Exhaustive Search Research Articles

Xeinabot: A Spoken Intelligent Agent approach Using Hidden Markov Model with Chatbot Technology in Android Environmen

The present study describes the designing and its execution of xeinabot, a public domains spoken smart agent in an Android system. The primary goal of this work is to design and construct a personal agent that combines chatbot technology and statistical methods for speech search. The former uses Hidden Markove System Algorithmic procedures and the N-gram technique for Automatically Speech Recognition, while the latter relies on chatbot systems for Speech Recognizing and uses Multinomial Naïve Bays Method for classification and brute force search for matching patterns. The main objective of this strategy is to create an agent that convinces people into thinking they are speaking with a human when, in reality, they are speaking with a machine. utilizing chatbot technology to make advantage. After evaluating roughly 500 phrases on various subjects, the evaluation findings show very good results and, in the majority of circumstances, the agent behaves like a person. The success task (correct voice recognition and speech understanding) amounts to 86.6%.

Experimental and numerical analysis of the geometry of a laboratory-scale overtopping wave energy converter using constructal design

An experimental study is conducted to investigate the influence of the design of a laboratory-scale onshore overtopping device over the mass of water accumulated in its reservoir, and generate a database to validate computational models. The geometric evaluation is based on the constructal design, being investigated one degree of freedom (ratio between the height and length of the ramp, H1/L1) for various depths of free surface of the water (h1). Four geometric constraints delimit the ramps configurations. The exhaustive search is applied to obtain the ramp geometry that maximizes the amount of water that enters the device reservoir, represented by the water level accumulated in the reservoir (hR). For h1 = 0.392 m cases, a numerical model based on the finite volume method is also used for validation purposes. Results indicated that the lowest H1/L1 ratios of the ramp led to the best performances for all studied magnitudes of h1, which agreed with previous findings of literature for similar wave conditions. The proposed numerical model was validated for predicting wave propagation, instantaneous amount of water discharge, and prediction of the effect of H1/L1 over hR, with a maximum relative error (RE) of 3.92 %.

Exhaustive Search of Dietary Intake Biomarkers as Objective Tools for Personalized Nutrimetabolomics and Precision Nutrition Implementation.

To conduct an exhaustive scoping search of existing literature, incorporating diverse bibliographic sources to elucidate the relationships between metabolite biomarkers in human fluids and dietary intake. The search for biomarkers linked to specific dietary food intake holds immense significance for precision health and nutrition research. Using objective methods to track food consumption through metabolites offers a more accurate way to provide dietary advice and prescriptions on healthy dietary patterns by healthcare professionals. An extensive investigation was conducted on biomarkers associated with the consumption of several food groups and consumption patterns. Evidence is integrated from observational studies, systematic reviews, and meta-analyses to achieve precision nutrition and metabolism personalization. Tailored search strategies were applied across databases and gray literature, yielding 158 primary research articles that met strict inclusion criteria. The collected data underwent rigorous analysis using STATA and Python tools. Biomarker-food associations were categorized into 5 groups: cereals and grains, dairy products, protein-rich foods, plant-based foods, and a miscellaneous group. Specific cutoff points (≥3 or ≥4 bibliographic appearances) were established to identify reliable biomarkers indicative of dietary consumption. Key metabolites in plasma, serum, and urine revealed intake from different food groups. For cereals and grains, 3-(3,5-dihydroxyphenyl) propanoic acid glucuronide and 3,5-dihydroxybenzoic acid were significant. Omega-3 fatty acids and specific amino acids showcased dairy and protein foods consumption. Nuts and seafood were linked to hypaphorine and trimethylamine N-oxide. The miscellaneous group featured compounds like theobromine, 7-methylxanthine, caffeine, quinic acid, paraxanthine, and theophylline associated with coffee intake. Data collected from this research demonstrate potential for incorporating precision nutrition into clinical settings and nutritional advice based on accurate estimation of food intake. By customizing dietary recommendations based on individualized metabolic profiles, this approach could significantly improve personalized food consumption health prescriptions and support integrating multiple nutritional data.This article is part of a Nutrition Reviews special collection on Precision Nutrition.

Predictive value of neutrophil to lymphocyte ratio for clinical outcome in patients with atrial fibrillation: a systematic review and meta-analysis.

The association between the Neutrophil-to-Lymphocyte Ratio (NLR) and the prognosis of Atrial Fibrillation (AF) has been extensively studied, yet clinical outcomes have varied. Consequently, this analysis was undertaken to explore the link between NLR and the prognostic markers of AF. We conducted an exhaustive search across electronic databases, including PubMed, Embase, Web of Science, and the Cochrane Library, to investigate the correlation between the NLR and indicators of adverse clinical outcomes associated with AF from the database establishment date through March 31, 2024. In this study, the recurrence rate of AF was the primary outcome measure, while the secondary outcome measures were mortality, stroke, and left atrial thrombus. Odds ratio (OR), relative risk (RR), hazard ratio (HR) and standard mean difference (SMD) with a 95% confidence interval (CI) were integrated for assessment, and the stability of prognostic outcomes and publication bias were verified by sensitivity analysis and Egger's test, respectively. Subgroup analyses were performed to pinpoint the sources of heterogeneity. This analysis included 20 studies, encompassing a total of 59,256 patients. Our statistical analysis of both categorical and continuous variables revealed that an elevated NLR was significantly associated with increased risks in AF patients for recurrence (categorical variable: OR = 1.39, 95% CI = 1.21-1.60; continuous variable: SMD = 0.49, 95% CI = 0.24-0.74), mortality (categorical variable: OR = 1.87, 95% CI = 1.59-2.20), stroke (categorical variable: OR = 1.56, 95% CI = 1.13-2.17; continuous variable: SMD = 0.77, 95% CI = 0.63-0.91), and left atrial thrombus (categorical variable: OR = 1.87, 95% CI = 1.27-2.75; continuous variable: SMD = 0.59, 95% CI = 0.30-0.89). Subgroup analyses found that high NLR was significantly linked to AF recurrence when the NLR was >3. High NLR was significantly linked to the risk of stroke in AF when the NLR was ≤3. This study suggested that a high NLR is significantly linked to prognostic risk markers of AF, and NLR may be an effective biomarker for the prognosis of AF in clinical practice. PROSPERO (CRD42024530970).

The Skin's Sun Story: Risks and Precautions

Background: The skin, representing the largest organ in the human body, serves numerous funtions crucial for homeostasis, as well as acts as the primary physical barrier protecting the organism from external environmental factors such as microorganisms, mechanical damage, heat or cold exposure, toxins, as well as solar radiation, posing various risks to the skin, including sunburn, photoageing and skin malignancies. Objectives: This study aims to explore the effects of solar radiation on the skin and to discuss preventive measures against its negative influence on the skin. Methods: An exhaustive search of references related to sun exposure and skin published in PubMed was undertaken, using the search terms: "solar exposure and skin, ultraviolet radiation and skin, visible light and skin, sunscreen, photoprotection”. Conclusions: Solar radiation, particularly ultraviolet radiation (UVR), induces acute and chronic damage to the skin, leading to conditions such as sunburn, photoaging, and skin malignancies. However, adequate exposure to sunlight is crucial for the synthesis of vitamin D3, which plays essential roles in calcium homeostasis, immune function, and potentially other physiological processes. Strategies such as proper sunscreen use and other photoprotection methods can mitigate the adverse effects of solar radiation while allowing for sufficient vitamin D synthesis.

Safeguarding Oscillators and Qudits with Distributed Two-Mode Squeezing

Recent advancements in multi-mode Gottesman-Kitaev-Preskill (GKP) codes have shown great promise in enhancing the protection of both discrete and analog quantum information. This broadened range of protection brings opportunities beyond quantum computing to benefit quantum sensing by safeguarding squeezing — the essential resource in many quantum metrology protocols. However, the potential for quantum sensing to benefit quantum error correction has been less explored. In this work, we provide a unique example where techniques from quantum sensing can be applied to improve multi-mode GKP codes. Inspired by distributed quantum sensing, we propose the distributed two-mode squeezing (dtms) GKP codes that offer benefits in error correction with minimal active encoding operations. Indeed, the proposed codes rely on a single (active) two-mode squeezing element and an array of beamsplitters that effectively distributes continuous-variable correlations to many GKP ancillae, similar to continuous-variable distributed quantum sensing. Despite this simple construction, the code distance achievable with dtms-GKP qubit codes is comparable to previous results obtained through brute-force numerical search \cite{lin2023closest}. Moreover, these codes enable analog noise suppression beyond that of the best-known two-mode codes \cite{noh2020o2o} without requiring an additional squeezer. We also provide a simple two-stage decoder for the proposed codes, which appears near-optimal for the case of two modes and permits analytical evaluation.

Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits

Qubit mapping is a critical aspect of implementing quantum circuits on real hardware devices. Currently, the existing algorithms for qubit mapping encounter difficulties when dealing with larger circuit sizes involving hundreds of qubits. In this article, we introduce an innovative qubit mapping algorithm, Duostra, tailored to address the challenge of implementing large-scale quantum circuits on real hardware devices with limited connectivity. Duostra operates by efficiently determining optimal paths for double-qubit gates and inserting SWAP gates accordingly to implement the double-qubit operations on real devices. Together with two heuristic scheduling algorithms, Limitedly Exhaustive Search and Shortest-Path Estimation, it yields results of good quality within a reasonable runtime, thereby striving toward achieving quantum advantage. Experimental results showcase our algorithm’s superiority, especially for large circuits beyond the NISQ era. For example, on large circuits with more than 50 qubits, we can reduce the mapping cost on an average 21.75% over the virtual best results among QMAP, t \(|ket\rangle\) , Qiskit, and SABRE. Besides, for mid-size circuits such as the SABRE-large benchmark, we improve the mapping costs by 4.5%, 5.2%, 16.3%, 20.7%, and 25.7%, when compared to QMAP, TOQM, t \(|ket\rangle\) , Qiskit, and SABRE, respectively.

Pairwise Stability in Weighted Network Formation Games: Selection and Computation

This paper is concerned with the selection and computation of pairwise stable networks when agents have differentiable and concave utility functions. We show that a pairwise stable network can be obtained by finding a Nash equilibrium of a noncooperative game played by the nodes and links in the network. Based on this observation, we introduce a logarithmic tracing procedure and a path-following algorithm for network formation games. We apply the algorithm to several models in the literature and make comparisons with two existing algorithms: a path-following algorithm based on the linear tracing procedure (LinTP) and a decompose and exhaustive search method (DaE). Numerical results indicate that the proposed method is more than four times as efficient as LinTP. Although DaE demonstrates exceptional efficiency for small-scale problems, our method outperforms it significantly for large-scale problems, where DaE may fail to find a solution. We also show that the decomposition technique of DaE can be used to further accelerate our algorithm for a special class of problems. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 12201289, 12122107, and 72394363/72394360], the Natural Science Foundation of Jiangsu Province [Grant BK20220754], the Guangdong Basic and Applied Basic Research Foundation [Grant 2021A1515110207], the Young Elite Scientists Sponsorship Program by CAST [Grant 2023QNRC001], and the Open Research Fund from the Guangdong Provincial Key Laboratory of Big Data Computing [Grant B10120210117-OF05]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0546 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0546 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Search for missing symmetry in the Inorganic Crystal Structure Database (ICSD).

An exhaustive search for missing symmetry was performed for 223 076 entries in the ICSD (2023-2 release). Approximately 0.65% of them can be described with higher symmetry than reported. Out of the identified noncentrosymmetric entries, ∼74% can be described by centrosymmetric space groups; this has implications for compatible physical properties. It is proposed that the information on the correct space group is included in the ICSD.

The Most Influential Publications Regarding Hair Transplantation: A Bibliometric Review.

This bibliometric review aims to assess the impact of significant publications within the field of hair transplantation. Citation counts will serve as a primary influence indicator. An exhaustive search was conducted using Clarivate's Web of Science database, yielding 260 publications related to hair transplantation. These were evaluated and sorted based on citations, narrowing down to the 50 most highly cited works for analysis. Parameters including citation density, authorship, institutional affiliations, country of origin, year of publication, article topic, and the level of evidence for each publication were obtained. Analyzed publications were cited a total of 1341 times. Authorship analysis revealed that the most significant contributors regarding hair transplantation were Bernstein and Rassman. We also identified the leading institutions affiliated with these works, highlighting the primary academic and research centers driving the field. Geographical analysis exhibited the US' dominance in producing impactful publications. Most publications were also classified within Level IV and Level V according to the Oxford Levels of Evidence system. This review provides a comprehensive snapshot of the pivotal publications shaping hair transplantation. Our findings underscore significant contributions within this field and may assist researchers and clinicians in understanding the evolution and the current state of the hair transplantation literature. This bibliometric analysis can serve as a roadmap for those seeking to delve into this rapidly evolving field, facilitating the identification of research gaps and formulating future research directions. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network

This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems.

A Thematic Synthesis of the Experiences of Social Prescribing Among Patients in the UK.

Social prescribing connects patients to resources or activities to meet their non-medical needs. In the UK, it is often implemented in primary care. In the social prescribing pathway, patients are directed to link workers to identify suitable solutions for their needs such as art workshops or welfare benefit guidance. Social prescribing marks a notable transition from traditional medical treatments to more comprehensive strategies focusing on holistic health and wellbeing strategies. Insights from patient experiences can improve the development of social prescribing to better meet their needs. This understanding can aid in improving the delivery and outcomes of social prescribing. To synthesise qualitative research on the experiences of social prescribing among patients in the UK. A qualitative systematic review - using thematic synthesis for peer-reviewed papers focused on experiences of social prescribing users in the UK. An exhaustive search was performed in six databases: ASSIA, CINAHL, EMBASE, MEDLINE, PSYCINFO, and SOCIAL SCIENCE CITATION INDEX via Web of Science. The CASP tool for qualitative research was used for quality assessment and PRISMA 2020 Checklist was used to ensure the report transparency. Titles and abstracts of 1,269 papers were screened. In total, 85 papers were full text-screened, and 19 studies were included in the review. Five analytical themes were developed from these papers: (a) Searching for hope in times of adversity; (b) Variability in temporal responsiveness; (c) Feeling supported and empowered by the social prescribing pathway; (d) Sustained change from a positive response; (e) Misalignment producing no response. Patients might experience lasting advantages from social prescribing if it aligns with their needs and expectations. Results highlighted the importance of matching social prescribing referral with patients' readiness to engage. Therefore, it is recommended that healthcare professionals evaluate patient suitability prior to beginning a social prescribing referral.

Prebiotic chiral transfer from self-aminoacylating ribozymes may favor either handedness

Modern life is essentially homochiral, containing D-sugars in nucleic acid backbones and L-amino acids in proteins. Since coded proteins are theorized to have developed from a prebiotic RNA World, the homochirality of L-amino acids observed in all known life presumably resulted from chiral transfer from a homochiral D-RNA World. This transfer would have been mediated by aminoacyl-RNAs defining the genetic code. Previous work on aminoacyl transfer using tRNA mimics has suggested that aminoacylation using D-RNA may be inherently biased toward reactivity with L-amino acids, implying a deterministic path from a D-RNA World to L-proteins. Using a model system of self-aminoacylating D-ribozymes and epimerizable activated amino acid analogs, we test the chiral selectivity of 15 ribozymes derived from an exhaustive search of sequence space. All of the ribozymes exhibit detectable selectivity, and a substantial fraction react preferentially to produce the D-enantiomer of the product. Furthermore, chiral preference is conserved within sequence families. These results are consistent with the transfer of chiral information from RNA to proteins but do not support an intrinsic bias of D-RNA for L-amino acids. Different aminoacylation structures result in different directions of chiral selectivity, such that L-proteins need not emerge from a D-RNA World.

SEMbap: Bow-free covariance search and data de-correlation.

Large-scale studies of gene expression are commonly influenced by biological and technical sources of expression variation, including batch effects, sample characteristics, and environmental impacts. Learning the causal relationships between observable variables may be challenging in the presence of unobserved confounders. Furthermore, many high-dimensional regression techniques may perform worse. In fact, controlling for unobserved confounding variables is essential, and many deconfounding methods have been suggested for application in a variety of situations. The main contribution of this article is the development of a two-stage deconfounding procedure based on Bow-free Acyclic Paths (BAP) search developed into the framework of Structural Equation Models (SEM), called SEMbap(). In the first stage, an exhaustive search of missing edges with significant covariance is performed via Shipley d-separation tests; then, in the second stage, a Constrained Gaussian Graphical Model (CGGM) is fitted or a low dimensional representation of bow-free edges structure is obtained via Graph Laplacian Principal Component Analysis (gLPCA). We compare four popular deconfounding methods to BAP search approach with applications on simulated and observed expression data. In the former, different structures of the hidden covariance matrix have been replicated. Compared to existing methods, BAP search algorithm is able to correctly identify hidden confounding whilst controlling false positive rate and achieving good fitting and perturbation metrics.

Integration of a novel 3D chaotic map with ELSS and novel cross-border pixel exchange strategy for secure image communication

This paper proposes a robust image encryption algorithm that utilizes a Novel three-dimensional (3D) Chaotic map and an Enhanced Logistic Sine System (ELSS). We leverage the unpredictability of 3D chaotic dynamics alongside the complexity of ELSS and DNA Sequence to forge a formidable image encryption scheme. Firstly, the image pixels are converted from decimal to hexadecimal notation and sorted in a 1D pixel array carrying a unique sequence of three channels of the RGB image. Secondly, the secret key is appended to XOR, the values with that 1-D pixels array. Thirdly, values are sorted by performing the binary right shift operation and encoded into DNA. Fourthly, a novel chaotic map is used to perform scrambling operations. Lastly, a novel enormous keyspace ELSS is used to perform efficient Border and Cross-Border (B &CB) pixel exchange, further enhancing the encryption quality of the proposed algorithm. Comprehensive security analysis proved that the proposed algorithm exhibits remarkable resilience against powerful known and chosen plaintext attacks and other prevalent cryptanalysis attacks, including differential attacks and exhaustive key search attacks. Henceforth, the proposed algorithm’s superior security and low computational cost make it an ideal choice for real-time secure image communication across various platforms, including satellite, multimedia, and military communications.

A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks.

Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation's complexity further. The simulation results show a significant improvement in the sum rate of the network.

Resveratrol: Challenges and prospects in extraction and hybridization with nanoparticles, polymers, and bio-ceramics.

Resveratrol (RSV), a bioactive natural phenolic compound found in plants, fruits, and vegetables, has garnered significant attention in pharmaceutical, food, and cosmetic industries due to its remarkable biological and pharmacological activities. Despite its potential in treating various diseases, its poor pharmacokinetic properties, such as low solubility, stability, bioavailability, and susceptibility to rapid oxidation, limit its biomedical applications. Recent advancements focus on incorporating resveratrol into innovative materials like nanoparticles, polymers, and bio-ceramics to enhance its properties and bioavailability. In this review, an exhaustive literature search was conducted from PubMed, Google Scholar, Science Direct, Scopus, and Web of Science databases to explore these advancements, to compares conventional and innovative extraction methods, and to highlights resveratrol's therapeutic potential, including its anti-inflammatory, anti-oxidative, anti-cancerogenic, antidiabetic, neuroprotective, and cardio-protective properties. Additionally, we discuss the challenges and prospects of hybrid materials combining resveratrol with nanoparticles, polymers, and bio-ceramics for therapeutic applications. Rigorous studies are still needed to confirm their clinical efficacy.

Clinical Implications of HELLP Syndrome in Anesthesia Management of Preeclamptic Pregnant Mothers: A Literature-Based Review

BACKGROUND:Hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome is a serious life-threatening obstetric emergency accompanied with heterogeneous presentation and multisystem dysfunction. It increases the perils of fetal morbidity and mortality and requires complex clinical management during medical and surgical interventions. Hence, it remains a major challenge for anesthesia providers during perioperative management. The current study aimed to explore the main clinical implications of HELLP syndrome in anesthesia management of preeclamptic pregnant mothers who underwent cesarean section.METHODOLOGY:An exhaustive search of the literature was conducted on the available relevant databases, such as PubMed, Google Scholar, and Cochrane Reviews, published from 1990 to 2023, using the terms HELLP syndrome, cesarean section, and anesthetic challenges/complications. All observational studies, randomized controlled trials (RCTs), cohorts, and nonrandomized studies on HELLP syndrome and anesthetic challenges during cesarean section reported in English were included. Nonpregnant mothers with HELLP syndrome and studies with no relevant reports or abstracts, case reports, or case studies were excluded.RESULTS:A systematic search of relevant databases identified 82 studies: 60 from PubMed, 18 from Google Scholar, and 4 from Cochrane Reviews. After removing duplicates, 48 studies were screened and recorded. Eighteen studies were assessed for eligibility after their titles and abstracts were reviewed. Twelve studies were excluded from the current study, as it did not fulfill the inclusion criteria. Finally, six studies, four retrospective studies, one prospective observational study, and one RCT, were included and reviewed.CONCLUSION:Perioperative anesthetic care in patients with HELLP syndrome remains a challenge. Hence, anesthetic techniques should be selected based on the severity of the condition, patient status, urgency surgery, fetal conditions, coagulation status, and safe platelet counts.

The 𝒯ℛ𝒪𝒴 project

Context. Co-orbital objects, also known as trojans, are frequently found in simulations of planetary system formation. In these configurations, a planet shares its orbit with other massive bodies. It is still unclear why there have not been any co-orbitals discovered thus far in exoplanetary systems (exotrojans) or even pairs of planets found in such a 1:1 mean motion resonance. Reconciling observations and theory is an open subject in the field. Aims. The main objective of the 𝒯ℛ𝒪𝒴 project is to conduct an exhaustive search for exotrojans using diverse observational techniques. In this work, we analyze the radial velocity time series informed by transits, focusing the search around low-mass stars. Methods. We employed the α-test method on confirmed planets searching for shifts between spectral and photometric mid-transit times. This technique is sensitive to mass imbalances within the planetary orbit, allowing us to identify non-negligible co-orbital masses. Results. Among the 95 transiting planets examined, we find one robust exotrojan candidate with a significant 3-σ detection. Additionally, 25 exoplanets show compatibility with the presence of exotrojan companions at a 1-σ level, requiring further observations to better constrain their presence. For two of those weak candidates, we find dimmings in their light curves within the predicted Lagrangian region. We established upper limits on the co-orbital masses for either the candidates and null detections. Conclusions. Our analysis reveals that current high-resolution spectrographs effectively rule out co-orbitals more massive than Saturn around low-mass stars. This work points out to dozens of targets that have the potential to better constraint their exotrojan upper mass limit with dedicated radial velocity observations. We also explored the potential of observing the secondary eclipses of the confirmed exoplanets in our sample to enhance the exotrojan search, ultimately leading to a more accurate estimation of the occurrence rate of exotrojans.

A Comparative Study Between Two Cybersecurity Attacks: Brute Force and Dictionary Attacks

In today's world, information drives not only business but nearly every aspect of human life. Therefore, safeguarding valuable information from malicious activities like attacks has become essential. Brute force search and dictionary attacks are prevalent cybersecurity threats, in which an attacker systematically attempts all possible passwords and passphrases to gain access to a user's account. These types of attacks are common due to the widespread reuse of simple password variations. The goal of this study is to discover as many passwords as possible and demonstrate their predictability and susceptibility. Our focus was on comparing two cracking methods, such as brute force search attacks and dictionary attacks, assessing their effectiveness and time requirements.