Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of synoptic and local meteorological conditions on surface ozoneconcentrations over Europe

Climate change is expected to alter global, regional and local meteorological conditions and as a result, the changes in the climate system will play an essential role on future air quality. Tropospheric ozone is considered one of the most harmful pollutants and it is strongly dependent on weather conditions. Therefore, understanding the impacts of near-term climate change on ozone concentrations is crucial for developing effective air quality policies. This dissertation focuses on the analysis of the influence of synoptic and local meteorological conditions on ground-level ozone over Europe and it provides a comprehensive spatial characterization of the most important meteorological key-driving factors of surface ozone concentrations over the whole domain. For this purpose two approaches are proposed: i) a weather types classification and ii) regression methods. Firstly, large-scale atmospheric circulation is examined through a weather types classification, implemented grid cell-by-grid cell over Europe. The ability of a suite of global climate models to reproduce realistic synoptic patterns in the present climate is evaluated against two reanalysis products. Additionally, the association between weather types and anomalies of maximum and minimum temperatures is investigated. In general, the models are able to capture realistic synoptic patterns when compared to the reanalyses. However, some limitations to reproduce the frequencies of certain weather types, such as low flow conditions over South Europe in summer and autumn are found. The projected changes in the frequency of weather types under future climate scenarios reveal an increase of anticyclonic days and warmer conditions affecting the British Isles in summer, and more westerlies and consequently mild winter conditions over Central Europe. As a result of a projected increase of low flow conditions over the Mediterranean basin, stagnant situations would become more frequent, favouring episodes of air pollution. Further analysis indicate that changes in the frequency of weather types represent a minor contribution of the total change of projected European temperatures. Thus, the temperature changes could be attributed to the so-called within-type variations (changes of the weather types themselves). In the context of climate change, that implies that global warming would also
The impact of seasonal flood peak dependence on annual maxima design quantiles

ABSTRACT If the maximum annual peak flow series are a mixture of summer and winter flows, a seasonal approach to flood frequency analysis is necessary. While considering seasonal maxima as mutually independent events, the annual maxima distribution is defined as the product of seasonal distributions. However, if the independency assumption does not hold, a bivariate approach with dependent margins should be applied, i.e. the copula approach. The impact of dependency on design quantiles is investigated here in the context of the Fréchet-Hoeffding inequality defining copula bounds and the definition of dependency. The results of the two approaches are compared using six catchments in the San River basin, where in four cases the dependency of seasonal maxima has been identified as positive significant and no strong dominance of any one season is observed. The product model leads to higher estimates of design quantiles than do models where the dependency is taken into account and, therefore, is safe. EDITOR R. Woods ASSOCIATE EDITOR A. Fiori
Astrocytes in the mouse suprachiasmatic nuclei respond directly to glucocorticoids feedback

The circadian timing system anticipates daily recurring changes in the environment to synchronize physiology. In mammals, the master pacemaker is the hypothalamic suprachiasmatic nuclei (SCN), which synchronizes “wake” functions by inducing the circadian release of Glucocorticoids (GCs) from the adrenal gland. GCs peak right before the active phase and set the time of peripheral clocks, however, it is still unclear whether the SCN respond to GCs feedback. While GCs influence directly the SCN during the perinatal period, the adult circuit is considered to be resistant to them, suggesting a reduction of GCs-sensitivity along development. To understand this mechanism, we followed the expression of GC receptor (GR) along mouse SCN development with single-cell resolution and show that GR is up-regulated in astrocytes as the circuit matures. We provide in vivo and in vitro evidence that the adult SCN stays responsive to circulating GCs through the activation of GR in astrocytes. Astrocytes’ communication is necessary to induce the GC-dependent shift on the SCN clock. Our data provides insight into the development of the SCN and highlight a new role of astrocytes as time-keepers in the adult. This finding might shed light on how the circadian system adapts to jetlag or shift work.
SARS-CoV-2 modulates NK cell responses via induction of HLA-E and triggers expansion of adaptive NK cells during acute infection

HLA-E expression plays a central role for modulation of NK cell function by interaction with inhibitory NKG2A and stimulatory NKG2C receptors on canonical and adaptive NK cells, respectively. Here, we demonstrate that infection of human primary lung tissue with SARS-CoV-2 leads to increased HLA-E expression and show that processing of the peptide YLQPRTFLL from the spike protein is primarily responsible for the strong, dose-dependent increase of HLA-E. Targeting the peptide site within the spike protein revealed that a single point mutation was sufficient to abrogate the increase in HLA-E expression. Spike-mediated induction of HLA-E differentially affected NK cell function: whereas degranulation, IFN-γ production, and target cell cytotoxicity were enhanced in NKG2C+ adaptive NK cells, effector functions were inhibited in NKG2A+ canonical NK cells. Analysis of a cohort of COVID-19 patients in the acute phase of infection revealed that adaptive NK cells were induced irrespective of the HCMV status, challenging the paradigm that adaptive NK cells are only generated during HCMV infection. During the first week of hospitalization, patients exhibited a selective increase of early NKG2C+CD57- adaptive NK cells whereas mature NKG2C+CD57+ cells remained unchanged. Further analysis of recovered patients suggested that the adaptive NK cell response is primarily driven by a wave of early adaptive NK cells during acute infection that wanes once the infection is cleared. Together, this study suggests that NK cell responses to SARS-CoV-2 infection are majorly influenced by the balance between canonical and adaptive NK cells via the HLA-E/NKG2A/C axis.
Chromosome-level genome assembly of the disco clam, Ctenoides ales, a first for the bivalve order Limida

The bivalve subclass Pteriomorphia, which includes the economically important scallops, oysters, mussels, and ark clams, exhibits extreme ecological, morphological, and behavioral diversity. Among this diversity are five morphologically distinct eye types, making Pteriomorphia an excellent setting to explore the molecular basis for the evolution of novel traits. Of pteriomorphian bivalves, Limida is the only order lacking genomic resources, greatly limiting the potential phylogenomic analyses related to eyes and phototransduction. Here, we present the first limid genome assembly, the disco clam, Ctenoides ales, which is characterized by invaginated eyes, exceptionally long tentacles, and a flashing light display. This genome assembly was constructed with PacBio long reads and Dovetail Omni-CTM proximity-ligation sequencing. The final assembly is ∼2.3Gb and over 99% of the total length is contained in 18 pseudomolecule scaffolds. We annotated 41,064 protein coding genes and report a BUSCO completeness of 91.9% for metazoa_obd10. Additionally, we report a completely annotated mitochondrial genome, also a first for Limida. The ∼20Kb mitogenome has 12 protein coding genes, 22 tRNAs, 2 rRNA genes, and a 1,589 bp duplicated sequence containing the origin of replication. The C. ales nuclear genome size is substantially larger than other pteriomorphian genomes, mainly accounted for by transposable element sequences. We inventoried the genome for opsins, the signaling proteins that initiate phototransduction, and found that, unlike its closest eyed-relatives, the scallops, C. ales lacks duplication of the rhabdomeric Gq-protein coupled opsin that is typically used for invertebrate vision. In fact, C. ales has uncharacteristically few opsins relative to the other pteriomorphian families, all of which have unique expansions of xenopsins, a recently discovered opsin subfamily. This chromosome-level assembly, along with the mitogenome, will be valuable resources for comparative genomics and phylogenetics in bivalves and particularly for the understudied but charismatic limids. Significance This high-quality chromosome-level genome assembly for Ctenoides ales, the disco clam, is the first genome sequenced from the bivalve order Limida, the only group of Pteriomorphia – a highly diverse and ecologically important clade – lacking genomic resources. The sequence and annotation of the C. ales genome will be a useful resource for molluscan phylogenetics and comparative genomics.
A Practical Framework for  $t$-Out-of- $n$ Oblivious Transfer With Security Against Covert Adversaries

Oblivious transfer plays a fundamental role in the area of secure distributed computation. In particular, this primitive is used to search items in decentralized databases. Using a variant of smooth projective hash previously presented by Zeng , we construct a practical framework for t-out-of- n oblivious transfer in the plain model without any set-up assumption. It can be implemented under a variety of standard intractability assumptions, including the decisional Diffie-Hellman assumption, the decisional N -th residuosity assumption, the decisional quadratic residuosity assumption, and the learning with error problem. It is computationally secure in the presence of covert adversaries and only requires four rounds of communication. Compared to existing practical protocols with fully-simulatable security against covert adversaries or malicious adversaries, our framework is generally more efficient.
A Systematic Approach to Digital Control Development for Four-Phase SRM Drive Using Single Current Sensor for Medium Power Applications

In the realm of medium-power high-volume applications, Switched Reluctance Motor (SRM) drives hold great advantages over other motors. However, the SRM drive must be optimized to reduce cost without compromising the performance for medium power applications. This paper presents a novel SRM drive utilizing a Miller converter-fed SRM motor with a single current sensor, offering a comprehensive control development procedure encompassing system modeling, design procedures, dynamic simulation, analysis, and experimental validation. The SRM is characterized through finite element analysis (FEA) to derive a MATLAB Simulink simulation model, and the conduction angle is optimized for drive efficiency through parametric simulation studies. The linear SRM model for control design is obtained via small signal analysis. Speed and current controllers are designed using the K-factor method, and the efficacy of the proposed drive is rigorously evaluated across various operating modes in MATLAB Simulink. Additionally, a hardware prototype is developed and the digital control algorithm is implemented on the DSP microcontroller TMS320F28379D based on the designed controllers to further assess drive performance. The results obtained validate the robustness and dynamic performance of the SRM drive across variable speed, variable torque, and constant power modes of operation.
Willingness to Pay for Smart Car Security

Smart cars have become more intelligent and technologically complex; however, consumers have raised concerns about their security because of hacking and technical safety. Although numerous studies have focused on the technological issues of smart cars, there is a need for new perspectives and discussions to examine drivers’ perceptions of smart car security and safety. Understanding drivers’ security perceptions and behaviors in a smart car environment is crucial for enhancing smart car security and driving industrial growth. Therefore, this study analyses the factors affecting drivers’ willingness to pay (WTP) for smart car security based on protection motivation theory and explores the relationship between protective motivations and post-behavior. This study analyses how drivers’ security vulnerabilities and severity perceptions in a smart car environment affect their WTP for security software. In addition, the comparative analysis discusses how security perceptions differ between traditional PC and smart car environments. The results of this study demonstrate that drivers are sensitive to smart car security and are willing to pay to strengthen security. Moreover, the results of this study suggest that the role of security solution companies is important for strengthening smart car security and expanding the market. This study has academic significance because it is an early study discussing driver behavior related to smart car security software on a theoretical basis. It is also significant because it provides practical implications for smart car security market growth and guides the formulation of effective security policies.
Feedback Control System for Angular Position Locking Fork of Forklifts

This research article proposes feedback control-based position control method for the Fork that effectively locks its position to prevent the pallet from slipping out. The proposed operation involves calculating the Forward Kinematics of the Fork to determine the angle adjustment positions and then installing a stepper motor to control the hydraulic valve and an encoder to receive feedback signals. To ensure stable and precise control, remote control is used for long-distance operation. The feedback signals of the system enable accurate control of the tilt angle of the Fork, which in turn provides precise positioning of the load. As a result, the fork can be effectively controlled and locked in the angle position by remote control. This research shows its importance in the field of forklift technology. It offers practical solutions to the challenges faced in material handling operations. By combining theoretical insights with the PID control mechanism to enhance load stability and prevent accidents.
Reducing Internet Gaming Disorder in Adolescents: Analysis of Gaming-Contingent of Self-Worth, Loneliness and Peer Attachment

This study aims to predict factors determining internet gaming disorder in adolescents. We test the variables of gaming contingent of self-worth, loneliness, and peer attachment. The sample used was 211 adolescents in Jakarta- Indonesia, using a non-probability sampling technique, namely purposive sampling. The instruments are the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), Gaming-Contingent of Self-Worth (GCSW), De Jong Gierveld Loneliness Scale, and Inventory Parent and Peer Attachment to measure Peer Attachment. The results showed that there was a significant effect between gaming-contingent self-worth, loneliness, and peer attachment on internet gaming disorder. The validation-seeking dimension of the gaming-contingent of self-worth variable has a significant effect where individuals may suffer from a decrease in self-worth due to the cessation of gaming. In contrast, the emotional loneliness dimension of the loneliness variable has a significant effect where individuals experience feelings of separation, parental abandonment, or parental divorce, significantly affecting internet gaming disorder.
An End-to-End Deep Learning Method for Potato Blight Disease Classification Using CNN

This paper proposes an end-to-end approach using deep learning to classify potato blight diseases using Convolutional Neural Networks (CNNs). The system is designed as a user-friendly web-based application and mobile app for farmers to easily predict early and late blight diseases in potato plants. Early detection and accurate identification of these diseases can save significant resources in agricultural practices. The proposed method utilizes deep learning and CNNs to process uploaded images of potato leaves, differentiating between early blight, late blight, and healthy plants with high accuracy. The project involves data collection, model building, MLOps using TensorFlow Serving, backend server development with FastAPI, and deployment on Google Cloud Platform (GCP) with GCP Functions. A React Native mobile app serves as the user interface. Future work includes publishing the application on Google and Apple Play Stores and expanding the model to cover additional plant diseases for a more comprehensive agricultural solution.
Detecting Mental Health Disorders Using NLP

The concept of mental fitness pertains to the state of possessing a sound and adaptable mind, characterized by good health and resilience. It encompasses the capacity to navigate challenges and setbacks in a constructive and flexible manner. Consequently, the cultivation and sustenance of mental fitness contribute significantly to the improvement of individual well-being and overall quality of life. In 2019, the WHO observation showed that close to a billion people around the world were living with a mental disorder especially depression and anxiety being the most common. There are various NLP projects which deal with classifying a specific mental health disorder based on analysis of text or speech but very few deal with multi-level classification. To address this we are doing a single model with multilevel mental health disorder classification-combining the text data from many data sets of individual classes. To address this we are creating a composite framework with multilevel mental health disorder classification-combining the text data of different classes from a dataset of reddit posts. Our experiments have shown that fine-tuned domain-specific transformer models and hybrid models combining transformer models and recurrent neural network models like LSTM and Bi-LSTM can detect mental health disorder with an fl-score of 88 % and above.
Peti-Chain – Blockchain Based Judicial Petition Filing System

The Petition-filing process in the Indian Judicial System is an integral part of the Indian Judiciary. The digital process of petition filing is very slow and cumbersome. Moreover, it is just dependent on the central system. This makes the overall system very vulnerable in terms of digital security and preservation of old records. This problem can be solved by making the whole system decentralized. By making use of the Blockchain technology, the petition-filing system would become more secure and easier to access. This process would also help the overall system by making the whole petition-filing process faster, for both the courts and the petition-filers. In this paper, we present a unique way of implementing the use of decentralized blockchain systems in the context of digital petition-filing.
Solar Powered Unmanned Aerial Vehicle

Drones, or unmanned aerial vehicles, are gaining popularity around the world due to their ease of use and vast range of applications. The biggest issue with UAVs is their battery endurance. Drones on the market nowadays use serially connected lipo batteries to generate power. In continuous motion, these serially connected lipo batteries can offer continual power for up to one hour. The lack of power for continuous use limits the use of drones in several fields. The paper aims to develop a system model that can use the abundant form of sunlight to power an unmanned aerial vehicle. This paper describes a theoretical model that switches between battery and solar panel based on the available power. The former part of the paper describes a basic model for the switching circuitry and the latter part describes a switching Circuit that switches between the solar panel and battery based on the power requirement during various levels of flight. The system is developed using MATLAB/SIMULINK model.
Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset.
Evolution of Signcryption based cryptosystem in 5G communication technologies using Design Thinking

With the evolution of communication technologies, various substantial applications help the human community. Various real-time applications rely on the 5G era to fulfil the baseline service requirements. These 5G network communications deploy different cryptosystems to handle the security issues between homogeneous and heterogeneous systems. Here, a novel signcryption scheme is proposed over various public cryptosystems to secure communication among the 5G network model. The anticipated model establishes mutual communication among the certificate-less public key infra-structure environment. The proposed scheme works well against the chosen adaptive ciphertext attack under various computational random problems. A bilinear pairing-based signcryption (BPS) method is proposed and executed over the MATLAB 2020a simulation environment. This model is secure over the random model and establishes a better security trade-off among the prevailing approaches. Moreover, this model analyses and quantifies the scheme's performance with metrics like time comparison over a key generation, signcryption and unsigncryption. Therefore, the model attains superior security and efficiency.
Enhanced Vulnerability Discovery in Embedded Firmware Binaries Through Function-Level Fuzzing

With the proliferation of Internet of Things (IoT) devices, the security of their firmware has emerged as a critical concern. This paper proposes the use of instrumentation techniques for function-level fuzzing. By employing instrumentation techniques, such as dynamic binary instrumentation (DBI), it demonstrates the benefits of fuzzing of an individual function within ARM binary of ELF format. The paper presents advantages of proposed technique over traditional whole-binary fuzzing approaches. The advantages include reduction in the fuzzing scope thereby resulting in improved time efficiency and reduced resource requirements. The proposed technique is implemented on an ARM based embedded hardware i.e Raspberry Pi thus demonstrating its ability to be implemented on IoT devices.