Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

Understanding the structural intricacies of RNA molecules is essential for deciphering numerous biological processes. Traditionally, scientists have relied on experimental methods to gain insights and draw conclusions. However, the recent advent of advanced computational techniques has significantly accelerated and refined the accuracy of research results in several areas. A particularly challenging aspect of RNA analysis is the prediction of its secondary structure, which is crucial for elucidating its functional role in biological systems. This paper deals with the prediction of pseudoknots in RNA, focusing on two types of pseudoknots: K-type and M-type pseudoknots. Pseudoknots are complex RNA formations in which nucleotides in a loop form base pairs with nucleotides outside the loop and thus contribute to essential biological functions. Accurate prediction of these structures is crucial for understanding RNA dynamics and interactions. Building on our previous work in which we developed a framework for the recognition of H- and L-type pseudoknots, an extended grammar-based framework tailored to the prediction of K- and M-type pseudoknots is proposed. This approach uses syntactic pattern recognition techniques and provides a systematic method to identify and characterize these complex RNA structures. Our framework uses context-free grammars (CFGs) to model RNA sequences and predict the occurrence of pseudoknots. By formulating specific grammatical rules for type K and type M pseudoknots, we enable efficient parsing of RNA sequences to recognize potential pseudoknot configurations. This method ensures an exhaustive exploration of possible pseudoknot structures within a reasonable time frame. In addition, the proposed method incorporates essential concepts of biology, such as base pairing optimization and free energy reduction, to improve the accuracy of pseudoknot prediction. These principles are crucial to ensure that the predicted structures are biologically plausible. By embedding these principles into our grammar-based framework, we aim to predict RNA conformations that are both theoretically sound and biologically relevant.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region's climate has a significant impact on how water is managed and used, mostly in the primary sector (agroforestry systems, livestock, and agriculture), and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the S. Francisco River basin (SFRB) is assessed in three periods (1970−2000, 1981−2022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot-summer (Csa) and warm-summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The Source and the mouth of SFRB are projected to endure the major impacts of climate change that is followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability, and quality for human use will all be impacted. Consequences on ecosystems, agricultural and for the socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating to a larger extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in the São Francisco River basin from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45%, and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$ 6,000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin's dispersed demographic and economic profile. To build routes towards a shared sustainable goal—namely, to adapt and reduce the expected climate change impacts in SFRB—it is imperative that integrated measures be conducted with the cooperation of stakeholders, local population, and decision-makers.

Corneal biomechanics is a hot topic in ophthalmology. The biomechanical properties of the cornea (BMPs) have important implications in the management and diagnosis of corneal diseases such as ectasia and keratoconus. In addition, the characterization of BMPs is crucial to model the predictability of a corneal surgery intervention, the outcomes of refractive surgery or follow-up of corneal diseases. The biomechanical behavior of the cornea is governed by viscoelastic properties that allow, among other structural implications, the damping of excess of intraocular pressure and reduce damage to the optic nerve. Currently, the most versatile and complete methods to measure corneal viscoelasticity are based on air-puff corneal applanation. However, those methods lack the ability to directly measure corneal viscosity. The aim of this work is to propose a new methodology based on the analysis of corneal air-puff measurements through the Standard Linear Solid model (SLSM) to provide analytical expressions to separately calculate the elastic and time-dependent (corneal retardation time and viscosity) properties. The results shown the mean values of elasticity (E), viscosity (Ƞ) and corneal retardation time (τ) in a sample of 200 young and healthy subjects. The influence of elasticity and viscosity on viscoelasticity, high-order corneal aberrations and optical transparency is investigated. Finally, the SLSM fed back from experimental E and Ƞ values was employed to compare the creep-relaxation response between normal, an ocular hypertension patient and Ortho-K user.

Delay in data transmission is one of key performance indicators (KPIs) of a network. The planning and project value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in an Long Term Evolution (LTE) network using three ML techniques - Neural Networks (NN), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that, in all three optimization approaches, the k-NN model is selected as the best solution. For the RReliefF optimization algorithm, the best model has 6 inputs and minimum relative error (RE), RE=0.109; for the Backward selection via the recursive feature elimination algorithm, the best model has 4 inputs and RE=0.041; and for the Pareto 80/20 rule, the best model has 11 inputs and RE= 0.049. The comparative analysis of the results concludes that according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

The aim of this work is to explain the behaviour of the multiresistance percentage of Pseudomona aeruginosa in some countries of Europe through a multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization and the World Bank. First, we will use a descriptive analysis and a principal components analysis. Then, we use a k-means clustering to determine the countries and regions that are most affected by the antibiotic resistance. Second, we expand the database by adding some socioeconomic, governance and antibiotic-consumption variables. We then run a data panel regression analysis to determine some functions that relates the multiresistance percentage with those new variables. Finally, we use machine learning techniques to validate a pooling panel data case, using XGBoost and random forest algorithms. The results of the data panel analysis indicate that the most important variables for the multiresistance percentage are corruption control and the rule of law. Similar results are found with the machine learning validation analysis, where the human development index is an additional important variable for the multiresistance percentage.

At present more than 9 million people die of cancer every year. Simple and broad-spectrum drugs are still an urgent need for cancer patients. Recently, we proposed a new hypothesis that intracellular osmotic pressure (IOP) is the driving force of cell division, and abnormal tumor proliferation is the result of uncontrolled IOP in cells. On the one hand, aneuploidy and abnormal function of Na⁺/K⁺ pump lead to a faster rise of IOP in tumor cell than normal cells, on the other hand, abnormality of cytoskeleton assembly leads to the decrease of tolerance limit of cell membrane (TLCM) of tumor cells for resisting IOP. This hypothesis predicts: 1)Tumor cells were more intolerant to hypotonic stress than normal cells. 2) Maligancies may be sellectively killed by a suddenn increase of IOP and combined with decrease of the TLCM of tumors. Na⁺/K⁺ pump inhibitors can promotely increase the IOP of tumor cells and cytoskeleton inhibitors can dramatically lower the TLCM of tumor cells. Therefore, Na⁺/K⁺ pump and cytoskeleton inhibitors may have a synergetic effect to kill tumor cells. 3) Molecules regulating cell osmolality may be new targets for cancer treatment.

Optical “fingerprints” are widely used in chemometrics-assited recognition of samples of different nature. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions occurring over time. The indicator reactions are usually carried out in aqueous solutions; in this study we have developed the reactions that occur in an organic solvent, which makes it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40 and 5W30 motor oils of 4 manufacturers, totally 6 samples. The procedure involved mixing of the dye, sample, and reagents (HNO3, HCl, or t-butyl hydroperoxide) in ethanolic solution in a 96-well plate and measuring absorbance or near-IR fluorescence intensity every several minutes during 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and k-nearest neighbors algorithm (kNN). The discrimination accuracy was evaluated by using the validation procedure. Reaction of oxidation of a dye with nitric acid allowed to recognize all 6 samples with 100% accuracy by LDA. Merging data of 4 reactions that did not provide complete discrimination ensured an accuracy of 93% by kNN technique. The developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetic-based discrimination strategy.

To ensure the security of spatial-temporal Top-k query in two-tiered wireless sensor networks, many schemes have been proposed in the literature in the past decade. However, most of them only consider the scenario where sensor nodes are static, and cannot achieve the security goal for spatial-temporal Top-k query in mobile sensor networks, because the mobility of the sensor nodes will affect the spatial-temporal relationships of the sensory data items generated by the sensor nodes. Although we have proposed some schemes for two-tiered mobile wireless sensor networks (TMWSNs) in our previous work, there is still large room to improve their performances. In this paper, we proposed a novel scheme named STQ-TMWSN for secure fine-grained spatial-temporal Top-k query in TMWSNs based on the virtual-grid construction and the size-order encryption binding. Theoretic analysis shows that STQ-TMWSN can achieve low computation complexity and high security performance. Simulation results indicate that STQ-TMWSN brings much lower communication cost than the state-of-the-art schemes on securing Top-k query in TMWSNs.

Different forces play a key-role in the stability of food colloid dispersions. Focus is here on those controlling attraction and/or repulsion, which concur to stabilization, phase separation, coagulation, and are quite evident in water-based systems. Combination of attractive and repulsive forces favors, or hinders, the association of colloid entities; such processes are often met in food technology. The above processes depend on the forces at work, and on colloid concentration in the medium (i.e. on inter-particle distance). Worked examples deals with milk manipulation procedures, ending in cheese formation. The whole milk-working sequence is controlled by the combination of forces leading to aggregation and phase separation of casein and other milk components. Thereafter, one gets either fresh, for prompt consumption, or aged cheeses. The combination of attractive (van der Waals, vdW, and depletion) with repulsive (double layer, DL, but also with steric) forces results in the dominance of aggregation versus dispersion modes in all steps of milk transformation, which depend on the distance among colloid particles, on the amplitude of the mentioned forces, and on their decay length. The combined role of double layer and van der Waals (vdW) forces is at the basis of the DLVO theory on colloid stability, which is properly modified when these forces overlap with steric stabilization and, eventually, with depletion. Steric effects are dispersive, depletion ones favor colloid nucleation in a single phase. The milk manipulation chain is a worked example of the intriguing association features controlled by the mentioned forces (and of ancillary ones, as well), and indicates which forces favor the formation of products such as Parmesan or Mozzarella cheese, but are not alien to the preparation of many other dairy products.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

Here we compile a detailed record of eddies and examine their intra-annual variability over the Spitsbergen Bank in the northwestern Barents Sea using spaceborne Sentinel-1 A/B synthetic aperture radar (SAR) data acquired in 2018. Analysis of 3070 SAR images enabled to register 1758 eddy manifestations in the marginal ice zone (MIZ) and 1631 signatures in the open water (OW). Submesoscale and small mesoscale eddies were observed within a range 0.2-40 km with the range of MIZ eddies being twice larger than that of OW eddies. We note a strong seasonal variability of eddy diameters and the ratio of cyclones to anticyclones. The eddies were present in all seasons showing an approximate parity in eddy generation intensity in the surface layer between winter and summer. The peak eddy activity is observed during the first (January to March) and third (July-September) quarters of the year. A well-pronounced seasonality of SAR-derived eddy intensity correlates with that of eddy kinetic energy in the surface layer of the Eurasian Arctic. Eddies were detected most frequently in Storfjorden, south of Edge Island, near Hopen Island and along the entire eastern flank of the Spitsbergen Bank. Very low eddy activity was observed in the central part of the bank. The MIZ eddies were recorded during eight months of the year peaking in February, and being spatially limited by a 200-m isobath. The OW eddies were widely dispersed across the study domain peaking in August and tending to accumulate in regions of relatively weak surface currents. In summer, about 20-25% of them were observed within the Barents Sea Polar Front.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

H-adaptivity is an effective tool to introduce local mesh refinement in FEM-based numerical simulation of crack propagation. The implementation of h-adaptivity could benefit the numerical simulation of fatigue or accidental load scenarios involving large structures such as ship hulls. In engineering applications, the element deletion method is frequently used to represent cracks. However, the element deletion method has some drawbacks such as strong mesh dependency and loss of mass or energy. In order to mitigate this problem, the element splitting method could be applied. In this study, a numerical method called ‘h-adaptive element splitting’ (h-AES) is introduced. The h-AES method is applied in FEM programs by combining h-adaptivity with the element splitting method. Two examples using the h-AES method to simulate cracks in large structures under linear-elastic fracture mechanics scenario are presented. The numerical results are verified against analytical solutions. Based on the examples, the h-AES method is proven to be able to introduce mesh refinement in large-scale numerical models that consist of structured coarse meshes. By employing the mesh refinement introduced in this paper, very small cracks are well represented in large structures.

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one’s health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; 2) The data, when communicated, are vulnerable to security and privacy issues; 3) The communication of the continuously collected data is not only costly but also energy hungry; 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks.This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a serviceoriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence, transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.

Background/Aim: Performing emergency surgery for bowel obstruction continues to place a significant strain on the healthcare system. Conventional assessment methods for outcomes in bowel obstruction cases often concentrate on isolated factors, and the evaluation of results for individuals with bowel obstruction remains poorly studied. This study aimed to examine the risk factors associated with major postoperative complications. Methods: We retrospectively analyzed 99 patients undergoing surgery from 2015 to 2022. We divided the patients into two groups: 1) benign related obstruction (n=68), 2) cancer related obstruction (n=31). We used logistic regression, KNN and XGBOOST. We calculated the receiver operating characteristic curve, and accuracy of the model. Results: Colon obstructions were more frequent in the cancer group (p=0.005). Operative time, intestinal resection and stoma were significantly more frequent in the cancer group. Major complications were 41% for the cancer group vs. 20 in the benign group (p=0.03). Uni-and multivariate analysis shows that significant risk factors for major complication were cancer related obstruction and CRP. The best model was KNN with an accuracy of 0.82. Conclusion. Colonic obstruction is associated with a tumor-related blockage. Malignant cancer and an increase in C-reactive protein (CRP) are significant risk factors for patients who have undergone emergency surgery due to major complications. KNN could improve the process of counseling and the perioperative management of patients with intestinal obstruction in emergency settings.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 (FIDIC 1999) framework. It aims to identify, categorize, and devise mitigation strategies for key types of Variations and Claims, aligning with the FIDIC Conditions of Contract. The research, drawing on inputs from construction industry professionals including Contract Administrators and Project Managers, focuses on the MENA re-gion. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts, predominantly through interviews, focusing on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the study uncovers the most prevalent and impactful causes of Variations and Claims, highlighting the critical need for managerial intervention. A key feature of this study is the integration of Scientometric Analysis for a quantitative review of current literature, providing a comprehensive academic context. A significant addition to the methodology is the implementation of a k-means clustering analysis. This advanced statistical technique further classifies the data into distinct clusters, each representing unique combinations of 'Frequency' and 'Impact' of Variations and Claims. The k-means analysis elucidates intricate patterns and potential solutions, offering profound insights into effectively managing these issues. These analytical advancements are crucial in identifying significant and manageable responses to reduce the frequency and impact of Variations and Claims in construction projects.

Cardiotonic steroids (CTS) are a group of steroid compounds derived from certain plants and animals. CTS are selective inhibitors of Na,K-ATPase, so for a long time they were widely used for medical purposes to treat heart failure and some other diseases. However, over time, doctors gradually began to refuse this group of drugs due to their narrow therapeutic range and a number of serious side effects. However, in parallel with the rise and fall of interest in CTS as a drug, interest in them as a single class of high-affinity Na,K-ATPase inhibitors has only increased. Numerous data on the effect of CTS on signaling cascades and cell viability made impelling a search for a rationale for the existence of such a subtle biological regulator. A large number of studies devoted to the potential use of CTS in the treatment of neurodegenerative, oncological, immune and other diseases "revitalized" the already "written off" group of the drugs. If initially the role of exogenous (mainly plant origin) CTS was studied, then by the 90s of the XX century the study of the role of endogenous CTS and their regulatory effects became a popular trend in biology and physiology. The role of endogenous CTS was studied in details in the pathogenesis of cardiovascular diseases [1]. A large number of researchers have unanimously stated that endogenous CTS are very complex regulators of the development of pathological processes; however, the exact cause-and-effect relationship that would unambiguously explain the processes of synthesis, utilization, and the role of endogenous CTS has not yet been established. The effects of CTS in the frame of various regulatory schemes have been described, but the predominant number of studies was devoted to arterial hypertension, heart and renal failure, cancer [2–4], aging [5,6], and neuroinflammation. The effects of both endo- and exogenous CTS on blood cells and have been studied to a much lesser extent.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 framework. The study aims to identify, categorize, and devise mitigation strategies for key types of variations and claims aligning with the FIDIC conditions of contract. The research is drawing on inputs from construction industry professionals including contract administrators and project managers, focusing on the MENA region. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts predominantly through interviews that focused on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the study uncovers the most prevalent and impactful causes of variations and claims, highlighting the critical need for managerial intervention. A key feature is the integration of scientometric analysis for a quantitative finding. A significant addition to the methodology is the implementation of a k-means clustering analysis. The survey had high internal consistency with a Cronbach's alpha of 0.97, and respondents reported frequent and significant claims like delayed drawings, ambiguous documents, and client changes. The results showed that effective claims management requires clear communication and balanced contracts, while poor design and contract documentation cause variations and claims. Spearman's correlation showed strong positive relationships between certain claim types and causes, emphasizing the importance of dealing with these factors to reduce claims. Most respondents said the survey could predict and reduce claims.

One of the most significant challenges that is facing urban-planning ,is the develop-ment of a transportation network, particularly in historical Syrian cities, which are characterized by their compact urban form. Historical Syrian cities, including historical city of Aleppo, present difficulties due to the essential balance required between saving cultural heritage and meeting the needs of a present-day population. This study basically addresses the city's key elements and planning issues, including traffic density, pedestrian safety, and the massive spreading of motor-ized vehicles. The objective of this study is to analyze the street structure, understanding its components and problems in order to develop a methodology that can be used to enhance urban mobility and to improve the structure of the historical city. for this purpose, two main methods were employed, method of analyzing geographical data based on Geographic Information Systems (GIS) and K-Means cluster analysis method. These two methods were employed to gain detailed and clear vision into mobility patterns of historical Aleppo. Which involved careful processing of data, such as street names, locations, components, quantities, and widths, based on the author’s observations and notes and a variety of local and international sources. This analysis process will not only reveal the current hierarchy of streets but also demonstrates the dynamical and complicated formation of transportation structures of the historical city. The findings of the study describe the challenges faced by historic city centers and highlight the interrelationship between the following: urban structure, objects of cultural heritage, tourism plans, and mobility patterns. The application of spatial analysis method serves as a technique that reveals the formulation of possible scenarios that helps to develop the transportation structure in the historical city of Aleppo. These generated scenarios will be used as typical solutions that can be generalized to all similar historic Syrian cities.

This paper presents work done as part of a transformation effort towards a greener and more sustainable Aluminium manufacturing plant. The effort includes reducing the carbon footprint by minimising waste and increasing operational efficiency. The contribution of this work includes the reduction of waste through the implementation of autonomous, real-time quality measurement and classification at an Aluminium casthouse. Data is collected from the MV20/20 which uses ultrasound pulses to detect molten Aluminium inclusions, which degrade the quality of the metal and cause subsequent metal waste. The sensor measures cleanliness, inclusion counts and distributions from 20 - 160 microns. The contribution of this work is in the development of business analytics to implement condition-based monitoring through anomaly detection, and to classify inclusion types for samples that failed. For anomaly detection, multivariate K-Means and DBSCAN algorithms are compared as they have been proven to work in a wide range of datasets. For classification, a two-stage classifier is implemented. The first stage classifies the success or failure of the sample, while the second stage classifies the inclusion responsible for the failed sample. The algorithms considered include logistic regression, support vector machine, multi-layer perceptron and radial basis function network. The multi-layer perceptron offers the best performance using k-fold cross-validation, and is further tuned using grid search to explore the possibility of an even better performance. The results reveal that the model has achieved a global maximum in performance. Recommendations include the integration of additional sensor systems and the improvements in quality assurance practices.

This paper presents an approach to the two-dimensional analysis of elastic isotropic deep beams using the finite difference method (FDM). Deep beams are subjected to in-plane loading and present a shear span to height ratio of less than 2.50; consequently, Euler-Bernoulli beam theory and Timoshenko beam theory do not apply. Deep beams analysis is generally conducted using numerical methods such as the finite element method and to a lesser extent the FDM; the strut-and-tie model and the stress field method are also widely utilized. Analytical approaches usually make use of the Airy stress function, where stresses are formulated in terms of the stress function; however, the exact solution of this function satisfying all of the boundary conditions can hardly be found, even for simple cases. In this paper, deep beams were analyzed using the FDM. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected nodes of the structure. Therefore, the deep beam was discretized with a two-dimensional grid, and additional nodes were introduced at the boundaries and at positions of discontinuity (openings, brutal change of material properties, non-uniform grid spacing), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at boundary nodes and satisfy the boundary and continuity conditions. An Airy stress function approach and a displacement potential function approach were considered in this study whereby strong formulations of equations (equilibrium, kinematic, and constitutive) were set. Stress and stability analyses were carried out with this model; furthermore, deep beams of varying stiffness, layered beams, and beams having openings were analyzed. For slender beams, the results obtained with the Airy stress function approach showed good agreement with those of the Euler-Bernoulli beam theory, and for deep beams, the shapes of stress distributions were in good agreement with a proper understanding of the behavior of structures. On the other hand, the displacement potential function approach delivered unsatisfactory results, probably due to the use of an inefficient equation solver; a more powerful tool will be needed in future research for this purpose.

This paper presents an approach to the Kirchhoff-Love plate theory (KLPT) using the finite difference method (FDM). The KLPT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally in the case of KLPT, the finite difference approximations are derived based on the fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection surface. The FOPH is made for the fourth and third derivative of the deflection surface while the SOPH is made for its second and first derivative; this leads to a 13-point stencil for the governing equation. In addition, the boundary conditions and not the governing equations are applied at the plate edges. In this paper, the FOPH was made for all of the derivatives of the deflection surface; this led to a 25-point stencil for the governing equation. Furthermore, additional nodes were introduced at plate edges and at positions of discontinuity (continuous supports/hinges, incorporated beams, stiffeners, brutal change of stiffness, etc.), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at the plate edges and to satisfy the boundary and continuity conditions. First-order analysis, second-order analysis, buckling analysis, and vibration analysis of plates were conducted with this model. Moreover, plates of varying thickness and plates with stiffeners were analyzed. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. In first-order, second-order, buckling, and vibration analyses of rectangular plates, the results obtained in this paper were in good agreement with those of well-established methods, and the accuracy was increased through a grid refinement.

The internet of things and growing demand for smaller and advanced devices has created the problem of high heat production in the electronic equipment which greatly reduces the work performance and life of the electronic instruments. Thermal interface materials (TIMs) are placed in between heat generating micro-chip and the heat dissipater to conduct all the produced heat to the heat sink. Development of suitable TIMs with excellent thermal conductivity (TC) in both in-plane and through-plane directions is a very important need at present. For efficient thermal management, polymer composites are potential candidates. But in general their thermal conductivity is low compared to that of metals. The filler integration into the polymer matrix is one of the two approaches used to increase the thermal conductivity of polymer composites and is also easy to scale up for industrial production. Another way to achieve this is to change the structure of polymer chains, which fall out of the scope of this work. In this review, considering the first approach, the authors have summarized recent development in many types of fillers with different scenarios by providing multiple cases with successful strategies to improve through-plane thermal conductivity (TPTC) (K⊥). For better understanding of TC the comprehensive background is also presented. In the end, it is given a detailed conclusion which provides drawbacks of some filler, multiple significant routes recommended by other researchers to build Thermally Conductive Polymer Composites, future aspects along with direction so that the researchers can get a guideline to design an effective polymer based Thermal interface materials. A number of methods to improve the effective (out-plane) thermal conductivity of polymer composites are also discussed.

Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combina-tions of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Co-pernicus Land Cover nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2 MSI for an acceptable overall accuracy over 85%. A com-parison of the overall classification accuracies shows that Sentinel-2 overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards integrating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.

Shown is the derivation of Lorentz-Einstein k-factor in SRT as an amplitude-term of oscillation-differential equations of second order.This case is shown for classical Lorentz-factor as solution of an equation for undamped oscillation as well as the developed theorem as a second solution for advanced SRT of fourth order with an equation for damped oscillation-states.This advanced term allows a calculation for any velocities by real rest mass.Also accelerated coordinate -frames are discussed.

Consciousness is a special type of interaction between subjects exchanging by lingua quanta (phonemes). A set of lingua quanta composes a thesaurus placed on the edge of chaos. Its library is a memory, modification of which is due to tuning of memristive neural elements scattered in the brain volume. The memristive neural model takes into consideration two types of neurons - excitatory and inhibitory and the current leakage, all at the body temperature, T = 309 K. At such temperatures only heavy ions like hydrogen ions - protons can pass robustly through the water medium of the brain. The robust ion transport is proton water wires supported by the Grotthuss mechanism. Final aims of the ions are the gap junctions (electric synapses) linking nearest neurons. Following these observations, a model of excitable nervous tissue is constructed. Namely, a difference mapping on the basis of sigmoid curves can reproduce chaotic modes of neural activity proved by positive values of the Lyapunov exponent. The edge of chaos is placed near the bifurcation boundary dividing the chaos and the periodic convulsive activity typical at the epileptic discharges. In this region the self-sustained spiral waves occur. An intermittent activity of the competing excitatory and inhibitory neurons is observed on the edge of chaos. The intermittent electrical activity of neural tissues is demonstrated by records both from different literature issues and records made by the author together with DR. A. Dudkin on slices of CA1 field of the hippocampus.

Organosilicate glass (OSG) films are a vital component in modern electronic devices, with their electrical properties playing a crucial role in device performance. This comprehensive review systematically examines the influence of chemical composition, vacuum ultraviolet (VUV) irradiation, and plasma treatment on the electrical properties of OSG films. Through an extensive survey of literature and experimental findings, we elucidate the intricate interplay between these factors and the resultant alterations in electrical conductivity, dielectric constant, and breakdown strength of OSG films. Key focus areas include the impact of diverse organic moieties incorporated into the silica matrix, the effects of VUV irradiation on film properties, and the modifications induced by various plasma treatment techniques. Furthermore, we discuss the underlying mechanisms governing these phenomena, shedding light on the intricate molecular interactions and structural rearrangements occurring within OSG films under different environmental conditions. This review not only consolidates existing knowledge but also identifies gaps in understanding and suggests avenues for future research, thereby advancing our comprehension of OSG film behavior and paving the way for enhanced performance in next-generation electronic and photonic devices.

This study investigates the application of regression neural networks, in particular the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the effectiveness of the models and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R² of 0.9900. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results.

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.

We extended our model of the S1 tubular segment to address the mechanisms by which SGLT1 interacts with lateral Na/K pumps and tight junctional complexes to generate isosmotic fluid reabsorption via tubular segment S3. The strategy applied allowed simulation of laboratory experiments. Reproducing known experimental results constrained the range of acceptable model outputs and contributed to minimizing the free parameter space. 1. In experimental conditions, published Na and K concentrations of proximal kidney cells were found to deviate substantially from their normal physiological levels. Analysis of the mechanisms involved suggested insufficient oxygen supply as the cause and indirectly, that a main function of the Na/H exchanger (NHE3) is to extrude protons stemming from mitochondrial energy metabolism. 2. The water path from the lumen to the peritubular space is through aquaporins on the cell membrane and claudin-2 at paracellular tight junctions, with a smaller additional contribution to water transport by the coupling of 1 glucose:2 Na:200 H2O in SGLT1. 3. A Na+-uptake component passed through paracellular junctions by solvent drag in Na- and water-permeable claudin-2, thus bypassing the Na/K-pump, in agreement with the findings of early studies. 4. Electrical cross talk between apical rheogenic SGLT1 and lateral rheogenic Na/K pumps results in tight coupling of luminal glucose uptake and transepithelial water flow. 5. Isosmotic transport is achieved by Na-mediated ion recirculation at the peritubular membrane.

Historically, macrophages have been long implicated in the control of the severity of infectious diseases. This has been based on the observations of a higher risk of severe disease and death upon rechallenge with viral variants due to antibody dependent enhance-ment (ADE) of infection into macrophages. The question remains as to what can account for this potent heterologous protection in macrophages? Here it is argued that the elusive defense mechanism of M1-like pro-inflammatory macrophages may pertain to a novel virus anti-virus response. This system initiates with high replication of human endogenous retrovirus K102 (HERV-K102), a non-pathogenic, protector foamy retrovirus of humans which generates M1-like pro-inflammatory foamy macrophages, glycolysis, and epigenetic changes, all characteristic of trained immunity. This virus-anti-virus system kills virally infected cells by several mechanisms, amplifies the innate interferon response via ‘viral mimicry’, has many unique components that interfere with exoge-nous virus replication, and may be especially adept at neutralizing enveloped exogenous pandemic viruses, such as SARS-CoV-2 and HIV-1. The goal of this treatise is to introduce the multifaceted HERV-K102 protector system, to illustrate how SARS-CoV-2 may target the HERV-K102 protector system by ADE, and to explore how this innate defense system may be exploited for pandemic preparedness.

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

Spinocerebellar ataxia type 2 (SCA2) is caused by polyglutamine expansion in Ataxin-2 (ATXN2). This factor binds RNA/proteins to modify metabolism after stress, and to control calcium (Ca2+) homeostasis after stimuli, thus exerting crucial neuroprotection for cerebellar ataxias and corticospinal motor neuron degeneration. Our Atxn2-CAG100-Knock-In mouse faithfully models features observed in patients at pre-onset, early and terminal stages. Here, its cerebellar global RNA profiling revealed downregulation of signaling cascades to precede motor deficits. Validation work at mRNA/protein level defined alterations that were independent of constant physiological ATXN2 functions, but specific for RNA/aggregation toxicity, and progressive across the short lifespan. Earliest changes were detected at 3 months among Ca2+ channels/transporters (Itpr1, Ryr3, Atp2a2, Atp2a3, Trpc3), IP3 metabolism (Plcg1, Inpp5a, Itpka), and Ca2+-Calmodulin dependent kinases (Camk2a, Camk4). CaMKIV–Sam68 control over alternative splicing of Nrxn1, an adhesion component of glutamatergic synapses between granule and Purkinje neurons, was found affected. Systematic screening of pre/post-synapse components, with dendrite morphology assessment, suggested early impairment of CamKIIα abundance together with weakening of parallel fiber connectivity. These data reveal molecular changes due to ATXN2 pathology, impacting communication and excitability of cerebellar neurons. Discovery of such risk versus progression markers improves the assessment of pre-symptomatic treatments in SCA2 and related disorders.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.

Gamma-aminobutyric acid (GABA) and glycine act as inhibitory neurotransmitters. Three types of inhibitory neurons and terminals, GABAergic, GABA/glycine co-releasing, and glycinergic, are orchestrated in the spinal cord neural circuits and play key roles in the regulation of pain, locomotive movement, and respiratory rhythms. Herein, we first describe GABAergic and glycinergic transmission and inhibitory networks, which consist of three types of terminals, in the mature mouse spinal cord. Second, we describe the developmental formation of GABAergic and glycinergic networks, with specific focus on the differentiation of neurons, formation of synapses, maturation of removal systems, and changes in their action. GABAergic and glycinergic neurons are derived from the same domains of the ventricular zone. Initially, GABAergic neurons are differentiated and their axons form synapses. Some of these neurons remain GABAergic in lamina I and II. Many of GABAergic neurons convert to co-releasing state. The co-releasing neurons and terminals remain in the dorsal horn, whereas many of co-releasing ones ultimately become glycinergic in the ventral horn. During the development of terminals and the transformation from radial glia to astrocytes, GABA and glycine receptor subunit compositions markedly change, removal systems mature, and GABAergic and glycinergic action shifts from excitatory to inhibitory.

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

In this paper we introduce the New/Generalized two parameter Gamma function and Pochhammer symbol. We named them, as Generalized p - k Gamma Function and Generalized p - k Pochhammer symbol and denoted as $ _{p}^{a}\Gamma_{k}(x) $ and $ _{p}^{a}(x)_{n,k} $ respectively. We prove the several identities for $ _{p}^{a}\Gamma_{k}(x) $ and $ _{p}^{a}(x)_{n,k} $ those satisfied by the classical Gamma function. Also we provide the integral representation for the $ _{p}^{a}\Gamma_{k}(x) $

Keywords: Electrical power systems, Support vector machines, Random Forest, Machine learning, Wavelet transform, Transmission lines fault, Electrical power quality, Short circuit, Classification of faults, Localization of faults, Decision trees, Ensemble learning, K-nearest neighbors.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (K_d), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC₅₀ approaches the K_d value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to K_d. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.