Cyclodextrin polymers have high applicability in pharmaceutical formulations due to better biocompatibility, solubility enhancement, loading capacity and controlled drug release than parent the cyclodextrins. The cytotoxicity and cell uptake of new cationic beta-cyclodextrin monomers and polymers were evaluated as suitable material for nasal formulations and their protective effects on cells exposed to hydrogen peroxide were studied. PC12 and CACO-2 cells were selected as the neuronal and epithelial type cells, respectively, to mimic the structure of respiratory and olfactory epithelia of the nasal cavity. All cationic beta-cyclodextrin polymers tested showed dose- and time-dependent toxicity; nevertheless, at 5 µM concentration and 60 min of exposure, the quaternary-ammonium-beta-cyclodextrin soluble polymer could be recognized as non-toxic. Based on these results, fluorescently labelled quaternary-ammonium-beta-cyclodextrin monomer and polymer were selected for uptake studies in CACO-2 cells. The monomeric and polymeric beta-cyclodextrins were internalized in the cytoplasm of CACO-2 cells; the cationic monomer showed higher permeability than the hydroxypropyl-beta-cyclodextrin, employed as comparison. Therefore, these cationic beta-cyclodextrins showed potential as excipients able to improve the nasal absorption of drugs. Furthermore, amino-beta-cyclodextrin and beta-cyclodextrin soluble polymers were able to reduce oxidative damage in PC12 and CACO-2 cells and thus could be studied as bioactive carriers or potential drugs for cells protection against oxidative stress.

A powder synthesis of PbMoO₄ from ancient lead (Pb) and deeply purified commercial MoO₃ powders was performed using a wet chemistry technique to achieve the low radioactivity scintillator for neutrinoless double beta decay search in ¹⁰⁰Mo. The synthesized powders were used to grow single crystals of PbMoO₄ by a Czochralski technique in Ar environment. The luminescence and scintillation properties were measured with excitations using UV, X- and γ-rays in temperature range of 10-300 K. Annealing of the grown PMO crystal in air atmosphere enhanced the scintillation light yield by ~3 orders of magnitude compared to that measured before annealing. The scintillation light yield of grown PMO crystal at 10 K is found to be 127% to that of a reference PMO crystal under 662 keV γ-rays excitation from a ¹³⁷Cs source. The background measurement of the grown crystal performed at 50 K shows negligible activity from ²¹⁰Pb compared to that of reference PMO crystal. These preliminary performances show that the PMO crystal grown from ancient Pb and deeply purified MoO₃ powders has the great potential to be used as cryogenic scintillator for the neutrinoless double beta decay search in ¹⁰⁰Mo.

The industrial sector plays a crucial role in driving global economic growth by creating employ-ment opportunities, generating local income, and significantly contributing to each country's Gross Domestic Product (GDP). However, the manufacturing industry is responsible for produc-ing a substantial amount of polluting effluents, which can harm the environment and compromise the life quality of future generations if not appropriately treated. Various methods, including physical, chemical, and biological processes, have been employed in the treatment of industrial ef-fluents. Filamentous fungi, in particular, have garnered attention as effective bioremediation agents due to their ability to produce enzymes capable of degrading recalcitrant compounds and adsorb different pollutant molecules. This study aimed to assess the collective body of research on using white and brown rot fungi to remove phenolic compounds from industrial effluents. The research employed a systematic review, incorporating scientometric analysis, with the primary goal of constructing a comprehensive overview of the evolution of this technology over time. The analysis also aimed to scrutinize its geographical distribution and identify gaps and trends within these research inquiries. Data for the study were obtained from the Web of Science (WoS) data-base using specific search terms (("white rot fung*" OR "brown rot fung*") AND (phenol* OR phenoloxidase OR "polyphenol oxidase*") AND (wastewater OR effluent* OR bioremediation)). The time frame considered ranged from 1945 to 2023, resulting in 464 publications. After refine-ment, 109 studies were selected for further analysis, focusing on using filamentous fungi in the bi-oremediation of industrial effluents. The findings revealed that white rot fungi were the most studied in industrial effluent remediation processes, accounting for 96.3% of the studies assessed, followed by brown rot fungi at 2.7%. Filamentous fungi demonstrate significant potential for treating industrial effluents due to their high capacity to degrade phenolic compounds, especially in wastewater from olive mills, paper production industries, and distilleries. Among the investi-gated species, Phanerochaete chrysosporium, Pleurotus ostreatus, Trametes versicolor, and Lentinula edodes have been the most extensively researched for this application. Notably, cultures with free cells (64.15%) stood out compared to cultures with immobilized cells, and cultures with isolated fungi surpassed microbial consortia. Geographically, Italy, Spain, Greece, India, and Brazil emerged as the most prominent countries in publications related to this area during the evaluated period.

The glucocorticoid receptor alpha (GRα), a pivotal component of the ancient glucocorticoid (GC) signaling system that emerged nearly 450 million years ago, has been instrumental in directing vertebrate evolution and is essential for survival, regulating fertility and fetal development, the function of every organ and circulating cells, metabolism, immune responses, and adaptation to stress. While its anti-inflammatory properties have been recognized since the mid-20th century, GRα’s broader role as the master regulator of homeostatic corrections, particularly in health and critical illness, has only recently been fully appreciated.GRα orchestrates three key phases of homeostatic correction: the Priming Phase, where it rapidly activates early immune responses, mobilizes energy reserves, and prepares the body to mount an immediate defense against various physiological stressors, including infection and injury. During this phase, GRα enhances glucose metabolism, supports mitochondrial function, and facilitates the release of key immune cells to the site of damage, ensuring the body is equipped to handle the initial assault. The Modulatory Phase follows, in which GRα fine-tunes the inflammatory response to prevent excessive tissue damage. By balancing pro- and anti-inflammatory signals, GRα helps contain the immune response, preventing the harmful effects of prolonged inflammation while maintaining immune surveillance. This phase also involves the regulation of oxidative stress, vascular tone, and the preservation of cellular integrity to protect vital organs from injury. Finally, in the Restorative Phase, GRα plays a critical role in resolving inflammation and initiating tissue repair. During this phase, GRα supports cellular regeneration, promotes the clearance of apoptotic cells and debris, and restores anatomical and physiological balance. It aids in rebuilding tissue architecture and returning the neuroendocrine-immune system to its baseline state, ensuring long-term recovery and stability.Throughout these phases, GRα manages complex interactions, including recently discovered co-regulation with pro-inflammatory transcription factors, while also regulating mitochondrial function to ensure bioenergetic and metabolic stability during stress. In critically ill patients, depletion of bioenergetic and micronutrient reserves severely impairs GRα’s capacity to regulate immune responses, heightening the risk of morbidity and mortality.This review, in light of this new understanding, underscores the need to re-evaluate current glucocorticoid (GC) treatment strategies, emphasizing the incorporation of micronutrient support to optimize GRα function across all phases of homeostatic correction. This integrated therapeutic approach has the potential to enhance both immediate immune responses and long-term recovery in critically ill patients, offering a more comprehensive strategy for managing the complex pathobiology of critical illness. Furthermore, this integrative approach could decrease glucocorticoid dose and duration requirements and potential side effects.

Alpha-methyl-p-tyrosine (AMPT; also referred as metyrosine) is an approved medication for the treatment of pheochromocytoma. As a tyrosine hydroxylase inhibitor AMPT may be a potential candidate for the treatment of diseases involving catecholamine alterations. However, only small-scale clinical trials have tested AMPT repurposing in few other illnesses. Chronic fatigue syndrome (CFS) is an heterogenous disorder with genetically associated vulnerability of catecholamine metabolism (e.g. catechol O-methyltransferase polymorphisms) holding an important impact of environmental factors. The current case report compiles genetic and longitudinal biochemical data for over a year follow-up of one patient sequentially diagnosed with sustained overstress, neurasthenia, CFS, and POTS (postural orthostatic tachycardia syndrome) over a 10-year period, and reports patient’s symptom improvement in response to low-medium doses of AMPT.

COVID-19 pandemic outbreak challenged the global public health in last couple of years. Throughout the pandemic period, numbers of mutant strains of SARS- CoV-2 created challenges for the infected patients with diverse pathophysiology and immune response. Variant of Concern (VOC) alpha (B.1.1.7), delta (B.1.617.2) and omicron (B.1.1.529) grew most notable for causing the epidemiological manifestations, which eventually caused elevated infectivity resulting in significant mortality. This review indicates the comparative analysis of the immune-pathophysiological mechanisms in respect to the aforementioned strains of SARS-CoV-2.

Background: Diabetic patients are more likely to experience morbidity and mortality as a result of microvascular complications such as retinopathy, neuropathy, nephropathy, and stroke. There are many synthetic anti-diabetic agents available which are expensive and have undesirable pathological effects. Thus, it is essential to look for cost-effective, natural, and safe antidiabetic agents. The aim of this study was to screen phytoconstituent and evaluate the in-vitro and in-silico α-amylase inhibitory activity of ethanolic extract of Adhatoda vasica leaves. Method: The extraction of Adhatoda vasica leaves was performed with ethanol through Soxhlet extraction process. Different concentrations (0.1 mg/ml to 1 mg/ml) of ethanolic extract, Acarbose, and Sitagliptin, were prepared and all concentrations were evaluated for α-amylase inhibitory activity through the spectrophotometric method. Molecular docking (AutodockVina 1.2.0) and toxicity profiling (SToPToX web server) studies were performed. Results: The plant extract showed highest inhibition of α-amylase (56.763±0.0035) at a concentration of 1 mg/ml. This inhibitory activity was supported by the in-silico study. Vasicoline (C5) and quercetin (C9), active constitute of plant Adhatoda vasica, showed the best binding energy of 8.3 and 8.0 Kcal/mol respectively with α-amylase enzyme (PDBID: 4W93). Toxicity study revealed the safety profile of plant extract. Conclusion: It was concluded that Adhatoda vasica leaves possess some bioactive compounds which are responsible for controlling blood glucose levels and its identification, purification, and isolation may lead to the development of newer therapeutic agent with lesser side effects.

Central adiposity is one of the significant determinants of obesity-related hypertension risk, which may arise due to the abdominal fat depot's pathogenic inflammatory nature. Pro-inflammatory cytokines and adipokines up-regulation through nuclear factor-kappa B (NF-κB) activation in adipose tissue has been considered an essential function in the pathogenesis of obesity-related hypertension. This study aimed to ascertain the NF-κB inhibitor (SN50) effect on TNF-α and angiotensinogen (AGT) secretion and expression in mediating the anti-inflammatory effect through its impact on NF-κB activity in humans adipose tissue. Primary human adipocytes were isolated from 20 subjects among 10 overweight and 10 obese with and without hypertension and treated with 10ng/ml LPS in the presence and absence of NF-κB inhibitor, SN50 (50μg/ml). TNF-α secretion and NF-κB p65 activity were detected in supernatants extracted from cultured cells treated and untreated with LPS (10ng/ml) and SN50 (50μg/ml) using enzyme-linked immunosorbent assay (ELISA). The western blot technique detected the protein of NF-κB p65 and AGT. Gene expression of TNF-α and AGT was detected in cells and performed using quantitative real-time polymerase chain reaction (RT-PCR). Treatment of AbdSc adipocytes with LPS (10ng/ml) caused a significant increase in NF-κB p65 among overweight and obese subjects with and without hypertension (P= 0.001) at 24 hours incubation. In contrast, SN50-NF-κB inhibitor causes a reduction of NF-κB p65 in overweight (P= <0.001) and obese subjects with and without hypertension (P= 0.001) at 24 hours incubation. Treatment of AbdSc adipocytes with 10ng/ml LPS caused a significant increase in TNF-α secretion in overweight and obese subjects at all-time points (P= <0.001), whereas SN50 leads to a decrease in TNF-α secretion at 3 and 12 hours incubation. Treatment of AbdSc adipocytes with LPS (10ng/ml) caused increased TNF-α and AGT gene expression twofold compared with untreated cells, whereas, in the presence of SN50, it reduces mRNA AGT levels in both groups. Taken together, these adipokines with NF-κB activation may represent essential biomarkers to evaluate hypertension risk and to provide insight into the pathogenesis of obesity-related hypertension.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

This article focuses on the leader-following consensus of fuzzy fractional order singular perturbation multi-agent systems (FOSPMASs) with order in 0,2. By employing the T-S fuzzy modeling approach, a fuzzy FOSPMAS is constructed. Subsequently, a fuzzy observer-based controller is designed and the error system corresponding to each agent is derived. Through a series of equivalent transformations, the error system is decomposed into fuzzy singular fractional order systems (SFOSs). According to the admissibility of SFOSs, the consensus conditions of the fuzzy FOSPMAS are obtained based on linear matrix inequalities (LMIs) without equality constraint. Finally, the effectiveness of the criteria is verified through an RLC circuit model.

In the fast-changing world of software development, the protection of software products has emerged as an imperative requirement. This paper presents a new way to increase the maturity of development teams to reach the highest level of software security. Based on these, the framework uses the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to systematically evaluate and enhance the people maturity of software development projects. The essence of the fuzzy logic and AHP technique interaction is to handle the uncertainty and complexity of human factors and team dynamics in the evaluation framework. Using the decision-making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into 9 categories to determine the ranks and weight of each category. Based on collected data, FAHP prioritized these practices; the category “C4: Skill Development and Stakeholder Engagement” is ranked highest at rank-1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank-1) HSF and HSV practice is “P15: Hands-On Practice and Stakeholder Communication”. Research evidence and case discussions show how the described framework assists in building secure software development (SSD) practices, which can be considered evidence of the application of team maturity to improve cybersecurity in organizations. Additional research directions include improving the framework, especially using highly developed learning techniques and applying the framework to other forms of development.

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes financial damage to the environment. In the case of air pollution, harmful and foreign substances in the air reach an amount and density above normal values. Air pollution also causes climate change, which affects global warming. Particulate matter is a harmful substance that harms the health of humans and other living things, causes air pollution, increases radiation, and negatively affects cloud formation. Particulate matter is extremely small solid particles and liquid droplets suspended in the air. PM10, which is less than 10 microns in diameter, is defined as small particles that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. Many criteria affect PM10 density and changes in density. The main criteria are vehicle traffic and fuel consumption, industrialization, coal, fuel oil and wood-burning heaters, greenhouse gas impact, and unregulated urbanization. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data between 2019 and 2023 obtained from the Air Monitoring Station in Kocaeli, Türkiye are used. After determining the criteria affecting PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking expert decision-maker opinions. The proposed decision-making model aims to guide obtaining the important causes of PM10 emission in terms of danger and focusing on these causes. Additionally, PM10 data is analyzed in the context of Covid-19 and a statistical analysis is made. One-way ANOVA is used to evaluate whether there is a significant difference in the average monthly data over the years. Games-Howell test, one of the post-hoc tests, is used for differences between groups (years). The study is important in that it provides a focus on the criteria affecting PM10 with an intuitive fuzzy perspective, along with statistical analysis.

This article describes an approach for optimising sensor-controlled systems through minimal intervention, utilising Fuzzy Linear Systems of Equations (FLSE). Staring with a generalised model of the system behaviour, incorporating an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem for FLSE. By achieving minimal intervention, we ensure that system adjustments effective, economically optimal and non-intrusive. A MATLAB-based implementation is presented.

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprised of fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed wing aircraft systems. Here the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity techniquesto recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models will enable us to avoid common control design methods, such as gain scheduling, where stability issues can be extremely challenging.

Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.

Several life contingency agreements are based on the assumption that policyholders have impaired life expectancy attributable to factors such as lifestyle, social class, or preexisting health issues. Quantifying two crucial variables, augmented death probabilities and the discount rate of projected cash flows, is essential for pricing such agreements. Information regarding the correct values of these parameters is subject to vagueness and imprecision, which further intensifies if impairments must be considered. This study proposes modelling mortality and interest rates using a generalization of fuzzy numbers (FNs), known as intuitionistic fuzzy numbers (IFNs). Consequently, this paper extends the literature on life contingency pricing with fuzzy parameters, where uncertainty in variables, such as interest rates and death probabilities, is modelled using FNs. While FNs introduce epistemic uncertainty, the use of IFNs adds bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis focuses on two agreements involving policyholders with impaired life expectancy: determining the annuity payment in a substandard annuity and pricing a life settlement over a whole life insurance policy. In particular, we emphasize modelling interest rates and survival probabilities using triangular intuitionistic fuzzy numbers (TIFNs) owing to their ease of interpretation and implementation.

This paper proposes a model that combines multi-criteria and multi-faceted risk assessment. Using two data sources, a fuzzy Technique for Order of Preference by Similarity to Ideal Solution FTOPSIS method combined with FMEA assessment. The FMEA method presented in this paper combines the technique of prioritising preferences according to FTOPSIS similarity to the ideal solution and conviction structure to overcome the defects of traditional FMEA indicators. The paper describes the exact process and tool selection, and the results obtained in the study verified accurate data. Finally, a numerical case study was presented on hospital reorganization services and case adaptation to prioritize surgical abandonment in Poland. The problem considered in the publication is the priority of patients’ operations in hospitals. The selection of relevant criteria, their importance and patient preferences are presented. The results obtained from the method provide a viable action plan for the proposed research problem. The proposed method is multi-faceted and can be part of an information system supporting reorganization, restructuring, and modification of the operational process.

In this paper, we introduce an innovative approach to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our method combines causal fuzzy logic rules with the Big Bird architecture, a Transformer-based Deep Learning algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach harnesses the complete dataset in tandem with our causal fuzzy Big Bird architecture. Additionally, we benchmark our results not only against previous Gliosarcoma/Glioblastoma studies but also against GPT-2 for a comprehensive evaluation.

Mild cognitive impairment (MCI) is when brain function declines. It is a transition stage between normal aging and Alzheimer's disease (AD) and may be an underlying symptom of AD. Improper use of test instruments or imperfect test methods in clinical practice often leads to variations in test results. In addition, there may be resistance from the subject if he/she has to undergo multiple screening tests simultaneously. Therefore, the objective of this study was to use virtual reality to create an image testing scenario that integrates Mini-Cog, Short Portable Mental Status Questionnaire (SPMSQ), Mini-mental Status Examination (MMSE), Saint Louis University Mental Status Examination (SLUMS), Clinical Dementia Rating (CDR), and Cognitive Abilities Screening Instrument (CASI), and combine fuzzy logic control (FLC) technology to develop an MCI forecasting system. There were 24 middle-aged to older adults aged 50 to 65 years who participated in the evaluation experiment. The results showed that the MCI forecasting system developed in this study is highly correlated with the traditional screening tests, including Mini-Cog, SPMSQ, MMSE, SLUMS, and CASI. The forecasting system can provide an integrated reference score for testers in making judgments. In addition, the distribution of the System Usability Scale (SUS) evaluation scores for the MCI forecasting system revealed that 87.5% were grade C (good to use) or above, and 29.2% were grade B (extremely good to use) or above. The forecasting system receives positive feedback from the subjects.

The influence of neutrosophy in the previous period is constantly growing in many areas of science and technology. Moreover, various applications of the neutrosophic approach have become more common in recent years. Our goal in this research is to utilize the neutrosophy to improve the performance of the Dai-Liao conjugate gradient (CG) method. Specifically, in this research, we propose and investigate a new neutrosophic logic system to calculate the key parameter t involved in the Dai–Liao CG iterations. Theoretical analysis and numerical experience indicate that the efficiency and robustness of the new rule for determining t. Combining the neutrosophy and the Dai-Liao conjugate gradient method, we propose and explore a new Dai-Liao CG iterations for solving large-scale unconstrained optimization models. The global convergence is established under common assumptions and the backtracking line search. Finally, by conducting numerical experiments, computational evidence demonstrates that the new fuzzy neutrosophic Dai-Liao conjugate gradient method is computationally effective and robust.

Many authors proposed analytical methods for solving fully fuzzy Sylvester matrix equation (FFSME) based on Vec-operator and Kronecker product. However, these methods are restricted to nonnegative fuzzy numbers and cannot be extended to FFSME with near-zero fuzzy numbers. The main intention of this paper is to develop a new numerical method for solving FFSME with near-zero trapezoidal fuzzy numbers that provides a wider scope of trapezoidal fully fuzzy Sylvester matrix equation (TrFFSME) in scientific applications. This numerical method can solve the trapezoidal fully fuzzy Sylvester matrix equation with arbitrary coefficients and find all possible finite arbitrary solutions for the system. In order to obtain all possible fuzzy solutions, the TrFFSME is transferred to a system of non-linear equations based on newly developed arithmetic fuzzy multiplication between trapezoidal fuzzy numbers. The fuzzy solutions to the TrFFSME are obtained by developing a new two-stage algorithm. To illustrate the proposed method numerical example is solved.

In this article, the concept of fuzzy $(\sigma,\mathcal{Z})$-contractive mapping has been introduced in fuzzy metric spaces which is an improvement over the corresponding concept recently introduced by Shukla et al. [Fuzzy Sets and system. 350 (2018) 85--94]. Thereafter, we utilized our newly introduced concept to prove some existence and uniqueness theorems in $\mathcal{M}$-complete fuzzy metric spaces. Our results extend and generalize the corresponding results of Shukla et al.. Moreover, an example is adopted to exhibit the utility of newly obtained results.

In a collaborative forecasting task, experts may have unequal authority levels. However, this has rarely been considered reasonably in the existing fuzzy collaborative forecasting methods. In addition, experts may not be willing to discriminate their authority levels. To address these issues, an auto-weighting fuzzy weighted intersection (FWI) fuzzy collaborative intelligence approach is proposed in this study. In the proposed auto-weighting FWI fuzzy collaborative intelligence approach, experts’ authority levels are automatically and reasonably assigned based on their past forecasting performances. Subsequently, the auto-weighting FWI mechanism is established to aggregate experts’ fuzzy forecasts. The theoretical properties of the auto-weighting FWI mechanism have been discussed and compared with those of the existing fuzzy aggregation operators. After applying the auto-weighting FWI fuzzy collaborative intelligence approach to a case of forecasting the yield of a DRAM product from the literature, its advantages over several existing methods were clearly illustrated.

A Mamdani-type fuzzy-logic model has been developed to link Mediterranean seagrass abundance to the prevailing environmental conditions. Big Databases, as UNEP-WCMC (seagrass abundance), CMEMS and EMODnet (oceanographic/environmental) and human-impact parameters were utilized for this expert system. Model structure and input parameters were tested according to their capacity to accurately predict seagrass families at specific locations. The optimum FIS comprised of four input variables: water depth, sea surface temperature and nitrates and bottom chlorophyll-a concentration, exhibiting fair accuracy (76%). Results illustrated that Posidoniaceae prefers cool (16-18oC) and low chlorophyll-a presence (&lt; 0.2 mg/m3); Zosteraceae favors cool (16-18oC) and mesotrophic waters (Chl-a &gt; 0.2 mg/m3), but also slightly warmer (18-19.5 oC) with lower Chl-a levels (&lt; 0.2 mg/m3); Cymodoceaceae lives from warm, oligotrophic (19.5-21.0oC and Chl-a &lt; 0.3 mg/m3) to moderately warm mesotrophic sites (18-21.3oC and 0.3 – 0.4 mg/m3 Chl-a). Finally, Hydrocharitaceae thrives in warm Mediterranaean waters (21-23oC) of low chlorophyll-a content (&lt; 0.25 mg/m3). Climate change scenarios showed that Posidoniaceae and Zosteraceae tolerate bathymetric changes, Posidoniaceae and Zosteraceae are mostly affected by sea temperature rise, while Hydrocharitaceae exhibits tolerance in higher sea temperature rise. This FIS could be used by national and regional policy-makers and public authorities.

Wireless Sensor Networks (WSNs) is considered to be a promising research area since it entails sensing capabilities. WSNs are confronted by fabricated explosion injection attacks and in return results into draining of vitality on sensor nodes routing mainly because of increased capabilities in a broad range of applications where sensor nodes use a battery as the power source. In this paper, we reveal and claim a state-of-the-art model to minimize vitality depletion of nodes within the network in return increases network period to more realistic stretch. It is revealed that the main cause is vitality consumption unbalances in different parts of the network direct resulting from having a static sink. More still, the network nodes neighboring the sink insistently partake in progressing data of other nodes to the sink. Consequently, the network nodes are supplementarily subjected to untimely vitality exhaustion. This paper mainly provides a novel movable (mobile) fuzzy logic sink approach and an intelligent system like expect systems that are able to scrutinize network situation, and make the superlative choice for spreading the network period robotically.

Objective: This paper aims to build an Effort Estimation Model for design, coding and testing Web Applications Based Fuzzy and Practical Models, which will help in optimizing the efforts in software development. Methods/Analysis: Soft computing approach is adopted and applied in the effort estimation and then compared with practical efforts in the development process with interpreting the historical data available for the existing functionalities. Findings: The effort estimation model presented in this paper focuses on the first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancement for a web application built on 3-tier architecture using Microsoft technologies. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. Novelty/Improvement: The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation tool would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data. And converts it into crisp value in the range [0…1] which helps to classify the complexity of the task and subtask in the design, coding and testing phases.

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

The aim of this article is introduced the concept of double framed T-soft fuzzy set (DFT-soft fuzzy set) which is the combination of soft set and fuzzy set. We also defined the notions and apply this concept in BCK/BCI-algebras. By using example, we also discussed the concept of double framed T-soft fuzzy algebra (DFT-soft fuzzy algebra) and double framed B-soft fuzzy algebra (DFB-soft fuzzy algebra) and also investigated their properties. Each double framed T-soft fuzzy algebra is double framed B-soft fuzzy algebra but by using example, we proved that converse may or may not be possible.

Oil has become the object of global exploits and fierce competition among the major world powers as it is a key strategic non-renewable resource. Transnational petroleum investment is therefore an important mechanism available to countries and international corporations to control oil resources even though there are numerous inherent uncertainties and risks. A new risk assessment index system is proposed in this paper based on use of the Delphi expert scoring system and fuzzy comprehensive evaluation that aims to minimize the potential risks inherent to multinational petroleum investment. This approach encapsulates political, legal, socioeconomic, and infrastructural factors to develop a technical method that can be used for transnational petroleum investment risk assessment. An evaluation of oil investment risk within a case study area is also presented; results provide reference data that can be applied by national and international oil companies to mitigate risks of transnational oil investment.

Intuitionistic fuzzy set, which can be represented using the triangular intuitionistic fuzzy number (TIFN), is a more generalized platform for expressing imprecise, incomplete and inconsistent information when solving multi-criteria decision-making problems, as well as for reflecting the evaluation information exactly in different dimensions. In this paper, the TIFN has been applied for solving some multi-criteria decision-making problems by developing a new triangular intuitionistic fuzzy geometric aggregation operator, that is the generalized triangular intuitionistic fuzzy ordered weighted geometric averaging (GTIFOWGA) operator, and defining some triangular intuitionistic fuzzy geometric aggregation operators including the triangular intuitionistic fuzzy weighted geometric averaging (TIFWGA) operator, the ordered weighted geometric averaging (TIFOWGA) operator and the hybrid geometric averaging (TIFHWGA) operator. Based on these operators, a new approach for solving multicriteria decision-making problems when the weight information is fixed has been proposed. Finally, the proposed method has been compared with some similar existing computational approaches by virtue of a numerical example to verify its feasibility and rationality.

In this paper we report the possibility of using the X-ray powder diffraction (XRPD) technique to detect the gamma-hydroxybutyric acid (GHB) as its sodium salt in different beverages but because it is not possible to freely buy them, beta-hydroxybutyric acid (BHB) and its sodium salt (NaBHB) were used as a model to fine tune an X-ray diffraction method for the qualitative analysis of the sodium salt of GHB. The method requires a small quantity of beverage, and an easy sample preparation, that consists only in the NaOH addition to the drink and a subsequent drying step. The dry residue obtained can be easily analyzed with XRPD using a single crystal X-ray diffractometer to exploit its high sensitivity that allows very fast patterns collection. Several beverages with different NaBHB:NaOH molar ratios were tested and the results show that NaBHB is detected in all drinks analyzed when NaBHB:NaOH molar ratio is 1:50 using characteristic peak at very low 2θ values that permits to detect its presence also in complex beverage matrices. Moreover, depending on the NaOH amount added, shifting and/or splitting of the characteristic peak of the NaBHB salt was observed, and the origin of this behavior was investigated.

Abstract: Breast cancer is one of the leading causes of death in the female population due to the re-sistance of cancer cells to many anticancer drugs used. Curcumin has cytotoxic activities against breast cancer cells, although it has limited use due to its poor bioavailability and rapid metabolic elimination. The synthesis of metal complexes of curcumin and curcuminoids is a relevant topic in the search for more active and selective derivatives of these molecular scaffolds. However, solubil-ity and bioavailability are concomitant disadvantages of these types of molecules. To overcome such drawbacks, the preparation of inclusion complexes offers a chemical and pharmacologically safe option for improving the aqueous solubility of organic molecules. Herein, we describe the preparation of the inclusion complex of dimethoxy curcumin magnesium complex with cyclodex-trin (DiMeOC-Mg-BCD, 5) in the stoichiometric relationship 1:1. This new inclusion complex's solubility in aqueous media (PBS) was improved by a factor of 6x over the free metal complex (DiMeOC-Mg). Furthermore, 5 affects cell metabolic rate, cell morphology, cell migration, induced apoptosis, and downregulation of the MMP-2 and MMP-9, IL-6, and STAT3 expression levels on MDA MB 231 cell lines. Results of an antitumor assay in an in-ovo model showed up to 30% inhi-bition of tumour growth for breast cancer (MDA-MB-231) when using 5 (0.650 mg/Kg dose), and 17.29 % inhibition with the free homoleptic metal complex (1.5 mg/Kg dose, DiMeOC-Mg). While the formulation of inclusion complexes from metal complexes of curcuminoids demonstrates its usefulness in improving the solubility and bioavailability of these metallodrugs, the new DiMeOC-Mg-BCD exhibits excellent potential for use as a therapeutic agent in the battle against breast cancer.

Carbapenem antibiotics are the drug of choice for treating multidrug-resistant bacterial infections. Metallo-beta-lactamases (MBLs) are carbapenemase capable of hydrolyzing nearly all therapeutically available beta-lactam antibiotics. Consequently, a need to assess the frequency and phenotypic resistance phenomena of two MBL genes in diarrheal and urinary tract infections (UTIs). Samples were collected through a cross-sectional study, with MBLs genes detected via PCR. Two hundred twenty eight diarrheal bacteria were isolated from 240 samples. The most predominant pathogens were Escherichia coli (32%) and Klebsiella spp. (7%). Phenotypic resistance to amoxicillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 50.4%, 65.6%, 66.8%, 80.5%, 54.4%, 41.6%, 25.7%, 41.2%, 37.2%, and 42.9%, respectively.  Total 142 UTI pathogens were obtained from 150 urine samples, with Klebsiella spp. (39%) and Escherichia coli (24%) are the major pathogens. Phenotypic resistance to amoxycillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 93.7%, 75.0%, 91.5%, 93.7%, 88.0%, 72.5%, 13.6%, 44.4%, 71.1%, and 43%, respectively.  Twenty four diarrheal isolates carried either blaNDM-1 or blaVIM genes; the overall MBL gene prevalence was 10.5%. Thirty six UTI pathogens carried either blaNDM-1 or blaVIM genes (25.4%). Seven isolates carried both blaNDM-1 and blaVIM genes.  MBL genes exhibited a strong association with phenotypic carbapenem and other beta-lactam antibiotic resistance. Resistance to carbapenems requires active surveillance and stewardship.

$\beta$-decay is one of the key factors to understand the $r$-process and evolution of massive stars. The Gamow-Teller (GT) transitions drive the $\beta$-decay process. We employ the proton-neutron quasiparticle random phase approximation (pn-QRPA) model to calculate terrestrial and stellar $\beta$-decay rates for 50 top-ranked nuclei possessing astrophysical significance according to a recent survey \cite{Nab21}. The model parameters of the pn-QRPA model effect the predicated results of $\beta$-decay. The current study investigates the effect of nucleon-nucleon pairing gaps on charge-changing transitions and the associated $\beta$ decay rates. Three different values of pairing gaps, namely TF, 3TF and 5TF, were used in our investigation. It was concluded that both GT strength distributions and half-lives are sensitive to pairing gap values. The 3TF pairing gap scheme, in our chosen nuclear model, resulted in best prediction with around 80$\%$ of the calculated half-lives within a factor 10 of the measured ones. The 3TF pairing scheme also led to calculation of biggest $\beta$-decay rates in stellar matter.

: Poultry products in Zambia form an integral part of the human diet in many households, as they are cheap and easy to produce. The burden of poultry diseases has, however, remained a major challenge. Growing consumer demand for poultry products in Zambia has resulted in non-prudent antimicrobial use on farms, intending to prevent and treat poultry diseases for growth optimization and maximising profits. This cross-sectional study aimed to identify the different types of bacteria causing diseases in chickens in Lusaka and to detect the extended-spectrum-lactamase (ESBL)-encoding genes. We collected 215 samples from 91 diseased chickens at three post-mortem facilities and screened them for gram-negative bacteria. Of these samples, 103 tested positive for various clinically relevant Enterobacteriaceae, including Enterobacter (43/103, 41.7%), Escherichia coli (20/103, 19.4%), Salmonella (10/103, 9.7%), and Shigella (8/103, 7.8%). Other isolated bacteria included Yersinia, Morganella, Proteus, and Klebsiella, which accounted for 21.4%. E. coli, Enterobacter, Salmonella, and Shigella were subjected to antimicrobial susceptibility testing. The results revealed that E. coli, Enterobacter, and shigella were highly resistant to tetracycline, ampicillin, amoxicillin, and trimethoprim-sulfamethoxazole, while Salmonella showed complete susceptibility to all antibiotics. The observed resistance patterns correlated with antimicrobial usage estimated from sales data from a large-scale wholesale and retail company. Six (6/14, 42.9%) E. coli isolates tested positive for bla_CTX-M, whilst eight (8/14, 57.1%) Enterobacter samples tested positive for bla_TEM. Interestingly, four (4/6, 66.7%) of the E. coli isolates carrying bla_CTX-M-positive strains were also positive for bla_TEM. Sanger sequencing of the PCR products revealed that five (5/6,83.3%) of the abovementioned isolates possessed the bla_CTX-M-15 allele. The results suggest the presence of potentially pathogenic ESBL-producing Enterobacteriaceae in poultry, threatening public health. Keywords:. Enterobacteriaceae, bla_CTX-M, bla_TEM

Ongoing beta cell death in type 1 diabetes (T1D) can be detected using biomarkers selectively discharged by dying beta cells into plasma. MicroRNA-375 (miR-375) ranks among top biomarkers based on studies in animal models and human islet transplantation. Our objective was to identify additional microRNAs that are co-released with miR-375 proportionate to the amount of beta cell destruction. RT-PCR profiling of 733 microRNAs in a discovery cohort of T1D patients 1 hour before/after islet transplantation indicated increased plasma levels of 22 microRNAs. Sub-selection for beta cell selectivity resulted in 15 microRNAs that were subjected to double-blinded multicenter analysis. This led to identification of 8 microRNAs that were consistently increased during early graft destruction: besides miR-375, these included miR-132/204/410/200a/429/125b, microRNAs with known function and enrichment in beta cells. Their potential clinical translation was investigated in a third independent cohort of 46 transplant patients, by correlating post-transplant microRNA levels to C-peptide levels 2 months later. Only miR-375 and miR-132 had prognostic potential for graft outcome and none of the newly identified microRNAs outperformed miR-375 in multiple regression. In conclusion, this study reveals multiple beta cell-enriched microRNAs that are co-released with miR-375 and can be used as complementary biomarkers of beta cell death.

Background: The extended-spectrum beta-lactamase (ESBL) Klebsiella pneumoniae is one of the leading causes of health-associated infections (HAI), whose antibiotic treatments have been severely reduced. Besides, HAI bacteria may harbor pathogenic factors such as siderophores, enzymes, or capsules, which increase the virulence of these strains. Thus, new therapies such as antimicrobial photodynamic inactivation (aPDI) are needed. Method: A collection of 118 clinical isolates of K. pneumoniae were characterized susceptibility and virulence through the determination of the minimum inhibitory concentration (MIC) of Amk, Cfx, Cfz, Imp, Mer, and Pip-taz, and by PCR, the frequency of the virulence genes, K2, magA, rmpA, entB, ybtS, and allS. Susceptibility to innate immunity, such as human serum, macrophages, and polymorphonuclear cells, was tested. All the strains were tested for sensitivity to the photosensitizer PSIR-3 (4µg/mL) in a 17µW/cm2 for 30 min aPDI. Results: A significantly higher frequency of virulence genes in ESBL than non-ESBL bacteria were observed. The isolates of the genotype K2+, ybtS+, and allS+ display enhanced virulence since they showed higher resistance to human serum as well as to phagocytosis. All strains are susceptible to the aPDI with PSIR-3 decreasing viability in 3log10. The combined treatment with Cfx improved the aPDI to 6log10 for the ESBL strains. The combined treatment is synergistic as it showed an FIC index value of 0.15. Conclusions: The aPDT effectively inhibits clinical isolates of K. pneumoniae, including the more risky strains of ESBL-producing bacteria and the K2+, ybtS+, and allS+ genotype. The aPDI with PSIR-3 is synergistic with Cfx.

Beta-thalassemia major (β-TM) is a severe form of thalassemia caused by mutations in the β-globin gene, resulting in partial or complete deficiency of β-globin chains. This deficiency results in oxidative stress, dyserythropoiesis, and chronic anemia. Cytokine dependent hematopoietic cell linker (CLNK) belongs to the adaptor protein family and has the capacity to interact with multiple signaling proteins thereby modulating signal transduction. The aim of the present study was to examine CLNK in sera of β-TM patients and examine its association with iron overload biomarkers. Sixty β-TM patients, aged 3–12 years old and undergoing blood transfusions, and 30 healthy control children were recruited and CLNK, ferritin and iron status parameters were measured. The results showed a significant increase (p < 0.001) in serum CLNK levels in β-TM patients as compared with normal controls. The increased levels of CLNK were significantly associated with increased ferritin levels. Increased CLNK levels in β-TM may be explained by reciprocal effects between immune signaling and immature erythrocytes, which, release soluble receptors and signaling molecules, including CLNK, in the blood.

It is well known that the primes and prime powers have a deep relationship with the nontrivial zeros of Riemann’s zeta function. This is a reciprocal relationship. The zeros and the primes are encoded in each other and are reciprocally recoverable. Riemann’s zeta is an extended or continued version of Euler’s zeta function which in turn equates with Euler’s product formula over the primes. This paper shows that the zeros of the converging Dirichlet or Catalan beta function, which requires no continuation to be valid in the critical strip, can be easily determined. The imaginary parts of these zeros have a deep and reciprocal relationship with the odd primes and odd prime powers. This relationship separates the odd primes into those having either 1 or 3 as a remainder after division by 4. The vector pathway of the beta function is such that the real part of its zeros has to be a half.

Prebiotic dietary fiber supplements are commonly consumed to help meet fiber recommendations and improve gastrointestinal health by stimulating beneficial bacteria and the production of short-chain fatty acids (SCFAs), molecules beneficial to host health. The objective of this research project was to compare potential prebiotic effects and fermentability of five commonly consumed fibers using an in vitro fermentation system measuring changes in fecal microbiota, total gas production and formation of common SCFAs. Fecal donations were collected from three healthy volunteers. Materials analyzed included: pure beta-glucan, Oatwell (commercially available oat-bran containing 22% oat β-glucan), xylooligosaccharides (XOS), WholeFiber (dried chicory root containing inulin, pectin, and hemi/celluloses), and pure inulin. Oatwell had the highest production of propionate at 12 h (4.76 μmol/mL) compared to inulin, WholeFiber and XOS samples (p<0.03). Oatwell’s effect was similar to those of the pure beta-glucan samples, both samples promoted the highest mean propionate production at 24 h. XOS resulted in a significant increase in the genus Bifidobacterium after 24 h of fermentation (0 h: 0.67 OTUs; 24 h: 5.22 OTUs; p = 0.038). Inulin and WholeFiber increased the beneficial genus Collinsella, consistent with findings in clinical studies. All analyzed compounds were fermentable and promoted the formation of beneficial SCFAs.

A human case of dengue virus 2 was detected from a febrile patient living in the Sare yoba, Kolda region (Southern Senegal). Phylogenetic analysis based on the partial sequence of the NS5 gene reveals that the virus belongs to the DENV2 Sylvatic genotype and is closely related to a strain (JF260983/ 98.98% identity) detected in Spain from a tourist who travelled to Guinee Bissau (bordering Kolda region) in 2009. This highlights a potential recent underreported circulation of sylvatic Dengue in the southern part of Senegal and calls for re-enforced integrated surveillance among humans, non-human primates and arboreal mosquitoes throughout a one-health approach.

Aggregated alpha-synuclein (-synuclein) is the main component of Lewy bodies (LBs), Lewy neurites (LNs), and glial cytoplasmic inclusions (GCIs), which are pathological hallmarks of idiopathic Parkinson’s disease (IPD) and multiple system atrophy (MSA), respectively. Initiating factors that culminate in forming LBs/LNs/GCIs remain elusive. Several species of -synuclein exist, including phosphorylated and nitrated forms. It is unclear which -synuclein post-translational modifications (PTMs) appear within aggregates throughout disease pathology. Herein we aimed to establish the predominant synuclein PTMs in post-mortem IPD and MSA pathology using immunohistochemistry. We examined the patterns of three -synuclein PTMs (pS87, pS129, nY39) simultaneously in pathology-affected regions of 15 PD, 5 MSA, 6 neurologically normal controls. All antibodies recognized LBs, LNs, and GCIs, albeit to a variable extent. pS129 -synuclein antibody was particularly immunopositive for LNs and synaptic dot-like structures followed by nY39 -synuclein antibody. GCIs, neuronal inclusions, and small threads were positive for nY39 -synuclein in MSA. Quantification of the LB scores revealed that pS129 -synuclein was the dominant and earliest -synuclein PTM followed by nY39 -synuclein, while lower amounts of pSer87 -synuclein appeared later in disease progression in PD. These results may have implications for novel biomarker and therapeutic developments.

The abnormal assembly of α-synuclein (α-Syn) is an initial step in the formation of Lewy bodies in the brain, which finally causes the neuronal death, being considered as a pathological hallmark in Parkinson’s disease (PD). Certain food bioactives or their metabolites at very low concentrations can trespass the blood brain barrier (BBB) that might, thereafter, act simultaneously. The aim of this work was to evaluate the inhibitory and destabilising capacities on α-Syn kinetics and the neuroprotective effects of three well-known bioactive compounds able to cross the BBB and present in foods; melatonin (MEL), protocatechuic acid (PCA) and hydroxytyrosol (HT), and their combinations. For this purpose, different in vitro techniques (Thioflavin T (ThT), Transmission Electronic Microscopy (TEM), electrophoresis and MTT assay) were used. All tested compounds and their combinations were able to abolish the toxicity induced by α-Syn. In addition, the combination of PCA (100 µM) +HT (100 µM) showed the highest inhibitory effect against α-Syn fibril formation and destabilises α-Syn fibrils (88 and 62%, respectively). This is the first time that MEL, PCA and HT prove a joint effect against α-Syn aggregation and toxicity when they are tested together.

This study addresses the challenge of predicting the dynamic behavior of the structures under seismic excitation. Accurate prediction of such systems' responses is critical for the design and evaluation of buildings and infrastructure. Traditional methods, including numerical models and differential equation solvers, often face significant computational burdens, especially with nonlinear hysteretic behaviors and large-scale problems. To overcome these limitations, a novel Fuzzy-based Convolutional Neural Network (FuzzyCNN) model is developed. This model integrates fuzzy logic principles with convolutional neural networks to effectively manage the uncertainties and complexities inherent in soil-structure interaction under seismic loads. The model's performance is validated through both numerical simulations and experimental data from a mid-rise concrete building subjected to seismic events. Comparative analysis with a traditional Physics-informed CNN (PhyCNN) model demonstrates the superior accuracy and robustness of the FuzzyCNN in predicting seismic responses. Key results show that the FuzzyCNN model not only enhances prediction accuracy but also handles uncertainties more effectively than the PhyCNN model. The findings suggest that the FuzzyCNN model can significantly improve the efficiency and accuracy of dynamic response predictions. This advancement offers valuable implications for engineering design, seismic risk assessment, and the development of more resilient infrastructure.

Unhealthy concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps atmospheric ozone and its precursors emitted from oil and gas operations. The winter-ozone system has two clear outcomes—occurrence or not—that is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language (“sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more constrained manner than alternative, more complex inference methods such as neural networks, increasing user trustworthiness of our model prototype before further optimization. Herein, we develop an ozone-forecasting system, Clyfar, based on knowledge of system dynamics and informed by an archive of meteorological conditions and ozone concentration. The inference system demonstrates proof-of-concept despite rudimentary tuning. We describe our framework for predicting future ozone concentrations if input values are drawn from numerical weather prediction forecasts as a proxy for observations as the system’s initial conditions. Our model is computationally cheap, allowing us to sample uncertainty with substantially more ensemble members than in traditional NWP. We evaluate hindcasts for one winter, finding our prototype demonstrates promise to deliver useful guidance for users concurrent with optimization of system parameters using machine learning.

Signal approximation, is the basic idea of signal processing and mathematical analysis where complex signals can be represented with simpler ones. This paper explores unifying two mathematical fundamentals, Dirichlet-type problems, and hyperbolic series identities, into signal approximation. In this paper, we address a well-known classic topic in mathematical analysis - the Dirichlet problem - to improve (the approximation of) signal using Fourier series. We also discuss using hyperbolic series representations to increase signal modeling flexibility. In this way, we hope to create new links that span some distance along this dimension and result in a somewhat theoretical view of signal behavior - present with the mathematical rigor needed for speculation but absent from an understanding capable of appropriately handling real-world complexity. This paper provides a theoretical investigation of the mechanics at play and reveals by analogy how these mathematical tools can potentially advance signal processing techniques and understanding. This paper illustrates Fourier series and fuzzy logic methods by two case studies on signal processing, satellite communication and financial risk measurement. The first case study performs signal approximation using the Fourier series and hyperbolic function identities to provide insight into how signals behave and are modeled. The second case study deals with applying fuzzy logic for risk evaluation of investment portfolios, emphasizing the utility in evasion systems solving uncertainty and imprecision problems related to financial decisions.

The present study is to address performance of the dairy supply chain and emphasizes the significance of reliability of the chain as a fundamental and critical attribute for fulfilling the safety requirements. In order to evaluate the performance of the dairy supply chain, the present study utilizes the reliability attribute, which is an essential and informative system performance indicator. The study considers not only the inputs and outputs but also internal activities and relationships between customers, manufacturers, and suppliers. To minimize calculations and resolve uncertainties and vagueness that may be experienced in the performance evaluation process, this study proposes the use of the Fuzzy Inference System (FIS) approach and evaluates the reliability at each level of the supply chain: supply, manufacturing, and distribution. Combining the reliability of different levels, the study finally calculates the reliability of the entire supply chain. The proposed methodology offers a practical approach for evaluating the reliability performance of a dairy supply chain and can be easily extended to other industries as well. To verify the proposed methodology, a real case in the Iranian dairy industry is chosen to be studied. Results indicate that the proposed methodology can effectively identify bottlenecks or weaknesses in the dairy supply chain and help develop effective strategies to address them. Analyzing factors influencing supply chain reliability can optimize operations, reduce costs, and improve the overall quality of dairy product quality delivered to consumers.

Considering the unquestionable role of education in economic development, it has an impact at a macroeconomic level—contributing to enhancing productivity and innovation, reducing poverty and promoting social cohesion—and at a personal level—ensuring the improvement of knowledge, skills and individual capabilities and promoting social values like empowerment and equality. Notably, quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study used a fuzzy set qualitative comparative analysis to assess the conditions for attaining higher levels of quality education based on the different indicators used to measure SDG 4. The analysis will allow comparisons of results among different countries and provide information about the initiatives that could be relevant to increasing quality education worldwide and for policymakers.

In this paper, experimental results about the performance of a Takagi-Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration for the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. The Takagi-Sugeno Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near from the upright position of the EV3BRS's body. The Parallel Distributed Compensation (PDC) approach is used to the design of the TSFC. The Linear Matrix Inequalities (LMIs) approach is used to get the feedback gains for every local linear controller guaranteeing global asymptotically stability, via a more relaxed stability condition, of the overall fuzzy control system. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry.

Machine Learning (ML) has proved its capabilities in different scientific and industrial fields, but it needs to be further investigated in the construction industry for practical utilization. One of the use cases of ML is delegating the structural calculation process. In this study, to discuss ML’s capabilities in performing the work of a structural designer, calculations of concrete sections based on ACI (American Concrete Institute) as a case study were selected. At first, all manual design steps and standard considerations for a concrete beam section were coded parametrically in MATLAB. After comparing with structural design references to prove the accuracy of codes in calculating shear and bending capacities, the parametric results were used as initial data (Look−up table) for training the ML operators. Regarding different types of ML techniques and as a comparison between them, in the next steps, all essential codes for Fuzzy Logic (FL), Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were coded in the same platform. The performance of the three coded ML operators to replace (delegate) standard calculations compared to direct calculations was individually investigated and displayed through parametric examples. After initial examples, the influences of the number of parameters and size of the Look−up table on the accuracy of each operator were discussed. The study concluded that although all three operators can delegate the standard calculation, the precision of the results differs considerably. In case of the desirable size of the Look−up table, ANFIS operators can represent the standard calculations with a different number of parameters and entirely high precision.

Fuzzy Rule Interpolation (FRI) offers an innovative methodology for inferring outcomes from data points that match no established rules in a sparse fuzzy rule base. Generally, a traditional fuzzy rule-based system suffers from the inability to perform inference while facing an unmatched observation, given a sparse fuzzy rule base that is derived from insufficient data or inadequate human experience and that fails to cover parts of a specific problem domain. Fortunately, starting with a sparse rule base, Fuzzy Rule Interpolation (FRI) can help to formulate interpolated rules specifically in instances where observations fail to activate any existing rule. An interpolated rule gives a result called consequent, which is related to the unmatched observation so that the inference system will not fail to generate an outcome corresponding to the observation as the output. Conventionally, these interpolated rules will be discarded after obtaining outcomes. However, valuable information may be potentially embedded within those discarded rules addressing the limitation regarding the coverage of the original sparse knowledge space. Moreover, without exploiting the potential value of the interpolated rules, the fuzzy inference system based on a sparse rule base will never improve its efficiency and robustness in the long run. Therefore, this underscores the necessity for Dynamic Fuzzy Rule Interpolation (D-FRI), which aims to collate interpolated rules that cover particular knowledge spaces absent in the sparse rule base. Intuitively, those frequently appeared interpolated rules are considered valuable because future observations are more likely to hit the respective problem areas. Promoting selected interpolated rules to the sparse rule base can enhance the overall coverage of the knowledge space and increase the inference efficiency over time. This paper introduces a framework for Dynamic Fuzzy Rule Interpolation that integrates the widely used Transformation-based Fuzzy Rule Interpolation (T-FRI) with an effective form of clustering of interpolated results. Experimental findings validate the effectiveness of this approach.

In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum

The selectin of a demand forecasting method is critical for companies aiming to avoid manufacturing overproduction or shortages in pursuit of sustainable development. Various qualitative and quantitative criteria with different weights must be considered during the evaluation of a forecasting method. The qualitative criteria and criteria weights are usually assessed in linguistic terms. Aggregating these various criteria and linguistic weights for evaluating and selecting demand forecasting methods in sustainable manufacturing is a major challenge. This paper proposes an extension of fuzzy Elimination and Choice Translating Reality (ELECTRE) I to resolve this problem. In the proposed method, fuzzy weighted ratings are defuzzified with the signed distance to develop a crisp ELECTRE I model. Moreover, an extension to ELECTRE I is developed by suggesting an extended modified discordance matrix and a closeness coefficient for ranking alternatives. The proposed extension can overcome the problem of information loss which can lead to incorrect ranking result when using the Hadamard product to combine concordance and modified discordance matrices. A comparison is conducted to show the advantage of the proposed extension. Finally, a numerical example is used to demonstrate the feasibility of the proposed method; furthermore, a numerical comparison is made to display the advantage of the proposed method.

With advances in information technology, big data, mobile communications, and robotics, digital technologies are increasingly being used in factories around the world. This digital transformation is named industry 4.0. Today, industrial companies are looking at how to adopt this era and implement these technologies 4.0 while improving their performance and generating more profits. The objective of this paper is to help companies to better choose the appropriate digital technologies according to their activities using a multi-experts-multi-criteria decision-making approach under hesitant fuzzy information. The proposed model is a generic model based on Multi-Agent Systems allowing to have an idea of the parameters necessary to apply the adopted approach. The adopted approach allows a better representation of uncertainty and subjectivity of experts’ judgments. It would be of great interest, especially, when exact quantitative data are not available. A real case company example is exposed (automotive company) towards putting into practice the proposed approach.

The bridge crane is often used in a complex environment, often subject to interference of all loads. Some uncertainties factors often have an inevitable impact on its swing. So the force situation of the bridge crane during a working cycle is analyzed, and a three-dimensional dynamic mathematical model of the bridge crane is built. Through the simulation analysis of the model under the action of driving force and wind load, the change law of the swing angle of the bridge crane is studied. Then, the fuzzy control theory is used to determine the control parameter in the anti sway control process. The position, swing angle deviation and deviation rate of the bridge crane are taken as the input, and the parameter correction is obtained after the fuzzification by using the center of gravity method, the anti sway fuzzy control system of the bridge crane is designed and simulated. The research results show that the fuzzy PID anti sway controller can not only improve the adaptability of the control system, but also overcome the large overshoot, quickly restrain the swing, and effectively realize the anti sway function of the bridge crane.

In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

This study evaluated the ranking of comprehensibility of the pictograms for judo, taekwondo, boxing, and wrestling used in the six games from the 27th Sydney Olympics in 2000 to the 32nd Tokyo Olympics in 2021. The evaluation was done using the Fuzzy TOPSIS method, one of the multi-criteria decision-making methodologies commonly used in economics and others fields. The results are as follows. The first, pictograms from the 2008 Beijing Olympics ranked first in three sports: taekwondo, boxing, and wrestling, but there were no pictograms that consistently ranked first or sixth in all sports. Second, the result of the sensitivity analysis shows a possibility that the ranking will be reversed if the weight of the evaluation factors changes, but in the 1000-time repetitive prediction, the better the evaluation ranking, the closer the value of the priority ranking to the ideal solution on average even if the weight changes.

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

In this paper we deal with the resolution of a fuzzy multiobjective programming problem using the level sets optimization. We compare it to other optimization strategies studied until now and we propose an algorithm to identify possible Pareto efficient optimal solutions.

Nowadays, model checking is applied as an accurate technique to verify software systems. The main problem of model checking techniques is the state space explosion. This problem occurs due to the exponential memory usage by the model checker. In this situation, using meta-heuristic and evolutionary algorithms to search for a state in which a property is satisfied/violated is a promising solution. Recently, different evolutionary algorithms like GA, PSO, etc. are applied to find deadlock state. Even though useful, most of them are concentrated on finding deadlock. This paper proposes a fuzzy algorithm in order to analyze reachability properties in systems specified through GTS with enormous state space. To do so, we first extend the existing PSO algorithm (for checking deadlocks) to analyze reachability properties. Then, to increase the accuracy, we employ a Fuzzy adaptive PSO algorithm to determine which state and path should be explored in each step to find the corresponding reachable state. These two approaches are implemented in an open-source toolset for designing and model checking GTS called GROOVE. Moreover, the experimental results indicate that the hybrid fuzzy approach improves speed and accuracy in comparison with other techniques based on meta-heuristic algorithms such as GA and the hybrid of PSO-GSA in analyzing reachability properties.

Two representable substructural logics ${\rm {\bf UL}}_\omega $ and ${\rm {\bf IUL}}_\omega $ are logics for finite UL and IUL-algebras, respectively. In this paper, the standard completeness of ${\rm {\bf UL}}_\omega $ and ${\rm {\bf IUL}}_\omega $ is proved by the method developed by Jenei, Montagna, Esteva, Gispert, Godo and Wang. This shows that ${\rm {\bf UL}}_\omega $ and ${\rm {\bf IUL}}_\omega $ are substructural fuzzy logics.

The performance evaluation of the Internet of Things (IoT) based supply chain is challenging due to the involvement of multiple decision makers, the multi-dimensional nature of the evaluation process, and the existence of uncertainty and imprecision in the decision making process. To ensure effective decisions are made, this paper presents a fuzzy multicriteria analysis model for evaluating the performance of IoT based supply chain. The inherent uncertainty and imprecision of the performance evaluation process is adequately handled by using intuitionistic fuzzy numbers. A new algorithm is developed for determining the overall performance index for each alternative across all criteria. The development of the fuzzy multicriteria group decision making model provides organizations with the ability to effectively evaluate the performance of their IoT based supply chains for improving their competitiveness. An example is presented for demonstrating the applicability of the model for dealing with real world IoT-based performance evaluation problems.

Hamacher operation which is generalization of the Algebraic and Einstein operation, can widely provide a large number of arithmetical operation with respect to uncertainty information, and Heronian mean can deal with correlations of the input arguments or different criteria and don’t make calculation redundancy, meanwhile, the normal intuitionistic fuzzy numbers (NIFNs) can depict distinctively normal distribution information in practical decision making. In this paper, a multi-criteria group decision-making (MCGDM) problem is researched under the NIFNs environment, and a new MCGDM approach is introduced on the basis of the Hamacher operation. Firstly, according to Hamacher t-conorm and t-norm, some operational laws of NIFNs are presented. Secondly, it is noticed that Heronian mean can’t only once take into account mutual relation between attribute values once, but also consider the correlation between input argument and itself. Therefore, we develop some operators and study their properties in order to aggregate normal intuitionistic fuzzy numbers information, these operators include Hamacher Heronian mean (NIFHHM), Hamacher weighted Heronian mean (NIFHWHM), Hamacher geometric Heronian mean (NIFHGHM) and Hamacher weighted geometric Heronian mean (NIFHWGHM). Furthermore, we apply the proposed operators to the MCGDM problem and present a new method. The main characteristics of this new method involve that: (1) it is suitable to make decision under the normal intuitionistic fuzzy numbers environment and more reliable and reasonable to aggregate the normal distribution information. (2) it utilizes Hamacher operation which can provide more reliable and flexible decision-making results and offer an effective and powerful mathematic tool for the MAGDM under uncertainty. (3) it uses the Heronian mean operator which can considers relationships between the input arguments or the attributes and don’t brings subsequently about redundancy. Lastly, an application is given for showing the feasibility and effectiveness of the presented method in this paper.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Recently, many authors have been interested to introduce fuzzy implications over t-norms and t-conorms. In this paper, we introduce (S,N) and residuum fuzzy implication for Dubois t-norm and Hamacher's t-norm. Also, new concepts so-called (T,N) and residual fuzzy co-implication in dual Heyting Algebra are investigated. Some examples as well as application are discussed as well.

Vaccine antigens must present the correct conformation of viral fusion glycoproteins to elicit effective immune responses. Virus-like particles (VLPs) serve as promising vaccine platforms because they mimic the membrane-embedded conformations of fusion glycoproteins on native viruses. Here, we employed SARS-CoV-2 VLPs (SMEN) presenting ancestral, Beta, or Omicron spikes to identify the variant that elicits potent and cross-protective immune responses in the highly sensitive K18-hACE2 mouse model. A combined intranasal and intramuscular administration regimen of the SMEN vaccine generated effective immune responses and was predominantly mediated by antibodies with minor contributions from T cells. Immunization with SMEN presenting an ancestral spike resulted in 100, 75, or 0% protection against ancestral, Delta or Beta VOC-induced mortality, respectively, whereas SMEN presenting the most divergent Omicron spike provided only limited protection (50%, 0%, and 25%) against ancestral, Delta, and Beta variants, respectively. By contrast, SMEN with a Beta spike offered 100% protection against the variants used in this study. Thus, the Beta variant not only overcame the immunity produced by other variants, but also elicited diverse and effective immune response. Our findings suggest that leveraging the Beta variant spike protein can enhance SARS-CoV-2 immunity, potentially leading to a more comprehensive vaccine against emerging variants.

Precision agriculture including camera-guided row detection and hydraulic steering are often used as an alternative because crop damage can be decreased and driving speed can be increased comparable to herbicide applications.The effects of different approaches as uncontrolled (UC), mechanical weed control (MWC), herbicide weed control (HWC) and mechanical + herbicide control (MWC+HWC) on weed density and yield of sugar beet were tested and evaluated in two trials (2021 and 2022) in South Transylvania at the tested intervals BBCH 19 and 31. Weed control efficacy (WCE) depends on the emergence of the weeds and a good timing of weed controls in all the trials and methods, though the highest yield of sugar beet roots was recorded in the treatment MWC+HWC, with an increase up to 12-15% (56.48 t ha-1) yield from HWC (50.22 t ha-1) and a yield increase of more than 35- 40% than MWC (42.34 t ha-1). Our trials show that is possible to increase yield and have less chemical applications with the introduction of the new precision technologies in agriculture included here the sensor-guided mechanical controls.

Cholinergic innervation in pancreas controls both the release of digestive enzymes to support the intestinal digestion and absorption, as well as insulin release to promote nutrient use in the cells of the body. The effects of muscarinic receptor stimulation are described in detail for endocrine beta cells and exocrine acinar cells separately. Here we describe morphological and functional criteria to separate these two cell types in situ in tissue slices and simultaneously measure their response to ACh stimulation on cytosolic Ca2+ oscillations [Ca2+]c in stimulatory glucose conditions. Our results show that both cell types respond to glucose directly in the concentration range compatible with the glucose transporters they express. The physiological ACh concentration increases the frequency of glucose stimulated [Ca2+]c oscillations in both cell types and synchronizes [Ca2+]c oscillations in acinar cells. The pharmacological ACh concentration further increases the oscillation frequency on the level of individual beta cells, inhibits the synchronization between these cells, and abolishes oscillatory activity in acinar cells. We discuss possible mechanisms leading to the observed phenomena.

Beta cells couple stimulation by glucose with insulin secretion and impairments in this coupling play a central role in diabetes mellitus. To clarify the effect of cAMP and the role of Epac2A in intracellular calcium signals and intercellular coupling, we performed functional multicellular calcium imaging in beta cells in mouse pancreas tissue slices after stimulation with glucose and forskolin in wild-type and Epac2A knock-out mice. Increased cAMP evoked calcium signals in otherwise sub-stimulatory glucose and beta cells from Epac2A knock-out mice displayed a faster activation. During the plateau phase, beta cells from Epac2A knock-out mice displayed a slightly higher active time in response to glucose compared with wild-type littermates, and increased cAMP increased the active time via a large increase in oscillation frequency and small decrease in oscillation duration in both Epac2A knock-out and wild-type mice. Functional network properties during stimulation with glucose did not differ in Epac2A knock-out mice, but the presence of Epac2A was crucial for the protective effect of increased cAMP in preventing a decline in beta cell functional connectivity with time. Finally, increased cAMP prolonged beta cell activity during deactivation in an Epac2A-independent manner.

In recent years, soil addition with organic amendments, such as biochar and compost, has gained attention as an effective agronomic practice to sustain soil fertility, enhance plant growth and crop yield. Well known are the positive effects of compost on yield of a wide crop varieties, while both positive and negative responses are reported for biochar Therefore, the aim of the study was to verify the effect of biochar mixed with three types of compost on quanti-qualitative response of Swiss chard (Beta vulgaris L. cycla), a leafy green vegetable rich in dietary antioxidants, largely consumed worldwide. A factorial experiment in pots with two factors, including biochar (without biochar and with biochar from vine pruning residues) and compost (without compost, with compost from olive pomace, with vermicompost from cattle manure and with compost from cattle anaerobic digestate), was setup. Two growth cycles were considered and a set of quantitative (height of plants, number, area and fresh weight of leaves) and qualitative parameters (carotenoid, chlorophyll, total N and NO3-content of leaves) were analyzed. Biochar decreased plant growth and NO3- leaf content; on the contrary, it increased total N leaf content, while compost improved all the considered parameters. The interactive effect of biochar and compost was evident only on total N and NO3- leaf content. In our experimental conditions, the compost showed to be the best option to improve Swiss chard growth and increase the phytopigments content, while the biochar-compost mixtures didn’t produce the expected effect.

In the paper, by convolution theorem for the Laplace transforms and analytic techniques, the author finds necessary and sufficient conditions for complete monotonicity, monotonicity, and inequalities of several functions involving polygamma functions. By these results, the author derives a lower bound of a function related to the sectional curvature of the manifold of the beta distributions. Finally, the author poses several guesses and open problems related to monotonicity, complete monotonicity, and inequalities of several functions involving polygamma functions.

Beta adrenoblockers is a large class of drugs mainly used to manage abnormal heart rhythms. Over the last couples of decades, the anticancer effects of these compounds has been extensively studied. There is much evidence about their activity in non-small cell lung, pancreatic, breast, colorectal, prostate and ovarian cancer. However, the mechanism of beta blockers anticancer activity is still not known, and more detailed studies are needed. The aim of our study was to evaluate the anticancer activity of beta adrenoblockers in non-small cell lung cancer cell lines A549 and H1299. In order to find the relationship with their selectivity to beta adrenoreceptors, in our study we used selective (atenolol, betaxolol, esmolol, metoprolol) and non-selective (pindolol, propranolol and timolol) beta blockers. The effect on cell viability was evaluated by MTT assay and the activity on cell ability to form colonies was tested by clonogenic assay. The type of cell death was evaluated by cell double staining with Hoechst 33342 and Propidium iodide. The most active adrenoblockers against both tested cancer cell lines were propranolol and betaxolol. They completely inhibited lung cancer cell colony formation at 90% of EC50 (half maximal effective concentration) value. Most tested compounds induced cell death through apoptosis and necrosis. In A549 cell lines apoptosis was mainly induced while in H1299 cell line compounds induced both apoptosis and necrosis. There was no correlation established between beta adrenoblocker anticancer activity and their selectivity to beta adrenoreceptors.

Parkinson’s Disease (PD) is a debilitating neurodegenerative disease, causing loss of motor function, and in some instances, cognitive decline and dementia, in those affected. The quality of life can be improved, and disease progression delayed through early interventions, however, current methods of confirming a PD diagnosis are extremely invasive. This prevents their use as a screening tool for the early onset stages of PD. We propose a surface imprinted polymer (SIP) electroimpedance spectroscopy (EIS) biosensor for detecting α-Synuclein (αSyn) and its aggregates, a biomarker that appears in saliva and blood during the early stages of PD as the blood brain barrier degrades. The surface imprinted polymer stamp is fabricated by low temperature melt stamping polycaprolactone (PCL) on interdigitated EIS electrodes. The result is a low-cost, small footprint biosensor that is highly suitable for non-invasive monitoring of the disease biomarker. The sensors were tested with αSyn dilutions in deionized water, and in constant ionic concentration matrix solutions with decreasing concentrations of αSyn to remove the background effects of concentration. The device response confirmed the specificity of these devices to the target protein of monomeric αSyn. The sensor limit of detection was measured to be 5 pg/L and its linear detection range was 5 pg/L – 5 µg/L. This covers the physiological range of αSyn in saliva and makes this a highly promising method of quantifying αSyn monomers for PD patients in the future. The SIP surface was regenerated, and the sensor reused to demonstrate its capability for repeat sensing as a potential continuous monitoring tool for the disease biomarker.

Emerging evidence demonstrated that using a new manufacturing technology to produce γ-aminobutyric acid (GABA)-fortified oolong (GO) tea could relieve human stress and exert versatile physiological benefits. The purpose of this human research aims to investigate therapeutic effects of daily GO tea consumption on the improvement of blood pressure, relaxation-related brain waves and quality of life (QOL) during a period of 28 consecutive days. Total polyphenols, major catechins and free amino acids were analyzed via HPLC assay. Changes of heart rate, blood pressure, α-brain wave (index of relaxation) and eight-item QOL score were investigated on day 0, 7, 14, 21 and 28, respectively. The chemical analysis results showed that GO tea contained the most abundant amino acids and GABA, contributing to the relaxation activity. Among all study participants, daily drinking GO tea could reduce systolic blood pressure on day 21 and diastolic blood pressure on day 28 (p &lt; 0.05, respectively). For participants with pre-hypertension, GO tea could effectively reduce heart rate, systolic and diastolic blood pressure on day 28 (p &lt; 0.05). At the end of the study, incremental changes in alpha-brain wave and QOL scores were also demonstrated (p &lt; 0.05, respectively). This study recommended that GO tea might potentially serve as a natural source of alternative therapy to benefit blood pressure, stress-relief and QOL.

SARS-CoV-2 is severe acute respiratory syndrome coronavirus 2 which induces severe pneumonia that has considerable death rate. In this article, we summarised the evolution of SARS CoV-2 from alpha to omicron variant. The origin, transmission capability, and innate immunity potential of the Omicron variety remain unknown in the aftermath of its appearance. It's also unclear whether further varieties based on Omicron may emerge in the future. However, there is no question that the Omicron version of SARS-CoV-2 would not be the last. The COVID-19 pandemic has become more difficult to control due to the constant appearance of new SARS-CoV-2 mutations. We have discussed about the epidemiology, treatment plans for SARS CoV-2 and preventive strategies for future pandemic.

Parkinson’s disease and Alzheimer’s disease are the most commonly diagnosed neurodegenerative disorders. Though these disorders differ in terms of their underlying pathophysiology as well as in their clinical features and course, there is a certain degree of overlap between them. This overlap may be partly related to α-synuclein-mediated neuropathological changes. Recent evidence has found that depression is associated with an increased subsequent risk of both these neurological disorders, and that α-synuclein may also play a pathogenic role in depression. The current study examines epidemiological, population genetic and environmental exposure data in relation to the estimated prevalence of depressive disorders, Parkinson’s disease and Alzheimer’s disease using a cross-sectional, country-level analysis. The results of this study are consistent with a significant relationship between depressive disorders and neurodegenerative disorders, a possible shared genetic vulnerability related to functional polymorphisms of the α-synuclein gene SNCA, and potential gene-environment interactions involving fine particulate matter pollution. The significance of these results is discussed in the light of existing translational, clinical and epidemiological research on the links between these disorders.

The potato tuber moth PTM, Phthorimaea operculella, is one of the most economically important potato pests worldwide. In the present study, the potential of Steinernema feltiae and Steinernema carpocapsae for controlling PTM in potato tubers was evaluated compared to alpha-cypermethrin. Steinernema carpocapsae in both concentrations (12.6×10⁶IJs and 6.2×10⁶IJs) showed a lower number of emerged insects than alpha-cypermethrin (10 mg ai l^-1). Alpha-cypermethrin (20 mg ai l^-1) showed the highest efficacy against PTM (81.17%), S.carpocapsae (12.6×10⁶IJs), and alpha-cypermethrin (10 mg ai l^-1) showed similar efficacy (72.53%) while S. feltiae (6.2×10⁶IJs) showed the lowest efficacy (39.04%). The results showed that S.carpocapsae in both concentrations and S. feltiae (12.6×10⁶IJs) were efficient the same as alpha-cypermethrin (10 mg ai l^-1) having no environmental and health adverse impacts issued in the chemical insecticides usage. Both concentrations of alpha-cypermethrin and S. carpocapsae showed the least tuber damage with no significant differences, while it was as high as the control (59.26%) in both concentrations of S. feltiae. This promising finding introduces EPNs as a part of the potato tuber protection program in storage. Accordingly, EPNs can be considered as an appropriate alternative to synthetic chemicals for PTM control without any residue and health problems.

Hepatitis B virus (HBV) infection is a major factor in development of various liver diseases such as hepatocellular carcinoma (HCC). Among HBV encoded proteins, HBV X protein (HBx) is known to play key role in development of HCC. Hepatocyte nuclear factor 4α (HNF4α) is a nuclear transcription factor which is critical for hepatocyte differentiation. However, the expression level as well as its regulatory mechanism in HBV infection have yet to be clarified. Here, we observed the suppression of HNF4α in cells which stably express HBV whole genome or HBx protein alone, while transient transfection of HBV replicon or HBx plasmid had no effect on the HNF4α level. Importantly, in the stable HBV- or HBx-expressing hepatocytes, the downregulated level of HNF4α was restored by inhibiting ERK signaling pathway. Our data showed that HNF4α was suppressed during long-term HBV infection in cultured HepG2-NTCP cells as well as in mouse model following hydrodynamic injection of pAAV-HBV or in mice intravenously infected with rAAV-HBV. Importantly, HNF4α downregulation increased cell proliferation which contributed to the formation and development of tumor in xenograft nude mice. The data presented here provided several proofs for the effect of HBV infection in manipulating HNF4α regulatory pathway in HCC development.

This treatise represents a significant contribution to the field by integrating quality constraint functions and predicates into Possibility Computation Tree Temporal Logic (PoCTL), thus giving rise to Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL). We provide a comprehensive definition of QFCTL's syntax, conduct an in-depth analysis of its logical characteristics, outline a precise model checking algorithm for QFCTL, and perform a meticulous complexity assessment of said algorithm. Moreover, by enriching QFCTL with a quantitative characteristic predicate operator, we innovate, culminating in the development of an enhanced Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL*). The treatise explores the logical facets of QFCTL*, formulates a bespoke model checking algorithm for QFCTL*, and conducts a rigorous analysis of its algorithmic complexity. To attest to the practical utility and robust expressive power of QFCTL and QFCTL*, we present a model checking example that serves as empirical evidence of the efficacy of the proposed model checking algorithms.

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is indeed innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has huge impact on the life of physically disabled people. Therefore, there is need to develop a robot which can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. The fast gradient filters using a fuzzy inference system (FIS) is employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the Fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using True Success Rate (TSR) and compared to other existing models. The results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

Currently, many health workers have died of being infected by dangerous infectious diseases. One solution is to prevent the spread of transmissions by minimizing contact between patients and staff or others using a mobile robot for delivering logistics services. We propose an approach by applies a navigation system using a position driver and obstacle avoidance using a fuzzy controller and particle swarm optimization for path planning in an unknown environment for a holonomic mobile robot. A Fuzzy Controller was used for obstacle avoidance so that the mobile robot could avoid obstacles using existing proximity sensors as the perception of the robot. In the path planning section, Particle Swarm Optimization is used to find intermediate points that must be reached by the mobile robot to reach their target. First, we performed simulation under the condition that the simulation resolution was similar to that of real conditions. It was found that the Trigonometric Function Adaptive PSO (TFAPSO) was better for finding the optimal solution based on the safest, shortest path, and lowest time. Finally, the holonomic mobile robot is tested directly in an unknown environment with the trajectory path generated by TFAPSO in real condition.

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPS). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPS. The Affinity Propagation clustering approach is utilized to dynamically determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy.

In this article, we first introduced new types of higher separation axioms called (r,s)-GFS-regular and (r,s)-GFS-normal spaces with the help of (r,s)-generalized fuzzy semi-closed sets (briefly, (r,s)-gfsc sets) and discussed some topological properties of them. Thereafter, we defined a stronger form of (r,s)-gfsc sets called (r,s)-g*fsc sets and investigated some of its features. Moreover, we showed that (r,s)-fsc set → (r,s)-g*fsc set → (r,s)-gfsc set, but the converse may not be true. In addition, we explored new types of fuzzy generalized mappings between double fuzzy topological spaces (U, τ, τ*) and (V, η, η*), and the relationships between these classes of mappings were examined with the help of some illustrative examples. Finally, some new types of compactness via (r,s)-gfso sets were defined and the relationships between them were introduced.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

The development of an equipment starts from an effective design activity. The concept selection process is an activity that is entailed in the design stage and its relevance in the design process cannot be overemphasized because it informs the choice of optimal conceptual design from a set of alternative design. Hence, there is a need to accrue efforts to the concept selection process because of its importance. This article presents the identification of optimal conceptual design as a Multicriteria decision making model by assessing the suitability of Fuzzy Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The Fuzzy MARCOS model was developed to access four alternative conceptual designs of briquetting machines considering eight design features with several sub features. The fuzzy MARCOS model was able to rank the conceptual designs and the results obtained from the decision analysis showed that the Fuzzy MARCOS model was able to rank the designs based on their performance and the final values of the overall utility function. The overall utility function is a based on the utility degree of the conceptual design alternatives in terms of the best and worst designs identified by the model. The utility degree created a platform for comparison on how the design alternatives varies from the best and worst design.

Investment management is a common process and practice used for achieving a desirable investment goal or outcome. Investment assessment should be carried out at various stages of project realization in accordance with capital investment volume. Investment risk management implies the effective control of all procedures and monitoring of risks in all phases of the investment project. Because of the reason that a single indicator in probability calculation of achieving optimal return from the investment does not exist, performing sufficiently reliable estimates of the quality of investments becomes a tedious task. There are many indicators, factors, and criteria required for consideration to reach the effective solution of the investment problem. Unfortunately, the systematic variation of economic situations in the marketplace stipulates the continuous and frequent changes of investment conditions and environment in which the investor should act and operate. Hence, the rules required for providing a reasonable quality of investment projects can be based only on investor’s management strategy and rely on investor’s intuition and practice. The importance of classification in investment management and decision making process is undeniable. The objects to be classified are described using assessments in accordance with various criteria which can be both quantitative and qualitative. With a competent formulation of the investment process, both methods are used in parallel. There exist various decision making approaches for the investment management, and simple additive weighting (SAW) is one of the well-known multi-criteria decision making (MCDM) methods aiming to provide an optimal decision for decision maker when solving various real-life problems, and particularly, investment problem. In this paper, fuzzy simple additive weighting (FSAW) method in group decision making is applied to undertake the capital investment expenditure for purchasing cars with the purpose of renting them to the public. The development of existing FSAW method is accomplished and this process involves the sensibility of outcomes to changes in the rate of fuzziness represented with decisions taken. Eventually, the degree of fuzziness involved in an analysis that directly attempts to model the immanent vagueness and imperfectness in particular precedency judgments made, is determined. A numerical example illustrates the importance and effectiveness of the suggested approach with the aim of ranking alternatives and hence, determining the most preferred alternative in MCDM problem.

In this paper, a fuzzy logic-based energy management system and multi-port quasi-z-source converter are proposed that utilizes hybrid renewable energy sources. The system ensures that each energy source module can be used individually by employing fuzzy logic to define power modes. This approach also helps prevent switching losses resulting from extra switching of the source modules. Also, the proposed energy management does not have a mathematical model, so applicability is simple and it is suitable for different multiple input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system because of the linguistic rules. Moreover, by adopting the quasi-z-source network to the end of the multi-port converter, the switching losses of the multi-port modules have been significantly reduced. The simulation results are obtained using MATLAB Simulink, and the experimental results are obtained by connecting the circuit to MATLAB Simulink via an Arduino Due.

The aim of this paper is the evaluation of the success of graduating with distinction in higher education (SGDHE) using the fuzzy DEMATEL method. The observation has been done using cause and effect criteria. 11 cause and 14 effect clusters have been used in this study. The study result of this work shows that all the effects are connected to the given causes and a cause-effect graph has been generated for each connection. This proposed approach is demonstrated with the empirical case of Dire Dawa University students in Dire Dawa Ethiopia.

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

We examined how combinations of corporate social responsibility (CSR) activities lead to high performance in Korean companies. This study addressed two related questions to expand our limited knowledge in this area. The first was what combinations of CSR activities achieve high performance. The second was to identify how CSR activities form an interdependent system, depending on different corporate situations. Korean Economic Justice Institute index data, from 2012 to 2018, were used with fuzzy set qualitative comparative analysis, and the results revealed several effective CSR activity factor combinations under given strategies and management environments. Companies with high performance exhibit complementarity between social contribution, environmental management, fairness, and employee satisfaction. By contrast, companies with low corporate performance show no complementarity between relatively unrelated activity factors. For companies whose CSR activities lead to low financial performance, most of the causal pathways focused only on activities at the primary stakeholder level, with weak diversity of CSR activities’ combinations at the primary and secondary stakeholder levels. These results indicate not only the appropriateness of CSR activity factor combinations for companies’ strategy and management environment contexts, but also their effectiveness, and are expected to provide companies with significant implications for CSR activities.

This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a Fuzzy Inference System (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules and one output. The FIS is responsible to monitor the restoration results, grade their validity and choose the one that yields to a better grade. These grades are produced autonomously by analyzing results of Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 um wire probe are compared with the restorations. Main aims are therefore a) Provide unsupervised deblurring for device independent EB measurement; b) Improve the reliability of the process; c) Apply deblurring without knowing the probe size. These, further facilitates the deployment and manufacturing of EB probes and probe independent and accurate EB characterization. It also makes restoration of previously collected EB measurements easier, where the probe sizes are not known or recorded.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relationships of the training time series. Firstly, we compare each data with the data of its previous day in historical training time series to generate a new fluctuation trend time series(FTTS). Then, fuzzify the FTTS into fuzzy-fluctuation time series(FFTS) according to the up, equal or down range and orientation of the fluctuations. Since the relationship between historical FFTS and the fluctuation trend of future is nonlinear, Particle Swarm Optimization (PSO) algorithm is employed to estimate the required parameters. Finally, use the acquired parameters to forecast the future fluctuations. In order to compare the performance of the proposed model with that of the other models, we apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets. The experimental results and the comparison results show that the proposed method can be successfully applied in stock market forecasting or such kinds of time series. We also apply the proposed method to forecast Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

Background : The multisystem manifestations of Fabry disease can create major challenges in patient care. Although enzyme replacement therapy with recombinant agalsidase beta has demonstrated clinical benefits, the standard fortnightly, multihour infusion regimen imposes a substantial burden on patients. Methods : We assessed the safety and feasibility of shortening the agalsidase beta infusion time to 90 minutes in adult patients with classic or later-onset Fabry disease in the absence of premedication. A total of 39 consecutive adult patients (agalsidase-naive: n=6; significant comorbidities: n=15) with no recent infusion-associated reactions underwent a total of 85 agalsidase beta infusions in our tertiary reference center for lysosomal diseases. Each infusion was administered at a constant rate (between 0.78 and 1.17 mg/min, depending on the patient’s body weight). Results : No adverse events of any type (including discomfort and infusion-associated reactions) were reported during or after infusions. The patients’ vital signs remained stable, and patient satisfaction was high. Seven patients requested even shorter infusions (50 to 60 minutes) under strict medical supervision. Conclusions : Our results suggest that shortening the agalsidase beta infusion time to 90 minutes is safe and feasible in stably treated adult patients with Fabry disease and no recent infusion-associated reactions.

Understanding the relationship between sugar yield and related traits is crucial for successfully breeding this important industrial crop. Thirteen sugar beet monogram cultivars were evaluated during 2018-2019. The study used a split-factorial design with two planting dates as the main plot, while cultivars and harvesting dates were laid out as CRBD as subplots. Significant differences were observed among the cultivars for all measured traits. The genetic correlation coefficients showed the highest positive and significant correlations between sugar yield and white sugar yield (WSY) (0.88**), sugar content (SC) (0.71**), and white sugar content (0.65**). Conversely, the highest negative and significant correlations were found between sugar yield and molasses sugar content (-0.62**), alkalinity coefficient (-0.58**), and root potassium content (-0.52**). In the stepwise multiple regression analysis, white sugar content (WSC), sugar content (SC), root α-amino nitrogen content, and root yield were included in the regression model, explaining 84% of the total variance in the sugar yield. Path analysis revealed that sugar content (1.12) and root yield (1.03) had the highest direct effects on sugar yield. These traits can be used as key criteria for developing superior hybrids with high economic productivity, thereby accelerating the selection process in various breeding programs.

In this paper, we report a comprehensive and consistent annotation of the locus encoding the horse T cell receptor b-chain (TRB), as inferred from the most recent genome assembly. The horse TRB locus spans approximately 1 Mb, making it the largest locus among the mammalian species stud-ied to date, with a significantly higher number of genes related to extensive duplicative events. In the region, 136 TRBV, belonging to 29 subgroups, 2 TRBD, 13 TRBJ and 2 TRBC genes, were iden-tified. The general genomic organization resembles that of other mammals, with a V-cluster of 135 TRBV genes, located upstream of two in-tandem aligned TRBD-J-C clusters, and an inverted TRBV gene at the 3’ end of the last TRBC gene. However, the horse b-chain repertoire would be affected by a high number of non-functional TRBV genes. Thus, we queried a transcriptomic da-taset derived from splenic tissue of a healthy adult horse, using each TRBJ gene as a probe to ana-lyse clonotypes encompassing the V(D)J junction. This analysis provided insights into the usage of the TRBV, TRBD and TRBJ genes and the variability of the juxtaposition region. Our results clearly demonstrated that the horse β-chain constitutes a complex level of variability broadly like that described in other mammalian species.