Binary Approach Research Articles

Discourse Analysis of Conflict and Resolution in History Textbooks: Representations of the Referendum in Indonesia

This study explores how Indonesian history textbooks represent conflict and its resolution with a focus on the theme of East Timor. The research applies the methodology of Historical Discourse Analysis, examining various elements of representation, including nomination, predication, argumentation, perspective, and intensification. The primary objects of study are two Indonesian history textbooks that are currently used in schools. The results show that textbook I adopts a pragmatic and binary approach, framing the Indonesian government as the protagonist that seeks to include East Timor as part of its national territory. In this narrative, the East Timorese people are portrayed as the antagonists who resist the integration process with Indonesia. This approach reflects a polarized view of the conflict, emphasizing the roles of both parties in opposition. On the other hand, Textbook II takes a more constructive and reconciliation-oriented approach. It presents a vision for future cooperation and understanding between Indonesia and East Timor, highlighting possibilities for peaceful resolution and long-term collaboration between the two countries. Despite these different approaches, this study identifies an ongoing challenge in the writing of Indonesian history textbooks, which can be termed as "schizo-history." This concept refers to the paradox faced by historians in balancing efforts to reconstruct historical events with the anxiety surrounding the potential impact of those narratives on national unity. The study argues that Indonesian historians must continue to seek alternative methodologies that can address these tensions while providing more nuanced and balanced representations of historical events. This research contributes to broader discussions on the role of history textbooks in shaping national identity and calls for further investigation into the challenges and opportunities in writing history textbooks in post-conflict contexts.

Cartographies of Emergence of Latin American Communities and British Latinx Imaginaries in London

This paper explores Latin American communities in London through the conceptual lens of cartographies of emergence in translocational perspective suggesting that it provides a less binary approach than the more commonly used perspective of ‘(in)visibility’. This is informed by the empirical analysis of Latin American community‐building through their negotiation of precarity and collective resistance. It also reflects on the contested nomenclatures used to denote the ‘community’ such as Latin American and British Latinx as well as plural interpretations of British Latinidades and British Latinx imaginaries, the latter to capture the mutable and dynamic identities and belonging.

Consensus-driven protocol for transanal irrigation in patients with low anterior resection syndrome and functional constipation.

This study aims to establish a consensus-based standard protocol for transanal irrigation (TAI) in patients with low anterior resection syndrome (LARS) and functional constipation. The Delphi method was utilized to reach a consensus among clinicians and nurses expert in the field of colorectal surgery and gastroenterology. To address various uncertainties concerning technical aspects, difficulties, and prescription of TAI, two questionnaires were developed and analyzed in two rounds. A binary approach was employed, setting a consensus threshold of 75% agreement. In the first round, nurses achieved consensus on all statements, while clinicians required a second round to reach consensus, particularly regarding prescription and technical aspects. Clinicians reached consensus on prescribing TAI as a second-line treatment for LARS and functional constipation, following the failure of conservative measures such as dietary and lifestyle interventions. Timing considerations for patients with LARS encompass avoiding TAI within 1month of stoma closure and waiting a minimum of 3months. For functional constipation, TAI is recommended for slow transit constipation, emphasizing its preference over surgical options. Consensus was also reached on the choice of catheter for patients with LARS, training requirements for patients and caregivers, preparation of the patient's intestine before TAI, and recommended irrigations. This consensus study successfully developed a standardized TAI protocol for LARS and functional constipation. It provides comprehensive guidelines for prescription and technical aspects, addressing the challenges encountered by healthcare professionals. The protocol aims to enhance patient care, improve treatment outcomes, and contribute to the advancement of TAI.

A Microfluidic Multiplex Sorter for Strain Development

AbstractSelecting strains with superior traits from strain improvement strategies is challenging, as it involves navigating the fitness landscape by applying selective pressures that drive variants from peaks of improvement to valleys over time. In recent years, the screening and selection is conducted via droplet microfluidic methods due to its high throughput capabilities. However, the oft‐used binary strategy, targeting only the high levels of improved traits, may not reflect the overall enhancement. A multiplexed sorting method capable of applying an additional threshold to sort traits by phenotypic strength is reported. The novel approach uses a droplet‐digital microfluidic sorter to screen different volumes of droplets using the same device design and sorting parameters. This method is used to sort glucoamylase enzyme mutants with two levels of activity (medium and high) from libraries of diastatic yeast that have been mutated with non‐genetically modified techniques. Using the multiplex system, medium‐performing strains with enhanced (up to 60%) fermentation kinetics in synthetic beverage media, which would have been missed with a binary screening approach, are identified. The multiplex sorting strategy efficiently finds strains with superior fermentation traits in the fitness landscape without requiring extensive screening rounds and mutations.

A Yin-Yang approach: Investigating students’ beliefs about teaching and learning in music among college students in Macau, China

This exploratory study aims to investigate the beliefs of college students in Macau, China, regarding teaching and learning in the field of music education, including factors influencing students’ views and their ideal music classroom environment. Employing a Rigorous Mixed Methods Framework that included surveys, observations, interviews, and reflective essays, a sample of ( n = 120). Chinese college students and ( n = 3) college music instructors were selected for participation. In the initial phase, findings revealed a nuanced perspective among students, demonstrating a stronger preference for the Constructivist Conception over the Traditional Conception. However, during the second and third phases, the findings revealed a preference for traditional and master-apprentice styles of learning in practice. Through the analysis of all three phases of research, this study unveiled a complex and juxtaposed ideology of music learning among Chinese students. While there was a misalignment between students’ ideologies and behaviors, they consistently expressed a desire for a more collaborative, creative, supportive, and open environment in which they can freely share their musical passion. Rather than adhering to a binary approach, the findings suggest the necessity of embracing a more nuanced “Yin-Yang” perspective in Macau.

Examining refusal and acceptance sequences employing a data-driven binary rating approach

This paper presents the development and preliminary validation of a data-driven analytic binary rating rubric for assessing roleplay-elicited pragmatic performance of learners. The roleplay task consists of two items—one lunch invitation to elicit a refusal and one gift offer to elicit an acceptance in Chinese, both of which involve initiating proposals to return an earlier favor. Responses to return-to-favor invitations or offers in Chinese are challenging for L2 learners as they involve complicated discursive features. To create a practical, reliable tool for evaluating L2 performance, the researchers conducted a discourse analysis of L1 Chinese performance data (N = 22) to identify key discursive features in two domains— Intention (i.e., refusing the return-for-favor intention) and Decision (i.e., refusing/accepting the offer/invitation). A roleplay can receive up to 4 points in both domains, including one point each for orientation/directness, position, modification, and justification, for 8 total possible points. Scores assigned to both L1 and L2 performance according to the rubric reflected high inter-rater reliability. The effect of proficiency was significant on learner scores and interacted with rating domains and scenarios. Lower-level learners scored lower with addressing the interlocutor's return-for-favor intention than with the invitation or offer itself; they also scored lower with refusing than accepting an offer. By focusing on key discursive features rather than exhaustive interactional details, the rubric streamlines the evaluation process while maintaining accuracy and reliability. Future studies may extend the use of the rubric to other speech act sequences and languages and apply it to automated grading systems.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Clinical Investigation of Short-Term Axial Elongation Control After Orthokeratology Lens Correction: Exploring Its Predictive Role in Long-Term Therapeutic Efficacy

Objective: This study was developed with the goal of exploring the efficacy of orthokeratology (OK) as an approach to controlling axial length (AL) growth and identifying factors predictive of the long-term efficacy of this approach. Methods: This was a retrospective study that enrolled 157 myopic children 7 to 15 years of age undergoing OK correction for over 3 years. The short- and long-term effectiveness of this approach at 1 and 3 years post-OK correction, respectively, was assessed. All participants were classified into two groups based on whether they exhibited good or poor long-term efficacy outcomes. Measurements of changes in AL were made at baseline and at 1 and 3 years post-OK. Univariate analyses and binary logistic regression approaches were used to evaluate baseline age, baseline spherical equivalent refraction, baseline keratometry (K) values of flat and steep meridians, and short-term effectiveness with the goal of identifying predictors of long-term efficacy. Results: Univariate analyses led to the identification of significant differences in baseline age, baseline spherical equivalent refraction, baseline AL, and short-term efficacy between these two groups (P<0.05), whereas gender, baseline flat K, and baseline steep K did not differ significantly (P>0.05). In binary logistic regression analyses, baseline AL (P=0.017) and short-term efficacy (P<0.001) were both found to significantly influence long-term efficacy. Receiver operating characteristic curves demonstrated that short-term efficacy offered an area under the curve value >0.8, consistent with its highly accurate performance as a predictor of long-term efficacy. Conclusion: These results suggest that short-term efficacy outcomes can feasibly be used to predict the long-term effectiveness of OK correction in children. In patients exhibiting poor short-term efficacy, timely replacement or the incorporation of additional treatment modalities may aid efforts to better control the progression of myopia.

Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails.

Accessible Trail Tourism: Trail Accessibility and Difficulty Rating Approach Designed for Individuals, Including With Mobility Impairments

ABSTRACTThis study introduces a novel approach to trail accessibility and difficulty rating tailored specifically for individuals, including those with mobility impairments, aiming to promote accessible trail tourism. Grounded in theoretical frameworks of inclusive outdoor recreation, equality, and complex adaptive systems, the proposed rating system incorporates criteria addressing physical access, terrain challenges, and amenities to accommodate diverse user needs. This study employed a sensor technology, called wheeled instrumentation sensor package (WISP) within the framework of the high‐efficiency trail assessment process (HETAP) to develop a new quantitatively based trail classification scheme. A case study trail and two hypothetical synthetic data trails were used to demonstrate both the trail wheelchair accessibility binary classification approach and the proposed trail difficulty classification approach. This research pioneers trail accessibility and difficulty rating and underscores the importance of integrating accessibility considerations into trail management practices to create truly inclusive outdoor recreational experiences.

Frustration Gap Bridging: A Second Look at Vocabulary Proficiency with the Yes/No Test

This research advances the scrutiny of the Yes/No test's (Meara: 1990) efficacy in evaluating vocabulary proficiency among language learners of high, middle, and low proficiency levels, focusing on its capability to mitigate the frustration commonly encountered by this demographic. As highlighted in the recent investigation (Goetz, 2023), the Yes/No test prompted respondents to signify their word familiarity through binary choices. Yes, they know a word, and No, they don’t. Involving a paltry 56 English language learners of lower proficiency to determine if the test's impact on enhancing vocabulary skills had merit, it was found that contrary to the anticipated outcomes, the test did not yield significant advancements in students' performance on subsequent unit tests compared to their counterparts who were not subjected to the Yes/No test. This absence of tangible benefits, combined with the test's failure to deliver a contextualized and nuanced comprehension of vocabulary, raises questions regarding its efficacy as a pedagogical tool for vocabulary enhancement. Nevertheless, the results did not dismiss the Yes/No test's potential utility. For this study, a second look will address two critical shortcomings: the limited sample size and the absence of a diverse range of proficiency levels among participants. This inquiry reevaluates the Yes/No test's capacity to effectively navigate the intricacies of language acquisition, proposing a more in-depth investigation into whether this binary and straightforward approach could indeed be beneficial or whether it underscores the imperative for a more holistic and contextually aware method in language instruction by expanding the sample size and including more proficiency levels. Questions remain regarding the efficacy of the Yes/No test. Is it optimally utilized as a straightforward, standalone tool, or should it be considered within the broader spectrum of comprehensive, context-sensitive learning strategies? This inquiry invites a nuanced evaluation of the test's role in language acquisition. It challenges researchers and practitioners to contemplate whether its simplicity is a virtue in isolation or if its true value emerges when integrated with a holistic approach to learning.

Graph neural network-based subgraph analysis for predicting adverse drug events

PurposeAdverse drug events (ADEs) are a significant global public health concern, and they have resulted in high rates of hospital admissions, morbidity, and mortality. Prior to the use of machine learning and deep learning methods, ADEs may not become well recognized until long after a drug has been approved and is widely used, which poses a significant challenge for ensuring patient safety. Consequently, there is a need to develop computational approaches for earlier identification of ADEs not detected during pre-registration clinical trials. MethodsThis paper presents a state-of-the-art network-based approach that models patients as subgraphs composed of nodes of International Classification of Diseases (ICD) codes and directed edges illustrating disease progression. Four Graph Neural Network (GNN) variants were employed to make sub-graph level predictions that answer three Research Questions (RQ): 1) whether ADE(s) would occur given a patient's prior diagnoses history, 2) when an ADE would occur, and 3) which ADE would occur. The first and second RQs were addressed using a binary classification approach. The third RQ was addressed using a multi-label classification model. ResultsThe proposed network-based approach demonstrated superior performance in predicting ADEs, with the GraphSage model exhibiting the highest accuracy for both RQ 1 (0.8863) and RQ 3 (0.9367), while the Graph Attention Networks (GAT) model was found to perform best for RQ 2 (0.8769). Furthermore, an analysis segmented by ADE classification revealed that while RQs 1 and 3 exhibited minimal variance across different ADE categories, a distinct advantage was observed for categories B, C, and E in the context of RQ 2 when applying this sub-graph method. ConclusionThe network-based approach demonstrates the potential of GNNs in supporting the early detection and prevention of ADEs. Accurately predicting ADEs could enable healthcare professionals to make informed clinical decisions, take preventive measures and adjust medication regimens before serious adverse events occur. The proposed prediction method could also lead to optimized usage of healthcare resources by preventing hospital admissions and reducing the overall burden of adverse drug events on the healthcare systems.

Stepwise methods for more nuanced adoption analysis: a case study of harvest and post-harvest mechanization in Bangladesh

The adoption of agricultural harvest and post-harvest mechanization is crucial for addressing drudgery, food losses, climate vulnerability and food security. Despite considerable efforts by government and development partners to prioritize agricultural mechanization, labour-intensive manual (post-)harvest activities continue to dominate in Bangladeshi smallholder systems. Explorations of this has been limited by simplistic binary approaches that ignore the dynamic pathways to usage outcomes. Instead, we apply non-binary analytical methods to district representative data to highlight the value in moving beyond binary adoption analysis. Results highlight that a national (post-)harvest mechanisation rate of 74% is insufficient to capture the true adoption status, given substantial disparity exists across machinery and by district. Deeper exploration of temporal and spatial differences enable the identification of key trends that warrant further in-depth explorations, while only 46% satisfaction with extension systems highlights the need to re-evaluate key information exchange mechanisms. We conclude that there is a clear need for district and machinery specific policy arrangements if Bangladesh is to achieve (post-)harvest mechanisation objectives that aim to reduce food loses and enable greater food security nationwide.

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

Comparison of Pre-Defined Automatic Machine Learning (AutoML) for MBTI Personality Prediction of Twitter Users using Binary Classification Approach

The Myers-Briggs Type Indicator (MBTI) is a personality test that is globally accepted and used as a method for identifying personality. MBTI uses a four-factor linear model to characterize a person's behaviour patterns. This feature is often used to pursue career opportunities, make decisions, manage leadership, and deal with stress. In particular, MBTI personality prediction has been widely conducted and well performed using Recurrent Neural Network (RNN) based on Twitter data because it indirectly reveals most of a person's personality through their tweets. However, deep understanding is needed in building RNN-based solutions. Hence it will take a lot of time and resources to produce an excellent model architecture and the parameters used. Therefore, this study proposed the Auto Machine Learning (AutoML) method with a pre-defined search space to determine the correct model architecture and hyperparameters based on the results of data analysis. Thus, the search algorithm can exploit environments with suitable configurations in general. There are two pre-defined search spaces employed in this study, i.e. (i) two RNN algorithms, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and (ii) pre-trained Word2Ves as word embedding. In addition, this study compares the model's performance that employs preprocessing and raw data (without preprocessing). The first result shows that the preprocessing increases the F1-Score values for LSTM and GRU by 2.35% and 2.02%, respectively. Subsequently, the LSTM outperformed GRU by the values of F1-Score at 0.35% and accuracy at 0.76%. The implementation of LSTM with pre-processed data in pre-defined AutoML with Word2Vec as a word embedding technique can provide good performance on long and complex data sequences such as Twitter data for predicting its user personality.

Age at marriage and its determinants in Kerala and Bihar

BackgroundMarriage is a significant milestone in one’s life, and the age at which individuals choose to get married can have various implications. By examining the age at marriage and its determinants in different regions, such as Kerala and Bihar, we can gain insights into Socio-demographic factors that influence this decision.MethodsThe National Family Health Survey (2019–2021) data was used. A total of 41,392 ever married women from Kerala (8, 579) and Bihar (32,813) were analysed using bivariate and multiple binary logistic regression approaches to meet the study aim.ResultThe significant association was found between explanatory factors and age at first marriage in Karala and Bihar. In Bihar, educated women from the poorest families had a higher rate of early marriages. Conversely, in Kerala, uneducated women from non-majority religions and larger households (more than 6 members) were more likely to be married before18. After accounting major effects of background characteristics, the adjusted odds ratio showed that In Kerala Muslim religion women have significantly higher odds of getting married before 18 (OR: 3.09, CI 2.67–3.57) then the Hindu women whereas in Bihar Muslim religion women has the significantly lower chance of getting married before turning 18 (OR: 0.87, CI 0.81–0.93) compared to Hindu. In both the states Kerala and Bihar General social group women had the significantly lower odds of getting married below 18 (OR: 0.73, CI 0.57–0.93 and OR: 0.88, CI 0.81–0.96) with respect to SC/ST social group women’s.ConclusionWomen in Bihar usually marry in younger age due to socioeconomic and traditional reasons, whereas in Kerala reverse is true, because of a greater emphasis on education and a more progressive social outlook. These regional variations reflect different cultural norms and socioeconomic environments, shape different marriage age patterns between the two regions.

Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.

The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action. We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages. A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes. Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts.

Probio-Ichnos: A Database of Microorganisms with In Vitro Probiotic Properties.

Probiotics are live microorganisms that, when consumed in adequate amounts, exert health benefits on the host by regulating intestinal and extraintestinal homeostasis. Common probiotic microorganisms include lactic acid bacteria (LAB), yeasts, and Bacillus species. Here, we present Probio-ichnos, the first manually curated, literature-based database that collects and comprehensively presents information on the microbial strains exhibiting in vitro probiotic characteristics (i.e., resistance to acid and bile, attachment to host epithelia, as well as antimicrobial, immunomodulatory, antiproliferative, and antioxidant activity), derived from human, animal or plant microbiota, fermented dairy or non-dairy food products, and environmental sources. Employing a rigorous methodology, we conducted a systematic search of the PubMed database utilizing the keyword 'probiotic' within the abstracts or titles, resulting in a total of 27,715 studies. Upon further manual filtering, 2207 studies presenting in vitro experiments and elucidating strain-specific probiotic attributes were collected and used for data extraction. The Probio-ichnos database consists of 12,993 entries on the in vitro probiotic characteristics of 11,202 distinct strains belonging to 470 species and 143 genera. Data are presented using a binary categorization approach for the presence of probiotic attributes according to the authors' conclusions. Additionally, information about the availability of the whole-genome sequence (WGS) of strains is included in the database. Overall, the Probio-ichnos database aims to streamline the navigation of the available literature to facilitate targeted validation and comparative investigation of the probiotic properties of the microbial strains.

Innovative binary nanofluid approach with copper (Cu - EO) and Magnetite ([formula omitted] - EO) for enhanced thermal performance

In this study, we present an innovative and detailed analysis of the motion of a binary nanofluid, specifically copper (Cu - EO) and Magnetite (Fe3O4 - EO), in a porous medium under solar radiation. Our research is driven by the potential to significantly enhance the thermal performance of a Parabolic Trough Solar Collector (PTSC) using a binary nanofluid. The boundary conditions, partial differential restrictions, and the governing PDEs are transformed into the ODEs system with appropriate similarity transformations. The finite element method (FEM) and semi-analytical technique memad Akbari-Ganji's Method (AGM) are used to solve the approximate solutions of ODEs. The findings of evaluating Copper (Cu - EO) and Magnetite (Fe3O4 - EO) were the two different nanofluids based on engine oil that were presented. Furthermore, the model's overall entropy variations are enhanced using Reynolds. Moreover, our study contributes to understanding fluidity and viscosity alterations in such systems. We use the Brinkman number to observe these alterations. We reveal that the thermal efficiency of (Cu - EO) is lower than (Fe3O4 - EO) by a range of 0.005–0.6. This indicates a substantial enhancement in the thermal performance of the PTSC.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.