More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

A detailed literature review is performed in this study to address solutions for the full-body design and control of an aerial manipulator. Deep Reinforcement Learning methods are growing to be utilized recently to cope with various uncertainties. The pros and cons of these theories will be explained as well as introducing the advantages of Fuzzy Reinforcement Learning methods. State-of-the-Art, possible challenges, potential approaches, and a summary of desired precision devices are discussed in this study.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

This research work deals hybrid control system based integrated Cuk converter fed brushless DC motor (BLDCM) for power factor correction. In this work, moth-flame optimization (MFO) and fuzzy logic controller (FLC) has been combined and moth –flame fuzzy logic controller (MFOFLC) has been proposed. Firstly, the BLDC motor modelling is composed with power factor correction (PFC) based integrated Cuk converter and BLDC speed is regulated using variable DC-Link inverter voltage which makes low switching operation with less switched losses. Here, with the use of switched inductor, the task and execution of proposed converter is redesigned. The DBR (diode bridge rectifier) trailed by proposed PFC based integrated Cuk converter operates in discontinuous inductor conduction mode(DICM) for achievement of better power factor.MFO is exhibited for gathering of dataset from the input voltage signal. At that point separated datasets is send to FLC to improve the updating function and minimization of torque ripple. However, our main objective is to assess adequacy of proposed method, the power factor is broke down. The execution of the proposed control methodology is executed in MATLAB/Simulink working platform and the display is assessed with the existing techniques.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

Bergamot flavonoids have been shown to prevent metabolic syndrome, non-alcoholic fatty liver disease (NAFLD) and stimulate autophagy in animal models and patients. To investigate further the mechanism of polyphenol-dependent effects, we performed RT2-PCR array analysis on 168 metabolism, transport and autophagy-related genes expressed in rat livers exposed for 14 weeks to different diets: standard, cafeteria (CAF) and CAF diet supplemented with 50 mg/kg of Bergamot Polyphenol Fraction (BPF). CAF diet caused a strong upregulation of the gluconeogenesis pathway (Gck, Pck2) and a moderate (>1.7 fold) induction of genes regulating lipogenesis (Srebf1, Pparg, Xbp1), lipid and cholesterol transport or lipolysis (Fabp3, Apoa1, Lpl) and inflammation (Il6). However, only one -oxidation gene (Cpt1a) and a few autophagy genes were modulated in the CAF group compared to controls. While most of these transcripts were significantly modulated by BPF, we observed a particularly potent effect on lipogenesis genes, like Acly, Acaca, Fasn and Scd1, which were suppressed far below the mRNA levels of control livers as confirmed by alternative primers-based RT2-PCR analysis and western blotting. These effects were accompanied by downregulation of pro-inflammatory cytokines (Il6, Tnfa, and Il10) and diabetes-related genes. Few autophagy (Map1Lc3a, Dapk) and no -oxidation gene expression changes were observed, suggesting minor transcriptional effects on autophagy. In conclusion, chronic BPF supplementation efficiently prevents NAFLD by modulating hepatic energy metabolism and inflammation gene expression programs, with no effect on -oxidation, but profound suppression of de-novo lipogenesis.

Statistical modeling of lifetime data plays an essential role in a wide range of practical fields, such as health and engineering. There have been a lot of studies done to develop statistical models that can better describe health data than traditional models. For the first time, we pioneer a novel family of continuous probability distributions called the generalized odd beta prime generalized (GOBP-G) family of distributions. The cumulative distribution and probability density functions of the new family are presented. A new generalization of the Weibull distribution called "generalized odd beta prime-Weibull" (GOBPW) is proposed using the pioneered GOBP-G family. The mixture representations of the new distribution are defined and derived. Some formal statistical properties of the GOBPW distribution, such as the moments, moment generating function, incomplete moments, information generating function, entropies, stress-strength function, quantile function, and order statistics, are derived. The estimation of the parameters of the proposed distribution is evaluated using the maximum likelihood estimation approach. Different cancer disease data sets, such as the bladder, head and neck, acute bone, and blood cancers, are used to illustrate the applicability and usefulness of the new model and were compared using several statistical accuracy measures with that of well-established extended Weibull distributions, which are the beta modified Weibull distribution, Kumaraswamy modified Weibull distribution, gamma generalized modified Weibull distribution, gamma log-logistic Weibull distribution, and beta log-logistic Weibull distribution. The results show that the proposed model gives better results than the competitive models. This study could guide the relevant stakeholders in choosing a suitable statistical model for the health data instead of relying on traditional models to enhance decision-making.

Nowadays, we can observe a worldwide trend towards the development of synthetic biomaterials. Numerous researches have been carried out in order to better understand the cellular behavior involved in the inflammation and bone healing processes related to living tissues. The aim of this study is to evaluate the tissue behavior of two different types of biomaterials: synthetic nano-hydroxyapatite / beta-tricalcium phosphate and hydroxyapatite in non-critical bone defects in rat calvaria. Twenty four rats underwent surgery in which two 3 mm defects in each cavity were performed. The rats were divided into 2 groups: Group 1, using xenogen hydroxyapatite (Bio oss ™); Group 2, using synthetic nano-hydroxyapatite / beta-tricalcium phosphate (Blue Bone ™). Sixty days after the surgery, the calvaria bone defect was filled with biomaterial, the animals were sacrificed and marked with Masson's trichrome and PAS staining, and immunohistochemistry (TNF-α, MMP-9) and electron microscopy analyzes were performed. Histomorphometric analysis indicates a greater presence in the protein matrix in Group 2, in addition to higher levels of TNF-α, MMP-9. The ultrastructural analysis shows an association of material with fibroblasts in the tissue regeneration stage. Paired statistical data indicates that Blue Bone ™ can improve bone formation / remodeling when compared to biomaterials of xenogenous origin.

This research work is an extension of a previous work in which the authors created an innovative family of fuzzy implication using as t-conorm probor, as fuzzy negation the 1-x and the type of Newton's binomial. The purpose of this paper is to compare the type of fuzzy implication probor with three other constructed fuzzy implications produced by max, Einstein and Lukasiewicz t-conorms maintaining in each case the same negation. T-conorms perform the fuzzy logical operation "or" of the corresponding fuzzy sets. The main methods used for the comparisons are the basic axioms of fuzzy logic. The authors performed six combinations of t-conorm comparisons, in order to find the rank order of the five fuzzy implications. Although the general type of fuzzy implication probor came out smaller than Hamacher, Einstein and Lukasiewicz and the type of Einstein smaller than Lukasiewicz authors make extensive tests using four different membership functions and different values to the parameter m so as to examine which out of the four fuzzy implications receive the best results. The application of isosceles trapezium to the fuzzy implications probor and Einstein give the best values (most values greater than or equal to 0.9 and equal to 1).

It aims to utilize more solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. Therefore, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking(MPPT) methods are utilized for the maximum power generation in photovoltaic panels systems under different weather conditions. In this paper, a novel hybrid and clever maximum power point monitoring fuzzy logic controller maximum power point tracking algorithm established on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in MATLAB/SIMULINK environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel were used for the simulation. The hybrid method proposed here was found to reduce the voltage and current ripple compared to the classical incremental conductivity method. The proposed intelligent method has been shown to stabilise the system more quickly in the event of sudden weather changes.

Cardiovascular disease (CVD) and obesity are prevalent public health concerns with grave implications for morbidity and mortality, necessitating tailored interventions. This research explores the utility of fuzzy c-means clustering paired with principal component analysis (PCA) in detecting at-risk groups and personalizing health strategies for these conditions. Fuzzy c-means clustering allows for the dynamic classification of individuals into groups based on unique risk factors and intervention outcomes, while PCA aids in distilling complex data sets to uncover underlying patterns. The conjoined use of these methods has shown promise in identifying diverse risk profiles and in forecasting intervention success rates. The study acknowledges limitations, including possible biases stemming from data set composition and analytical parameter selection. Future research aims to refine these tools for clinical application. The results support implementing fuzzy c-means clustering and PCA for delineating specific target populations for health interventions, emphasizing careful use of these analytical approaches. Subsequent studies should focus on correlating these techniques with concrete clinical results to enhance public health measures.

Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover, the integration of blockchain technology provides a decentralized and tamper-proof infrastructure for securely storing and managing authentication and key agreement data. This ensures transparency and trust in the system, mitigating the risks of unauthorized access and data manipulation. The blockchain-based architecture also enables efficient resource utilization and scalability, allowing the system to handle authentication requests in a timely manner, even in large-scale digital healthcare environments.The proposed method is evaluated by using the NIST Special Database 302 and it shows superior performance compared to existing methods, with minimum False Rejection Rate (FRR), False Acceptance Rate (FAR), and response time. Moreover, the proposed method minimizes communication overhead during the authentication process and resists different cyber attacks including a Replay attack, Man-in-the-middle attack, Denial of Service (DoS) attack, and Impersonation attack. The proposed method achieves excellent performance in terms of security, efficiency, and resistance to various cyber-attacks, making it a promising approach for secure data sharing in P2P cloud environments.

In the context of economic development and international economic integration, the logistics industry in Vietnam is developing to meet the market demand for the transportation of goods; thus, many logistics enterprises have been formulated and grown up in recent years. This research aims to measure the efficiency of logistics enterprises and recommend a feasible solution to improve their future performance by integrating the Super Slacked-based Measure model (Super-SBM) in Data Envelopment Analysis and Fuzzy Analytic Hierarchy Processes (Fuzzy AHP) in multi-criteria decision-making. The super-SBM model was utilized to conduct the efficiency scores of logistics enterprises from 2016 to 2022 based on calculating the ratio between input and output variables; the empirical result determined each enterprise's effectiveness and ineffectiveness. Next, the Fuzzy AHP method evaluated and ranked criteria that directly impacted the operational process of logistics enterprises based on experts' opinions; the examined result suggested a feasible direction to improve their business efficiency in the future. The proposed hybrid models are a helpful solution for efficiency determination and development direction for logistics enterprises. An overall picture of the logistics enterprises was also drawn to describe their operational business process.

Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system.

Growing agricultural demands for the global population are unlocking the path to developing innovative solutions for efficient water management. Herein, an intelligent variable rate irrigation system (fuzzy-VRI) is proposed for rapid decision-making to achieve optimized irrigation in various delimited zones. The proposed system automatically creates irrigation maps for a center pivot irrigation system for a variable-rate application of water. Primary inputs are spatial imagery on remotely sensed soil moisture (SSM), soil adjusted vegetation index (SAVI), canopy temperature (CT), and nitrogen content (NI). To eliminate localized issues with soil characteristics, we used the crop nitrogen content map to provide a focused insight on issues related to water shortage. The system relates these inputs to set reference values for the rotation speed controllers and individual openings of each central pivot sprinkler valve. The results showed that the system can detect and characterize the spatial variability of the crop and further, the fuzzy logic solved the uncertainties of an irrigation system and defined a control model for high-precision irrigation. The proposed approach is validated through the comparison between the recommended irrigation and actual irrigation at two field sites, and the results showed that the developed approach gives an accurate estimation of irrigation with a reduction in the volume of irrigated water of up to 27% in some cases. Future research should implement the fuzzy-VRI real-time during field trials in order to quantify its effect on irrigation use, yield, and water use efficiency.

The part transportation efficiency is a main factor of aircraft sortie generation rate. Part transportation is used to transport spare part from base to carrier. Transportation strategy depends on both demand on carrier and inventory in transportation base. The transportation time and stochastic demand will induce fluctuations of cost and inventory. Thus, a Takagi-Sugeno fuzzy system of dynamic part transportation is established considering transportation time and stochastic demand. And a novel Takagi-Sugeno fuzzy robust control is designed for dynamic part transportation, which will keep transportation cost and part inventory stable. First of all, a fuzzy model with stochastic demand and transportation time is proposed. Then, a novel robust control with cross rule groups is conducted according to production and transportation strategy, which will reduce fluctuations induced by strategies switch. Moreover, robust stability is guaranteed and part can be supplied in time under a low cost. Finally, simulation illustrates usefulness and quickness of the novel Takagi-Sugeno fuzzy robust control. Besides, the proposed method will be useful in other transportation electrification systems with delay time and uncertainty.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

Extended-spectrum beta-lactamase (ESBL) -producing and multidrug resistant Enterobacterales pose a major threat to both human and animal health. This study assessed the prevalence of ESBL-producing Escherichia coli (ESBL-Ec) and Klebsiella spp. (ESBL-K) in cattle farms in Ouagadougou, Burkina Faso, using a One Health approach. From May 2021 to September 2022, cattle faeces, farmers’ stools, their drinking water and farm soil samples were collected from semi-intensive and traditional farms. ESBL-selective medium was used to obtain resistant isolates, which were further characterized using biochemical tests. An-timicrobial susceptibility testing was performed using the Kirby-Bauer disc dif-fusion method. ESBL-Ec and/or ESBL-K were detected in 188 of 322 samples (58.0%). The prevalence of ESBL-Ec isolates was 42.2% (136/322) and ESBL-K isolates 24.5% (79/322). Notably, 156 of the 188 ESBL isolates (83.0%) exhibited multidrug resistance. The highest resistance rates were observed against tetracy-cline and cotrimoxazole. Importantly, no isolates showed resistance to mero-penem, which was used to test for carbapenem resistance. This study highlights the presence of ESBL-Ec and ESBL-K among humans, animals and the envi-ronment of the cattle farms. Good hygiene and biosafety practices are essential to limit the potential spread of multidrug resistant bacteria between different inter-faces on farms.

Background: beta blockers of three generations are first-line drugs in the treatment of chronic heart failure (CHF). However, to date there is no clear understanding of the effect of beta blockers on myocardial energy metabolism disorders in CHF. The aim of the study: to conduct a study of beta-blockers of different generations on myocardial energy metabolism in the experimental CHF. Methods: CHF was modeled in white outbred rats by administering doxorubicin. The study drugs were administered intragastrically – new drug Hypertril (1-(β-phenylethyl)-4-amino-1,2,4-triazolium bromide)-3.5 mg/kg, metoprolol - 15 mg/kg, Nebivolol -10 mg/kg, Carvedilol 50 mg/kg, Bisoprolol, 10 mg/kg. In the myocardium, the main indices of energy metabolism were determined - ATP, ADP, AMP, malate, lactate, pyruvate, succinate dehydrogenase (SDH) activity, NAD-dependent malate dehydrogenase (NAD-MDH) activity. Results: traditional second-generation beta-blockers (Metoprolol and Bisoprolol) did not affect the studied indices of energy metabolism, and third-generation beta-blockers with additional properties - Carvedilol and, especially, Nebivalol and Hypertril improved myocardial energy metabolism. Conclusions: obtained results will help to expand our understanding of the effect of beta-blockers of various generations used to treat cardiovascular diseases on energy metabolism, and are also an experimental justification for the practical choice of these drugs in the complex therapy of CHF.

Starting from acid chlorides, alkynes, tryptamines, and acroyl chloride, 21 densely substituted tetrahydro-beta-carbolines are prepared in a four-component one-pot reaction. In this study, the aza-Michael addition step to generate intermediate enaminones is optimized in the presence of ytterbium triflate. Moreover, apart from acroyl chloride, all reactants can be deployed in almost equimolar ratios, which increases the atom economy of the sequence. For mechanistic rationali-zation, the concluding aza-anellation was investigated by DFT calculations on potential inter-mediates and corresponding activation energies, revealing that the aza-anellation rather pro-ceeds via ene-reaction than via electrocyclization.

Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy, largely due to metastasis and drug resistant recurrences. Fifteen percent of ovarian tumors carry mutations in BRCA1 or BRCA2, rendering them vulnerable to treatment with PARP inhibitors such as olaparib. Recent studies have shown that TGFβ can induce “BRCAness” in BRCA wild-type cancer cells. Given that TGFβ is a known driver of epithelial to mesenchymal transition (EMT), and the con-nection between EMT and metastatic spread in EOC and other cancers, we asked if TGFβ and EMT alter susceptibility of EOC to PARP inhibition. Epithelial EOC cells were transiently treated with soluble TGFβ and their clonogenic potential, expression and function of EMT and DNA repair genes, and response to PARP inhibitors compared with untreated controls. A second epithelial cell line was compared to its mesenchymal derivative for EMT and DNA repair gene expression and drug responses. We found that TGFβ and EMT resulted in downregulation of genes responsible for homologous recombination (HR) and sensitized cells to olaparib. HR efficiency was reduced in a dose-dependent manner. Furthermore, mesenchymal cells displayed sensitivity to olaparib, cis-platin, and the DNA-PK inhibitor Nu-7441. Therefore, treatment of disseminated, mesenchymal tumors may represent an opportunity to expand clinical utility of PARP inhibitors and similar agents.

: Supplement the use of ergogenic aids in cyclist’s directly have been improved the body metabolism and hemodynamic factors that are micro supplement in chancing reactions on the body muscle mass and limb muscle. Mostly knowing that, muscle power development progressive fast glycolytic and short time oxidative systems reactions. Sport competition intervals, therefore, during periods has been used specific drinks supported to cyclists. But, be obtained during should be long race times. Athletes directly needed some drug and fluid intake to prevented from metabolic breakdown rapidly the dynamic physiologic performance factors. Beta-alanine supplementation can be direct muscle performance development affects the anaerobic metabolism and capacity. It should be de-termined how the cyclists will use the competitive and training period intervals can increase the cyclists specific sprint and endurance race performance. Science cyclist International Road doses will be created in which, intervals can random effectively the investigate. This study random a cohort studies is examined the effects of beta-alanine supplementation on aerobic and anaerobic power output in specific cyclists. Therefore, we have been databases PubMed, Scopus and Medline initial search 10 August 2020 were created prospective effect the quality of bias work concluded effect size (ES) 95% confidence interval (CI) were used in participant. Participations (N=66) have age range 25 to 38 of the using beta-alanine in training periods to endurance muscle performance, aerobic power, anaerobic power, and sprint time trials. As a result of beta-alanine improved an-aerobic and aerobic power output on 4-week time-dependent trial performance condition. Signifi-cant values are obtained level factor alpha <0.05 and p-value analysis pre-post interactive stand-ardization.

In this study, we first discover some new concept coordinated UD-convex mappings with fuzzy-number values. After that, we look into Hermite-Hadamard type inequalities via fuzzy-number-valued coordinated UD-convex fuzzy-number-valued mapping (coordinated UD-convex FNVM). In the case of coordinated UD-convex FNVM, novel conclusions are derived by making particular decisions in recently proven inequalities. Additionally, it is demonstrated that the recently discovered inequalities are expansions of comparable findings in the literature. It is important to note that the main outcomes are validated by nontrivial examples.

Modern war is sudden and rapid, and once the battle starts, the demand for equipment maintenance and guarantee will show an outburst situation, which puts equipment maintenance and guarantee capability to a severe test. To accurately grasp the civil-military integration of aviation maintenance and guarantee capabilities, it is necessary to evaluate their capabilities. To this end, the evaluation indicator system is established by using the three-dimensional indicator system construction model of "maintenance task → task demand → capability demand", and the evaluation method based on intuitionistic fuzzy entropy and normal grey cloud model is proposed, using intuitionistic fuzzy entropy theory to determine the expert weights and indicator weights, and the grey cloud model theory to determine the capability evaluation level. Finally, the application process of the evaluation model is demonstrated through specific examples, and the evaluation results are analyzed to verify the effectiveness of the evaluation model.

Modern war is sudden and rapid, and once the battle starts, the demand for equipment maintenance and guarantee will show an outburst situation, which puts equipment maintenance and guarantee capability to a severe test. To accurately grasp the civil-military integration of aviation maintenance and guarantee capabilities, it is necessary to evaluate their capabilities. To this end, the evaluation indicator system is established by using the three-dimensional indicator system construction model of "maintenance task → task demand → capability demand", and the evaluation method based on intuitionistic fuzzy entropy and normal grey cloud model is proposed, using intuitionistic fuzzy entropy theory to determine the expert weights and indicator weights, and the grey cloud model theory to determine the capability evaluation level. Finally, the application process of the evaluation model is demonstrated through specific examples, and the evaluation results are analyzed to verify the effectiveness of the evaluation model.

Resource conflicts represent a significant global challenge in regions abundant with natural re-sources. Modelling the myriad factors driving natural resource-based conflicts (NRBCs), spanning environmental, health, socio-economic, and political dimensions, is a complex endeavor exacer-bated by data scarcity. Furthermore, existing quantitative studies often focus solely on large-scale conflicts. This article introduces a novel algorithm, the Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management (SEFLAME-CM), which integrates the local knowledge of stakeholders into spatial decision-making technologies to support sustainable peace efforts. The results are validated with spatial multi-criteria evaluation (SMCE) using spatial statistics. The Moran’s I scatter plots for the overall conflicts reveal significant values of 0.99 and 0.98 for both the SEFLAME-CM and SMCE, respectively, with significant spatial autocorrelation. While there re-mains room for improvement in enhancing the model's quality, SEFLAME-CM demonstrates its capacity to transparently model complex real-world problems. The findings underscore the im-perative for a holistic approach to addressing environmental degradation, socio-economic, and political drivers of resource conflicts at the community level. Our paper demonstrates the signif-icance of spatial information technologies and knowledge exchange between experts and local stakeholders in effectively managing resource conflicts. These insights should inform national policies and international interventions, ensuring that the complex underlying issues are ad-dressed while prioritizing the knowledge and needs of affected communities.

This article discusses a transformer-free, high-efficiency DC-DC converter besides renewable energy applications. The traditional buck-boost, classic Zeta, Sepic, and Cuk converter does have the benefits of a simple design, low cost, as well as the capacity to execute voltage step-up and step-down. Conversely, because of the detrimental consequences of the parasitic constraints of the device, the voltage conversion gain of the traditional DC-DC converter is much more restricted and the efficiency is also significantly smaller, whereas this proposed converter does have a higher voltage gain and efficiency because it is used in a single power switch, resulting in reduced switching losses and voltage stress. The said converter's design is very simple, which simplifies the operation control and reduces switching and conduction losses, leading to an efficiency of 97.4 percent. This converter seems to have the same capabilities as the Zeta converter, including continuous desired output current and desired buck-boost operation. Such an article offers the operation principle and steady evaluation, as well as a comparison with other existing high step-up configurations. The proposed converter employs a fuzzy logic controller, which improves the voltage level as well as reduces the time taken to set the voltage output of a conventional PI and ANN controller, especially in comparison to the FLC controller. For deployment, Experimental Result and MATLAB/Simulink has been used, and the modeling results indicate that the proposed controller performance has improved

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

In the course of SARS-CoV-2 infection, the 3CL or nsp5 protease plays a pivotal role as the most important viral protease required for the maturation of viral proteins during host infection. Herein, we simulated for 500 ns 3CLproWT, 3CLproH41A, , 3CLproBeta, and 3CLproOmicron, in complex with the substrates nsp 4|5 and nsp 5|6. Our results show that mutations in the 3CLpro present in the SARS-CoV-2 variant of concerns (VOCs) did not lead to significant conformational changes or changes in substrate binding affinities. However, significantly high cleavage rates for the boundary between nsp4 and nsp5 were obtained for 3CLproBeta and 3CLproOmicron and may play a key role in the viral replication and virus fitness gain. Our molecular dynamics data suggest that the cleavage rate of nsp4|5 may be related to the increased amount of viral load observed for these VOCs, releasing more nsp4 than other non-structural proteins. Based on our hydrogen bonding analyses, we also discovered that Gly143 and Glu166 are key residues in substrate recognition, suggesting that these residues may be incorporated as pharmacophoric centers for Beta and Omicron variants in drug design. Our results suggest that Gly143 and Glu166 are essential residues to interact with Gln6 of the different substrates and, therefore, are potential broad-spectrum pharmacophoric centers of SARS-CoV-2 3CLpro.

Diabetes mellitus (DM) is one of the major risk factors for COVID-19 complications as it is one of the chronic immune-compromising conditions especially if patients have uncontrolled diabetes, poor HbA1c &/or irregular blood glucose levels. Diabetic patient’s mortality rates with COVID-19 are higher than cardiovascular or cancer patients. Recently Bacillus Calmette–Guérin (BCG) has shown successful results in reversing diabetes in both rats and clinical trials based on different mechanisms from aerobic glycolysis to Beta cells regeneration. BCG is a multi-face vaccine that has been used extensively in protection from TB and leprosy and has been repositioned for treatment of bladder cancer, diabetes & multiple sclerosis. Recently, the COVID-19 epidemiological study confirmed that universal BCG vaccination reduced morbidity and mortality in certain geographical areas. Countries without universal policies of BCG vaccination (Italy, Nederland, USA) have been more severely affected compared to countries with universal and long-standing BCG policies that have shown low numbers of reported COVID-19 cases. Some countries have started clinical trials that included a single dose BCG vaccine as prophylaxis from COVID-19 or an attempt to minimize its side effects. This proposed research aims to use BCG vaccine as a double-edged weapon countering both COVID-19 & diabetes, not only as protection but also as therapeutic vaccination. The work includes a case study of regenerated pancreatic beta cells based on improved C-peptide & PCPRI laboratory findings after BCG vaccination for a 9 years’ patient. The patient was re-vaccinated based on a negative tuberculin test & no scar at the site of injection of the 1st BCG vaccination at birth. Furthermore, the authors in the present article described a prospective BCG multi-dose clinical study in full details that they will apply in case of acceptance of their submitted grant & the ethical committee approval. The aim of the clinical study is to check if double dose BCG (4 weeks apart) will show a significant difference in the protection of health care professionals in Egypt. The authors suggest and invite the scientific community to take into consideration the concept of direct BCG re-vaccination (after 4 weeks) because of the reported gene expressions & exaggerated innate immunity consequently. As the diabetic MODY-5 patient (mutation of HNF1B, Val2Leu) was on low dose Riomet® while eliminating insulin gradually, a simple analytical method for metformin assay was recommended to ensure its concentration before use as it is not approved yet by the Egyptian QC labs.

The risk classification of barrier lake is the key to conducting emergency treatment in a scientific manner. Such difficulties are faced as short time for risk evaluation, complex evaluation indicators, difficulty in obtaining information quickly and quantifying index weights. Based on this, this paper constructs a quantitative risk classification model for barrier lakes based on D-AHP. On the basis of studies on nearly 100 cases of barrier lakes, an 8-factor evaluation index system and quantitative classification are proposed. The methods of rapid calculation of reservoir capacity curve of barrier lake and intelligent identification of particles on the surface of barrier body were developed, which realized the rapid acquisition of 8-factor evaluation index information in emergency environment. The D-AHP method dealt with inconsistent weight assignment to evaluation factors by experts, which helped achieve weight quantification of 8 factors. The risk assessment on15 barrier lakes such as Tangjiashan barrier lake show that the conclusion drawn with risk classification method proposed in this paper is basically consistent with that of the traditional table-lookup method. However, the table-lookup method ignores cumulative loss’s impacts on the risk level of barrier lakes, and considers the extremely severe loss of barrier lakes as a sufficient condition for the evaluation level to be grade I, and thus a deviation in the evaluation. The risk classification method proposed in this paper is more reasonable and reliable.

This paper presents a novel real time control strategy on four-wheel independent steering system for electric vehicle with stepper motors using neural networks combined with fuzzy logic. The control of the four wheels is based on a variation of Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system is made up of the four aforementioned blocks where they work in sync and inseparable from each other with the common goal of driving all the steering stepper motors at the same time. Simulation results demonstrate that the proposed control method has strong robustness and practicability under different angles and speeds.

The electricity generated from fossil fuel and nuclear energy has negative impacts on the environment and has shown a need to search for and promote the use of clean, renewable and sustainable energy resources such as wind, solar and geothermal resources. Nowadays, wind energy resource has emerged as the fastest-growing source of energy on an overall scale. However, the selection of the best location for constructing a wind farm is a vital issue that can be considered a site selection problem, which involves various conflicting factors. Therefore, it is classified as a multi-criteria decision-making (MCDM) problem. In this research, the site selection of wind power plants is conducted in Burundi country. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weigh the criteria considering their relative importance. The main factors affecting optimal wind farm location considered in this study are wind speed, slope, distance from the grid network, distance to roads, and land use/land cover (LULC). Furthermore, a geographic information system (GIS) is utilized to generate the final suitability wind farm locations map. The obtained results show that 20.91% of the total study area is suitable; nevertheless, only 1.96% is highly suitable for wind farm placement. The western part of Burundi is concluded to be the most suitable area for wind farm construction and the most area is situated in Lake Tanganyika.

Stevia Rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The op-timization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, ni-trogen, phosphorus, and potassium. The system employs a wireless sensor network to collect qua-si-real-time data, which is transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide to farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a line-ar-regression model, the system demonstrated acceptable performance, with a determination coef-ficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

Supply Chain Management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance as a whole. Since most of these problems involve uncertainty and hesitation on the part of Decision Makers (DMs), various studies have emerged recently that present SCM applications of techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and HFLTS extensions. Given the relevance of this subject and the lack of literature review studies, this study presents a systematic review of HFLTS and HFLTS extension applications to SCM decision-making problems. In order to answer a set of research questions, the selected papers have been classified in accordance with a group of factors that are pertinent to the origins of these studies, SCM, HFLTSs, and decision-making. The results demonstrate that the Source and Enable processes have been studied with greater frequency, while the most common problems have to do with supplier selection, failure evaluation, and performance evaluation. The companies of the automotive sector and Sustainable SCM and Green SCM strategies predominate in the analyzed studies. Even though most of the studies use techniques based on HFLTSs, we have identified applications of seven distinct HFLTS extensions, with Double Hierarchy Hesitant Fuzzy Linguistic Term Sets and Probabilistic Linguistic Term Sets being the most utilized. The identification of gaps in the literature presents avenues for future studies focused on innovative applications, integrations of techniques, comparisons of techniques, group decision-making, and validation procedures for new models. The results of this study offer a panorama of the state of the art in regard to this subject and can help researchers and practitioners develop new studies which involve the use of methods that employ HFLTSs and HFLTS extensions in SCM decision-making problems.

At present, aeroponic control is mainly regulated according to the environmental factors of aeroponics, such as the temperature, humidity, light intensity, carbon dioxide concentration, pH, and electrical conductivity (EC) of the nutrient solution. There is a lack of knowledge available in the literature for an aeroponic control system for plant growth information as the primary feedback signal. Therefore, a fuzzy logic-controlled aeroponics system was developed for the growth of lettuce as a feedback signal. After designing, the effectiveness test of system was performed with experiment having three treatments T1 (180C), T2 (220C) and without control (CK). The results show an increasing trend in shoot length, root length, and shoot area over time in all treatments. In terms of shoot area, the maximum of 345 cm² was observed in the T1 treatment, while the CK treatment had a minimum of 241.8 cm². Statistical analysis indicated that the T1 and T2 treatments were not significantly different, but both were significantly different from the CK treatment. The study demonstrates the effectiveness of the fuzzy logic control system and an 18°C nutrient solution temperature, resulting in improved lettuce growth and reduced root-to-shoot ratio. The findings suggest the potential of using fuzzy logic control in aeroponics for optimized cultivation outcomes.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

Abstract. This article proposes new fuzzy controller to regulation the voltage, damp the oscillations and limit the overshoot behavior in single-machine infinite-bus power system. Two different independent MISO fuzzy controllers are designed. First controller, is voltage stabilizer and it composes of two adaptive fuzzy modules and proportional gain AVR. The voltage stabilizer is designed to regulate the terminal voltage, damp the voltage oscillation and limit the voltage overshoot to the industrial maximum allowable limit. The input signals of this stabilizer is are taken from the terminal voltage and the imposed voltage disturbance that can be read from any smart relay. The fuzzy output is used as feedback signal to control the AVR in order to achieve stabilized voltage equal to the reference voltage. The second fuzzy controller is designed with two input signals; “mechanical angle” and its derivative, and one output which is used as feedback signal to adapt the damping torque in order to damp the mechanical oscillation fast and with maximum overshoot not exceeding 10%, and hence damp the power angle oscillation that is important in the PSS improvement. MATLAB-SIMULINK was used for modelling and the test cases simulation. The results show very good performance for the proposed fuzzy controllers.

The synchronous generator, as the main component of power systems, plays a key role in these system’s stability. Therefore, utilizing the most effective control strategy for modeling and control the synchronous generator results in the best outcomes in power systems’ performances. The advantage of using a powerful controller is to have the synchronous generator modeled and controlled as well as its main task i.e. stabilizing power systems. Since the synchronous generator is known as a complicated nonlinear system, modeling and control of it is a difficult task. This paper presents a sum of squares (SOS) approach to modeling and control the synchronous generator using polynomial fuzzy systems. This method as an efficacious control strategy has numerous superiorities to the well-known T–S fuzzy controller, due to the control framework is a polynomial fuzzy model, which is more general and effectual than the well-known T–S fuzzy model. In this case, a polynomial Lyapunov function is used for analyzing the stability of the polynomial fuzzy system. Then, the number of rules in a polynomial fuzzy model is less than in a T-S fuzzy model. Besides, derived stability conditions are represented in terms of the SOS approach, which can be numerically solved via the recently developed SOSTOOLS. This approach avoids the difficulty of solving LMI (Linear Matrix Inequality). The Effectiveness of the proposed control strategy is verified by using the third-part Matlab toolbox, SOSTOOLS.

Fermatean fuzzy linguistic (FFL) set theory provides an efficient tool for modeling a higher level of uncertain and imprecise information, which cannot be represented using intuitionistic fuzzy linguistic (IFL)/Pythagorean fuzzy linguistic (PFL) sets. On the other hand, the linguistic scale function is the better way to consider the semantics of the linguistic terms during the evaluation process. In the present paper, we first define some new modified operational laws for Fermatean fuzzy linguistic numbers (FFLNs) based on linguistic scale function (LSF) to overcome the shortcomings of the existing operational laws and prove some important mathematical properties of them. Based on it, the work defines several new aggregation operators (AOs), namely, the FFL-weighted averaging (FFLWA) operator, the FFL-weighted geometric (FFLWG) operator, the FFL-ordered weighted averaging (FFLOWA) operator, the FFL-ordered weighted geometric (FFLOWG) operator, the FFL-hybrid averaging (FFLHA) operator and the FFL-hybrid geometric (FFLHG) operator under FFL environment. Several properties of these AOs are investigated in detail. Further, based on these operators, a multiple attribute group decision-making (MAGDM) approach with FFL information is developed. Finally, to illustrate the effectiveness of the present approach, a real-life supplier selection problem is presented where the evaluation information of the alternatives is given in terms of FFLNs.

Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

Background: the nature of alpha-D-mannose - natural aldohexose sugar, C-2 glucose epimer, whose intended use is for preventing urinary tract infections - in the interaction with E. coli is addressed in order to drive the issue of its regulatory classification as a medicinal product or medical device. Methods: PRISMA systematic review approach was applied; Delphi Panel method used to target consensus on statements retrieved from evidence. Results: Based of regulatory definitions and research evidences, the mechanism of D-mannose does not involve a metabolic or immunological action while there is an uncertainty regarding the pharmacological action. Specific interaction between the product and the bacteria within the body occurs, but its nature is inert: it does not induce a direct response activating or inhibiting body processes. Moreover, the action of D-mannose takes place, even if inside the bladder, outside the epithelium on bacteria that have not yet invaded the urothelial tissue. Therefore, its mechanism of action is not directed to host structures but to structures (bacteria) external to the host's tissues. Conclusions: From a regulatory perspective, D-mannose maintains the current medical device classification: new regulations and legal judgments can add further considerations. From a pharmacological perspective, research is driven versus synthetic mannosides: no further considerations are expected on alpha-D-mannose.

Oxidative stress and chronic inflammation, both at the systemic and central level, are critical early events in atherosclerosis and Alzheimer’s disease (AD). Purpose. To investigate the oxidative stress, inflammatory, and Tau-phosphorylation lowering effects of pomegranate polyphenols (PP) (punicalagin, ellagic acid, peels, and arils extracts). Methods. We used flow cytometry to quantify protein expression of proinflammatory cytokines (IL-1β) and anti-inflammatory mediators (IL-10) in THP-1 macrophages, as well as M1/M2 cell-specific markers (CD86 and CD163) expression in human microglia HMC3 cells. IL-10 protein expression was also quantified in U373-MG human astrocytes. The effect of PP on human amyloid beta 1-42 (Aβ1-42)-induced oxidative stress was assessed in microglia by measuring ROS generation and lipid peroxidation, using respectively two ′,7′-dichlorofluorescein diacetate (DCFH-DA) and thiobarbituric acid reactive substances (TBARS) tests. Neuronal viability and cell apoptotic response to Aβ1-42 toxicity were assayed using the MTT (3-(4, 5-dimethyl thiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay and the annexinV-FITC apoptosis detection kit; respectively. Finally, flow cytometry analysis was also performed to evaluate the ability of PP to modulate Aβ1-42-induced Tau-181 phosphorylation (pTau-181). Results. Our data indicate that PP was significantly (p<0.05) effective in countering Aβ1-42-induced inflammation through increasing the anti-inflammatory cytokines (IL-10) (in U373-MG astrocytes and THP1 macrophages) and decreasing proinflammatory markers (IL-1β) expression in THP1 macrophages. PP was also significantly (p<0.05) effective in inducing the phenotypic transition of THP-1 macrophages and microglial cells from M1 to M2 by decreasing CD86 and increasing CD163 surface receptor expression. Moreover, our treatments have a significant (p<0.05) beneficial impact on oxidative stress, illustrated in the reduction of TBARS and ROS generation. Our treatments have significant (p<0.05) cell viability improvement capacities and anti-apoptotic effects on human H4 neurons. Furthermore, our results suggest that Aβ1-42 significantly (p<0.05) increases pTau-181. This effect was significantly (p<0.05) attenuated by arils, peels, and punicalagin and drastically reduced by ellagic acid treatments. Conclusion. Our results are attributed to PP's anti-inflammatory, antioxidant, anti-apoptotic, and anti-Tau pathology potential. Future studies should aim to extend our knowledge of the potential role of PP on A1-42-induced neurodegeneration, mainly its association with the tauopathy involved in AD.

The lymphatic system of the brain meninges and head plays a crucial role in the clearance of amyloid-β protein (Aβ), a peptide thought to be pathogenic in Alzheimer’s disease (AD), from the brain. The development of methods to modulate lymphatic clearance of Aβ from the brain coild be a revolutionary step in the therapy of AD. The opening of the blood-brain barrier (OBBB) by focused ultrasound is considered as a possible tool for stimulation of clearance of Aβ from the brain of humans and animals. Here, we propose an alternative method of non-invasive music-induced OBBB that is accompanied by the activation of clearance of fluorescent Aβ (Fαβ) from the mouse brain. Using confocal imaging, fluorescence microscopy and magnetic resonance tomography, we clearly demonstrate that OBBB by music stimulates the movement of Fαβ and Omniscan in the cerebrospinal fluid and lymphatic clearance of Fαβ from the brain. We propose the extended detrended fluctuation analysis (EDFA) as a promising method for the identification of OBBB markers in the electroencephalographic (EEG) patterns. These pilot results suggest that music-induced OBBB and the EDFA analysis of EEG can be a non-invasive, low cost, labelling free, clinical perspective and completely new approach for the treatment and monitoring of AD.

Clinical pathogens especially Gram-negative bacteria developing resistance to third-generation cephalosporins are making the clinical outcome more complicated and serious. This study was undertaken to evaluate the distribution of extended-spectrum beta-lactamases in Tamil Nadu regions in India. For this study, clinical samples were collected from five different hospitals located in Tamil Nadu and ESBL producing Gram-negative isolates were characterized. Minimal inhibitory concentration (MIC) was performed using cefotaxime and ceftazidime. The blaESBL producing genes were screened using multiplex PCR for the genes, CTX-M group-1,-2,-8,-9,-26. Conjugation studies were performed using E. coli AB1157 as a recipient for the isolates harbouring plasmid-borne resistance following broth-mating experiment. In total, 1500 samples were collected and 599 Gram-negative bacteria were isolated that included Escherichia coli (n=233), Klebsiella pneumoniae (n=182), Pseudomonas aeruginosa (n=79), Citrobacter spp. (n=30), Proteus mirabilis (n=28), Salmonella spp. (n=21), Acinetobacter baumannii (n=12), Serratia spp. (n=6), Shigella spp. (n=4), Morganella morganii (n=3) and Providencia spp. (n=1). MIC results showed that 358 isolates were resistant to cefotaxime and ceftazidime. Further, ESBL gene amplification results showed that 19 isolates had CTX-M group-1 gene including E. coli (n=16), K. pneumoniae (n=2) and P. aeruginosa (n=1) whereas one M. morganii isolate had CTX-M group-9 gene in their plasmid. Through conjugation studies, 12/20 isolates were found to be involved in the transformation of its plasmid-borne resistance gene. Our study highlighted the role of horizontal gene transfer in the dissemination of plasmid-borne blaCTX-M resistance genes among ESBL producing isolates.

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by Hybrid Energy Storage Systems (HESS), this paper proposes a State of Charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

This study explores the implications of merging two fundamentally different types of banks: Islamic and conventional banks. The research aims to provide insight into such a merger's unique opportunities and challenges and offer strategic guidance for future mergers. A balanced scorecard-based strategic analysis using Quantum Spherical Fuzzy Decision-Making Approach was used to develop the merged bank's short- and long-term strategic plans. The balanced scorecard included 12 key performance indicators (KPIs) in 4 groups, and the methodology also incorporated several questions to guide the analysis. The study results offer valuable insights into the potential opportunities and challenges of merging these two types of banks and strategic recommendations for stakeholders at all levels. The study is a valuable guideline for future mergers between similar or different types of banks. Overall, the findings suggest that a well-planned merger strategy is essential for avoiding challenges and maximizing the benefits of merging Islamic and conventional banks. By integrating the strengths of both types of banks, a merged entity could create a competitive advantage and potentially improve financial performance. However, this requires careful consideration of cultural differences, regulatory challenges, and other factors that could impact the merger's success.

The estimation of the efficiency of road transport vehicles remains a significant problem for contemporary transport companies, as the technological process is influenced by numerous stochastic impacts, such as demand stochasticity, road conditions uncertainty, transport market fluctuations, etc. To consider the uncertainty related to the estimation of the vehicles’ fleet efficiency, we propose a fuzzy-logic approach, where the efficiency of a given vehicle is described by a membership function. The efficiency of the whole fleet and its rational structure in that case can be evaluated as a fuzzy set. To demonstrate the developed approach, we depict a case study of using cargo vehicles for deliveries of agricultural products in the Republic of Kazakhstan. The numeric results are presented for the selected models of vehicles that a transport company uses to service a set of clients located in Northern Kazakhstan.

This article investigated the Automatic Generation Control(AGC) of multi-area multi-source interconnected systems with hydropower plants, thermal power plants, and wind energy. Adaptive Neuro-fuzzy controller integrated with the cascaded proportional-integral-derivative with filter (PIDF-PIDF) is a new cascaded controller (ANF-PIDF-PIDF) that has been presented as a secondary controller for applied hybrid power systems. The recent Skill Optimization Algorithm (SOA) is employed to optimize PIDF- PIDF controller parameter gains and the Adaptive Neuro-Fuzzy controller's inputs and output scaling factors. SOA is used to update the controller parameters with integral square error (ISE) employed as the objective function. A 1% step load disturbance was considered simultaneously in all three areas. The controller's performance is evaluated and compared with and without considering the effects of wind energy sources and non-linearity for ANF-PIDF-PIDF, PIDF-PIDF, and PIDF and it was determined that the ANF-PIDF-PIDF was the most efficient. The dynamic system performance is also compared with parallel high voltage direct current (HVDC) tie-lines. The investigation clearly shows that incorporating HVDC tie-line with multi-area, multi-source provides better dynamic performance in maximum amplitude, oscillation, and settling time. Additionally, sensitivity analysis is done and the optimum controller gains does not need to be reset to uncertain values in system loading conditions. All simulation results were evaluated using MATLAB 2016b.

In the era of sustainable development, green supplier selection has become a key component of supply chain management, as it considers criteria such as carbon footprint, water usage, energy usage and recycling capacity. Since the green supplier selection problem involves subjective criteria and uncertainty in preferences, it is well suited to using multi-criteria decision-making methods (MCDM). Although few researchers have investigated MCDM for green supplier selection under uncertainty using intuitionistic and Pythagorean fuzzy sets. Still, the newly developed fermatean fuzzy can handle greater data uncertainty than intuitionistic and Pythagorean fuzzy sets. Since interval extension of fuzzy theory provides more accurate modeling, in this paper, we propose an interval extension of fermatean fuzzy set and discuss its fundamental set operations, arithmetic operations and related properties. We propose the Hamming distance function and the score function of the interval-valued fermatean fuzzy numbers. The recently developed Multi-attribute Border Approximation Area Comparison (MABAC) method is also considered due to its stable and simple computation compared to other conventional MCDM methods. Therefore, an interval-valued fermatean fuzzy MABAC method is proposed and solved to select a green supplier based on multiple criteria. The proposed method's comparative analysis and sensitivity analysis validate the proposed method's effectiveness and robustness.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Recently, the medical image processing is extensively used in several areas. In earlier detection and treatment of these diseases is very helpful to find out the abnormality issues in that image. Here there are number of methods available for segmentation to detect the lung nodule of computer tomography (CT) image. The main result of this paper, the earlier detection of lung nodules using Pre-processing techniques of top-hat transform, median and adaptive bilateral filter was compared both filtering methods and proved the adaptive bilateral filter is suitable method for CT images. The proposed segmentation technique uses novel strip method and the image is split into number of strips 3, 4, 5 and 6. A marker- watershed method based on PSO and Fuzzy C-mean Clustering method was proposed method. Firstly, the input image was reduced noise reduction and smoothing and the filter image is using strips method and then the image is segmented by marker watershed method. Secondly, the enhanced PSO technique was used to locate the better accurate value of the clustering centers of Fuzzy C-mean Clustering. Final stage, with the accurate value of centers and the enhanced target function and the small region of the segmented object was clustered by Fuzzy C-mean. In segmentation algorithm presented in this paper gives 95% of accuracy rate to detect lung nodules when strip count is 5.

In the context of healthcare technology management (HTM) in Saudi Arabia and the Gulf region, this study addresses a significant gap by exploring both external and internal risk factors affecting HTM performance. Previous research has primarily focused on modeling or predicting failures in medical devices, mostly examining internal (endogenous) factors that impact device performance and the development of optimal service strategies. However, a comprehensive investigation of external (exogenous) factors has been notably absent. This research introduces a novel hierarchical risk management framework, designed to accommodate the broad array of healthcare technologies, not limited to medical devices. It significantly advances the field by thoroughly investigating and validating a comprehensive set of 53 risk factors and assessing their influence on HTM. Additionally, this study embraces the perspective of enterprise risk management (ERM) and expands it to identify and incorporate a wider range of risk factors, offering a more holistic and strategic approach to risk assessment in healthcare technology management. The findings reveal that several external and internal factors previously underexplored significantly impact HTM performance. Notably, the Fuzzy AHP survey identified “design risk” under facility and environmental risks as the highest risk for HTM in the region. Furthermore, the study reveals that three out of the top ten risks are related to “facility and internal environmental” factors, six are related to technological endogenous factors, and only one is related to managerial factors. This distribution underscores the critical areas for intervention and the need for robust facility and technology management strategies. In conclusion, this research not only fills a critical void by providing a robust framework for healthcare technology risk assessment but also broadens the scope of risk analysis to include a wider array of technologies, thereby enhancing the efficacy and safety of healthcare interventions in the region. Additionally, the proposed hierarchy provides insights into the underlying risk factors for healthcare technology management, with potential applications extending beyond the regional context to a global scale. Moreover, the equation we propose offers a novel perspective on the key risk factors involved in healthcare technology management, presenting insights with far-reaching implications applicable not only regionally but also on a global level. This framework also supports sustainability goals by encouraging the efficient and responsible utilization and management of healthcare technologies, essential for ensuring the long-term economic and environmental sustainability of medical technology use.

This paper presents the mathematical modeling for designing a 10[kW] DC-AC inverter system from 48 [V] DC to 120 V ACrms, considering its non-linear operation, allowing to obtain a detailed simulation to assess the proposed fuzzy PI control (with anti-windup action) and its optimal operation. The performance assessment of the converter was carried out with the discrete treatment of the voltage and current signals using spectral analysis, filtering considerations, and evaluation of limit cycle technique to determine the system's stability. The main results showed that THD at the filter output (transformer and capacitor) was around 0.0322% and 0.0632% at nominal load in open loop and closed loop, respectively. The inverter system presented more sensitivity in the current signal with a ripple at 120 [Hz] of 2.02% and at 5000 [Hz] of 10.62% (at nominal load). However, when the system operates in a closed loop, the current ripple at 120 [Hz] is 2.38% and at 5000 [Hz] is 10.62%.

The diffusion of molecules in aqueous solutions in the domain of membrane technology is very critical in the efficiency of chemical engineering and purification processes. In this study, the diffusion in high and low concentration regions is simulated with finite difference method (FDM), and then the results of numerical computations are coupled with adaptive network-based fuzzy inference system (ANFIS) and bilayered neural network method (BNNM). Machine learning approach can individually predict diffusion phenomena across the domain based on understanding of the machine instead of the discretization of an ordinary differential equation (ODE). The findings of the machine learning method are in good agreement with those of FDM at different times of the simulation. In addition to numerical computation, the error of the system is computed for different iterations. The results show that by increasing the number of iterations and training datasets, all errors reduce significantly for both training and testing. BNN method is also used to train the prediction process of diffusion for a more accurate comparison. This technique is similar to ANFIS method in terms of prediction capability. According to the findings, ANFIS approach predicts diffusion slightly better than BNN method. In this regard, ANFIS technique produces R>0.99 while BNN method produces R around 0.98. Both machine learning methods are accurate enough to predict diffusion throughout the domain for different time steps. The computational time for both algorithms is less than that of FDM method to predict low and high concentrations in the domain. Besides, based on the results, artificial intelligence (AI) can find the relationship between inputs and outputs and determine which input has the main influence on the output in this study to optimize the process. As such, future studies can be focused on AI and other methods for faster prediction and optimization processes.

This paper attempts to compare two different approaches to solve the problem of accident rates prediction based on human factors for industrial workers. One of the methods has already been done using Fuzzy c-Means Clustering and proved to be working with decent results. The second method which will be covered in this paper is using Artificial Neural Networks. The primary goal of this work is to insure that ANN will work efficiently in such prediction problem. The second goal is to reveal the fact that which one of the two selected methodologies is better at defining the estimation of accident rates among people who work in different industrial fields. The purpose has been achieved when the outcome of the ANN was obtained and compared accordingly with the output of the research previously carried out with Fuzzy c-means clustering method. Comparing the outcomes of these two different methods gave an immense insight on which features are more important than others when it comes to laborers properties with completely different background such as varying levels of health, knowledge, experience, training and physical properties. At the end of the research, it becomes clear that accident rates estimation for laborers with properly trained Artificial Neural Network gives better results when it is compared with Fuzzy c-Means Clustering method. Standard deviation method was used to calculate the validity of ANN technique. The result was compared with Fuzzy c-mean clustering technique. Impressive improvement of 8.8% in the accident rate prediction was achieved using Tailored-Made-ANN.

This paper aims to investigate impact of employees’ gender on OCB as per the employees’ perception in Jordanian governmental hospitals. A convenient sample of 126 employees working in the three main governmental hospitals in north of Jordan has been taken for the purpose of this study. The collected data includes linguistic terms that suffer from uncertainty which, in turn, cannot be dealt with traditional numerical values. The result prove that gender impact on OCB has shown statistically significant differences at (α=0.05) as far as altruism, courtesy, and civic virtue are concerned; and this variable stands in favor of males with the total score of 0.011%. Similarly, as far as the effect of age factor on OCB is concerned, there have been statistically significant differences at (α=0.05) in relation to courtesy, sportsmanship, and civic virtue with the total score of 0.27%. Finally, the results provide a baseline data for further studies which may contribute more significant in the field of OCB.

This paper is an extension to previous work that introduced successful classical-control for indoor lighting system that simultaneously control LED system and roller curtain to achieve accurate light intensity for indoor lighting applications. In this paper, Multi Input Multi Output Fuzzy Logic Controller (MIMO FLC) is proposed to replace the classical-controller. Detailed method is provided to illustrate the steps to design the required MIMO FLC that has two fuzzy input, two fuzzy output and forty-five fuzzy rules with mamdani fuzzy engine. The proposed MIMO FLC is simulated in Matlab using Simulink tools. The results are illustrated, analyzed and compared with the classical-control. The response showed very close performance to the classical-controller. The maximum average error and the maximum error with respect to “Desired Lux” inside the room is -6% and -10% respectively.

This paper discusses domestic problem of waiting hot water for the shower use till it reaches satisfactory temperature, which result a lot of wastage in fresh water. The outcome from research survey shows that there is no satisfactory solution till now as all solutions were either expensive or with no effective results. Local small inline electric heater equipped with fuzzy logic controller is proposed in this paper to be installed just before the showerhead to measure the water temperature and flow before the showerhead, as control input-variables, and decide the operating voltage of the heater, as control output-variable (MISO). MATLAB Simulink is used to model the proposed system. Different test cases are simulated to prove the performance and the safe operation of the system. Techno-economic study is carried out to determine the “Direct Benefits” and “Indirect Benefits” that can be achieved if such system is implemented in wide range. Azerbaijan data is taken as an example to calculate the economic benefits. The results show important benefits not only for economy but also for climate and the reduction of CO₂ gas emission. Different economic indices are provided to be an easy reference for decision makers and project managers.

This study investigates the factors influencing consumers' intentions to purchase overseas travel packages via social media in Thailand, with a focus on consumer trust and perceived risks. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. In the qualitative phase, the Fuzzy Set Delphi Method was applied to gather expert consensus from 19 participants. For the quantitative phase, a survey was conducted among 600 individuals who had experience purchasing overseas travel packages on social media in Thailand. First-order and second-order confirmatory factor analyses were used to examine the relationships between eight identified factors: social media influencer, e-WOM, trust, perceived risk, brand image, rating review, personal attitude, and destination image. The results indicate that trust and perceived risk are the most significant factors affecting purchase intention. Trust plays a crucial role in customers' decision-making processes, while perceived risk influences their tendency to avoid purchasing packages if the risk is perceived as high. The insights gained from this study contribute to the understanding of factors influencing purchase intentions for overseas travel packages through social media in Thailand, providing valuable information for travel agencies to develop effective marketing strategies.

The paper presents an adaptation of numerical solution of first order linear differential equation in fuzzy environment. The numerical method is re-established and studied with fuzzy concept to estimate its uncertain parameters whose values are not precisely known. Demonstrations of fuzzy solutions of the governing methods are carried out by the approaches, namely Modified Runge Kutta method and Runge Kutta Merson method. The results are compared with the exact solution which is found using generalized Hukuhara derivative (gH-derivative) concepts. Additionally, different illustrative examples and an application in industry of the methods are also undertaken with the useful table and graph to show the usefulness for attained to the proposed approaches.

This study addresses the urgent need to reduce carbon emissions in the power sector, a major contributor to global greenhouse gas emissions, by employing system engineering principles coupled with machine learning techniques. It focuses on analyzing the interplay between regional marginal prices (LMP) and carbon emissions within electricity markets. Leveraging a dataset that encompasses hourly LMP and carbon emissions data across various regions of New York State, the paper explores how market designs and operational strategies influence carbon output. The analysis utilizes neural networks to simulate and predict the effects of different market scenarios on carbon emissions, highlighting the role of LMP, loss costs, and congestion costs in environmental policy effectiveness. The results underscore the potential of system engineering to provide a holistic framework that integrates market dynamics, policy adjustments, and environmental impacts, thereby offering actionable insights into optimizing market designs for reduced carbon footprints. This approach not only enhances the understanding of the complex interactions within electricity markets but also supports the development of targeted strategies for achieving sustainable energy transitions.

The World Wide Web today is growing at a phenomenal rate. The crawling approach is of vital importance to leverage the efficiency of web crawling. The existing crawling algorithms on multicore platforms easily suffer from time consuming and can not support large data well. In order to exploit the potential parallelism and efficiency of crawling on Spark, based on the software thread level speculation, this paper proposes a Speculative parallel crawler approach (SpecCA) on Apache Spark. By analyzing the process of web crawler, the SpecCA firstly hires a function to divide the whole crawling process into several subprocesses which can be implemented independently and then spawns a number of threads to speculatively implement every subprocess in parallel. At last, the speculative results are merged to form the final outcome. Compared with the conventional parallel approach on multicore platform, SpecCA is very efficiency and leverages a high parallelism degree by adequately using the resources of the cluster. Experiments show that SpecCA could achieve a significant speedup improvement with compare to the traditional approach in average. Additionally, with the growing number of working nodes, the execution time decreases gradually while the speedup scales linearly. The results indicate that the efficiency of web crawling can be significantly enhanced by adopting this speculative parallel algorithm.

Polycrystalline zinc oxide (ZnO) thin films were deposited on soda-lime glass substrates using the chemical spray pyrolysis method at 450 ºC. The samples were irradiated with 8 keV H+ ions at three different fluences using a Colutron ion gun. The effects of the irradiation on the structural, morphological, and optical properties were studied with different techniques, including Rutherford Backscattering Spectrometry (RBS), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Ultraviolet and Visible Spectroscopy (UV-Vis). The results show that ion irradiation enhances crystallinity, narrowing the optical band gap. The changes in transmittance are related to defect formation within the material, which acts as light absorption and re-emission centers. A shifting of the film's preferred growth orientation to the c-axis and changing the grain morphology and size distribution was detected. We observed an increase in the lattice parameters observed after irradiation, suggesting an expansion of the crystalline structure due to ions incorporation and defects within the ZnO crystal lattice. The morphological study shows an increase in the average size of the large particles after irradiation. This change is attributed to the emergence of defects and nucleation centers during irradiation. The average size of small particles remained relatively constant after irradiation, suggesting that small particles are more stable and less susceptible to external influences, resulting in fewer changes due to irradiation.

Electricity markets are transitioning to zonal and nodal pricing to maximize social welfare, improve price signals, and enhance congestion management. South Korea, traditionally reliant on uniform pricing, is evaluating this transition but lacks a detailed impact analysis. This study assessed the impacts of various zonal and nodal pricing schemes on power systems and provided policy implications for this transition. We (1) modeled the power system at various levels of granularity, obtaining a detailed 4,579-node representation; (2) constructed a set of zonal and nodal pricing schemes reflecting changes in market-clearing models and pricing mechanisms; and (3) performed quantitative analyses through simulations for each scheme. Under the current system marginal price (SMP)-based uniform pricing, the schemes with least market impact are SMP-based zonal pricing with two bidding zones and extended locational marginal pricing. These results can guide the development of an appropriate pricing transition pathway, although a market price reduction of 4.8–7.0% appears inevitable. Within the Korean electricity market, wherein Transco is a monopoly retailer, we identified potential conflicts of interest for Transco in zonal and nodal pricing. By focusing on South Korea, this study offers valuable insights into any electricity market considering the transition to zonal and nodal pricing.

The rare abscopal effect during radiotherapy is thought to be caused by the release of immune-stimulated damage-associated molecular patterns, such as high mobility group box-1 protein (HMGB1), from cancer cells. Although external irradiation of cancer cells increases HMGB1 release, it is not clear if HMGB1 is released from cells after administering 131I-labeled m-iodobenzylguanidine (131I-MIBG) as an internal targeted radiotherapeutic agent. This study aimed to determine if HMGB1 is released from human-derived cancer and normal cells after administering 131I-MIBG. Methods: Extracellular lactate dehydrogenase (LDH) and HMGB1 released from H441 (human-derived lung adenocarcinoma cell line) and HaCaT (human keratinocyte cell line) 1 day after 2- and 10-Gy X-ray irradiation were measured. Accumulations of 131I-MIBG in SH-SY5Y (human-derived neuroblastoma cell line) and HaCaT were measured 60 min after administering 131I-MIBG (0.37, 1.85, 3.7 MBq/well). Extracellular LDH and HMGB1 released from SH-SY5Y and HaCaT 1 day after administering 131I-MIBG were also measured. Results: The extracellular LDH and HMGB1 released from H441 after 10-Gy X-ray irradiation were significantly increased. However, the extracellular LDH and HMGB1 released from HaCaT after 2-Gy and 10-Gy X-ray irradiation were not increased. After administering 1.85 MBq and 3.7 MBq 131I-MIBG, the extracellular LDH and HMGB1 released from SH-SY5Y were both significantly increased, but only the extracellular LDH released from HaCaT was significantly increased. Conclusions: HMGB1 was released from neuroblastomas but not from normal cells after 131I-MIBG administration, suggesting that a combination of 131I-MIBG and immunotherapy may be feasible.

Neuraminidase (NA)-based immunity could reduce the severity of infection caused by the new antigenic variants of influenza viruses. Detection of NA-inhibiting (NI) antibodies simultaneously with antibodies to hemagglutinin (HA) may enhance research of antibody response to influenza vaccines. Methods. The study examined 64 pairs of sera from patients vaccinated in 2018 with seasonal inactivated trivalent influenza vaccines (IIVs) with the formula recommended by WHO for the 2018-2019 influenza season. Antibodies to drift influenza viruses A/Guangdong-Maonan/SWL1536/2019(H1N1)pdm09 and A A/Brisbane/34/2018(H3N2) were studied before vaccination and 21 days after vaccination. To assess NI antibodies, we used the enzyme-linked lectin assay (ELLA) with pairs of reassortant viruses A/H6N1 and A/H6N2, and anti-HA antibodies were detected in hemagglutination inhibition (HI) test. The microneutralization (MN) test was performed in the MDCK cell line with A/H6N1 and A/H6N2 viruses. Results. Seasonal IIVs induce a substantial immune response of NI antibodies to influenza A/H1N1pdm09 and A/H3N2 viruses. A significantly reduced ‘herd’ immunity to drift influenza viruses A/H1N1pdm09 and A/H3N2 was shown, compared to previously circulating strains, but most of all to N2. Seasonal IIVs caused increases in antibodies to homologous and drift viruses, but to drift viruses more often among older patients. The level of NI antibodies to later A/H1N1pdm09 virus in response to IIVs was statistically significantly lower among younger people. After IIV vaccination, the total proportions of individuals with HI antibody levels ≥1:40 and NI antibodies ≥ 1:20 were 32.8% to drift A/H1N1pdm09 virus and 17.2% to drift A/H3N2 virus. Antisera containing HI and NI antibodies exhibit neutralizing properties in vitro against viruses with unrelated HA of H6 subtype. Conclusions. Drift A/H1N1pdm09 and A/H3N2 viruses demonstrated significantly lower reactivity with NI and NI antibodies to early influenza viruses. In response to seasonal IIVs, the level of seroprotection increased, including to drift viruses A, but to a greater extent to A/H1N1pdm09. Based on the data obtained, it can be hypothesized that in the case of a mismatched HA, it will most likely be necessary to vaccinate against the N1-containing influenza virus those who under 60 years of age, and the entire population against virus possessing N2.

The aim of our research was to evaluate and analyze serum 25(OH) vitamin D and parathyroid hormone (PTH) levels to investigate whether vitamin D deficiency serves as a risk factor for an increased incidence of acute respiratory infections (ARI) in children. Serum PTH levels were used as an indicator of vitamin D sufficiency, as normal PTH levels require an optimal concentration of 25(OH) vitamin D. The study included 129 children, divided into five subgroups: children with acute bronchopneumonia (n=42), acute laryngotracheitis (n=7), acute bronchiolitis (n=32), acute bronchitis (n=18), and a control group (n=30). No statistically significant differences in 25(OH)D levels were observed between the overall population of children with ARI and the control group (p=0.073). However, significant differences in 25(OH)D levels were identified between the control group and children with bronchopneumonia, acute bronchitis, and laryngotracheitis (p<0.01, p<0.05). Vitamin D insufficiency was found in 28.3% of children with ARIs and 10% in the healthy group. Sufficient levels were present in 60% of healthy children and 48.5% of those with ARIs. Deficiency was observed in 4% of ARI patients and 3.3% of the control group. Regarding PTH levels, statistical significance was found between the control group and the acute bronchiolitis group, due to the high percentage of children with hypervitaminosis in this subgroup. These results highlight the crucial role of vitamin D in the onset and progression of acute respiratory tract infections in children, emphasizing its impact on their overall respiratory health.

Photocatalytic water splitting offers a promising route for sustainable hydrogen production, but its efficiency remains limited by complex interactions between material properties, operating conditions, and reaction mechanisms. This study presents a data-driven approach to optimize photocatalytic water splitting for enhanced hydrogen evolution. By integrating experimental data, density functional theory calculations, and machine learning algorithms, we identified key descriptors governing photocatalyst performance. A predictive model was developed to screen optimal catalyst compositions, morphologies, and operating conditions, leading to a significant increase in hydrogen production rates. The results demonstrate the potential of data-driven optimization to accelerate the discovery of high-performance photocatalysts, paving the way for scalable and efficient solar-driven hydrogen production.

Sentiment analysis has emerged as a vital application of Natural Language Processing (NLP), enabling the extraction of subjective information from textual data. This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as contemporary techniques such as Random Forest, Gradient Boosting, and deep learning approaches like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. Using a comprehensive dataset sourced from social media platforms and product reviews, we evaluate the performance of these algorithms based on accuracy, precision, recall, and F1-score. Our findings highlight the strengths and weaknesses of each algorithm in handling sentiment classification tasks, emphasizing the influence of feature extraction techniques, such as Bag of Words and Word Embeddings, on model performance. The results indicate that while deep learning models generally outperform traditional algorithms, the choice of algorithm should be tailored to the specific context and requirements of the analysis. This study contributes to the ongoing discourse on the efficacy of machine learning methods in NLP, offering insights that can guide researchers and practitioners in selecting appropriate algorithms for sentiment analysis tasks.

Human-Computer Interaction (HCI) has evolved significantly with advances in Artificial Intelligence (AI) and Computer Vision (CV), paving the way for more intelligent and adaptive user interfaces. This paper explores the design and implementation of user interfaces that leverage AI and CV to create more intuitive, responsive, and personalized experiences. By integrating techniques such as facial recognition, gesture control, and eye tracking, interfaces can adapt in real-time to users’ actions and intentions, thus enhancing accessibility, efficiency, and engagement across various applications, from healthcare to gaming. The study reviews current advancements and challenges in using AI and CV for HCI, including data privacy, model interpretability, and computational efficiency. Experimental results demonstrate the potential of these technologies to improve interaction quality and user satisfaction. This work aims to contribute to the growing field of intelligent HCI design, offering insights into practical implementations and future research directions in AI-driven interface development.

Recycling of composite materials has become a very important topic to develop sustainable solutions for the management of waste generated by industries that use composite materials. This paper presents a bibliographical review on the main recycling methods of composite materials such as: mechanical, chemical, thermal and hybrid. In mechanical recycling, the material is crushed or ground, generally using the resulting material as filler, which causes limitations in the applications due to the type of resulting fibers (short, long, non-uniform). On the other hand, chemical recycling breaks down polymers through a chemical reaction and recovers fibers, so in some cases the mechanical properties of the fibers are affected. Thermal recycling uses high temperatures to break down materials, transforming them into energy or reusing them in other products. Finally, hybrid recycling uses two or more processes as a means to recover the material; among the most used methods in this category is the mechanical process combined with the thermal process. This review describes the advantages and disadvantages of each method, as well as the environmental impact. Although each type of recycling offers special characteristics for the material obtained, it is necessary to continue researching and developing technologies for recycling processes that promote recycling and reuse of composite materials in a better way.

Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. Eureka Xecs SafeTrucks-project (Heavy traffic safety improvements by advanced dynamics and road weather services) develops real-time vehicle-specific weather and safety services tailored to each vehicle, based on the vehicle’s own sensor observations combined with data from the service systems and an analysis of the vehicle's own dynamics. The services are also analyzed by Digital Twin modelling, in order evaluate and refine them in a controlled environment. The pilot services are ultimately tested in pilot system within operative heavy traffic. This paper presents the concept and architecture of the platform, with preliminary results of pilot services operation and system evaluation.

As global food demand continues to grow, enhancing rice seed setting rate and yield has emerged as a crucial research topic. The stigma exsertion rate in rice, a pivotal determinant of the outcrossing seed setting rate in sterility lines, is essential for facilitating the propagation and efficient seed production of hybrid rice varieties. This article reviews the research progress on stigma exertion rate in rice, systematically analyzing the latest molecular biology and genetics findings to uncover the key genes and molecular mechanisms regulating stigma exertion. Furthermore, it explores the application of molecular marker-assisted selection technology in rice breeding, aiming to optimize stigma exertion traits to enhance the stigma exertion rate and outcrossing habit of rice sterility lines. By integrating existing research outcomes, this article not only provides researchers with a theoretical foundation for a deeper understanding of the regulatory mechanisms of stigma exertion but also offers practical strategies for rice breeding practices.

The stability of slopes is a critical challenge in various civil engineering projects, such as embankments, cut-slopes, landfills, dams, transportation infrastructure, and riverbank restoration. Stabilizing slopes using bioengineering methods is a sustainable approach that limits the negative impact of engineering works; such methods should be implemented and adopted worldwide. Geosynthetic materials and plant roots are sustainable for preventing erosion and surface landslides. The plants used for this paper are known to have beneficial effects on erosion control, namely Festuca arundinaceous, Dactylis glomerata, Phleum pratensis, Trifolium pratense, and Trifolium repens. Using vegetation as a bio-reinforcement method is often more cost-effective and environmentally friendly than traditional engineering solutions. The paper interprets the erosion process that occurred on sandy slopes protected by organic soil layers and geosynthetic materials under rainfall simulation on scale model tests.

Myxomatous mitral valve disease (MMVD) is a leading cause of cardiac morbidity and mortality in dogs, requiring targeted intervention at various stages of disease progression. MMVD is characterized by thickening of the mitral valve leaflets, leading to mitral regurgitation and cardiac remodeling. This progressive disease impairs valve function, causing volume overload in the left atrium and ventricle, which can eventually lead to congestive heart failure. MMVD involves complex mechanisms, including genetic predisposition, neurohormonal activation, and mechanical stress on the valve. Thickened valve leaflets fail to seal properly during systole, resulting in mitral regurgitation and increased left atrial pressure. This triggers compensatory mechanisms like activation of the renin-angiotensin-aldosterone system (RAAS), contributing to further cardiac remodeling. Chronic RAAS activation results in fluid retention, vasoconstriction, and fibrosis, all of which can worsen heart failure. This mini-review provides an overview of current therapeutic strategies for the management of MMVD and discusses preliminary findings from our consortium regarding the preclinical and clinical efficacy of the sodium-glucose co-transporter type-2 (SGLT-2) inhibitor, dapagliflozin, in dogs.

The paper presents a new method of path planning for an industrial robot manipulator performing thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser trian-gulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed based on the application of discrete exterior calculus methods characterized by high computational efficiency. The developed algorithms and their software implementation were experimentally tested through robotic mi-croplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral raw materials.

The Median Based Unit Weibull is a new 2 parameter unit Weibull distribution defined on the unit interval (0,1). Estimation of the parameters using MLE encountered some problems like large variance. Using generalized method of moments (GMMs) and percentile method may ameliorate this condition. This paper introduces GMMs and the percentile methods for estimating the parameters of the new distribution with illustrative real data analysis.

Magnesium (Mg) is not prominent among the list of well known anti-aging agents. Yet the signs and symptoms of aging mimic those of Mg deficiency. Mg is a required cofactor for over 800 enzymatic reactions (as of 2022). This review does not correlate Mg status with clinical data on agents linked to longevity. The approach is physiologic and highlights specific Mg dependent reactions required by these longevity linked biomarkers. Many of these share common pathways to extend healthspan. Mg is a required cofactor in the synthesis of vitamin D and melatonin and activation of five of the eight B vitamins. It is a required cofactor for all CYP450 enzymes. It is directly responsible for the appropriate methylation of proteins and DNA, which control the epigenome. The MTHFR (methylenetetrahydrofolate reductase) 677T allele that compromises methylation is present in a majority of Americans. Aberrant methylation predicts the severity of Covid-19 and its persistence into long Covid. Mg is a silent benefactor that may indirectly link these longevity agents, but only if viewed in context with calcium (Ca), i.e., Ca:Mg. Both compete for the same receptor. To fully exploit these longevity agents sufficient Mg is required. The pertinent physiology is presented.

.Citizen or community science (CS) projects in the marine environment rarely consider carbon footprint and sustainability. In this case study, we assessed the effectiveness of ten CS methods used by tourists in the Great Barrier Reef Marine Park (GBRMP) and Coral Sea Marine Park (CSMP). We also assessed baseline coral reef knowledge of the tourists, observations of marine species and the communication of our results to the public. Specifically, the tourists utilised up to ten methods: iNaturalist, CoralWatch, Great Barrier Reef Census, Eye on the Reef (EoR), environ-mental DNA (eDNA) testing kits, photogrammetry, social surveys, and Red Map, as well as ma-rine debris and marine vegetation collections. A total of 10,421 data points were collected across 14 days, including 5,390 records (52 % of the total) uploaded to iNaturalist, comprising 640 plant animal species. Public awareness of the CS expedition reached over 700,000 people based on estimates from advertising, media, social media, family and friends, and conference presenta-tions. We estimated the total carbon footprint for the expedition as 268.7 tonnes of CO2 or 4.47 tonnes of CO2 per person, equivalent to $112 AUD needed to offset this input. Based on these re-sults, our recommendations to leverage CS methods include governmental review strategies, temporal replication to allow the measurement of changes through time, integrating sustainabil-ity into CS ecotourism platforms, and encouraging broad participation.

This study analyzes the effects of primary energy consumption and alternative energy patents on CO2 emission intensity and CO2 emissions per capita, and their relationship using canonical correlation analysis. Our results are as follows. First, CO2 emissions from natural gas and liquefied petroleum gas have positive effects, while CO2 emissions from gasoline, fuel oil, diesel, and kerosene have negative effects on CO2 emission intensity. Second, patents for devices for producing mechanical power from muscle energy have large positive effects. Finally, the more the patents use waste heat, geothermal energy, hydro energy, and wind energy, the higher the CO2 emissions from liquefied petroleum gas, gasoline, and crude oil and the lower the CO2 emissions from diesel, which are conducive to controlling CO2 emissions. Therefore, energy policies will be more effective based on the CO2 “emissions” level from primary energy consumption and the “control” degree of CO2 emissions by alternative energy.

The molten salt reactor (MSR) specific 3D spatial dynamics code leveraging the neutronics and thermal-hydraulics (TH) coupling method, TMSR3D, has been extended for static and transient analysis of large advanced light water reactors (LWRs), particularly focusing on control rod ejection accidents. The code is built upon the neutron diffusion model leveraging the widely used "two-step" strategy in three-dimensional Cartesian coordinates. A polynomial nodal expansion method (NEM) is utilized to handle spatial variables, while the neutron kinetics solver employs the exponential transformation technique. The code also integrates a single-channel TH module and a steam table set at 15.5 Mpa. The accuracy of the code has been validated through three transient benchmarks, including the 3D-LRA benchmark for boiling water reactors (BWRs), the NEACRP 3D benchmark, and the MOX/UO2 core benchmark for pressurized water reactors (PWRs). These benchmarks cover both static and transient behaviors, and the results have been thoroughly analyzed. The close alignment of extended version of TMSR3D outcomes with reference data and other codes supports its reliability, making it a promising tool for future dynamic analyses of large-scale advanced PWRs.

Antimicrobial resistance (AMR), a consequence of the ability of microorganisms, especially bacteria, to develop resistance against conventional antibiotics hampering the treatment of common infections, is recognized as one of the most imperative health threats of this century. Antibacterial photodynamic therapy (aPDT) has emerged as a promising alternative strategy, utilizing photo-sensitizers activated by light to generate reactive oxygen species (ROS) that kill pathogens without inducing resistance. In this work, we synthesized silica nanoparticles of different sizes (20 nm, 80 nm, and 250 nm) functionalized with the photosensitizer Rose Bengal (RB) and a gluconamide ligand, which targets Gram-negative bacteria, to assess their potential in aPDT. Comprehensive characterization, including dynamic light scattering (DLS) and photophysical analysis, confirmed the stability and effective singlet oxygen production of the functionalized nanoparticles. RB loading was size-dependent, decreasing with increasing nanoparticle diameter. Consequently, the largest nanoparticles (250 nm) displayed insufficient RB surface density for effective aPDT, while the smallest (20 nm) and intermediate (80 nm) particles were more promising. Bacterial assays in E. coli revealed minimal dark toxicity and significant light-activated phototoxicity for the RB-loaded nanoparticles. The addition of gluconamide notably enhanced phototoxic activity, particularly in the smallest nanoparticles (RB-G-20@SiNP), which demonstrated the highest photo-toxicity-to-cytotoxicity ratio. These findings indicate that small, gluconamide-functionalized silica nanoparticles are highly effective for targeted aPDT, offering a robust strategy to combat AMR.

Reactive oxygen species (ROS) encompass various molecular oxygen derivatives naturally produced during aerobic metabolism, including superoxide anion, hydrogen peroxide, and hydroxyl radical. Excessive ROS production leads to oxidative distress, causing cellular damage and contributing to various pathologies, often alongside inflammation. Endogenous sources of ROS include mitochondrial activity and NADPH oxidases. The antioxidant system, comprising enzymes such as superoxide dismutase, peroxiredoxin, and catalase, mitigates ROS-induced damage. This review explores the regulation of ROS by membrane receptors, focusing on B1 and B2 kinin receptors and histamine H2 receptors, which are implicated in vasodilation, angiogenesis, inflammation, and gastric acid secretion. Understanding these interactions provides insights into ROS modulation and its role in disease mechanisms.

Wang et al. (Nature 623, 340—350; 2023)¹ used satellite observations and phenological signatures to map and estimate the extent of planted rubber (Hevea brasiliensis Müll.Arg.), and associated deforestation, for eight Southeast Asian countries. Their assessment implies that planted rubber may have caused over 4 Mha of deforestation since 1993 — a value greater than assumed in setting deforestation policies. Unfortunately, their estimates rely on incorrect methods. In addition, their definition of “deforestation” is misleading because it includes all tree cover loss, not just natural forests, but also from established rubber gardens, and other plantations. Our assessment indicates that since 2001 the actual extent of deforestation associated with planted rubber is approximately one quarter that reported by Wang et al.

Breast cancer is the most commonly diagnosed cancer in women in both developed and developing countries. Due to the increasing alarms in the detection of cases every year, it is considered a priority to evaluate new molecules as potential antitumoral agents. Among these molecules, cationic peptides have emerged as promising therapeutic agents with a membrane-based mechanism of action very hard to counteract by the cancer cells. This biophysical study seeks to offer preliminary insights into how NA-CATH:ATRA-1-ATRA-1, a synthetic cationic peptide derived of the peptide NA-CATH, isolated from the species Natra aja or Chinese cobra, is a potential antitumoral compound at the membrane level on breast cancer cell lines. The interaction of the NA-CATH-ATRA-1-ATRA-1 with two lipid systems representative of tumoral and non-tumoral membranes was followed by differential scanning calorimetry and Fourier-transform infrared spectroscopy. The results showed a strong interaction of NA-CATH-ATRA-1-ATRA-1 with the lipids present in the eukaryotic membranes, especially phosphatidylserine, suggesting a mechanism based on the electrostatic interaction of the peptide with the anionic lipids of the tumoral cancer membranes. The biophysical studies are essential to understand the mechanism of action of a potential pharmaceutics.

Among women globally, breast cancer is a major cause of cancer-related death. Accurate and timely diagnosis is essential, and results can be significantly improved. A new era in image analysis has been brought about by the emergence of artificial intelligence (AI), which has made significant progress in the diagnosis and customization of treatment plans for breast cancer possible. This study aimed to develop a comprehensive AI framework for breast cancer detection by integrating convolutional neural networks (CNNs) for image analysis with an artificial neural network (ANN) for clinical data. Using a dataset of ultrasound and pathology images, along with clinical features from 569 patients, we trained CNN models to classify breast tissue as benign or malignant, and the ANN to process clinical data for the same task. The combined approach significantly enhanced the accuracy of breast cancer diagnosis, achieving over 96% accuracy by leveraging both imaging and clinical data. Class Activation Maps (CAM) and heatmaps provided valuable interpretability for CNN predictions, improving trust and transparency in the model’s decision-making process. The results demonstrate that the fusion of CNNs and ANNs enhances diagnostic accuracy and offers a promising tool for early breast cancer detection, while providing crucial insights for clinical validation.

Background/Objectives: Biallelic pathogenic variants in the CEP290 gene are typically associated with severe, early-onset inherited retinal dystrophies (IRDs) in both syndromic and non-syndromic forms. This study highlights the phenotypic variability of CEP290-related non-syndromic IRDs, focusing on two siblings with biallelic CEP290 variants. One sibling presents with a milder phenotype, expanding the known spectrum of CEP290-related IRDs and emphasizing the importance of personalized clinical monitoring; Methods: Whole-exome sequencing (WES) was used to identify CEP290 pathogenic variants in the siblings. Comprehensive ophthalmologic evaluations were performed to assess the severity and progression of retinal degeneration; Results: Both siblings were found to carry compound heterozygous pathogenic variants in CEP290, inherited in trans. Clinical evaluations revealed significantly preserved retinal function in the sister, whereas the brother exhibited a more aggressive and progressive retinal dystrophy; Conclusions: This study expands the phenotypic spectrum of non-syndromic CEP290-related IRDs, demonstrating that biallelic CEP290 variants can result in a wide range of severity, from mild to severe. These findings highlight the need for personalized monitoring and tailored management strategies in CEP290-related IRDs.

This work presents an innovative tri-generation system for the simultaneous production of desalinated water, electrical energy, and cooling, aimed at addressing the growing challenges of water scarcity and climate change. The system integrates an 8-stage MSF process and a 6-effect MED process, along with an expander-generator that improves cycle efficiency by utilizing and reintroducing steam within the desalination stages. A parametric study was conducted using ASPEN PLUS to evaluate the system’s performance. The analysis showed that the system operates over a broader pressure and temperature range than conventional MED systems, from 28.3 to 0.8 kPa and from 68 to 4°C, thanks to a dual ejector-condensation process. In cogeneration mode, the system achieves a PR of 12.06 and an RR of 54%0.54, producing 67,219.2 L/day of desalinated water and reducing electrical consumption by 12.03%. In tri-generation mode, it achieves a PR of 17.81 and an RR of 0.80%, with a cooling capacity of 1225 kW (COP of 3.278), producing 99,273.6 L/day of desalinated water while reducing electrical consumption by 3.69%. This system offers an efficient solution to meet the growing water and energy needs of coastal communities, combining water desalination, energy production, and cooling in a single integrated process.

This work aims to verify whether the continuum mechanics assumption holds for the numerical simulation of a typical sample delivery system in Serial Femtosecond Crystallography (SFX). Knudsen numbers were calculated based on the numerical simulation results of helium flow through the gas-focused liquid sheet nozzle into the vacuum chamber, representing the upper limit of Knudsen number for such systems. The analysed flow is considered steady, compressible and laminar. The numerical results are mesh-independent, with a Grid Convergence Index significantly lower than 1 % for global and local analysis. The study is based on an improved definition of the numerical Knudsen number, a combination of cell Knudsen number and physical Knudsen number. In the analysis, no-slip boundary and low-pressure boundary slip conditions are compared. No significant differences are observed. The study justifies using computational fluid dynamics (CFD) analysis for SFX sample delivery systems based on the assumption of continuum mechanics.

Background/Objectives: Thoracic aortic aneurysms (TAA) represent medical conditions having causes that can range from genetic determinations to various risk factors, having potential life-threatening complications, the familial TAA (FTAA) being a cluster of non-syndromic thoracic aortic aneurysms. This review aims to emphasize the importance of genetic testing in patients diagnosed with thoracic aortic aneurysms, the genetic implications accounting for roughly 20% of these cases. The reported case presents a patient who was genetically tested 10 years after a Bentall procedure, which was performed for acute DeBakey type I aortic dissection developed in her second pregnancy, when the patient started presenting recurrent prolonged febrile syndrome. She was tested positive for a missense variant in the actin alpha-2 (ACTA2) gene, in heterozygous status, but also serological tests revealed the presence of an infection with Coxiella burnetii. Methods: For a better understanding of both conditions, our research was conducted on two directions, one reviewing the literature on patients with Coxiella burnetii blood culture-negative infective endocarditis (BCNIE) and the other one, focusing on patients having familial thoracic aortic aneurysm (FTAA) due to ACTA2 mutation. This review incorporates studies found on PubMed and ResearchGate up to August 2024. Conclusions: BCNIE represents a condition with several diagnosis challenges and may lead to severe complications if timely treatment is not initiated. Also, diagnosing FTAA requires more frequent genetic testing, enabling a better follow-up and management.

The tight sandstone gas of the Upper Paleozoic in the Ordos Basin has become a key field for increasing reserves and production of natural gas in the basin. As a high-quality tight sandstone gas reservoir, the 8th member of the Shihezi Formation only has good gas production in the south of the basin. For this reason, the types of natural gas source rocks in the north and south of the Upper Paleozoic were summarized, and the main types of gas source rocks in the north and south were clarified. The sedimentary facies of the 8th member of the Shihezi Formation and the Shanxi Formation were analyzed. The results show that the main gas-producing source rock in the northern part of the basin is coal rock, and the main gas-producing source rock in the southern part of the basin is dark mudstone. The Permian Shanxi Formation in the northern part of the basin mainly develops the delta front, which provides good conditions for the development of coal and rock. Compared with the northern Shanxi Formation, the Permian Shanxi Formation in the southern Ordos Basin mainly develops marine facies and marine-continental transitional facies, and the coal seam is not created. Therefore, the natural gas produced by the coal of the Shanxi Formation can easily migrate to the good reservoir in the 8th member of the box, which is also the reason for the difference in the enrichment of tight sandstone gas in the north and south of the Ordos Basin.

Cardiovascular surgeries, though lifesaving, can lead to complications requiring further interventions, including amputations. This study uses Sankey diagrams, Sunburst plots, and Icicle plots to analyze treatment pathways in patients with peripheral artery disease (PAD), focusing on those that lead to amputation versus those that do not. We found that non-amputation outcomes commonly involve anti-platelet and lipid-lowering medications, while amputation pathways, although less frequent, often share similar initial treatments but diverge later, suggesting areas for early intervention. These visualizations highlight patterns in patient care and emphasize the importance of tailored treatment strategies to reduce amputations, offering insights to predict likely outcomes based on treatment sequences.

In slope engineering, the foundation grooves of slope protection gird beams are mostly excavated manually. However, manual excavation presents problems, such as low construction efficiency, uneven groove bottoms, inconsistent cross-section groove widths, and curved groove axis, which reduces the quality of cast-in-place grid beams or complicates the installation of prefabricated grid beams. To address these issues, a mechanized grooving device for slope protection grid beams has been developed and successfully applied in the project. The grooving device is composed of interlocking dual spiral cutter heads, a mechanism for the groove width and depth adjustment, a cutter head working platform, a platform azimuth adjuster, a cutter head sliding mechanism, a hydraulic motor, a hydraulic power system and a platform anchoring mechanism. It can be used to groove on soil and strongly weathered rock slopes at different slope angles, and the foundation grooves of slope protection gird beams of different depths and widths can be carved according to the design requirements. Moreover, the grooved trenches feature flat bottoms, consistent widths, and straight axis, making it easy to install prefabricated grid beams and greatly improving the quality of cast-in-place grid beams. According to the construction statistics of a slope project in Guangdong, the grooving speeds of the grooving device on the slope of soil and strongly weathered rock are about 1.5m/min and 0.5m/min respectively, which greatly improves the construction efficiency and quality.