Accurate and reliable project cost estimates are fundamental to achieve successful municipal capital improvement (CIP) programs. Engineering cost estimates typically represent critical information for key decision makers to authorize and efficiently allocate the necessary funds for construction, budgeting, to generate a request for proposals, contract negotiations, scheduling, etc. for these reasons, cost estimators are using different estimating methods and approaches that allow for required levels of accuracy. As the project’s scope becomes more detailed and the potential risks are identified and/or the project design stage progresses these cost estimates are revised and updated. In this paper, the most common project cost estimation methods and approaches were collected and categorized into two main groups of (1) probabilistic and (2) deterministic methods. Under these groups overall ten different methods were identified and discussed addressing their requirements, advantages, and shortcomings, including the potential risk that can positively or negatively affect the project’s cost outcome. This paper will be a good resource for professionals who are in budget development and/or are seeking to a better understanding of different methods in determining an appropriate base cost margin and produce a meaningful and reliable project cost estimate.

We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensemble (NODE) and Conditional Continuous Normalizing Flows (CNF). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE’s output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow’s efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow’s end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.

Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak and accumulated rainfall-based damage are likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S), derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in literature, to monitor flash flood risks on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V) and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data is available, offering crucial information for flood risk monitoring, assessment, and the development of effective mitigation strategies.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

Probabilistic inference problems have very broad practical applications. To solve this kind of problems under conditions of certainty, an effective mathematical apparatus has been developed. In real situations, obtaining deterministic estimates of relevant probabilities is often difficult; therefore, problems with handling uncertain estimates of probabilities appear. This paper examines the problem of probabilistic inference with probability trees provided that the initial probabilities are given in the form of intervals of their possible values.

A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking as fundamental the concept of a path, the continuity equation and Cauchy's equation for fluid dynamics arise naturally, while the specific information about the system can be included using the Maximum Caliber (or maximum path entropy) principle.

Abstract: The Willows, belonging to the genus Salix, encompass a vast array of species and hybrids, totaling between 330 to 500 species along with approximately 200 hybrids. These plants are found across continents such as Africa, North America, Europe, and Asia, exhibiting diverse forms ranging from trees to shrubs and prostrate plants. Traditionally, Willows have been utilized in folk medicine due to their rich reservoir of biologically active compounds, with salicin being a notable example, serving as a prodrug for salicylic acid.The chemical composition of Willows is highly diverse, with 322 secondary metabolites identified thus far. These include various classes such as flavonoids (94 types encompassing flavonols, flavones, flavanones, isoflavones, flavan-3-ols like catechins and procyanidins, chalcones, dihydrochalcone, anthocyanins, and dihydroflavonols), phenolic glycosides (76), organic acids (28), non-phenolic glycosides (17), sterols, and terpenes (17), simple phenolics (13), lignans (7), as well as volatiles and fatty acids (69). This rich chemical di-versity underpins the various pharmacological activities exhibited by Willows. The pharmacological properties attributed to Willows are extensive and include analgesic, an-ti-inflammatory, antioxidant, anticancer, cytotoxic, antidiabetic, antimicrobial, antiobesity, neuroprotective, and hepatoprotective effects. These properties make Willows a subject of con-siderable interest in the fields of medicine and pharmacology. In summary, Willows represent a valuable natural resource with a diverse chemical composition and a wide range of pharmacological activities. This study aimed to enhance our comprehension of the medicinal characteristics of willow bark extract, focusing on its antimicrobial properties. This investigation holds significance in validating the traditional usage of willow bark and investi-gating its potential utility in contemporary medicine.

Since its post-World War II inception, the science of record linkage has grown exponentially and is used across industrial, governmental, and academic agencies. The academic fields that rely on record linkage are diverse, ranging from history to public health to demography. In this paper, we introduce the different types of data linkage and give a historical context to their development. We then introduce the three types of underlying models for probabilistic record linkage: Fellegi-Sunter based methods, machine learning methods, and Bayesian methods. Practical considerations such as data standardization and privacy concerns are then discussed. Finally, recommendations are given for organizations developing or maintaining record linkage programs, with an emphasis on organizations measuring long-term complications of disasters such as 9/11.

The treatment of acute myeloid leukemia (AML) with adverse genetics remains unsatisfactory, with very low response rates to standard chemotherapy and shorter durations of remission commonly observed in these patients. The complex biology of AML with adverse genetics is continuously evolving. Herein, we discuss recent advances which have investigated the contribution of cell intrinsic mechanisms as well as of the immune system to myeloid leukemogenesis in this specific subset of AML. We focus on the biological rationales for combining targeted therapy and immunotherapy, which are currently being investigated in ongoing trials, and could hopefully ameliorate the poor outcomes for these patients

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7,389 participants and 53 variables. Initially, unsupervised K-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (Light-GBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised K-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.

This article introduces a novel approach, termed the Asymmetric Probabilistic Tsetlin (APT) Machine, which incorporates the Stochastic Point Location (SPL) algorithm with the Asymmetric Steps technique into the Tsetlin Machine (TM). APT introduces stochasticity into the state transitions of Tsetlin Automata (TA) by leveraging the SPL algorithm, thereby enhancing pattern recognition capabilities. To enhance random search processes, we introduced a decaying normal distribution into the procedure. Meanwhile, the Asymmetric Steps approach biases state transition probabilities towards specific input patterns, further elevating operational efficiency. The efficacy of the proposed approach is assessed across diverse benchmark datasets for classification tasks. The performance of APT is compared with traditional machine learning algorithms and other Tsetlin Machine models, including the Asymmetric Tsetlin (AT) Machine, characterized by deterministic rules for Asymmetric transitions, and the Classical Tsetlin (CT) Machine, employing deterministic rules for symmetric transitions. Strikingly, the introduced APT methodology demonstrates highly competitive outcomes compared to established machine learning methods. Notably, both APT and AT exhibit state-of-the-art performance, surpassing the Classical Tsetlin Machine, emphasizing the efficacy of asymmetric models for achieving superior outcomes. Remarkably, APT exhibits even better performance than AT, particularly in handling complex datasets.

The Collatz conjecture, a longstanding mathematical puzzle, posits that, regardless of the starting integer, iteratively applying a specific formula will eventually lead to the value 1. This paper introduces a novel approach to validate the Collatz conjecture by leveraging the binary representation of generated numbers. Each transition in the sequence is predetermined using the Collatz conjecture formula, yet the path of transitions is revealed to be intricate, involving alternating increases and decreases for each initial value. The study delves into the global flow of the sequence, investigating the behavior of the generated numbers as they progress toward the termination value of 1. The analysis utilizes the concept of probability to shed light on the complex dynamics of the Collatz conjecture. By incorporating probabilistic methods, this research aims to unravel the underlying patterns and tendencies that govern the convergence of the sequence. The findings contribute to a deeper understanding of the Collatz conjecture, offering insights into the inherent complexities of its trajectories. This work not only validates the conjecture through binary representation but also provides a probabilistic framework to elucidate the global flow of the sequence, enriching our comprehension of this enduring mathematical mystery.

The literature study delves into novel therapeutic strategies in transplantation medicine, highlighting unique approaches that could revolutionize current practices. It emphasizes novel patterns that have the potential to transform the current landscape. Commencing with a thorough examination of the historical environment, it traces crucial advances and changes in transplantation. The focus is on traditional techniques, emphasizing their limitations and challenges. An extensive examination of contemporary transplantation therapies offers valuable insights into the most recent progress in organ, cellular, and tissue transplantation. This analysis examines the immunological obstacles and outlines the current advancements and constraints. The exploration further navigates into novel models and concepts such as immunomodulation, developments in organ preservation techniques, gene editing, biomaterials, scaffold-based solutions, and the integration of precision medicine, artificial intelligence, and machine learning applications. Each section analyzes the potential for change and current progress in various areas, backed by relevant scientific sources up to 2023. The subsequent sections delve into innovative cellular therapies, biotechnological progress, current clinical trials, and case studies demonstrating effective applications. This examination delves into the ethical and regulatory considerations of innovative therapeutic procedures, emphasizing ethical implications, regulatory frameworks, and the importance of balancing innovation with patient welfare. The literature review concludes by offering a forward-looking perspective, emphasizing potential progress, anticipated challenges, and advocating for collaboration and interdisciplinary approaches to overcome limitations in the field of transplantation medicine.

(1) Background: The prediction of cervical lesions evolution is a challenge for clinicians. The prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors for the prediction of progression, regression or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions that underwent follow-up. Descriptive statistics and sensitivity analysis for individual or combined index tests were used for data analysis; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model that comprised a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least 3 clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesions regression or persistence was modest; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction.

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modelling of gene expression networks that involve large amounts of data. The integration of data from DNA, RNA, and protein activities has resemblance to pharmacogenomics, which is of utmost importance in the development of cancer drugs. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Expanding the study and application of functional genomics has great promise to advance the process of discovering and repurposing drugs, especially in the field of oncology.

Dysferlinopathies comprise a spectrum of muscular dystrophies characterized by progressive muscle weakness and degeneration due to mutations in the DYSF gene, which encodes the dysferlin protein critical for muscle membrane repair. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We explore the phenotypic heterogeneity of dysferlinopathies, highlight the incomplete understanding of genotype-phenotype correlations, and discuss the implications of various DYSF mutations. Furthermore, we examine the utility of animal models in elucidating disease mechanisms and the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease's progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. By culminating the complexities inherent in dysferlinopathies, this article emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

With a view to understand and resolve the complexity of the food matrix, ohmic technologies alone or in combination were extensively employed. In this sense, the newest developments and advances of proteomics, metabolomics, lipidomics whith their unique benefits could simplify and understand the link between physiological and pathological activities in biology, physiology, pathology, and food science and processing. This review aims to briefly introduce the basis of proteomics, metabolomics, lipidomics, then expansively review their impact on the assessment of meat quality and safety. Here, also, we discuss the application of proteomics, metabolomics, lipidomics for the authentication and adulteration of meat and meat derivatives from different sources, and provides some perspectives regarding the use of emerging techniques such as rapid mass spectrometry (MS) and non-invasive measurements for the analysis of meat quality and safety. This paper summarizes all significant investigation in these matters, and underlines the advances in analytical chemistry technologies and meat science areas. By emphasizing the requirement for additional examinations, this paper attempts a comprehensive knowledge of “foodomics” and their potential to improve their employment in meat science.

Log collection and storage is a crucial process for enterprises around the globe. Log analysis helps identify potential security breaches and, in some cases, is required by law for compliance. However, enterprises often delegate these responsibilities to third-party Cloud Service Providers (CSPs), where the logs are collected and processed for anomaly detection and stored in a data warehouse for archiving. Prevalent schemes rely on plain (unencrypted) data for anomaly detection. More often, these logs can reveal sensitive information about an organization or the customers of that organization. Hence, it is best to keep it encrypted at all times. This paper presents "SigML++," an extension of work done in "SigML." We utilize Fully Homomorphic Encryption (FHE) with the Cheon-Kim-Kim-Song (CKKS) scheme for supervised log anomaly detection on encrypted data. We use an Artificial Neural Network (ANN) based probabilistic polynomial approximations using a Perceptron with linear activation. We probabilistically approximate the Sigmoid activation function (σ(x)) in the encrypted domain for the intervals [−10,10] and [−50,50]. Experiments show better approximations for Logistic Regression (LR) and Support Vector Machine (SVM) for low-order polynomials.

In this study, the probabilistic exergoeconomic analysis was performed for four industrial gas turbine (GT) units comprising of two (GT16 and GT19) units of 100MW GE engine and two (GT8 and GT12) units of 25MW Hitachi engine at Transcorp Power Limited, Ughelli. These four industrial GT engine units were modelled and simulated using natural gas as fuel. The design point (DP) simulation results of the modelled GT engines were validated with the available DP thermodynamic data from original equipment manufacturer (OEM). This was done before the off-design point (ODP) simulation was carried out which represents the plant operations. The results obtained from exergy analysis at full load operation show that the turbine has the highest exergy efficiency followed by compressor and combustion having the least. For turbines these were 96.13% for GT8 unit, 98.02% for GT12 unit, 96.26% for GT16 unit, and 96.30% for GT19 unit. Moreover, the combustion chamber has the highest exergy destruction efficiency of 55.16% GT8 unit, 56.58% GT12 unit, 43.90% GT16 unit, and 43.30% GT19 unit respectively. The exergy analysis results obtained from the four units show that the combustion chamber (CC) is the most significant exergy destruction with lowest exergy efficiency and highest exergy destruction efficiency of plant components. The exergoeconomic analysis results from four units showed combustion chamber energy destruction cost of 531.08 $/h GT8 unit, 584.53 $/h GT12 unit, 2351.81$/h GT16, and 2315.93$/h GT19 unit. The probabilistic results analysis based on the input parameters distributions evaluated and discussed.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

Reassessment of the fatigue life for wind turbines structural components is typically performed using deterministic methods with the same partial safety factors as used for the original design. However, in relation to life extension, the conditions are generally different from the assumptions used for calibration of partial safety factors; and using a deterministic assessment method with these partial safety factors might not lead to optimal decisions. In this paper, the deterministic assessment method is compared to probabilistic and risk-based approaches, and the economic feasibility is assessed for a case wind farm. Using the models also used for calibration of partial safety factors in IEC61400-1 ed. 4 it is found that the probabilistic assessment generally leads to longer additional fatigue life than the deterministic assessment method. The longer duration of the extended life can make life extension feasible in more situations. The risk-based model is applied to include the risk of failure directly in the economic feasibility assessment and it is found that the reliability can be much lower than the target for new turbines, without compromising the economic feasibility.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these popular distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is the upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of a random discrete variable. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

By combining stochastic variational inference with message passing algorithms we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases the robots shares the operating space and needs to cross each other’s paths multiple times without colliding.

Short video addiction refers to a psychological dependency on short video applications, characterized by an intense attachment and an inability to control usage, leading to typical addiction symptoms. While its definition bears similarities to substance addiction, significant differences also exist. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), published in 2013, short video addiction has not yet been officially classified as an addiction disorder. To facilitate more in-depth and standardized research on short video addiction, it is essential to synthesize and organize existing studies on its theoretical foundations and mechanisms.

The impacts of inflation on international finance are generally reflected in the decline of buying power, risks of investment and deposit loss from currency devaluation. For the purpose of optimized allocation and management of asset values, i.e., annual income or research grants, a framework of a three-level model for static financial management is presented to maximize banking interests, bonuses, and the remaining balance after spending necessary expenditures. Meanwhile, in retrospect to higher-level financial inflation, strategies of dynamic investment, i.e., posing regular tests of flipping coins, exploiting variations of the currency exchange rate, as well as applying the theory of broad money, are presented to increase the appreciation rate of net assets in and post the era of COVID-19. Quantitative evaluations in case studies verify the feasibility of improving the financial situation, indicating broader applications for budgeting and investing when US inflation rate is below 3.0%. Besides, combining approaches of static and dynamic strategies, the expected annual growth rate of individual zero-risk asset investment is predicted as within the range of 8.30~9.62% according to budgeted appreciation on net assets in 12 months, which is capable of overcoming negative impacts from higher level annual inflation, i.e., those as highest as 8.01% in the Year 2022.

Lyme disease is a highly prevalent tick-borne infection caused by Borrelia burgdorferi spirochetes in North America and Eurasia. Diagnosis of the early disease remains difficult with the currently approved serological tests in the absence of noticeable tick bite or erythema migrans due to presentation of non-specific symptoms and the absence of antibodies early in infection. Furthermore, it is difficult to distinguish persistence of disease and reinfection in the endemic regions. If not treated early, infection spreads throughout the body and affects most organs with disease manifestations including arthritis, musculoskeletal pain, neuroborreliosis and acrodermatitis chronicum atrophicans. Although most patients recover after antibiotic treatment, ~10-12% of show recurrence of symptoms known as post-treatment Lyme disease syndrome (PTLDS). Various causes of PTLDS are not well-defined; however, the spirochete persisters, antigenic debris, dysregulation of immunological response in individuals or a combination of these factors are proposed to result in continuation of Lyme disease manifestations. Based on these considerations, this review highlights currently employed treatment approaches with antimicrobials and vaccines being tried for prevention of B. burgdorferi infection. We also summarize combinatorial approaches that could be employed to alleviate symptoms to improve the quality of life of the individuals suffering from long Lyme disease.

Acute lower respiratory tract infection is a major health problem that affects more than 15% of the total population of Saudi Arabia each year. Epidemiological studies conducted over the last three decades have indicated that viruses are responsible for the majority of these infections. The epidemiology of respiratory viruses in Saudi Arabia is believed to be affected mainly by the presence and mobility of large numbers of foreign workers and the gathering of millions of Muslims in Mecca during the Hajj and Umrah seasons. Knowledge concerning the epidemiology, circulation pattern, and evolutionary kinetics of respiratory viruses in Saudi Arabia are scant, with the available literature being inconsistent. After reviewing the available data on epidemiology and evolution of respiratory viruses, this study examines the effect of varying vaccination rates across different age groups during the 2019/2020 influenza season in Saudi Arabia. Utilizing an age-structured SEIR (Susceptible–Exposed–Infectious–Recovered) model, it develops customized vaccination policies by considering age-specific incidence rates. This pioneering research segments the population into five age groups—infants and school-aged children, junior and older youth, young adults and adults, middle-aged, and seniors—across five regional areas in Saudi Arabis: Eastern, Central, Northern, Western, and Southern. A contact matrix, compiled from multiple sources, is used to delineate the dynamics of influenza transmission. The study undertakes a thorough comparison of three distinct vaccination scenarios: no vaccination, national vaccination, and optimized vaccine allocation policy, under various potential pandemic situations. It examines their impact on vaccination campaigns in terms of the total number of infections, mortality, and morbidity rates. The results emphasize the crucial impact of population demographics on disease transmission and vaccine distribution. According to the findings, the optimized vaccination scenario proved to be the most effective strategy in all scenarios.

Lightning is responsible for most annually burned area by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm designed to run on a uniform 20-km grid over the Continental USA and Alaska. The algorithm consists of a large set of logistic equations parameterized via logistic regression using long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 Principal Components (strong predictors) from a list of 611 potential predictors. Our statistical analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: horizontal temperature distribution by pressure levels, amount of low-level atmospheric moisture, and wind vectors. These physical variables impact the vertical separation of electric charges in the lower troposphere during storms causing the voltage potential between ground and the cloud deck to increase to a level that triggers electrical discharges. Results from a forecast verification using independent data showed excellent model skill, thus making this algorithm suitable for inclusion into software systems forecasting the chance of wildfire ignitions.

Probabilistic latent semantic analysis is a statistical technique developed for information retrieval and spanned many fields. It yields intuitive and solid results. However, the rigidity of the assumptions and the iterative nature derived from the Expectation-maximization algorithm generate several problems, dividing detractors and enthusiasts. In this manuscript, we first describe the Probabilistic latent semantic analysis. After, we discuss reformulations that attempt to solve these problems. We pay special attention to the works relating Probabilistic latent semantic analysis and the Singular value decomposition Theorem. Also, Probabilistic latent semantic analysis can be the basis for other techniques, such as kernelization or probabilistic transfer learning, and those that extend the descriptive character of the Principal component analysis to an inferential tool and open a window of opportunities.

Calculus, as a starting point to everyone's study of mathematics, was developed along with classical mechanics. Though not rigorously formulated, the intuition for differential form and infinitesimal motion makes calculus not hard to understand. And hence it does not require great effort to grasp the principles of classical mechanics. However, the situation becomes quite different when it comes to the quantum world. Given a particle system, what of interest is always the physical quantity of this system, such as energy, position, momentum, entropy, etc. But it is no longer straightforward.

Modern building automation systems implement plenty of advanced control and monitoring functions considering various parameters like users’ activity, lighting, temperature changes etc. Moreover, new solutions based on Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a new approach in building automation systems design to provide effective way of their implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. In this paper, the authors discuss development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with Internet of Things considering organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements from EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator.

This paper presents a scenario evolution model for maritime accidents using Bayesian networks (BN) to predict the most likely causes of specific types of maritime incidents. The BN nodes encompass accident type, life loss contingency, accident severity, the quarter and time period of the accident, and the type and gross tonnage of the ships involved. We analyzed 5,660 global maritime accidents from 2005 to 2020. Using Netica software, we constructed a Tree Augmented Network (TAN) model, accounting for interdependencies among risk influencing factors. We validated the results through sensitivity analysis and historical accident records. Forward causal inference and reverse diagnostic inference were then performed on each node variable to investigate the accident development trend and evolution process under predetermined conditions. The findings indicate that the model can effectively predict the likelihood of various accident scenarios under specific conditions, as well as the extrapolation of accident consequences. Forward causal reasoning reveals that general cargo ships with a gross tonnage of 1-18,500 t are most likely to experience collision, grounding, and stranding accidents in the first quarter. Reverse diagnostic reasoning indicates that during early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1-18,500 t are less likely to involve life loss in the event of collision accidents.

The age of information (AoI) metric was proposed to measure the freshness of messages obtained at the terminal node of a status updating system. In this paper, the AoI of discrete time status updating system with probabilistic packet preemption is investigated by analyzing the steady state of a three-dimensional discrete stochastic process. Assuming the queue used in system is Ber/Geo/1/2*/η, which represents that the system size is 2 and the packet in buffer can be preempted by fresher packet with probability η. Instead of considering system’s AoI separately, we use a three-dimensional state vector (n,m,l) to simultaneously track the real time changes of the AoI, the age of packet in server, and the age of packet waiting in buffer. We give the explicit expression of system’s average AoI, and show that the average AoI of system without packet preemption is obtained by letting η=0. When η is set to 1, the mean of AoI of system having Ber/Geo/1/2* queue is obtained as well. Combining the results we have obtained and comparing them with corresponding average continuous AoIs, we propose a possible relationship between average discrete AoI with Ber/Geo/1/c queue and the average continuous AoI with M/M/1/c queue. For each of two extreme cases where η=0 and η=1, we also determine the stationary distribution of AoI using the probability generation function (PGF) method. The relations between average AoI and the packet preemption probability η, as well as AoI’s distribution curves in two extreme cases are illustrated by numerical simulations. Notice that the probabilistic packet preemption may occur, for example, in an energy harvest (EH) node of wireless sensor network, where the packet in buffer can be replaced only when the node collects enough energy. In particular, to exhibit the usefulness of our idea and methods and highlight the merits of considering discrete time systems, in this paper we give much more explanations showing that how the results about continuous AoI is derived by analyzing the corresponding discrete time system, and how the discrete age analysis is generalized to the system with multiple sources. In terms of packet service process, we also propose our idea to analyze system’s AoI when the service time distribution is relaxed to be arbitrary.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

Microgrids can be islanded to improve the reliability of the electrical energy supply during contingencies such as faults. It is still unclear what stability phenomena can occur in microgrids without inertia during and immediately after the fault-initiated islanding transient, and how the stability should be evaluated. To allow robust planning and operation, a stability analysis methodology should be able to take into account the inherent uncertainty of load and renewable energy sources. Therefore, this paper proposes a probabilistic stability analysis methodology for fault-initiated islanding of microgrids based on a thorough analysis of the root causes of instability during islanding transients. To perform stability analysis within reasonable time and allow control actions during operation, dynamical microgrid models in the dq reference frame are described and validated by comparing time-domain simulations results to previously validated component-based models. The stability analysis methodology is demonstrated in a case study of a generic microgrid. The instability phenomena and the dq microgrid models are successfully validated, and the results indicate that the stability analysis methodology can assist the microgrid operator to improve fault-initiated islanding capability in order to improve reliability of supply.

Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem.To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the " true, false and inconclusive" in three-valued semantics. They cannot give accurate quantitative description of correctness when "inconclusive" is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such method, one can give early warning once the probability of correctness is lower than pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.

Background: This study focuses on different manual handling methods and their effect on psychomotor skills concerning physical measurements (height, weight, age) and pinch strength. Objective: To assess the effects of three manual handling techniques on psychomotor skills and to establish the prediction of pinch strength by physical measurements. Methods: Three manual handling methods were tested: handling with a 90-degree elbow angle, handling with one hand, and handling with both hands. Psychomotor skills were quantified through handgrip strength, dexterity tests, and perceived exertion scales. Linear and random forest regression models were used to predict pinch strength from physical measurements. Results: Handling with a 90-degree elbow angle significantly increased perceived exertion and error rates while decreasing post-task handgrip strength. The one-hand method showed the least negative effect on psychomotor abilities. Weight emerged as the most influential predictor of pinch strength, followed by height and age. The random forest regression model outperformed the linear regression model, indicating its suitability for capturing complex non-linear relationships among variables. The Random Forest Regression model excelled in predicting weight with a high R² score of 0.78, showing strong predictive power. It was moderately effective for height with an R² score of 0.42. However, it performed poorly for age prediction, with an R² score of 0.01, indicating ineffective features for predicting age. Conclusion: The results emphasize the importance of ergonomic interventions and training programs to optimize manual handling practices, aiming to reduce physical strain and enhance worker productivity and safety. Maintaining muscle strength is crucial in older age groups due to the decline in pinch strength with age. These findings can inform intervention studies and the modification of occupational health and safety practices. Future research should aim to validate these results across diverse populations and explore additional factors influencing handgrip strength.

This review examines methodologies studying soil carbon in urban green spaces, a field requiring higher attention as their ecosystem services are valued and urban mitigation plans against climate change are developed. In the growing global urbanization, urban green spaces play a crucial role as nature-based solutions to address climate challenges, demanding efficient carbon accounting and reduction strategies. In this research, a systematic review was conducted assessing publications from 2021 to 2023 in the databases Web of Science and Scopus. Findings expose the lack of standardization in the methodologies used to study soil carbon in urban green spaces, evidencing in situ measurements at different sampling depths and studies without longitudinal monitoring over time. In addition, studies originated primarily in universities from China, United States and Spain, which focused on carbon in the form of Soil Organic Carbon and Total Carbon. Based on these findings, recommendations are made advocating for the combination and standardizing methodologies. This review offers valuable insights that shed light on an ignore and often overseen issue. Further studies and standardized methodology are needed, to help cities efforts in their pursuit of carbon neutrality by acknowledging the influential role of urban soils, especially green spaces.

Lumpy skin disease (LSD), caused by a virus within the Poxviridae family and Capripoxvirus genus, induces nodular skin lesions in cattle, spreading through direct contact and insect vectors, significantly affecting global cattle farming. Despite available vaccines, their effectiveness is limited by poor bioavailability and adverse effects. Our study aimed to identify potential inhibitors targeting LSD-associated DNA polymerase protein, selecting LSDV039 for further investigation through comprehensive analysis and computational methods. Virtual screening revealed rhein and taxifolin as potent binders among 380 phytocompounds, with respective affinities of -8.965 and -7.195 kcal/mol. Canagliflozin and tepotinib exhibited strong affinities (-9.858 and -8.856 kcal/mol) among 718 FDA-approved antiviral drugs. Molecular dynamics simulations of canagliflozin, tepotinib, rhein, and taxifolin highlighted taxifolin's superior stability and binding energy. Rhein displayed compactness in RMSD and RMSF but fluctuations in Rg and SASA, while canagliflozin demonstrated stability compared to tepotinib. The study highlights the promising potential of repurposed drugs and phytocompounds as LSD therapeutics. However, extensive validation through in-vitro, in-vivo, and clinical trials is crucial for their practical application.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

An approach to the assessment of probabilistic inference is described which quantifies the performance on the probability scale. From both information and Bayesian theory, the central tendency of an inference is proven to be the geometric mean of the probabilities reported for the actual outcome and is referred to as the “Accuracy.” Upper and lower error bars on the accuracy are provided by the arithmetic mean and the -2/3 mean. The arithmetic is called the “Decisiveness” due to its similarity with the cost of a decision and the -2/3 mean is called the “Robustness”, due to its sensitivity to outlier errors. Visualization of inference performance is facilitated by plotting the reported model probabilities versus the histogram calculated source probabilities. The visualization of the calibration between model and source is summarized on both axes by the arithmetic, geometric, and -2/3 means. From information theory, the performance of the inference is related to the cross-entropy between the model and source distribution. Just as cross-entropy is the sum of the entropy and the divergence; the accuracy of a model can be decomposed into a component due to the source uncertainty and the divergence between the source and model. Translated to the probability domain these quantities are plotted as the average model probability versus the average source probability. The divergence probability is the average model probability divided by the average source probability. When an inference is over/under-confident, the arithmetic mean of the model increases/decreases, while the -2/3 mean decreases/increases, respectively.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

This narrative review examines the crucial role of physical activity in managing long COVID, characterized by persistent symptoms such as fatigue, breathlessness, and cognitive impairments following SARS-CoV-2 infection. The methodology involved a comprehensive literature search across PubMed, EBSCO (EDS), and Google Scholar, selecting peer-reviewed articles from December 2019 focusing on long-term COVID physical activity interventions. The review highlights that tailored exercise programs, adjusted to individual health status and abilities, significantly alleviate symptoms and enhance psychological well-being. Key findings emphasize the importance of personalized exercise prescriptions due to the variability in patient responses and the need for a multidisciplinary approach in developing and monitoring interventions. Despite promising outcomes, the review identifies research gaps, including the need for long-term studies, randomized controlled trials, and deeper mechanistic insights. It suggests that standardized reporting, templates like CERT, and integrating digital health tools are essential for optimizing interventions. Overall, the review advocates including personalized physical activity programs in standard care for long COVID to improve patient outcomes and quality of life.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

This research examines sustainable approaches in academic career development by analyzing the interactions between job satisfaction, professional commitment, and research performance among early-career researchers in Romanian institutions. Using a quantitative cross-sectional design, data were collected from 96 individuals classified as R1 and R2 by the European Commission. A detailed questionnaire and Web-Assisted Personal Interviewing (WAPI) identified key relationships. Findings show a significant positive correlation between job satisfaction and research perfor-mance, underscoring the role of professional satisfaction in fostering sustainable academic ex-cellence. Professional commitment is also a crucial predictor of research performance, aligning with existing studies and highlighting the importance of sustainable career practices. Statistical analysis via SPSS version 23.0 validated the initial hypotheses, emphasizing each dimension's significance in a sustainable academic career. Results reflect Romania's research environment, emphasizing the need for sustainable working conditions, effective research evaluation, sup-portive career frameworks, and strong professional commitment to motivate young researchers. This research proposes innovative, sustainable practices for enhancing academic performance and career management, advocating clear objectives, leadership development, continuous competency improvement, and a collaborative, inclusive work environment. Understanding these factors aids individuals and institutions in making informed decisions that support sustainable academic career trajectories and professional growth.

Nepal is one of the most seismically active regions in the world, as highlighted by the recent devastating 2015, Mw7.8 Gorkha earthquake and a robust assessment of seismic hazard is paramount for the design of earthquake-resistant structures. In this study we present a new probabilistic seismic hazard assessment (PSHA) model for Nepal. We considered data and findings from recent scientific publications, which allowed us to develop a unified homogenized seismicity catalogue, propose alternative Seismic Source Characterization (SSC) models including up-to-date parameters of major thrust faults like Main Frontal Thrust – MFT and Main Boundary Thrust – MBT, while also considering existing SSC models and various hazard modelling strategies within a logic tree framework. The sensitivity analyses show the hazard levels are generally higher for SSC models integrating the major thrust faults, followed by homogenous volume sources and smoothed seismicity approach. The hazard maps covering entire Nepal is presented as well as the Uniform Hazard Spectra (UHS) for 5 selected locations (Kathmandu, Pokhara, Biratnagar, Nepalganj and Dipayal) at return periods of 475- and 2,475-years considering Vs,30=760 m/s. The results obtained are generally consistent with most recent studies. However, a notable variability in hazard levels and several discrepancies with respect to the Nepal Building Code NBC105: 2020 [1] and Global Hazard Model, GEM [2] are noted and possible causes are discussed.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

Driven by the importance of early detection for improving patient outcomes, this study investigates the effectiveness of various machine learning algorithms in predicting lung cancer based on clinical data. Lung cancer remains a significant global health concern, and this research seeks to contribute to advancements in early diagnosis through the application of machine learning techniques. The dataset consists of 385 training and 97 test samples, with a k-fold cross-validation approach (k=5) utilized to mitigate overfitting. Additionally, the ADASYN oversampling technique is employed to address class imbalance. Our analysis includes traditional algorithms such as Logistic Regression and Decision Trees, as well as ensemble methods like XGBoost, LGBM, and AdaBoost. Furthermore, we explore the effectiveness of deep learning models, particularly Deep Neural Networks (DNN). Results demonstrate promising accuracy rates across all models, with some outperforming others in terms of precision, recall, and F1-score. Our findings reveal that LGBM achieves the highest accuracy among all models, with an accuracy rate of 96.91%. It also exhibits superior performance in terms of precision, recall, and F1-score, particularly in classifying both positive and negative instances of lung cancer.

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation using neural networks with two outputs to make probabilistic predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), and loss, which is the integration of these two metrics, by removing unnecessary features through feature selection. When features with high gain were selected by random forests (RF), in the forecast of 14.7-kW PV systems, loss improved by 1.57 kW, PICP by 0.057, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise in LUBE and reduce the prediction accuracy.

The trigeminal nerve is responsible for innervating the periorbita. Ultrasound-guided trigeminal block is employed in humans for trigeminal neuralgia or periorbital surgery. There are no studies evaluating this block in dogs. This study aims to evaluate and compare two approaches (coronoid and temporal) of the trigeminal nerve block. We hypothesized superior staining with the coronoid approach. Thirteen dog heads were used. After a preliminary anatomical study, two ultrasound-guided injections per head (right and left, coronoid and temporal approach, randomly assigned), with an injectate volume of 0.15 mL cm-1 of cranial length were performed (iodate contrast and tissue dye mixture). The ultrasound probe was placed over the temporal region, visualizing the pterygopalatine fossa. For the temporal approach, the needle was advanced from the medial aspect of the temporal region in a dorsoventral direction. For the coronoid approach, it was advanced ventral to the zygomatic arch in a lateromedial direction. CT scans and dissections were conducted to assess and compare the position of the needle, spread of injectate, and nerve staining. No significant differences were found. Both approaches demonstrated effective interfascial distribution of injectate, with some minimal intracranial spread. Although the coronoid approach did not yield superior staining as hypothesized, it presents a viable alternative to the temporal approach. Studies in live animals are warranted to evaluate clinical efficacy and safety.

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disor-ders, even nearly a century after its discovery, due to the elusive nature of its causes. The develop-ment of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, open-ing new possibilities for treatment. Despite these advancements, conventional treatments for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, under-scoring the need for innovative solutions. This necessity has led to the incorporating of advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

This article presents a low-cost insurance system developed for small-hold farms in disaster-prone regions, primarily using free Earth Observation (EO) data and Free Open Source Software (FOSS) – collectively termed sustainable geoinformatics - with a proprietary mobile phone app that enables verification. The study examined 30 farms in Risaralda department, Colombia. A Digital Elevation Model (12.5m pixels) from the ALOS PALSAR satellite sensor was used with a Geographical Information System (GIS) to map the terrain, drainage and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out Time Series Analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was piloted using GPS-tagged, time-stamped mobile phone photos to verify crop health. Key features of this insurance system are its low operational cost and rapid damage verification, relative to conventional approaches to farm insurance. It is an affordable form of insurance for small-scale farmers, with the rapid verification and payment of claims. This relatively fast, low-cost and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policy-holder farmers to recover more quickly from disasters.

Conventional monitoring relies on "in vivo" (non-destructive) measurements of aboveground organ traits (such as stems, branches, and leaves) to analyze plantation growth trends and assess the effectiveness of management practices like watering and fertilization. This study reveals the necessity of “ex vivo” (destructive) approaches, post-excavation, to ensure fully reliable monitoring of plantation performance, including belowground organ growth and development trends. We showcase the effectiveness of dendrometric analysis, which measures the annual ring width in the secondary xylem (wood) of the taproot and stem. Monitoring above- and belowground organ growth trends allow for the exploration of similarities and differences in how different tree organs respond to internal and external factors. Such monitoring is especially crucial for assessing plantation performance in challenging climates, such as those found in arid and semi-arid lands.

Understanding mechanisms of ageing remains a complex challenge for biogerontologists, but recent adaptations of evolutionary ageing theories offer a compelling lens in which to view both age-related molecular and physiological deterioration. Ageing is commonly associated with a progressive loss of biochemical processes and degeneration of molecular material, however, the mechanisms of diminishing function are not simply a result of aberrant expression of these pathways. Natural selection pressures are at their highest in youthful periods to select genes advantageous to maximising reproductive capacity. After sexual maturation, selective pressure diminishes, subjecting individuals to maladaptive pleiotropic gene function once beneficial for developmental growth, but pathogenic later in life. Due to this selective ‘shadowing’ in ageing, mechanisms to counter hyperfunctional genes are unlikely to evolve within a given species. These genetic ‘run-on’ programmes are not dysfunctional per se and, in fact, remain highly robust. Thus, interventions capable of optimising gene function during the ageing process represent as attractive therapeutic modalities. Caenorhabditis elegans offers a unique model to explore high-throughput screening of interventions to combat hyperfunctional pathways involved in ageing. However, the majority of research to date employ whole-life vs late-life pathway manipulation of developmental/growth-related systems to claim ageing attenuation, despite significant impairments in early-life fitness and discordance with healthspan. Here, we discuss data supporting hyperfunctional changes at a global molecular and functional level in C. elegans, and how classical lifespan-extension mutants alter these dynamics. We later discuss the relevance of such mutant models for exploring mechanisms of ageing, highlighting that post-reproductive gene manipulation represents a far more translatable approach for C. elegans research not routed within pleiotropic constraints. More so, lifespan has become a routine readout for quantifying ‘ageing’, however, we make the argument that a compression of gerospan vs lifespan extension is a more meaningful goal for anti-ageing regimes with current knowledge. Similarly, mechanisms governing lifespan are distinct from those regulating health, and slowing senescent pathology accumulation appears more applicable for the abstraction to human ageing.

The BP neural network and PSO algorithm are applied into analyzing the reliability of long-span Concrete Filled Steel Tubular (CFST) arch bridges. Firstly, using BP neural network to fit the structural performance function. And then the PSO method was used to calculate the reliability index. A long-span Concrete Filled Steel Tubular arch bridges reliability indices were calculated whether considering the geometric nonlinearity or not. The calculation and analysis results showed that the BP neural network and PSO algorithm compensated the deficiency of the traditional reliability analysis methods, improved the calculation accuracy, provided a new thought and means for the research on the reliability of long-span bridge structure, and well applied to the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges. Additionally, the reliability analysis of long-span Concrete Filled Steel Tubular arch bridges on service limit state must consider the geometric nonlinearity effect, or the results will tend to be unsafe.

Energy storage systems (ESSs) are the key elements to improve the operation of power systems. On the other hand, these elements challenge the power system planners. The difficulties arise as a result of the ESSs' economic and technological features. The cycle life of ESSs is a critical aspect that influences the choices made during expansion planning processes. In this manuscript, we have focused on a new model for the expansion planning of ESSs considering the impacts of technical properties, such as the cycle life and depth of discharge. For this purpose, the proposed model consists of the hourly operation planning of ESSs in the sample days of year. A new indicator is proposed to determine the daily charging/discharging cycles of ESSs. Numerical results show the ability of the proposed model to determine the optimal technology and capacity of ESSs.

Blockchain technology has been gaining great interest from a variety of sectors including healthcare, supply chain, and cryptocurrencies. However, Blockchain suffers from its limited ability to scale (i.e. low throughput and high latency). Several solutions have been proposed to tackle this. In particular, sharding proved that it is one of the most promising solutions to Blockchain’s scalability issue. Sharding can be divided into two major categories: (1) Sharding-based Proof-of-Work (PoW) Blockchain protocols, and (2) Sharding-based Proof-of-Stake (PoS) Blockchain protocols. The two categories achieve good performances (i.e. good throughput with a reasonable latency), but raise security issues. This article focuses on the second category. In this paper, we start by introducing the key components of sharding-based PoS Blockchain protocols. Then, we briefly introduce two consensus mechanisms, namely PoS and practical Byzantine Fault Tolerance (pBFT), and discuss their use and limitations in the context of sharding-based Blockchain protocols. Next, we provide a probabilistic model to analyze the security of these protocols. More specifically, we compute the probability of committing a faulty block and measure the security by computing the number of years to fail. Finally, we evaluate the effectiveness of the proposed model via numerical analysis.

Engineering Education needs to be approached in different, innovative ways to meet the needs of society, learners, and education providers. This ranges from new educational programs and innovative pedagogies in individual classrooms, to online and immersive learning, to micro-credentials and modularity along with how to enable rapid adaptation for reskilling and upskilling, to entirely new pathways of restructuring education. Gamification is the application of game elements and digital game design techniques to non-game problems, such as learning challenges. It is about using game-based mechanics, aesthetics, and game thinking to enhance users’ engagement, motivate action, and support learning. At first view, the concept of gamification does not contain game design elements, but with the addition of additional elements such as the video game component that is applied in different contexts, the users are encouraged to participate in learning activities and complete learning challenges successfully. The first step in the process of designing a gamification activity is to think like a game designer. According to Cook (2013), any process can be gamified when the following requirements are covered: (i) the gamified activity is easy to learn, (ii) user progress can be measured, (iii) continuous feedback is provided to users.

Probiotics are microorganisms infused in products for health benefits including acceleration of nutrient digestion, however, it is also important to ensure the safety prior to incorporation. Here, we present evidence of the ability of two probiotic isolates,Lacticaseibacillus paracasei BCRC-16100 and Lacticaseibacillus paracasei ZFM54, in the (1) enhancement of carbohydrate digestion, (2) tolerance to stress, and (3) antibiotic resistance. Approaches include whole genome sequence (WGS) analysis and bioactivity assays. WGS revealed genes suggesting the ability of the two isolates to promote carbohydrate digestion, tolerance to stress and antibiotic resistance. Carbohydrate digestive ability was confirmed through a biochemical assay where the two isolates cause glucose release from starch. The two isolates also showed versatility in a range of temperature and alcohol concentration. For antibiotic resistance particularly on vancomycin, there are three mechanisms namely transporter control, transcriptional regulation, and efflux pump. Furthermore, promoter and transposable element analysis showed that some of the active antibiotic resistant (AbR) genes can be laterally transferred. Altogether, we show the potential of two probiotic isolates to be incorporated in products for improved carbohydrate digestion and the need to address the removal of active and mobile antibiotic resistance genes that may compromise safety.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from atmospheric reanalysis over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM, as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation, as compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy.

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is used widely in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties, including anti-inflammatory, antiviral and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures, but also new synthetic routes developed during the last years as well as promising therapeutic applications.

Multivariate statistical approaches (MSA), such as principal components analysis and multidimensional scaling, seek to uncover meaningful patterns within datasets by considering multiple response variables in a concerted fashion. Although these techniques are readily used by ecologists to visualize and explain differences between study sites, they could theoretically be employed to differentiate organisms within an experimental framework while simultaneously identifying response variables that drive documented experimental differences. Therefore, MSA were used herein to attempt to understand the response of the common, Indo-Pacific reef coral Seriatopora hystrix to temperature changes using data from laboratory-based temperature challenge studies performed in Southern Taiwan. Gene expression and physiological data partitioned experimental specimens by time of sampling, treatment temperature, and site of origin upon employing MSA, signifying that S. hystrix and its dinoflagellate endosymbionts display physiological and molecular signatures that are characteristic of sampling time, site of colony origin, and/or temperature regime. These findings promote the utility of MSA for documenting biologically meaningful shifts in the physiological and/or sub-cellular response of marine invertebrates exposed to environmental change.

Bacillus Calmette-Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG's efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and enhanced stimulation of anti-tumor immune responses. Key strategies involve using functional BCG cell wall components and co-delivering BCG with chemotherapeutics to achieve synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG's limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG's mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.

Despite many calls to reduce or eliminate the use of mechanical restraint, it is still widely used in many countries. Studies using patient interviews have a very clear message: Patients experience mechanical restraint as the most humiliating intervention. There seems to be a lack of alternatives with violent patients. We developed a method using 30 kg bags, originally designed for fitness purposes, to be attached to a patient's wrist or ankle under 1:1 supervision. The method was tested with 10 experienced nurses and de-escalation trainers. A video was made and presented to six outpatients who had previously experienced mechanical restraint. All participants were interviewed. Transcribed interviews were analysed using qualitative content analysis. All participants approved of the method as a milder and less humiliating alternative to mechanical restraint. Nurses' main concerns were the risk of falls and the use of the bags as a weapon. The latter could be controlled by using an additional bag. Patients were generally positive, especially if there was a history of abuse. The method should be further developed to replace at least part of mechanical restraints. As with all ‘milder means‘, care should be taken to really replace restraint and not to introduce additional coercion.

Multicriteria decision aid requires a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g. means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensate for this relaxation. The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences (CPP) method in multicriteria problems with data from Likert scales. The CPP is capable of allaying the protests raised and obtaining more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of CPP with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the CPP.

To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

Wheat, a component of the staple diet globally, is a common food allergen in children. The symptoms of wheat allergy (WA) range from skin rash to shortness of breath, significantly impairing quality of life. Following initial clinical suspicion, individuals may undergo wheat allergen-specific skin prick test (SPT), blood test for serum immunoglobulin E (sIgE) levels or oral food challenge. Conventional management of WA lies in wheat avoidance, yet accidental consumption may be inevitable owing to the ubiquity of wheat in various food products. This article aims to provide an overview of the immunologic pathway of WA, followed by its novel diagnostic methods, namely alcohol-soluble SPT extracts, component-resolved diagnosis and basophil activation test (BAT). The mechanisms underlying wheat allergen-specific oral immunotherapy (OIT) as well as a summary of the efficacy, tolerability and safety of related clinical trials will then be discussed.

This work aims to generate some types of M_(j )–neighborhoods. Relationships among these M_(j )-neighborhoods and some other types of neighborhoods are discussed. Additionally, M_(j )-lower, M_(j )-upper approximations and M_(j )-accuracy in terms of M_(j )-neighborhoods are presented. Their properties, with some examples, are investigated. Finally, We addressed the comparisons among M_(j )approach and other approaches.

Scrub typhus (ST) is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the ‘tsutsugamushi triangle’, diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters to human body through the bite of trombiculid mite (Chigger) of the genus Leptotrombidium deliense.The diagnosis of the disease becomes challenging as its early symptoms mimic other febrile illnesses like dengue, influenza, and corona viruses. Lack of rapid, reliable, and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent population, habitat characteristics, and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns, and behavioral interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

For a significant period, conventional breeding and genetic modification were the key techniques that were effective in managing biotic and abiotic stresses in crops and adding desirable traits. However, the recent appearance of novel diseases and unexpected climatic changes that have substantial implications for agriculture worldwide have urged scientists to look for alternative methods to quickly manage seasonal crises. The primary emphasis of this chapter is on the obstacles and diverse biotechnological methods employed to enhance crop resilience against a range of biotic and abiotic stressors in plants. Furthermore, we consider gene transformation, omics techniques, genome editing, and other sophisticated biotechnological tools that utilize transcriptomic, proteomic, metabolomic, phenomics, RNA interference, and epigenome modifications for enhancing plant resilience. Lastly, we examine the potential of merging these eco-friendly and innovative methods with conventional breeding to promote modern agriculture and aid in enhancing tolerance to different biotic, abiotic stresses and growth promotion.

Early disease detection in livestock allows for target treatment decreasing antibiotics use and allow advancements in precision veterinary medicine. MicroRNA (miRNA) -driven signaling cascades play a crucial role in the context of farm animal disease diagnostics and prediction, and their proper understanding remains a challenge. In livestock farm animals, only a small number of miRNAs have been fully validated with respect to disease conditions and physiological or behavioral traits. Low abundance of miRNAs in blood and bodily fluids, along with a small number of nucleotides, makes detection and discrimination tedious and challenging task in. miRNAs usually are homologous, owing to which detection specificity becomes next to impossible when screening for multiple miRNAs in the same analyte sample. Hence, a concurrent, multiplexing, approach becomes crucial for the development of on-farm point-of-care based detection systems. Comprehensive screening methods demand broad dynamic range and enhanced specificity. For on-farm handheld platform development, the ability to screen for multiple varieties of miRNA is essential. In this review paper, I provide an overview of the recent developments of miRNA sensing and the current bottlenecks in the realization of the sensors for detecting miRNAS as target analyte for various livestock disease detection applications. Due to the nascent stages of this research, the possibilities of exploiting miRNAs as a biomarker opens up ways to move from reactive to predictive possibilities in diseases detection in the modern digital livestock farming.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). This review paper provides a comprehensive overview of fNIRS, EEG, and fMRI, including their principles, advantages, limitations, and integration with other modalities for advanced BCIs. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Applications of advanced BCIs in clinical, assistive, gaming, and entertainment domains are also discussed. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature on data acquisition approaches and a comparison of the ease of use for each of the modalities.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty for the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research uses both univariate and multivariate ML techniques to perform probabilistic price prediction modelling for the Canadian beef industry, taking into account beef production, commodity market, and international trade features to enhance accuracy. We apply multivariate algorithms by using support vector regression (SVR), random forest (RF), and adaboost (AB) and univariate algorithms by using autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and adaboost (AB) in multivariate modeling show superior performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.

There is growing interest in the discovery of novel xanthine oxidase inhibitors for gout prevention and treatment with fewer side effects. This study aimed to identify the xanthine oxidase (XO) inhibitory potential and drug-likeness of the metabolites present in the methanolic leaves extract of Anastatica (A.) hierochuntica L. using in vitro and in silico models. The extract-derived metabolites were identified by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Molecular docking predicted the XO inhibitory activity of the identified metabolites and validated the best scored in vitro XO inhibitory activities for experimental verification as well as predictions of their anticancer, pharmacokinetic and toxic properties, oral bioavailability, and endocrine disruption using SwissADMET, PASS, ProTox-II and Endocrine Disruptome web servers. A total of 12 metabolites with a majority of flavonoids were identified. Rutin, quercetin and luteolin flavonoids demonstrated the highest ranked docking score of -12.39, -11.15 and -10.43, respectively, while the half-maximal inhibitory concentration (IC50) values of these metabolites against XO activity were 11.35 µM, 11.1 µM and 21.58 µM, respectively. In addition, SwissADMET generated data related to physicochemical properties and drug-likeness of the metabolites. Similarly, PASS, ProTox-II and Endocrine Disruptome prediction models stated the safe and potential use of these natural compounds. However, in vivo studies are necessary to support the development of prominent and promising therapeutic use of A. hierochuntica methanolic leaves extract-derived metabolites as XO inhibitors for the prevention and treatment of hyperuricemic and gout patients. Furthermore, the predicted findings of the present study open a new paradigm for these extract-derived metabolites by revealing novel oncogenic targets for the potential treatment of human malignancies.

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment, where three typical problems arise. First, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Second, heterogeneous devices record different received signal strength (RSS) levels due to the variations in chip-set and antenna attenuation. Third, APs are not necessarily visible in every scanning cycle leading to missing data. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. Considering the spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) is fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP has been investigated in this research, and demonstrated the efficiency of differentiation between cells. The proposed system is able to achieve comparable localization performance. The filed test results present a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network without any prior knowledge of the environment.

No doubt pollution is a global problem which must be holistically tackled. In doing this, adequate knowledge of the sources of pollution is important, therefore the aim of this paper is to review source apportionment with reference to top-down and bottom-up methods. In this paper, dispersion modeling, emissions inventory, and sampling methods were discussed. Also, analytical methods involved in top-down source apportionment were mentioned. The two techniques are needed to evaluate pollutants and their sources. Based on these two approaches, pollution control strategy would be developed and decisions can be made in deciding the right approach to solve or reduce the pollution problems.

Legionella pneumophila can cause a large panel of symptoms beside the classic pneumonia presentation. We present here a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of legionnaires’ disease in the same ward. While the first one was easily assumed as nosocomial based on the date of symptoms onset, the other case needed to rely on clear typing results to be either assigned as nosocomial and related to the same environmental source as the first one, or community acquired. To untangle this specific question, we applied Core-genome MultiLocus Typing (MLST), whole-genome Single Nucleotide Polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large scale epidemiological investigations, NGS typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in environment.

In the last two centuries, land use change (LUC) has been the most important direct changes driver for terrestrial ecosystems. To contrast the consequent ecosystems degradation, forward-looking spatial policies and target landscape and land-use planning processes, promoting a sustainable land use change, are needed. The present paper proposes a framework of action including different landscape planning and ecological approaches: from the spatial modelling to recognize the LUC and build different scenarios, to the ecosystem services (ESs) assessment to evaluate the possible environmental impacts. Three different scenarios were built: Trend, No-Tillage and Energy crops. The Sediment Delivery Ratio and Carbon Storage and Sequestration ESs were assessed and compared for each scenario. The aim of the paper is to support decision-makers and local communities into the landscape planning process. Results show that a regional development in line with past trend could lead to further land degradation. Instead, the two scenarios proposed in compliance with EU policies, could bring benefits only if related to moderate LUCs and respecting the naturally grass-vegetated land. From the local to global scale, a guided and shared LUC management allows implementing sustainable development, basing on a deep knowledge of physical-environmental but also social and economic issues.

We discuss the influence played by different statistical models in the prediction of porosity and litho-fluid facies from logged and post-stack inverted acoustic impedance (Ip) values. We compare the inversion and classification results obtained under three different a-priori statistical assumptions: an analytical Gaussian distribution, an analytical Gaussian-mixture model and a non-parametric mixture distribution. The first model assumes Gaussian distributed porosity and Ip values, thus neglecting their facies-dependent behaviour caused by different lithologic and saturation conditions. Differently, the other two statistical models relate each component of the mixture to a specific litho-fluid facies, so that the facies-dependency of porosity and Ip values is taken into account. Blind well tests are used to validate the final predictions, whereas the analysis of the maximum-a-posteriori (MAP) solutions, the coverage ratio and the contingency analysis tools are used to quantitatively compare the inversion outcomes. This work points out that the correct choice of the statistical petrophysical model could be crucial in reservoir characterization studies. Indeed, for the investigated zone it turns out that the simple Gaussian model constitutes an oversimplified assumption, while the two mixture models provide more accurate results, although the non-parametric one yields slightly superior predictions with respect to the Gaussian-mixture assumption.

There has been a surge in the interest for (semi)transparent photovoltaics (sTPVs) in recent years, since the more traditional, opaque, devices are not ideally suited for a variety of innovative appli-cations spanning from smart and self-powered windows for buildings to those for vehicle inte-gration. Additional requirements for these photovoltaic applications are a high conversion effi-ciency (despite the necessary compromise to achieve a degree of transparency) and an aesthetical-ly pleasing design. One potential realm to explore in the attempt to meet such challenges is the bio-logical world, where evolution has led to highly efficient and fascinating light-management structures. In this mini-review, we explore some of the biomimetic approaches that can be used to improve both transparent and semitransparent photovoltaic cells, such as moth-eye inspired structures for improved performance and stability or tuneable, coloured, and semi-transparent devices inspired by beetles’ cuticles. Lastly, we will briefly discuss possible future developments for bio-inspired and potentially bio-compatible sTPVs.

Despite several decades’ effort to detect and identify phytoplasmas (Tenericutes: Mollicutes) using PCR and Sanger sequencing focusing on diseased plants, knowledge of phytoplasma biodiversity and vector associations remains highly incomplete. To improve protocols for documenting phyto-plasma diversity and ecology, we used DNA extracted from phloem-feeding insects and com-pared traditional Sanger sequencing with a next-generation sequencing method, Anchored Hy-brid Enrichment (AHE) for detecting and characterizing phytoplasmas. Among 22 of 180 leaf-hopper samples that initially tested positive for phytoplasmas using qPCR, AHE yielded phyto-plasma 16Sr sequences for 20 (19 complete and 1 partial sequence) while Sanger sequencing yield-ed sequences for 16 (11 complete and 5 partial). AHE yielded phytoplasma sequences for an addi-tional 7 samples (3 complete and 4 partial) that did not meet the qPCR threshold for phytoplasma positivity or yielded non-phytoplasma sequences using Sanger sequencing. This suggests that AHE is more efficient for obtaining phytoplasma sequences. Twenty-three samples with sufficient data were classified into eight 16Sr subgroups (16SrI-B, I-F, I-AO, III-U, V-C, IX-J, XI-C, XXXVII-A), three new subgroups (designated as 16SrVI-L, XV-D, XI-G) and three possible new groups. Our results suggest that screening phloem-feeding insects using qPCR and AHE sequencing may be the most efficient method for discovering new phytoplasmas.

Metabolite transport across cellular membranes is a key feature of living organisms. Specialized proteins or protein complexes mediate such transport processes and are accessible to genetic engineering approaches. This special issue will reflect on the different transport between cellular compartments. It will provide an overview about different transporter classes and metabolic engineering approaches in different organisms ranging from Bacteria to Eukarya. The transport mechanisms energetics of these compounds were thought little of, and most consideration was given to the designing of metabolic pathways. Gram-negative microbes discharge a wide scope of proteins whose capacities incorporate biogenesis of organelles, for example, pilli and flagella, supplement securing, destructiveness, and efflux of medications and different poisons. Six particular emission frameworks have been appeared to intervene protein send out through the inward and external layers of Gram-negative microbes. These pathways are exceptionally monitored all through the Gram-negative bacterial species. In Gram-positive microscopic organisms, discharged proteins are usually moved across the single layer by the Sec pathway or the two-arginine (Tat) pathway. Recently efforts focused on transporter and metabolite engineering, visualize the improvement and enhancement of microbial cell to pass such metabolites these metabolite carrier proteins are inserted into the inner membrane of mitochondria across the inner membrane, specialized carrier proteins are necessary that mediate the transport into and out of the mitochondrial matrix. Two main strategies for metabolic engineering in plants: the introduction of genes encoding new enzymes and the use of TFs controlling specialized metabolic pathways

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

This paper addresses the development of knowledge and assessment-centered learning approaches within a reflective learning framework in a first year physics class in a university faculty. The quality of students’ reflections was scored using a Self-reporting Reflective Learning Appraisal Questionnaire at the end of each learning approach. The results showed the differences between the approaches based on reflections on the learning control through self-knowledge, by connecting experience and knowledge, as well as through self-reflection and self-regulation. Assessment-centered activities fundamentally help students identify aspects of their attitudes towards, as well as regulate, their sustainability learning education.

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

Multiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real- time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Stroke represents one of the neurological diseases most responsible for death and permanent disability in the world. Different factors, such as thrombus, emboli and atherosclerosis, take part in the intricate pathophysiology of stroke. Comprehending the molecular processes involved in this mechanism is crucial to developing new, specific and efficient treatments. Some common mechanisms are excitotoxicity and calcium overload, oxidative stress, and neuroinflammation. Furthermore, non-coding RNAs (ncRNAs) are critical in pathophysiology and recovery after cerebral ischaemia. ncRNAs, particularly microRNAs, and the long non-coding RNAs (lncRNAs) are essential for angiogenesis and neuroprotection, and they have been suggested to be therapeutic, diagnostic and prognostic tools in cerebrovascular diseases, including stroke. This review summarizes the intricate molecular mechanisms underlying ischemic and hemorrhagic stroke and delves into the function of miRNAs in the development of brain damage. Furthermore, we will analyze new perspectives on treatment based on molecular mechanisms in addition to traditional stroke therapies.

Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are secreted from the gut upon nutrient stimulation and regulate postprandial metabolism. These hormones are known as classical incretin hormones and are responsible for a major part of postprandial insulin release. The incretin effect is severely reduced in patients with type 2 diabetes, but it was discovered that administration of GLP-1 agonists was capable of normalizing glucose control in these patients. Over the last decades, much research has been focused on the development of incretin-based therapies for type 2 diabetes. These therapies include incretin receptor agonists and inhibitors of the incretin-degrading enzyme dipeptidyl peptidase-4. Especially the development of diverse GLP-1 receptor agonists has shown immense success, whereas studies of GIP monotherapy in patients with type 2 diabetes have consistently been disappointing. Interestingly, both GIP-GLP-1 co-agonists and GIP receptor antagonists administered in combination with GLP-1R agonists appear to be efficient with respect to both weight loss and control of diabetes, although the molecular mechanisms behind these effects remain unknown. This review describes our current knowledge of the two incretin hormones and the development of incretin-based therapies for treatment of type 2 diabetes.

Malware detection is a crucial pillar of cybersecurity and aims to identify and neutralize malware threats. This study investigates how three deep learning algorithms - Convolutional Neural Net-works with One-Dimensional Architecture (CNN1D), Neural Networks (NN), and a hybrid model of one dimensional convolutional neural networks (CNN1D) and Long Short-Term Memory (LSTM) - help detect malware in Portable Executable working (.exe) files. Using a large dataset of tagged malware and benign samples, the effectiveness of these algorithms in distinguishing dangerous from harmless executables is extensively tested. In addition, the study evaluates the computational efficiency of the proposed methods for a variety of hardware configurations, including Central Processing Units (CPU), Graphics Processing Units (GPU), Raspberry Pi (IoT device), and Tensor Processing Units (TPU). Performance metrics provide insight into the scalability and effectiveness of the model across different hardware configurations. The results of this study have significant impact on the development of malware detection, particularly in the context of IoT security. By demonstrating the capabilities and limitations of CNN1D, Neural Networks, and CNN1D with LSTM models, the study provides practitioners with efficient and scalable solutions to real-world malware detection difficulties. In addition, insights from performance tests on various hardware platforms provide important assistance in developing robust security measures in IoT contexts, strengthening cybersecurity defenses in an ever-changing digital landscape.

This study addresses the central question that how can a precise measurement of the gluon Parton Distribution Function (PDF) be attained, thereby enhancing our understanding of the intricate quark-gluon composition within nucleons? Through a dual approach involving Quantum Chromodynamics (QCD) and high-energy particle collisions, the research combines computational tools and experimental techniques, focusing on protons and neutrons. Scattering experiments and advanced detectors are utilized to probe nucleon structure. Theoretical frameworks like QCD equations and the Momentum Sum Rule investigate Parton Distribution Functions at specific momentum fractions and energy scales. The primary goal is to achieve an accurate measurement of the gluon PDF, crucial for unraveling strong force complexities and internal nucleon dynamics. The study's theoretical plots and equations illustrate hypothetical behaviors of gluon distribution, quark distribution, and splitting functions, contributing to a deeper understanding of fundamental particles.

Bacteriotherapy is emerging as a strategic and effective approach to treat infections by providing putatively harmless bacteria (i.e., probiotics) as antagonists to pathogens. A proper delivery of probiotics or their metabolites (i.e., post-biotics) can avail itself of biomaterial encapsulation by innovative manufacturing technologies. This review paper aims at providing the most recent biomaterial-assisted strategies proposed to treat infections or disbiosis using bacteriotherapy. We revised the encapsulation processes across multiscale biomaterial approaches, which could be ideal to target different tissues and suit diverse therapeutic opportunities. Hydrogels, and in particular polysaccharides, are the focus of this review, as they have been reported to better sustain vitality of the live cells incorporated. Specifically, the approaches for fabricating hydrogel-based devices with increasing dimensionality (D), namely, 0D (i.e., particles), 1D (i.e., fibers), 2D (i.e., fiber meshes), and 3D (i.e., scaffolds) endowed with probiotics, were detailed by describing their advantages and challenges, along with a future overlook in the field. Electrospinning, electrospray and 3D bioprinting were investigated as new biofabrication methods for probiotic encapsulation within multidimensional matrices. Finally, examples of biomaterial-based systems for cell and post-biotic release were reported.

The study of the development of the vertebrate retina can be addressed from several perspectives: from purely qualitative to a more quantitative approach that takes into account its spatiotemporal features, its three dimensional structure and also the regulation and properties at the systems level. Here we review the ongoing transition towards a full four-dimensional characterization of the developing vertebrate retina, focusing on the challenges at the experimental, image acquisition, image processing and quantification. Using the developing zebrafish retina, we illustrate how quantitative data extracted from these type of highly dense three-dimensional tissues depends strongly on the image quality, image processing and algorithms used to segment and quantify. Therefore, we propose that the scientific community that focuses on developmental systems could strongly benefit from a more detailed disclosure of the tools and pipelines used to process and analyze images from biological samples.

In this paper, we propose 2 novel methods for brain tumor detection in MRI images. In the first proposed approach, we build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. In the second approach, we expand upon prior studies on convolutional neural networks: a convolutional neural network involving a specific module of layers is used for classification. The first approach achieved accuracy scores of 0.98 and the second approach achieved a score of 0.863, outperforming a benchmark VGG-16 model. Considerations to granular computing and circuit complexity theory are given in the paper as well.

Background: Differentiating essential tremor (ET) and functional tremor (FT) remains challenging due to their overlapping clinical presentations. Objective: In this study, we aimed to elucidate the demographic and psychometric differences between the aforementioned tremor types to enhance the diagnostic accuracy and therapeutic strategies. Methods: In this prospective study, we included 96 patients diagnosed with ET or FT and analysed their related demographic data, clinical symptoms, and psychometric evaluation scores. Our statistical analysis involved Pearson’s chi-square tests, Fisher’s exact tests, and logistic regression to assess how the different variables impact tremor diagnosis. Results: Our study demonstrated a higher ET prevalence in males (p = 0.015). Furthermore, we demonstrated that patients with ET displayed a significantly lower body mass index and a lower age of onset compared to those with FT (p = 0.050 and p = 0.023, respectively). Our psychometric assessments revealed higher cognitive and body image scores in patients with ET, whereas those with FT scored higher on depression and anxiety scales. The misdiagnosis rate was 14.5%, emphasising the need for improved diagnostic criteria. Conclusions: We established specific demographic and psychometric distinctions between ET and FT, which could potentially benefit clinicians in making accurate diagnoses and tailoring treatment approaches. These findings support the inclusion of comprehensive psychometric evaluations into standard diagnostic procedures to better differentiate tremor types.

Spinal cord tumors, though rare, present formidable challenges in clinical management due to their intricate nature. Traditional treatment modalities like surgery, radiation therapy, and chemotherapy have been the mainstay for managing these tumors. However, despite significant advancements, challenges persist, including the limitations of surgical resection and the potential side effects associated with radiation therapy. In response to these limitations, a wave of innovative approaches is reshaping the treatment landscape for spinal cord tumors. Advancements in gene therapy, immunotherapy, and targeted therapy are offering groundbreaking possibilities. Gene therapy holds the potential to modify the genes responsible for tumor growth, while immunotherapy harnesses the body's own immune system to fight cancer cells. Targeted therapy aims to strike a specific vulnerability within the tumor cells, offering a more precise and potentially less toxic approach. Additionally, novel surgical adjuncts are being explored to improve visualization and minimize damage to surrounding healthy tissue during tumor removal. These developments pave the way for a future of personalized medicine for spinal cord tumors. By delving deeper into the molecular makeup of individual tumors, doctors can tailor treatment strategies to target specific mutations and vulnerabilities. This personalized approach offers the potential for more effective interventions with fewer side effects, ultimately leading to improved patient outcomes and a better quality of life. This evolving landscape of spinal cord tumor management signifies the crucial integration of established and innovative strategies to create a brighter future for patients battling this complex condition.

Musculoskeletal injuries such as equine osteoarthritis, osteoarticular defects, tendonitis/desmitis, and muscular disorders are prevalent among sport horses, with a fair prognosis for returning to exercise or previous performance levels. The field of equine medicine has witnessed rapid and fruitful development, resulting in a diverse range of therapeutic options for musculoskeletal problems. Staying abreast of these advancements can be challenging, prompting the need for a comprehensive review of commonly used and recent treatments. The aim is to compile current therapeutic options for managing these injuries, spanning from simple to complex physiotherapy techniques, conservative treatments including steroidal and non-steroidal anti-inflammatory drugs, hyaluronic acid, and polyacrylamides, to the promising regenerative therapies such as hemoderivatives and stem cell-based therapies. Each therapeutic modality is scrutinized for its benefits, limitations, and potential synergistic actions to facilitate their most effective application for intended healing/regeneration of the injured tissue/organ and subsequent patient recovery. While stem cell-based therapies emerge as particularly promising for equine musculoskeletal injuries, a multidisciplinary approach is underscored throughout the discussion, emphasizing the importance of considering various therapeutic modalities in tandem.