore, the correct representation of cloud condensate vertical profile and stratiform/convective rain are crucial for the better simulation of excess and deficient monsoon, which may further improve the variability of ISM in climate model. This study connotes the urgent need of the improvement of cloud parameterization in coupled climate models for the betterment of monsoon variability and realistic representation of deficient/excess monsoon rainfall patterns over Indian subcontinent.