Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of Osmotic Dehydration and Further Drying to Improve the Quality of Dried Carrots

The food industry needs carrots as a processed product for dry products using drying technology to maintain product quality. The aim of the research was to examine the effect of osmotic dehydration temperature with ternary solution on the quality of carrots. The treatments studied were osmotic media temperatures of 25°C and 50°C combined with oven drying and infrared until the water content reached ±10%. Parameters measured after osmotic dehydration were loss of water and increase in solids, quality parameters after further drying were water content and post-storage quality parameters were carotenoids and rehydration test. The dehydration treatment resulted in a reduction of water of 27.25%-44.24% and addition of solids of 15.37%- 18.31%. The initial water content of carrots before osmotic treatment was 90%, the water content after osmotic at 25°C was 65.72% and 50°C was 63.29%. Combination of osmotic with oven requires 22-24 hours of drying time while infrared requires 8- 10 hours of drying time. The best carotenoid value was osmotic dehydration at 25°C followed by an oven or infrared with a value of 32.95(mg/100g)–31.94(mg/100g). Whereas at 50°C the rehydration values were in the range 271.14%-301.42% Keywords: Carrots, Infrared drying, Osmotic dehydration, Osmotic solution, Oven drying
IMG2InChI: Extracting Molecular Big Data from Chemical Images Using Transformer Models

Machine learning methods are extensively used to develop new drugs and materials, and molecular big data plays a vital role in this process. A large amount of chemical molecules in documents should be well utilized for the construction of molecular big data. Although optical character recognition technology has been widely applied to extract molecular data from scanned images, the recognition accuracy should be improved because of the complexity and sparsity of molecular structure and the fuzziness of scanned molecular images. In this paper, a novel Transformer-based model is used to automatically extract molecular features from images. Furthermore, the extracted molecular features are translated into InChI descriptors by another Transformer model. The experimental results suggest that the proposed method outperforms other deep learning based methods including ResNet and LSTM (Long Short Term Memory). Moreover, the data extraction process is visualized so that the interpretability of the model could be guaranteed. This is significant in understanding the mechanism of molecule representation. Meanwhile, the results also demonstrate that the proposed method could extract molecular features in a fine granularity, such as atoms and chemical bonds.
Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma

Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. Methods: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67–0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70–0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.
higher RESEARCH ARTICLE Relationship between cultural intelligence and career and work adaptability in nursing students

Objective: To investigate the relationship between cultural intelligence and career and work adaptability among nursing students. Method: The descriptive, cross-sectional study was conducted at Kilis 7 Aralık University Nursing Department in Turkey from April to May 2019, and comprised nursing students of either gender. Data was gathered using Cultural Intelligence Scale and Career and Work Adaptability Questionnaire. Data was analysed using SPSS24. Results: Of the 277 subjects, 162(58.5%) were females and 115(41.5%) were males. The overall mean age was 21.21±1.81 years. The mean Cultural Intelligence Scale score was 95.17±18.16. The mean Career and Work Adaptability Questionnaire score was 115.69±19.38. There was a positive correlation between the total scores and subscale scores of both the scales ( r =598, p <0.001). The student’s father’s occupation, desire to work overseas, feeling like a good fit for nursing, and feeling prepared for professional life significantly aﬀected cultural intelligence ( p <0.05). The student’s father’s occupation significantly aﬀected career and work adaptability ( p =0.001). Conclusions: There was a positive correlation between the total scores and subscale scores of Cultural Intelligence Scale and Career and Work Adaptability Questionnaire.
Thermodynamic Analysis to Evaluate the Effect of Diet on Brain Glucose Metabolism: The Case of Fish Oil

Inefficient glucose metabolism and decreased ATP production in the brain are linked to ageing, cognitive decline, and neurodegenerative diseases (NDDs). This study employed thermodynamic analysis to assess the effect of fish oil supplementation on glucose metabolism in ageing brains. Data from previous studies on glucose metabolism in the aged human brain and grey mouse lemur brains were examined. The results demonstrated that Omega-3 fish oil supplementation in grey mouse lemurs increased entropy generation and decreased Gibbs free energy across all brain regions. Specifically, there was a 47.4% increase in entropy generation and a 47.4 decrease in Gibbs free energy in the whole brain, indicating improved metabolic efficiency. In the human model, looking at the specific brain regions, supplementation with Omega-3 polyunsaturated fatty acids (n-3 PUFAs) reduced the entropy generation difference between elderly and young individuals in the cerebellum and particular parts of the brain cortex, namely the anterior cingulate and occipital lobe, with 100%, 14.29%, and 20% reductions, respectively. The Gibbs free energy difference was reduced only in the anterior cingulate by 60.64%. This research underscores that the application of thermodynamics is a comparable and powerful tool in comprehending the dynamics and metabolic intricacies within the brain.
Evaluation of the effects of different water demand scenarios on downstream water availability: The case of Thuli river basin

Thuli river basin, in south western Zimbabwe, is situated in a semi-arid area, where surface water resource availability is a constraint. There is intensive use of blue water in the upper catchment more than its lower reaches. The paper presents the evaluation of the effects of upstream water demand scenarios on downstream users in the river basin. A model was applied as a tool to simulate the effects. The impacts of different water demand scenarios on downstream water availability were evaluated. The water demand scenarios used were based on government recommendations and future plans on water resources development, drought risk mitigation, implementation of environmental water requirement and implementing inter basin transfer (IBT) to Bulawayo, the second largest city in Zimbabwe. The study showed that implementing IBT will increase water shortages for downstream users while enforcement of environmental water requirements, implementation of government plans on water resources development in the catchment and drought risk reduction; decreases water shortages for downstream users. It is therefore clear that while the IBT is an important development for Bulawayo, the river basin management of the Thuli river basin requires a holistic approach. Downstream users in the form of domestic and agricultural users should be considered while allocating water for the IBT.
The cognitive approach in analyzing legal terms in the international treaties’ texts

Cognitive linguistics dealing with the description and explanation of mental structuresand processes connected with language knowledge is viewed as a flexible framework rather thanone widely accepted theory; therefore, it is applicable in the study of terms. The article aimsto analyze legal terms from cognitive linguistics’ perspective. More specifically, we apply acognitive approach in interpreting legal terms and explain the way being used in the internationaltreaties’ texts by presenting the document’s frame structure. We reveal the legal document’sframes with the terminals and lexical expressions in Kazakh and English. The study analyzesthe agreement document as a script due to a F. Ungerer and H-J. Schmidt’s framework. Theterms are undergone the concept structure research technique with presenting paradigmatic andsyntagmatic relationships.
Automating Machine Learning Model Development: An OperationalML Approach with PyCARET and Streamlit

OperationalML (Automatic Machine Learning) refers to tools and techniques that automate various machine-learning model development aspects, including data preparation, feature engineering, model selection, hyperparameter optimization, and evaluation. OperationalML can significantly reduce the time and expertise required for building high-quality machine- learning models while making the process more transparent and scalable. In this study, we present an implementation of OperationalML using two open-source libraries: PyCARET and Streamlit. Our work focuses on evaluating the performance and usability of our implementation across classification tasks from publicly available datasets. Results show that our OperationalML system outperforms manual approaches, especially in speed and ease of use. Additionally, we conduct sensitivity analysis to assess the effectiveness of each component within the system and offer insights into future improvements that could enhance the overall quality of the generated models. This work contributes to the growing body of literature on Machine learning in Production, serving as a valuable resource for practitioners, researchers, and educators who want to quickly develop effective predictive models without spending extensive time on training machine learning.
Impact of Hardware Impairments on Covert Cooperative Backscatter Communications

In this paper, we investigate a covert cooperative backscatter communication (BackCom) network under residual hardware impairments (RHIs) that are unavoidable in practical radio frequency transceivers and may have an impact on the communication covertness and reliability. By exploiting the availability of statistical channel state information, we derive analytic expressions for the eavesdropper's detection error probability (DEP) and system outage probability (SOP) to evaluate the system covertness and communication reliability, respectively. Using the derived results, we prove that RHIs can improve the communication covertness but significantly degrade the outage performance. We also show that under RHIs and extremely high transmit signal-to-noise levels, the system outage performance has a non-zero asymptote, while the DEP still approaches one, similar to the scenario with perfect hardware. Computer simulations corroborate our analytical results.
Towards Open-Set APT Malware Classification under Few-Shot Setting

Advanced Persistent Threat (APT) malware group classification has attracted more attention recently. Previous methods have two downsides. First, most use conventional classifiers ignoring the bias caused by the sparse number of revealed malware. Second, they conducted on closed-set without considering the constant stream of novel APT groups. In this paper, we propose a framework for open-set APT malware classification under a few-shot setting. First, the pre-trained encoder extracts the dynamic behavioral features of APT malware. Then the prototypes of known APT groups are calculated. Based on these prototypes the classification probability of the test sample is calculated. Finally, we devise plug-and-play open-set loss and dynamic triplet threshold modules to construct clear boundaries of known categories to achieve open-set recognition. Experimental results conducted on two datasets show that our approach achieves state-of-the-art performance, enabling the detection of known APT malware and recognition of unknown malware with few known APT-labelled malware.
GNN-Based Neighbor Selection and Resource Allocation for Decentralized Federated Learning

Decentralized federated learning (DFL) enables clients to train a neural network model in a device-to-device (D2D) manner without central coordination. In practical systems, DFL faces challenges due to the dynamic topology changes, time-varying channel conditions, and limited computational capability of devices. These factors can affect the performance of DFL. To address the aforementioned challenges, in this paper, we propose a graph neural network (GNN)-based approach to minimize the total delay on training and improve the learning performance of DFL in D2D wireless networks. In our proposed approach, a multi-head graph attention mechanism is used to capture different features of clients and channels. We design a neighbor selection module which enables each client to select a subset of its neighbors for the participation of model aggregation. We develop a decoder which enables each client to determine its transmit power and CPU frequency. Experimental results show that our proposed algorithm can achieve a lower total delay on training when compared with three baseline schemes. Furthermore, the proposed algorithm achieves similar performance on the testing accuracy when compared with the full participation scheme.
PID Controller with Robotic Arm using Optimization Algorithm

- The traditional Proportional-Integral-Derivative (PID) controller utilized Optimized social spider technique for robotic arm model has been investigated. the fuzzy technique method has been inserted as artificial intelligence technique to introduce the better performance for robotic arm in utilize steady state error and positions for the coordination to all angles of robotic arms. The techniques had been solved the stability it’s expressed as the main control problem in additional to that two forward and one inverse kinematics conversion has been settled to the model of robotic arms and according to the parameters estimation to the steady state error is zero and the ideal circle is a very closely to the proposed method and this work has been applied in MATLAB R2021a.
Ethical Considerations in AI: Navigating Bias, Fairness, and Accountability

: The reconciliation of man-made reasoning (artificial intelligence) and enormous information examination in dynamic cycles has introduced another period of mechanical headways and extraordinary abilities across different areas. Notwithstanding, this expanding collaboration has likewise caused a corresponding ascent in moral quandaries and contemplations. This examination article explores the complex scene of moral issues in man-made intelligence fueled direction, with a specific accentuation on the moral contemplations related with large information driven choice cycles. Drawing from an extensive survey of the current writing, this article enlightens the different moral systems pertinent to man-made intelligence and enormous information morals. It takes apart unambiguous moral issues that arise with regards to computer based intelligence navigation, including algorithmic predisposition, straightforwardness, and responsibility, while likewise investigating the unpredictable moral contemplations involved in the assortment and use of huge information, like information protection, security, and informed assent. To empirically investigate the scope and repercussions of these ethical quandaries, the study employs a mixed-method approach that combines qualitative and quantitative data analysis. The discoveries highlight the squeezing need to create and carry out moral structures to direct artificial intelligence and huge information navigation, as well as to propose useful suggestions for alleviating these moral difficulties.
Numerical analysis of the influence of number of blades on the power performance of vertical axis wind turbines

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Cellular and Molecular Biology

: The increasing incidence of antimicrobial resistance bacterial infection and decreasing effectiveness of conventional antibiotics to treatment have caused serious problems worldwide. The demand for new generationantibiotics to combat microbial pathogens is imperative. Cationic antimicrobial peptides (AMPs) with different sources from prokaryotic to complex eukaryotic organisms, with variable length, amino acid composition and secondary structure, have been consideredduring the past decades. The advantages of large number of AMPs are related to broad spectrum and morphogenetic activities, low resistance rate among microorganismswithout side effect on human cells, rapid killing of bacteria via membrane damage and intracellular targets,and their critical roles in anti-inﬂammatory. Ribosomal synthesized peptides of Gram positive bacteria with various post translational modificationsrepresent extended types of antimicrobial peptide with different structural and functional diversity. These types of peptides have been considered as new therapeutic agents for pharmaceutical development . In addition, non-ribosomal synthesized peptides are a wide range of peptides , an extremely extensive range of biological activities and pharmacological properties that are not synthesized by ribosomes, show interesting biological properties ranging from antibiotic to bio surfactants. This review focused on genetics, mechanism of action and modifications, resistance mode of Gram positive bacteria to AMPs and the biotechnological application of
Evaluation of the geothermal energy potential of Switzerland-a techno-economic approach

This research evaluates the geothermal energy potential in Switzerland focusing on different geothermal systems (hydrothermal, EGS, AGS) and purposes (heat or electricity). The evaluation utilises subsurface properties, expected costs for the infrastructure and drilling, and demand for the generated heat or power. A techno-economic model is developed to assess the feasibility of utilising geothermal resources for energy generation, with a specific focus on economic viability as the most critical factor. The results are presented through potential maps, highlighting favourable locations for a geothermal system. The maps aim to simplify the site search process, reducing costs associated with preliminary work for geothermal projects. The research considers three different temperature scenarios for heat (60 °C and 100 °C) and electricity purposes (150 °C). The results show that the Molasse Basin is a favourable region for hydrothermal systems, while the Jura region presents a potential for EGS projects.
Haploidentical Hematopoietic Stem Cell Transplantation for Patients with Severe Aplastic Anemia—Single-Centre Experience

Severe aplastic anemia (SAA) is a life-threatening type of aplastic anemia for which allogeneic stem cell transplantation or immunosuppressive therapy are the principal treatment modalities. Only about 25–30% of patients have a matched sibling donor, and finding an unrelated donor in ethnic minorities is a challenge. The use of related haploidentical donor transplants in severe aplastic anemia is uncommon. We would like to report our experience with the first four patients who underwent haploidentical transplants for severe aplastic anemia. This is a retrospective study. We collected data from our transplant database of all haploidentical hematopoietic stem cell transplants for SAA from 1 January 2020 to 31 December 2021. The transplant protocol used was the Hopkins’ protocol. There were three patients who underwent haploidentical transplants as primary therapy for SAA. A fourth patient received a haploidentical transplant after immunosuppressive therapy failure. The median age of the patients at transplant was 24 y (range 20–29). All patients were engrafted. Neutrophil engraftment occurred at a median of 21 days (range 17–22). Any active infections resolved with the recovery of blood counts. The median hospitalization time was 27 days (range 22–41). Only one patient had grade 2 acute GVHD involving the skin. There was no chronic GVHD. All patients had complete lymphoid and myeloid donor chimerism on day 60. Based on our experience and the emerging literature, haplo-identical transplantation should be considered for select young patients with SAA who have low chances of responding to immunosuppressive therapy.
Self-Attention based Bidirectional LSTM and Machine Learning Approaches for Android Malware Classification

Android-based mobile and other electronics devices dealing with medical and security applications bear huge amount data processing at heterogeneous environment causing a serious problem of security threats and system crash. To meet the business requirements, the android-based OS maintains much anticipated and smart environment for creating the developer’s comforts zone. Therefore, from the prospective of maintaining personal information secured, it is utmost important to focus on safety and security of digital devices. In this paper, we gave the assurance for better malware classifiers utilizing the popular machine learning and deep learning based methods. In particular, performing the extensive set of experiments using machine learning classifiers on two dataset, one is created by API call and another Prem++ is created permission over APK. The API call dataset contains 1156 features, whereas prem++ contains 130 features. The methodology adopted with self-attention based features which are processed by setting of bidirectional LSTM. The noticeable advantages of our approach are that the system follows simple static approach and track the malware without dynamic analysis. More generally, APIs and permission settings are part of every android device which votes our approach satisfactorily applicable.