Scientific Research Research Articles

Introducing machine learning to analyze factors aimed at successful development of the individual social qualities

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

Micro-Opto-Electro-Mechanical Systems for High-Precision Displacement Sensing: A Review

High-precision displacement sensing has been widely used across both scientific research and industrial applications. The recent interests in developing micro-opto-electro-mechanical systems (MOEMS) have given rise to an excellent platform for miniaturized displacement sensors. Advancement in this field during past years is now yielding integrated high-precision sensors which show great potential in applications ranging from photoacoustic spectroscopy to high-precision positioning and automation. In this review, we briefly summarize different techniques for high-precision displacement sensing based on MOEMS and discuss the challenges for future improvement.

An Eye Movement Classification Method based on Cascade Forest.

Eye tracking technology has become increasingly important in scientific research and practical applications. In the field of eye tracking research, analysis of eye movement data is crucial, particularly for classifying raw eye movement data into eye movement events. Current classification methods exhibit considerable variation in adaptability across different participants, and it is necessary to address the issues of class imbalance and data scarcity in eye movement classification. In the current study, we introduce a novel eye movement classification method based on cascade forest (EMCCF), which comprises two modules: (1) a feature extraction module that employs a multi-scale time window method to extract features from raw eye movement data; (2) a classification module that innovatively employs a layered ensemble architecture, integrating the cascade forest structure with ensemble learning principles, specifically for eye movement classification. Consequently, EMCCF not only enhanced the accuracy and efficiency of eye movement classification but also represents an advancement in applying ensemble learning techniques within this domain. Furthermore, experimental results indicated that EMCCF outperformed existing deep learning-based classification models in several metrics and demonstrated robust performance across different datasets and participants.

Breaking the Silence: The Role of Health Professionals in the Gaza Crisis

Having the opportunity to write something in this moment, I would use it to raise my voice on behalf of the health professional community for the people of Gaza. As mentioned by Sualeha Shekhani and Dr. Aamir Jafarey, doctors represent the moral voice of their era. However, in light of the events of October 7, 2023, and the subsequent armed assault by Israeli forces on Gaza, which has led to a significant humanitarian crisis, the response from the global bioethics community seems muted (Shekhani & Jafarey, 2024). This silence can be partly explained by editorial restraints that may prevent a more robust response from the health community towards the Gaza crisis. Health professionals are uniquely positioned to influence policy decisions and advocate for equitable healthcare. They possess specialized knowledge of medical ethics and healthcare structures, enabling them to champion the causes of the vulnerable and reduce disparities across society. Physicians, trained in evidence-based medicine, can ensure that policy decisions are grounded in scientific research rather than political expediency (Fernandes, 2024). The medical community's silence regarding the Gaza crisis is troubling. Healthcare workers in Gaza are targeted, and doctors work without essential medicines and resources, risking their lives for the population they serve. The current health sector in Gaza is at a breaking point, exacerbating the public health crisis and psychological trauma experienced by the residents (Fernandes, 2024).

Teaching abductive reasoning for use as a problem-solving tool in organic chemistry and beyond

The second-year undergraduate Organic Chemistry course sequence is often cited as one of the most, if not the most, challenging for students in the US. Thus, a persistent question remains: What is it about Organic Chemistry that makes the course so difficult for students? Herein, we put forward the hypothesis that a new mode of thinking and problem solving is expected of the students; these skills have not yet been developed in their prior scientific coursework and are often not deliberately taught in Organic Chemistry. This form of reasoning and problem solving, known as abductive reasoning, is highlighted for its connection to medical diagnosis and scientific thinking. We provide examples to showcase how instructors could explicitly foreground the reasoning process in their classroom. Ultimately, we argue that teaching how to reason using abduction may benefit students in both the short term (in the course) and the long term (in their careers as scientists and medical practitioners).

Understanding the physiological transmission mechanisms of photoplethysmography signals: a comprehensive review.

The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications. Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints. The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals.&#xD.

Preparation, Quality control and Standardization of a Destructive Distillate <i>Swasakasa Nei</i> (SKN), A Traditional Siddha Herbal Pharmaceutic for <i>Swasakasam</i> (Bronchial Asthma)

Standardization is essential to ensure the quality and reliability of traditional medicines. This study aimed to collect, authenticate and purification of raw materials, preparation of <i>Swasakasa Nei</i> (SKN) according to SOP, and Quality evaluation and Standardization procedures following Pharmacopoeial Laboratory of Indian Medicine (PLIM) guidelines. SKN, prepared as <i>Kuzhithylam</i>, is used to treat <i>Swasakasam</i> (Bronchial Asthma) in the Siddha system of medicine. The organoleptic characters of <i>Swasakasa Nei</i> reveal that SKN was a black, thick semisolid with a smoky odour, greasy consistency, and free-flowing nature drug. Physicochemical analysis showed values for Saponification Value (202.19), Iodine Value (30), Acid Value (7.25%), Peroxide Value (0.58%), Refractive Index (1.462), Specific Gravity (0.9131 g/ml), Loss on Drying (34.17%), Total Fatty Matter (60.18%), Free Fatty Acids (1.06%), and Unsaponifiable Matter (0.98%). The congealing point was 19°C, with no mineral oil and definite oxidation on rancidity. HPTLC fingerprint analysis revealed multiple prominent peaks at various wavelengths. Microbial contamination tests showed no specific microbes, and aflatoxin assays confirmed SKN was free from Aflatoxins B1, B2, G1, and G2. Pesticide residue analysis detected no organochlorine, organophosphorus, or pyrethroids, and heavy metal analysis showed no trace elements or heavy metals. These standards affirm the authenticity, genuineness, and safety of SKN, contributing to its scientific validation.

How to close the loop with citizen scientists to advance meaningful science

AbstractCitizen science yields increased scientific capacity in exchange for science literacy and promises of a more responsive science to society’s needs. Yet, citizen science projects are criticized for producing few scientific outputs and having exploitative relationships with the citizens who participate. In the eagerness to capture new data, scientists can fail to see the value of citizen scientists’ expertise beyond data generation and can forget to close the loop with outputs that benefit the public interest. Citizen scientists are experts in their local environments who, when asked, can improve scientific processes and products. To the degree that citizen scientists are relegated to data collection, we shortchange opportunities to advance science. Rather than merely critique, we present an evidence-based engagement approach for listening to citizen scientist participants and incorporating their input into science processes and products that can be retrofitted onto existing citizen science projects or integrated from a project’s inception. We offer this adaptable blueprint in four steps and illustrate this approach via a crowdsourced hydrology project on the Boyne River, USA. We show how engaging voices of citizen scientists at key points in the project improves both the products of science (a real-time ecohydrological model) and the process of conducting the science (adaptations to help improve data collection). Distinct from outreach or education, considering citizen scientists as an equally interesting site of inquiry can improve the practice and outputs of science.

Organellar Genomes of Sargassum hemiphyllum var. chinense Provide Insight into the Characteristics of Phaeophyceae

Sargassum hemiphyllum var. chinense, a prevalent seaweed along the Chinese coast, has economic and ecological significance. However, systematic positions within Sargassum and among the three orders of Phaeophyceae, Fucales, Ectocarpales, and Laminariales are in debate. Here, we reported the organellar genomes of S. hemiphyllum var. chinense (34,686-bp mitogenome with 65 genes and 124,323 bp plastome with 173 genes) and the investigation of comparative genomics and systematics of 37 mitogenomes and 22 plastomes of Fucales (including S. hemiphyllum var. chinense), Ectocarpales, and Laminariales in Phaeophyceae. Whole genome collinearity analysis showed gene number, type, and arrangement were consistent in organellar genomes of Sargassum with 360 SNP loci identified as S. hemiphyllum var. chinense and two genes (rps7 and cox2) identified as intrageneric classifications of Sargassum. Comparative genomics of the three orders of Phaeophyceae exhibited the same content and different types (petL was only found in plastomes of the order Fucales and Ectocarpales) and arrangements (most plastomes were rearranged, but trnA and trnD in the mitogenome represented different orders) in genes. We quantified the frequency of RNA-editing (canonical C-to-U) in both organellar genomes; the proportion of edited sites corresponded to 0.02% of the plastome and 0.23% of the mitogenome (in reference to the total genome) of S. hemiphyllum var. chinense. The repetition ratio of Fucales was relatively low, with scattered and tandem repeats (nine tandem repeats of 14–24 bp) dominating, while most protein-coding genes underwent negative selection (Ka/Ks < 1). Collectively, these findings provide valuable insights to guide future species identification and evolutionary status of three important Phaeophyceae order species.

Talking across worlds: The ontological turn and communication in natural resource co-management with Indigenous communities

The ontological turn in critical social theory provokes emerging calls for new approaches to natural resource management where Indigenous perspectives, worldview, knowledges, and values are prioritized in the stewardship of Indigenous lands. Yet, scant literature focuses on the ontological implications for communication within environmental decision-making, where Habermas’ communicative action theory, with its norms of privileging argumentation, formality, expertise, institutional authority, rationality, and language, continues to shape spaces of public participation since the communicative turn in the 1990s. Growing calls for participatory decision-making, as well as the mounting failures of scientific management approaches espoused by conventional natural resource management, have fuelled the rise of co-management since the 1980s. The emerging emphasis in co-management approaches on community collaboration and meaningful communication was strengthened with the emergence of adaptive co-management and adaptive governance in the early 2000s. Yet, the ontological turn unveils communicative tensions which continue to persist, rooted in ontological difference and onto-epistemic violence. Rethinking communication under the ontological turn in co-management with Indigenous communities, this paper reviews the literature and further proposes the idea of ethical equivocation as a communicative tool and starting point toward learning to talk across worlds.

Effect of curvature variations on the hydrodynamic performance of heaving and pitching foils

AbstractThe use of heaving and pitching fins for underwater propulsion of engineering devices poses an attractive outlook given the efficiency and adaptability of natural fish. However, significant knowledge gaps need to be bridged before biologically inspired propulsion is able to operate at competitive performances in a practical setting. One of these relates to the design of structures that can leverage passive deformation and active morphing in order to achieve optimal hydrodynamic performance. To provide insights into the performance improvements associated with passive and active fin deformations, we provide here a systematic numerical investigation in the thrust, power, and efficiency of 2D heaving and pitching fins with imposed curvature variations. The results show that for a given chordline kinematics, the use of curvature can improve thrust by 70% or efficiency by 35% over a rigid fin. Maximum thrust is achieved when the camber variations are synchronized with the maximum heave velocity, increasing the overall magnitude of the force vector while increasing efficiency as well. Maximum efficiency is achieved when camber is applied during the first half of the stroke, tilting the force vector to create thrust earlier in the cycle than a comparable rigid fin. Overall, our results demonstrate that curving fins are consistently able to significantly outperform rigid fins with the same chord line kinematics on both thrust and hydrodynamic efficiency.

Development of the Competency Evaluation Scale for Clinical Nutritionists in China: A Delphi Study

Background: Clinical nutritionists are responsible for nutritional therapy in clinical practice, which significantly enhances patients’ nutritional status. This study aims to develop and validate a competency evaluation scale to effectively assess the abilities of clinical nutritionists. Methods: The competency evaluation scale for clinical nutritionists was developed based on the iceberg model, utilizing literature review, semi-structured interviews, and the Delphi method. The weights of each indicator were calculated using the Analytic Hierarchy Process (AHP), and the validity and reliability of the scale were confirmed through questionnaire surveys. Results: The competency evaluation scale of clinical nutritionists comprised five primary indicators, twelve secondary indicators, and sixty-six tertiary indicators. The primary indicators, including professional theoretical knowledge, professional practical skills, humanistic practice ability, interpersonal communication ability, and professional development capability, have respective weights of 0.2168, 0.2120, 0.2042, 0.2022, and 0.1649. The Cronbach’s α coefficients of the five dimensions of the scale were 0.970, 0.978, 0.969, 0.962, and 0.947, respectively. The results of the Exploratory Factor Analysis showed that the prerequisites for factor analysis were satisfied. Additionally, Bartlett’s test of sphericity yielded a significance level of p < 0.001, confirming the scale’s reliability and validity. Conclusions: The competency evaluation scale for clinical nutritionists developed in this study is of high scientific reliability and validity, which provides assessment criteria for the training and assessment of clinical nutritionists.

Towards a minimum data set for the female stress urinary incontinence surgical literature: a collaborative work from Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction, American Urogynecologic Society, and International Continence Society.

There has been a need for an acceptable common minimum data set in the scientific literature as regards the surgical treatment of female stress urinary incontinence (SUI). Such a data set, if widely adopted, would improve the quality of the literature and allow objective comparisons between and across interventions. The surgical treatment of female stress urinary incontinence has evolved considerably over the past few decades. The corresponding body of literature has grown exponentially describing the outcomes of hundreds of studies of these novel interventions. However, historically, the literature in this space has been of uneven quality. In order to improve the reporting of clinical studies, and ultimately patient outcomes, a standard minimum data set for trial design and publications was created by a collaborative group formed from leading scientific societies. The consensus document created from this novel collaboration between members of SUFU (Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction), AUGS (American Urogynecologic Society), and ICS (International Continence Society) provides clear guidance for the structure of clinical studies and reporting of results in the peer-reviewed literature. This has substantial potential ramifications for scientific journals, journal editors, peer reviewers, investigators, regulatory agencies, industry, clinicians, and patients.

Large-Scale Satisfaction Rating-Driven Selection of New Energy Vehicles: A Basic Uncertain Linguistic Information Bonferroni Mean-Based MCGDM Approach Considering Criteria Interaction

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations.

Quantinar: a blockchain peer-to-peer ecosystem for modern data analytics

AbstractThe power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, p-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar, to support quantitative analytics knowledge paired with code in the form of Quantlets or software snippets. The integration of blockchain technology allows Quantinar to ensure fully transparent and reproducible scientific research.

Eco-Theological Implications of Psalms 8 in The Context of Biodiversity Preservation and Field Hunting in Osun Groove

One of the prominent natural ecosystems in Nigeria that has attracted global attention with rich cultural, social, economical, political and religious relevance is Osun-Osogbo Sacred Grove. In spite of the rising global scholarly attention on eco-theology and biodiversity, there is still inadequate scientific research on eco-theological implications on Osun-Osogbo grove and biodiversity preservation in the light of Psalms 8. Thus, this study aims at assessing the implication of eco-theological implications of Psalms 8 in the context of biodiversity preservation and field hunting in Osun-Osogbo grove, Osogbo, Osun State. The study employed historical and exegetical analyses using secondary sources of data. The pioneering work of Jürgen Moltmann serves as the theoretical basis of the study. The grove is regarded as the abode of the goddess of fertility, Osun, one of the pantheon of the Yoruba gods, the landscape of the grove and its meandering river is dotted with sanctuaries and shrines, sculptures and art works in honor of Osun and other deities. Osun-Osogbo Festival has attracted a lot of tourists to the grove contributing to the economic growth of the state. Plant, trees and animal species are gradually decreasing; thus, some flora and fauna are endangered. In the context of Psalms 8, biodiversity preservation needs to be enabled as it was observed that encroachment into the grove from the buffer zone as well as uncurbed daily activities along the road are beginning to have negative effect on the rich ecotourism potentials that are being conserved. Conclusively, the grove remains an integral part of the cosmological system needed to be governed by man and not man be governed by it.

Genome-wide assessment of shared genetic landscape of idiopathic pulmonary fibrosis and its comorbidities.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease accompanied by both local and systemic comorbidities. Genetic factors play a role in the development of IPF and certain associated comorbidities. Nevertheless, it is uncertain whether there are shared genetic factors underlying IPF and these comorbidities. To bridge this knowledge gap, we conducted a systematic investigation into the shared genetic architecture between IPF and ten prevalent heritable comorbidities (i.e., body mass index [BMI], coronary artery disease [CAD], chronic obstructive pulmonary disease [COPD], gastroesophageal reflux disease, lung cancer, major depressive disorder [MDD], obstructive sleep apnoea, pulmonary hypertension [PH], stroke, and type 2 diabetes), by utilizing large-scale summary data from their respective genome-wide association studies and multi-omics studies. We revealed significant (false discovery rate [FDR] < 0.05) and moderate genetic correlations between IPF and seven comorbidities, excluding lung cancer, MDD and PH. Evidence suggested a partially putative causal effect of IPF on CAD. Notably, we observed FDR-significant genetic enrichments in lung for the cross-trait between IPF and CAD and in liver for the cross-trait between IPF and COPD. Additionally, we identified 65 FDR-significant genes over-represented in 20 biological pathways related to the etiology of IPF, BMI, and COPD, including inflammation-related mucin gene clusters. Several of these genes were associated with clinically relevant drugs for the treatment of IPF, CAD, and/or COPD. Our results underscore the pervasive shared genetic basis between IPF and its common comorbidities and hold future implications for early diagnosis of IPF-related comorbidities, drug repurposing, and the development of novel therapies for IPF.

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability. However, obtaining QM descriptors often requires CPU-intensive computational chemistry calculations. To identify when QM descriptors help graph neural networks predict chemical properties, we conduct a systematic investigation of the impact of atom, bond, and molecular QM descriptors on the performance of directed message passing neural networks (D-MPNNs) for predicting 16 molecular properties. The analysis surveys computational and experimental targets, as well as classification and regression tasks, and varied data set sizes from several hundred to hundreds of thousands of data points. Our results indicate that QM descriptors are mostly beneficial for D-MPNN performance on small data sets, provided that the descriptors correlate well with the targets and can be readily computed with high accuracy. Otherwise, using QM descriptors can add cost without benefit or even introduce unwanted noise that can degrade model performance. Strategic integration of QM descriptors with D-MPNN unlocks potential for physics-informed, data-efficient modeling with some interpretability that can streamline de novo drug and material designs. To facilitate the use of QM descriptors in machine learning workflows for chemistry, we provide a set of guidelines regarding when and how to best leverage QM descriptors, a high-throughput workflow to compute them, and an enhancement to Chemprop, a widely adopted open-source D-MPNN implementation for chemical property prediction.

Efficacy of atomic layer deposition of Al2O3 on composite LiNi0.8Mn0.1Co0.1O2 electrode for Li-ion batteries

LiNi0.8Mn0.1Co0.1O2 (NMC811) is a popular cathode material for Li-ion batteries, yet degradation and side reactions at the cathode-electrolyte interface pose significant challenges to their long-term cycling stability. Coating LiNixMnyCo1−x−yO2 (NMC) with refractory materials has been widely used to improve the stability of the cathode-electrolyte interface, but mixed results have been reported for Al2O3 coatings of the Ni-rich NMC811 materials. To elucidate the role and effect of the Al2O3 coating, we have coated commercial-grade NMC811 electrodes with Al2O3 by the atomic layer deposition (ALD) technique. Through a systematic investigation of the long-term cycling stability at different upper cutoff voltages, the stability against ambient storage, the rate capability, and the charger transfer kinetics, our results show no significant differences between the Al2O3-coated and the bare (uncoated) electrodes. This highlights the contentious role of Al2O3 coating on Ni-rich NMC cathodes and calls into question the benefits of coating on commercial-grade electrodes.

Reviving Ancient Wisdom: Evidence-Based Study of Unani Medicines on Management of Migraine and Arthritis.

Unani medicine, rooted in Greco-Arabic traditions, offers a holistic approach to healthcare, focusing on the balance of bodily humors and natural medications from plants, minerals, and animals. Unani medicine's holistic perspective emphasizes comprehensive care by addressing the root causes of diseases, particularly in chronic conditions like cancer and neurological disorders. The system's emphasis on Mizaj, preventive measures, and health promotion reflects its relevance in contemporary healthcare. Integrating traditional knowledge with modern scientific validation, Unani medicine offers valuable insights into alternative approaches to health management. This review article explores the management of migraine and arthritis using Unani medicine, drawing insights from various case reports. The objective is to evaluate the efficacy of Unani treatments in providing relief and improving the quality of life for patients suffering from these conditions. The review analyzes multiple case reports detailing the use of specific Unani formulations and therapies. Key findings indicate that Unani medicine, with its holistic approach and reliance on natural medications, has shown promising results in managing both migraine and arthritis.Unani medicine offers a valuable alternative approach to managing migraine and arthritis, aligning traditional knowledge with modern therapeutic needs.