The Logarithmic Number System (LNS) has emerged as a promising alternative to floating-point arithmetic, particularly in digital signal and image processing applications. While LNS efficiently manages multiplication, division, and square-root operations through fixed-point arithmetic, addition and subtraction remain challenging due to inherent non-linearity in both operations and a singularity issue unique to subtraction. These problems require significant memory resources to maintain floatingpoint precision, using interpolation techniques to defeat non-linearity and co-transformation procedures to resolve singularities in subtraction. This review provides a complete overview of the evolutionary development of LNS, highlighting these main innovations and new developments aimed at reducing memory usage and hardware efficiency in LNS implementations. The paper finishes with an exploration of promising directions aimed at increasing the efficiency of LNS so that it will be a competitive option to floating-point arithmetic in a wider variety of computing environments.

Electroencephalography (EEG) feature extraction plays a fundamental role in translating raw neural signals into meaningful representations for applications such as neurological diagnostics, brain-computer interfaces (BCIs), and cognitive state monitoring. This study presents a comprehensive comparative analysis of mathematical approaches employed in EEG feature extraction, categorized into time, frequency, time-frequency and nonlinear domains. In addition to reviewing existing methods, an experimental study was conducted using real EEG data from the Bonn University dataset. Features including statistical descriptors, Hjorth parameters, band powers, spectral entropy, approximate entropy, fractal dimension, Hurst exponent, and Lempel-Ziv complexity were systematically extracted and compared between healthy and seizure recordings. Furthermore, timefrequency representations were generated using Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to capture transient and non-stationary dynamics. The results demonstrate that a multi-domain feature extraction strategy significantly enhances the ability to characterize and discriminate pathological EEG signals. Key challenges such as data variability, limited dataset availability, and the need for standardized analysis pipelines are also discussed, along with future directions including the development of benchmark datasets, explainable AI-driven feature selection, and real-time EEG processing optimization. By integrating theoretical insights with experimental validation, this study aims to support the development of more reliable, interpretable, and scalable EEG-based systems for scientific and clinical use.

Background:In the context of modern challenges such as the growth of cybercrime, the spread of cryptocurrencies and the strengthening of transnational criminal networks, the legal systems of many countries are forced to adapt to new realities and improve their approaches to combating money laundering. These changes require both deep theoretical understanding and the formation of an effective legal framework to protect the economy and financial system from the penetration of criminal proceeds. Methods: This study was conducted using a set of methods traditionally used in legal science, allowing for a comprehensive and in-depth analysis of the topic. To achieve the goal, the authors of the article used a set of methods, including content analysis and the case study method, as a result of which an analysis of the national and regional legislation of Kazakhstan was carried out, as well as a study of judicial practice, identifying certain problems associated with digitalization and the use of artificial intelligence in the legal system.The article substantiates the need to strengthen interdepartmental cooperation, introduce advanced analytical methods and automated monitoring of financial flows. The purpose of the study is to analyze the delinquency of digitalization as a tool for legalizing the laundering of criminal proceeds from the point of view of prevention, economic and legal regulation and improvement of counteraction mechanisms. Results and conslusions: The results of the study emphasize the need to improve coordination between structures, introduce modern analytical methods and monitoring technologies to strengthen the fight against crimes related to money laundering and terrorist financing. The cognitivism of digitalization delinquency reflects not only the transformation of criminal behavior, but also the use of high technology, artificial intelligence, and other digital tools, which complicates the process of identifying criminal operations and their participants. This requires law enforcement agencies and the judicial system to develop modern investigation methods and apply legal instruments to collect evidence.

Background: The influence of artificial intelligence (AI) across different spheres of life continues to increase, and the pharmacy profession has not been left behind. Across research in different countries, AI emerges as an instrumental technology transforming how pharmacy professionals undertake their routine duties. However, with the increase in the use of AI in pharmacy practice emerges a critical need to understand the ethical implications that this new trend has within the pharmacy sector. The purpose of the current study was to systematically review published studies to understand how the adoption of AI in pharmacy practice impacts ethical practice from the perspectives of the challenges it presents, its influence in decision-making and the existing regulatory frameworks, and understand possible solutions to challenges emerging within the pharmacy profession as a result of increasing adoption of AI. Methods: Using PRISMA guidelines, the researcher conducted a systematic review of articles published between 2014 and 2024 and which were focused on elements of ethical implications of AI for its adoption and application within the pharmacy practice. Four electronic databases; PubMed, Scopus, Web of Science, and Google Scholar were searched for data sources, from which a total of3732 articles emerged from the initial search. Upon screening for suitability, 17 articles remained and were systematically reviewed. Data extraction and synthesis was carried out and summarized in a standard Table. Results: The review revealed a dual impact of AI in pharmacy practice, enhancing efficiency while posing ethical risks. It emerged that AI-driven decision-making tools serve to improve medication safety but at the same time do require robust ethical frameworks for the purpose of ensuring fairness, transparency, and accountability. Conclusions: The findings point to the conclusion that there is a need for continued ethical discourse, regulatory development, and pharmacist education from all relevant stakeholders for the purpose of maximizing AI’s benefits while mitigating risks.

Introduction: Quarantining is a widely used public health measure for controlling pandemics that can spread throughout the population. However, the economic impact of quarantine, especially for less severe viruses, has led to debates regarding its necessity. The purpose: This study aimed to estimate the direct and indirect costs associated with setting up a quarantine facility for the Mpox (formerly called monkeypox) virus. Methods: This study modeled a facility in Saudi Arabia housing 100 Mpox patients for a 14-day incubation period. Direct costs encompassed accommodation and treatment expenses, while indirect costs reflected the loss in productivity due to employees missing work. Results: The findings show that direct costs amounted to approximately $152,500, with $150,000 attributed to accommodations. In contrast, indirect costs were approximately $129,500. If patients were to self-isolate at home, indirect costs would significantly exceed the direct costs. Conclusion: This study concluded that enforcing quarantine could have a negative economic impact due to job absenteeism. Given the low public risk of Mpox and the availability of affordable treatments, alternative measures such as mass vaccination, remote working, and improved hygiene practices are recommended to manage the spread of the virus.

Introduction: Muscle weakness is a significantly problematic symptom and condition that arises from a range of medical conditions and which has in previous studies been linked to impacting millions of people with varying conditions globally. Accordingly, the effective management of muscle weakness is essential because it has a strong association with broader health outcomes that include but are not limited to mobility, cardiovascular health, and mortality. Method: This study was a comprehensive analysis of pharmacological interventions for muscle weakness using data from ClinicalTrials.gov. The initial search targeted studies directly focused on interventions for muscle weakness and a total of 1201 trials emerged. Of the 1,201 trials initially identified, 94 studies met inclusion criteria in that they were specifically targeting pharmacological interventions to improve muscle function. Results: The findings showed that interventions for muscle weakness encompass a range of therapy modalities that include anabolic medicines, creatine supplementation, neuromuscular pharmacotherapies, anti-inflammatory medications, and device-based therapies. An evaluation of these findings shows that they emphasize the diversity in therapeutic approaches, from anabolic agents to neuromuscular drugs, revealing the need for individualized treatment strategies based on patient demographics, underlying health conditions, and specific drug mechanisms. Conclusion: This analysis contributes valuable insights into the effectiveness and safety of pharmacological treatments for muscle weakness, highlighting promising directions for future research.

Background: Vaccination is a critical public health measure that prevents infectious diseases and mitigates severe health complications. However, adults with disabilities often face barriers to vaccination, including healthcare accessibility, vaccine hesitancy, and underlying medical conditions that may heighten their vulnerability to vaccine preventable diseases. Despite their increased risk, research on vaccination outcomes, accessibility, and efficacy in this population remains limited. Objective: This review aims to assess the current landscape of clinical trials on vaccination in adults with disabilities by analyzing data from ClinicalTrials.gov. The objective is to identify trends, challenges, and gaps in research to inform future studies and policy recommendations for improving vaccine accessibility and uptake in this demographic. Methods: A search was conducted on ClinicalTrials.gov on December 26, 2024, using the keywords "vaccination and disability." Completed interventional studies with available results focusing on adults with disabilities were included. Data extracted included study design, intervention type, outcome measures, demographic details, and trial phases. Results: The search identified 215 relevant clinical trials, of which 78 met the inclusion criteria. The majority of trials focused on immunogenicity and safety, employing vaccines such as Tetanus-Diphtheria, Influenza, Pneumococcal, and COVID-19 vaccines. Most studies utilized randomized interventional designs, with primary outcomes assessing immune response through geometric mean titers (GMTs) and adverse event monitoring. The trials spanned various phases, from early safety assessments (Phase 1) to large-scale efficacy studies (Phase 3 and 4). Inclusion criteria in these studies varied, with some focusing on older adults and individuals with immunocompromised conditions, highlighting a need for more comprehensive research on diverse disability subgroups. Conclusion: While substantial research has been conducted on vaccination in adults with disabilities, gaps remain in addressing accessibility, vaccine hesitancy, and long term efficacy. Future studies should prioritize inclusive trial designs, targeted interventions, and policy frameworks that enhance vaccine equity and public health outcomes for this vulnerable population.

To evaluate the microvascular features in patients diagnosed with both acute and convalescent stages of Vogt-Koyanagi-Harada disease (VKH) using optical coherence tomography angiography (OCTA), a meta-analysis was conducted. The Embase, PubMed, and Cochrane databases were searched up to January 18, 2025. Eight studies were deemed eligible, examining vessel density (VD) in both the superficial capillary plexus (SCP) and deep capillary plexus (DCP) utilizing OCTA. The results indicated that convalescent VKH patients exhibited a slight reduction in SCP VD compared to healthy controls, with significant decreases observed in the macular whole enface (MD -1.29, 95% CI: -2.09, -0.49), foveal (MD -2.46, 95% CI: -4.80, -0.13), and parafoveal (MD -1.56, 95% CI: -2.24, -0.87) regions. A comparable decline was noted in DCP VD, particularly in the parafoveal regions (MD -3.00, 95% CI: -5.07, -0.93). In the active VKH cohort, foveal DCP VD (MD -1.39, 95% CI: -2.52, -0.27) was significantly lower than that of healthy controls. Furthermore, the choriocapillaris (CC) flow area was significantly diminished in convalescent VKH patients relative to healthy controls (MD = -0.06, 95% CI: -0.10, -0.02). The findings of the meta-analysis underscore the utility of OCTA in providing quantitative assessments of microvasculature in VKH disease.

Abstract – Nowadays, the rates of online networking action have reached tremendous heights. Due to the increased volume of online users and their usage has proceeded to the rapid development of electronic data. So it is difficult to recognize the demanding information from vast text of information. The growing complication of electronic information has forced to present optimization technique. Attracted by the behavior, communication, movements and hunting nature of spider, we initiate a spider hunting technique to find Topic and Sub-Topic of a corpus. The suggested technique could effectively recognize the desired corpus from text data. The Enhanced Spider Hunting technique is evaluated against broadly utilized standard algorithm and the recommended technique has predominant execution compared to other Meta heuristics.

Abstract: Wireless nodes are time varying behavior in its nature, because energy usage of each node is not being constant. Packet transmission among cluster nodes get overloaded, when multi input and multi output is made; multi task is performed cause more energy usage, also selfish node could not transfer the data packet to target node. Selfish node need to hide some important data, and then only transmit normal data’s that tries to misuse those data, so packet delivery rate is reduced. This normal method not identifies selfish nodes in routing path. Proposed Incentive Sorted Path selection Scheme (ISPSS) obtains traffic free packet transmission among wireless sensor nodes, since routing paths are sorted based on distance and energy usage, which path use minimum energy and also minimum distance, that are considered as effective path from sender node to sink node. Selfish node available in cluster they are identified by using progressive stable algorithm. It improves the packet delivery rate, network lifetime, and reduces energy usage and end to end delay

This paper presents a method to analyze the traffic flow pattern in Velachery Vijaya Nagar, Chennai using waiting time in the signal in different time intervals with the help of Fuzzy Cognitive Maps(FCM) and Induced Fuzzy Cognitive Map(IFCM). FCMs and IFCMs are fuzzy-graph modelling approaches based on expert’s opinion. This is the non-statistical approach to study the problem with imprecise information. FCMs and IFCMs are the best suited tool when the data is an unsupervised one.

A pot experiment was carried out to evaluate the effect of clay minerals, compost and the interaction between them (Zeolite = Z, Bentonite = B, compost = C, zeolite + bentonite = ZB, zeolite + compost = ZC and bentonite + compost = BC) as soil amendments and their effect on reducing heavy metals (HMs = Cd, Ni and Pb) accumulation in barley plant (Hordeum vulgare L. Var. Giza 132) and their mobility in two contaminated soils (Abou-Rawash, Giza Governorate and Kafr El-Sheikh Governorate). The previous soil amendments were add by three rates (0, 1and 2%). Barley plants were harvested at 90 day. All treatments led to increasing fresh and dry weight of barley plants as well as decrease its concentration and content of HMs compared with control. Zeolite produced the highest growth while the lowest accumulation of HMs was resulted by bentonite addition. Generally, clay minerals are able to improving growth of plants in polluted areas in addition to decreasing HMs uptake by plants in absence or presence of compost.

It has been proven that due to generation and recombination of charge carriers at the presence of an external constant electric field concentration fluctuations of charge carriers and electric field occur in semiconductors with deep traps. For the first time a Van der Pol type equation was obtained for these semiconductors for the alternating electric field. From the solution of the obtained equation both the amplitude and oscillation frequency were determined in the first approximation by the method of N.N. Bogoliubov and Y.A. Mitropolsky. It was shown that the frequency of oscillations in the first approxi-mation is more important than in the zero approximation. The amplitude of oscillations tends to a finite value at very high time value . This proves that there is a steady dynamic mode. A graph indicating the dependence of amplitude on time was developed. The values of the electric field and the constants of generation and recombination of charge carriers were determined.

In India, every year over 2 lakh people lose their lives due to liver disease. To diagnose the various types of liver diseases, the medical imaging based schemes have been used. In this paper, the ultrasound image has been considered due to their benefits like safe and low cost compared to overall medical imaging modalities for liver disease classification by using machine learning with optimization schemes. In this work, the Spiking Neuron based Neural Network (SNNN) with Cat Swarm Optimization (CSO) is proposed for classifying normal, fatty and heterogeneous liver images. The ultrasound images have been affected by the speckle noise, which is reduced by using bilateral filter in pre-processing. In order to increase detection accuracy, the Gray Level Co-Occurrence Matrix (GLCM), Linear Discriminant Analysis (LDA), Gray Level Different Statistics (GLDS) and statistical features are extracted. After that, the extracted features are combined and then the efficient features are selected by Dragon Fly (DF) algorithm for classification. Finally, the SNNN is used for liver disease classification. To reduce the pattern recognition issue and achieve better accuracy with low computational complexity, the proposed scheme introduced a bio inspired algorithm of CSO as a learning approach to training a SNNN. The simulation results demonstrate that the proposed SNNN-CSO has obtained better performance and less false error rate compared to exist the Support Vector Machine (SVM).

Abstract: The mobile nodes are generally focused for path selection, but path is not perfectly predicted by sender node, it transmitting the data packets continuously. Genuine time routing is difficult for normal path selection scheme, because unfortunately the nodes behavior is changed, it block data packets, and blocks the time, when transmitting data packets. It obtains the inefficient communication. It increase packet loss rate, and reduces network lifetime. So, proposed Efficient routing for unpredictable path (ERUP) method is used to achieve effective communication in mobile network, the current path status is analyzed and after that assign the routing path. Routing path is judged easily by this methods. This does not block data flow and time. Multirate leveraging situation analyzer algorithm is constructed, it analyze the leveraging condition of routing path nodes in various rates, such that rates are resources utilization of nodes. It increase network lifetime and minimize packet loss rate