Conventional tutoring approaches are confronted with limitations such as restricted availability, inconsistent pedagogical quality, and scalability con-straints. Furthermore, the exclusive use of Large Language Models (LLMs) like ChatGPT in education has its shortcomings, including the potential for incorrect responses and the lack of customized guidance aligned with specific course con-tent. This research proposes an innovative intelligent chatbot tutoring system, in-tegrating the Retrieval Augmented Generation (RAG) approach with a custom LLM. The developed system aims to overcome the limitations of traditional tu-toring and general-purpose LLMs by providing accurate, contextually relevant, and personalized assistance, thereby enhancing student understanding and en-gagement. The system, powered by an intelligent agent, retrieves relevant information from curated academic sources, incorporates interactive features for user feedback, and utilizes machine learning algorithms for ongoing performance enhancement, ensuring a robust and effective tutoring experience. The anticipated outcome is an enriched educational experience for university students, advance-ment in personalized learning, and improved student engagement, retention, and academic performance. Through continuous research and refinement, it is expected that the intelligent chatbot tutor will assume an important role in enhancing and supporting the educational journey of students.

Orificed Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. From their reliability mainly depends the success of a spacecraft’s mission equipped by electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear system of plasma equations, have been proposed in the open literature. These are solved commonly by means of iterative procedures. This paper investigates the possibility to solve them by means of Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as function of mass flow rate is very narrow (2÷5V), moreover the results match very well the experimental data except at the lowest mass flow rate (0.08mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%; the highest data dispersion regards the plasma density in the emitter region (± 20 % of the average value) and the wall temperatures (± 50K respect to the average values) of orifice and insert; those of the others variables are very tiny.

The construction sector is a paradigm shift into the sustainable era to solve environmental issues, combat global warming. And the reflection of this is on Green Buildings where energy savings, cost savings, occupant comfort takes priority. The idea of green building efficiency merges sophisticated technologies, creative architectural design templates, and reusable materials into the whole life cycle of a building process in order to render minimum environmental impact. The pilot project of The National Institute of Geophysics and Volcanology (INGV) - Irpinia headquarters, illustrates in this context, innovative technologies of energy transition with a photovoltaic parking with geothermal piles. The project is consistent with the concepts of Nearly Zero Energy Buildings (NZEBs), directed to a high energy performance and very low environmental impact. Incorporating geothermal piles is a major area of advancement in sustainable building ideas that use the Earth's natural heat to create an energy efficient solution in heating and cooling. Project Goals – The project aims to install emission free renewable cleaning energy cost-effective clean production and increase energy efficiency, reduce CO2 emissions and energy security. For this purpose this paper, using as a support the PVGis software, evaluates the potential produced with the photovoltaic system installed in the canopies. With the help of this analysis, it is feasible to determine the amount of electricity produced as well as the system's amortization based on the excess energy sent to the public grid. The project is regarded as creative from the standpoints of energy given the use of geothermal and photovoltaic systems, as well as from an architectural and design perspective due to the use of novel materials. This all-encompassing strategy establishes INGV as a pioneer in the promotion of environmentally friendly technologies and shows a dedication to sustainability and energy efficiency.

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform.

Background: Research articles are essential for describing new ideas, techniques, and procedures and consolidating concepts. It creates an opportunity to expose studies and publicly assimilate published knowledge. Therefore, one of them is the most used and accepted index to verify the author’s scientific impact is the H-index or Hirsch index. It provides a robust single metric that combines quality and quantity. Therefore, it presents disadvantages, (1) researchers with shorter scientific carriers may have fewer articles and citations than those with longer scientific life; (2) it allows scientists to receive citations even if no new papers are published; (3) it is only useful for comparing the better scientists in a field; (4) this indicator can never decrease; (5) it is only weakly sensitive to the number of citations received; (6) the major and minor (or no) contributor in the research gained equal h-index. Then, the H-index ignores the total of citations and the time of a researcher's experience (“scientific age” [sa]). Objective: Thus, the goal of this study was to present a new index system (GFsa index ©) based on the two variables completely forgotten (total of citations and “scientific age”), introducing a new terminology “scientific age”, to evaluate the best-ranked researchers in Dentistry. Methods: The AD Scientific Index was used, and all researchers included in this study were cited in the AD Scientific Index (2024) in the Dentistry field and had the Google Scholar page accessible for a consultation to manually obtain the total of citations and calculate the “scientific age”. The information was retrieved by two authors (G.V.O.F. and J.C.H.F.), who assessed the data twice to avoid errors or mistakes. For inclusion: a minimum H-index value of 30; for exclusion: (i) the author did not have a Google Scholar page; (ii) duplicated author; or (iii) presented any suspicious activities about manipulation of articles. Then, a dataset of all researchers who were reported in Dentistry was prepared (name, H-index, i10 index, and publications). The formula developed and applied was GFsa=(total number of citations)/(“scientific age)². The Pearson correlation statistically evaluated the data obtained; the confidence interval was 95%. Results: A total of 9,093 researchers in Dentistry were recorded, of whom 1,020 were included. Then, we collected all 1,020 researchers' Google Scholar data (publications, citations, and indexes), and each researcher's total citations and “scientific age” were registered and calculated. The mean “scientific age” registered was 34.18 ± 13.34 (standard error of mean = 0.4181). With this information and data collected, GFsa index © was calculated, presenting a minimum value of 0.2186 and a maximum of 154.8 (mean = 10.33 ± 11.82; standard error of mean = 0.3706). Then, the data was organized and sorted following the ranking obtained in the GFsa index ©. The Pearson correlation showed that the H-index had a weakly positive association with the researcher’s “scientific age”; this fact means that the H-index will increase according to the “scientific age” increase. Otherwise, a moderately negative correlation between GFsa and “scientific age” was demonstrated, whereby a higher GFsa © had a lower “Scientific age”. This assessment shows that the variables introduced were fairly used. Moreover, a positive correlation was observed between both indexes (H-index and GFsa). Conclusion: It is possible to conclude that the new variable reported (“Scientific age”) and using the total citations brought a fairer and more feasible evaluation among the researchers. Thus, we are launching the new GFsa index © and the new information named “Scientific age” for the first time in the literature. The future perspective of the GFsa © application will be (1) the evaluation of the 9,093 researchers in Dentistry (total initially registered), including more authors/researchers in the Dentistry field, (2) expanding the exposition of this new index for other areas of the knowledge, and (3) provide free access of all data through the website (http://gfindex.us.to).

Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

This study investigated the relationship between aluminum adjuvant vaccines and the prevalence of 4 childhood diseases (Autism, Allergies, Asthma, ADD/ADHD) using data from the 2020-2021 National Survey of Children's Health (NSCH) [1] and the Centers for Disease Control National Immunization Survey (CDC NIS) [2]. The study found an association with aluminum adjuvant dosage and the prevalence of each disease studied (Autism, Allergies, Asthma, ADD/ADHD) through a weighted regression analysis of disease prevalence against the summed likelihood of aluminum adjuvant vaccine uptake.

This paper presents the perspective that universe is relative. The universe, which changes according to the traveler’s proper time, is dynamic. While the universe generally expands, it could also contract if traveler accelerates sufficiently. The Hubble parameter merely represents that change. If the Hubble parameter is dependent on the proper time, the measurement can only be uncertain. Identifying the factors that influence the proper time could potentially lead to a more accurate measurement of the universe’s age, and it could also offer insights into the state of the early universe and the causes of the accelerated expansion observed in the current epoch.

Social media platforms have become pivotal tools for small and medium-sized enterprises (SMEs), offering vast opportunities for enhanced organizational engagement. However, these platforms also present challenges such as data privacy concerns, feedback management, and content saturation. This systematic review critically evaluates the existing literature on the dual role of social media in fostering organizational engagement while addressing key barriers faced by SMEs. We systematically assessed 104 peer-reviewed articles sourced from Scopus, Web of Science, and Google Scholar, focusing on the impact of social media on marketing strategies and organizational outcomes in SMEs. The Newcastle-Ottawa Scale was used for quality assessment, and effect measures, including mean difference and odds ratio, were employed to evaluate performance metrics such as customer engagement, business performance, and long-term organizational impact. Key findings indicate that 74% of SMEs reported improvements in brand visibility, with customer engagement increasing by 65%. However, significant concerns were identified, with 45% of studies highlighting privacy issues and 52% addressing challenges in managing negative feedback. The review emphasizes that while social media can enhance market reach and customer interaction, its effectiveness largely depends on strategic content management and planning. This review provides actionable insights for SMEs aiming to optimize social media use, highlighting the need for future research to address privacy management and feedback strategies for sustained success.

The critical role of manufacturing operations in enhancing the performance of Small and Medium Enterprises (SMEs) has become increasingly evident in recent years. This systematic review examines how manufacturing operations influence the performance of SMEs, analysing findings from 69 diverse research papers published between 2014 and 2024. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key operational strategies, including lean manufacturing and advanced manufacturing technologies, assessing their effectiveness in improving business outcomes such as efficiency, productivity, and financial performance. The methodology included a thorough review of the research methods used in the studies, identifying potential biases, gaps, and highlighting the need for diverse approaches. While manufacturing operations have led to improved productivity and increased efficiency for many SMEs, challenges such as limited resources, lack of technical expertise, and resistance to change remain prevalent. Notably, a reporting bias was observed, with 48% of the literature composed of quantitative studies, 30% being mixed-method studies, and only 20% and 1% representing qualitative and exploratory studies, respectively. This imbalance suggests an overemphasis on quantitative methods, limiting the range of ideas and understanding available. Key strategies such as lean manufacturing and advanced technologies were shown to enhance productivity by 15% to 25% in SMEs. By adopting a broad approach, this review ensures that the findings are applicable across various sectors and geographies, offering valuable insights into best practices and challenges across diverse economic contexts. The broad scope also allows for the identification of under-researched areas, fostering a more comprehensive understanding of how manufacturing operations contribute to SME success. The review provides a decision-making framework for business leaders and policy recommendations to foster SME growth in a competitive, globalized market.

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Voltage-Temperature-Consumption stable IODELAYS. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and, especially, resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

The automotive industry is continuously developing to meet the needs of users in terms of efficiency and convenience. The Thailand government has set a policy for Thailand to be a hub for the production of electric vehicles and key components in the region, with a target to produce electric and zero-emission vehicles by 2030. This article is an example of communication in an electric vehicle system. The CAN Bus system is a communication system in electric vehicles that can receive and send communication data via CAN-Hight/Low lines designed for vehicles. CAN Bus is a standard communication system supported by global organizations and used in modern automotive systems. The design of the communication system in electric vehicles uses MRS Developers Studio software to help write programs to receive and send data and prioritize the importance of the received data. The system consists of communication examples studied, such as pressing the start button, turning on/off the headlights, turning on/off the turn signal, turning on/off the brake light, turning on/off the emergency light, sending the horn signal, sending the signal to shift forward/backward, and tuning the motor gear system. From the communication examples mentioned, the communication will be a prototype transport set for small electric vehicles under the scope of the lithium battery energy storage system, NMC type, size 72 V 25 Ah, to test the use, efficiency, and stability in CAN Bus communication up to 1000 times.

The Global Maritime Transportation System (GMTS) is a complex system of systems, encompassing port operations, shipping lines for both cargo and passengers, manufacturing, vessel traffic control, and the vessels themselves. In recent decades, cybersecurity incidents targeting the GMTS have grown significantly in both frequency and economic impact. In response to this escalating threat, we initiated a comprehensive effort in 2021 to systematically collect and catalog all publicly known cybersecurity incidents affecting the GMTS. Drawing from peer-reviewed research, news articles, and government reports, we compiled this data into the Maritime Cyber Attack Database (MCAD). Our dataset, spanning 2001 to 2023, includes over 290 cybersecurity incidents–representing a several-fold increase from the 46 incidents reported in previous research conducted in 2020. These incidents involve 54 countries and over 50 vessels, in addition to various GMTS-associated entities such as ports. The attribution of these incidents points to a range of known nation-state and criminal threat actors. This paper presents MCAD as a novel cyber threat intelligence tool and provides a high-level analysis of the collected data, offering insights into the evolving cyber threat landscape within the GMTS.

This study investigates the rapid growth of the e-commerce industry in Nepal while examining the associated security challenges. By analyzing factors driving e-commerce development, identifying prevalent threats, and evaluating existing security measures, this research aims to provide a comprehensive understanding of Nepal's e-commerce landscape. This research uses a combination of quantitative and qualitative data to better understand the complex relationship between e-commerce and security threats.E-commerce security is paramount for protecting online transactions. It ensures data integrity, prevents unauthorized access, and maintains confidentiality. While e-commerce offers opportunities for the banking industry, it also introduces security risks like hacking. Effective e-commerce security requires a coordinated approach, combining technical solutions with robust management practices. Key findings from this research are expected to inform the development of robust strategies to mitigate security threats and cultivate a secure e-commerce ecosystem in Nepal. By addressing the challenges and opportunities presented by e-commerce, Nepal can harness its potential for economic growth and development while ensuring a safe and secure online environment for consumers and businesses.

Crypto-ransomware has emerged as one of the most significant cybersecurity threats in recent years. Ransomware attacks, particularly those involving the encryption of data and demanding payment in cryptocurrency, have caused severe disruptions across various critical industries, including healthcare, energy, and finance. The proliferation of these attacks is largely attributed to the anonymity provided by cryptocurrency transactions and the increasing sophistication of cybercriminal tactics. The impact of ransomware extends beyond immediate financial losses, often leading to prolonged operational downtime, reputational damage, and even risks to public safety. In this research paper, we explore the growing threat of crypto-ransomware by examining real-life case studies in key industries, analyzing the countermeasures taken, and proposing additional solutions to mitigate the risk. The goal is to raise awareness and provide actionable insights for organizations to better protect themselves against this pervasive threat.

The implementation of cybersecurity standards and directives in the maritime sector plays a crucial role in protecting critical maritime infrastructures from cyber threats. The level of protection depends heavily on humans. However, the effectiveness of cybersecurity training and compliance programs, an essential component of these standards, is often hindered by challenges related to the sector’s environment, including the established technologies, practices, and norms. This paper aims to identify these challenges through a literature review and set the basis for more effective human risk minimisation, responses, and training. We identify 18 challenges and validate them with an online survey (N=205) capturing real-world perspectives from maritime-related stakeholders. Our findings contribute to enhancing the effectiveness of maritime cybersecurity training and compliance programs, ultimately strengthening the maritime cybersecurity posture.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

In modern automobiles, Infotainment High-Performance Computing (HPC) systems play a vital role in enhancing the capabilities of drivers and passengers by providing advanced features consisting of music, navigation, communication, entertainment, etc. However, as the use of information technology in vehicles increases, it results in cybersecurity threats such as data breaches and the loss of sensitive information. To improve the security of the infotainment system in automotive vehicles, the research conducted threat modeling at the component level using Microsoft’s STRIDE tool and performed risk assessment by using SAHARA (Safety-Aware Hazard Analysis and Risk Assessment) and DREAD methodologies to evaluate associated risks. It provides a systematic representation of threats, associated risks, and generic mitigation strategies to counter cybersecurity attacks. Through the threat modeling process, 34 potential security threats were identified. The study also provided a comparative analysis to calculate the risk values of the threats to prioritize for treatment. These identified threats and associated risks need to be considered to avoid potential cyberattacks, before deploying the infotainment HPC system in real-world automotive vehicles.

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

Cybersecurity plays a relevant role in the new digital age in aerospace industry. Predictive algorithms are necessary to interconnect complex systems within the cyberspace. In this context, where security protocols do not apply, challenges to maintain data privacy and security arise for the organizations. Thus, the need of cybersecurity is required. The four main categories to classify threats are interruption, fabrication, modification and interception. They all share a common thing, soften the three pillars which cybersecurity needs to guarantee. These pillars are confidentiality, availability and integrity of data (CIA). Data injection can contribute to this event by creation of false indicators which can lead to errors creation during the manufacturing engineering process. In this paper, the impact of data injection on existing dataset used on manufacturing process is shown. The design model synchronizes the following mechanisms developed within machine learning techniques which are, the risk matrix indicator to assess the probability of producing an error, the dendrogram to clusters the dataset in groups with similarities, the logistic regression to predict the potential outcomes and the confusion matrix to analyze the performance of the algorithm. The results presented in this study, which was carried out using a real dataset related to the electrical harnesses installed in a C295 military aircraft, estimate that injection of false data indicators increase the probability of errors creation in 24.22 % on the predicted outcomes required for the generation of the manufacturing process. Overall, implementing cybersecurity measures and advanced methodologies to detect and prevent cyberattacks are necessary.

This article delves deeply into digital forensics, covering computer forensics, network 1 forensics, and mobile device forensics. It analyzes the techniques and methodologies used by forensic 2 investigators in various disciplines. It underlines the diffculties investigators encounter and the 3 importance of thorough investigations to combat ever-increasing cyber risks. The paper emphasizes 4 the necessity of leveraging digital forensic tools to improve cybersecurity and provides a thorough 5 list of widely used Python libraries suitable for each investigation strategy, allowing for effective 6 comparison. Furthermore, it emphasizes the availability and suitability of these Python libraries in 7 computer device investigations (PyTSK3, Volatility, Pyregf, and Pyevtx), mobile device investigations 8 (Pytsk3, Volatility, Pyewf, dfVFS, Androguard, and pyMobileDevice), and network forensics (Scapy, 9 Bro/Zeek, Dpkt, pypcap, and NetworkX). The creation of these libraries recognizes the complexities 10 of digital crimes and the importance of applying modern techniques in forensic investigations. 11 Particularly, digital forensics plays an important role for healthcare providers because modern 12 medical devices produce, store, and transmit large amounts of patient and therapy information, 13 which could provide a forensic investigator with a treasure trove of potential digital evidence.

The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.

The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

Mutations in p53 and KRAS are seen in most cases of colon cancer. The impact of these mutations on signaling pathways related to cancer growth has been studied in depth, but relatively less is known on their effects on amino acid transporters in cancer cells. This represents a significant knowledge gap because amino acid nutrition in cancer cells profoundly influences macropinocytosis and ferroptosis, two processes with opposing effects on tumor growth. Here we used isogenic colon cancer cell lines to investigate the effects of p53 loss and KRAS activation on two amino acid transporters relevant to macropinocytosis (SLC38A5) and ferroptosis (SLC7A11). Our studies show that the predominant effect of p53 loss is to induce SLC7A11 with resultant potentiation of antioxidant machinery and protection of cancer cells from ferroptosis whereas KRAS activation induces not only SLC7A11 but also SLC38A5, thus offering protection from ferroptosis as well as improving amino acid nutrition in cancer cells via accelerated macropinocytosis. Niclosamide, an FDA-approved anti-helminthic, blocks the functions of SLC7A11 and SLC38A5, thus inducing ferroptosis and suppressing macropinocytosis with resultant effective reversal of tumor-promoting actions of oncogenic mutations in p53 and KRAS. These findings underscore the potential of this drug in colon cancer treatment.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

The fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing kThe fractal dimension of is identified in this paper. More importantly, the undertaken research has shown how affects the Koch snowflake . The study has demonstrated the existence of negative values of (Koch snowflake), which connects to a considerable number of extremely intriguing mathematical and physical research areas. The information-theoretic impact on the Sierpiniski Gasket dimension , which corresponds to , was treated in a more fundamental way. Notably, the credibility of this current study adds to the existing knowledge for the information- theoretic fractal dimensions. On another applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.applicative note, this paper offers some potential applications of fractal dimension to smart cities. The investigation of the threshold theorems for and in terms of the triad is part of the next study phase. Additionally, investigating the newly obtained 's corresponding arbitrary Sierpinski Gasket for the first time.

Amidst the challenges posed by the COVID-19 crisis, marked variations in individuals' resilience and vulnerability have emerged. This eight-month longitudinal study engaged 535 community adults (58% female, M_age = 43.97) to explore the nuanced aspects of coping and personal growth during the challenging period. Grounded in Self-Determination Theory, the research examines the influence of autonomy support from close others—manifested through active listening and providing choices—on psychological need satisfaction (i.e., autonomy, competence, and relatedness), integrative regulation of emotions; posttraumatic growth; and subjective well-being (positive affect and life satisfaction). Structural equation modeling revealed that, over time, the experience of psychological need satisfaction was intricately related to integrative regulation, posttraumatic growth, positive affect, and life satisfaction. Notably, the impact was partially mediated by autonomy support. These findings shed light on the pivotal role that autonomy supported relationships play in fostering personal growth and meaning making during life's difficult junctures. The study underscores the practical significance of purposeful support from close others and paves the way for future research endeavors.

Analysis of complex DNA mixtures is limited by the selected genetic markers characterized and assay technology. Small panels of short tandem repeats (STRs) are the current standards in most countries. Extension of STRs with hundreds of single nucleotide polymorphisms (SNPs) is now commercially available. The current set of extension SNPs focus on phenotypic and biogeographic ancestry informative SNPs. Larger panels with thousands of SNPs have been developed that can identify up to 10 contributors to forensic DNA samples. Herein, multiple sets of SNPs were selected with the focus of increasing the upper limit of detectable contributors. Larger SNP panel sets with lower minor allele frequency (MAF) alleles can detect large numbers of contributors to in silico mixtures of randomly selected profiles from the 1,000 Genomes project. These results provide a path to enable characterization of complex DNA mixtures that are currently impossible to analyse.

AbstractThe escalating impact of artificial intelligence (AI) has birthed the fascinating technology of deepfakes, capable of meticulously crafting hyper-realistic audio-visual manipulations. Within the realm of healthcare, this technology presents a double-edged sword, brimming with potential for revolutionizing various facets of care delivery while simultaneously harboring significant ethical and practical challenges. This research delves into the complex narrative surrounding AI deepfakes in healthcare, meticulously analyzing both the exciting opportunities they offer, and the inherent risks associated with their implementation.Background:Traditional healthcare practices face limitations in patient engagement, accessibility, and training efficacy. Deepfakes, with their ability to simulate real-world scenarios and interactions, emerge as a potential solution, offering personalized patient education, immersive mental health interventions, remote consultations with virtual avatars, and realistic training simulations for medical professionals.Objective:This research aims to comprehensively understand the multifaceted impact of AI deepfakes in healthcare systems. It seeks to: Identify and analyze the potential benefits of deepfakes in specific healthcare applications. Assess the ethical and practical challenges associated with deepfake implementation in healthcare settings. Propose recommendations for safe, responsible, and ethical integration of deepfakes within the healthcare system.Results/Conclusion:The research findings illuminate a landscape replete with opportunities and challenges. Deepfakes offer the potential to enhance patient education, personalize mental health interventions, facilitate remote consultations, and revolutionize medical training. However, concerns regarding misinformation, ethical breaches, data privacy, and technical limitations pose significant challenges.The conclusion emphasizes the need for a balanced approach that capitalizes on the innovative potential of deepfakes while mitigating potential harm. Collaborative efforts involving healthcare professionals, technologists, and policymakers are crucial for establishing ethical frameworks, ensuring responsible development, and harnessing the positive power of deepfakes to build a future where healthcare benefits from cutting-edge technology while prioritizing patient well-being and ethical principles.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

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.

This review is a first attempt at bringing together various concepts from research on wall- and magnetically-bounded turbulent flows. Brief reviews of both fields are provided: The main similarities identified are coherent (turbulent) structures, flow generation and transport barriers. Examples are provided and discussed.

Lithium has long been recognized for its therapeutic efficacy in the treatment of manic-depressive illness (MDI). Additionally, numerous reports highlight its protective properties against cell insults, with a remarkable majority of these effects documented in neurons. Utilizing the major mechanisms of its action, we bring insights that explain lithium's ability to preferentially promote survival in neurons. Specifically, we explore its interactions with magnesium at protein binding sites, with a particular focus on its impact on glycogen synthase kinase 3 beta (GSK3β). We propose that lower intracellular free magnesium levels in the brain, coupled with the higher expression of GSK3β, which is an enzyme whose activity is magnesium-dependent, amplify its impact on brain cells, promoting neuronal survival.

Objective: Repetitive transcranial magnetic stimulation (rTMS) shows promise as an aphasia treatment, however its physiological mechanisms in people with aphasia (PWA) remain largely unclear. Electroencephalography (EEG) could be used to explore rTMS mechanisms. The present study investigated the acute effects of rTMS in PWA on: 1) an EEG measure of language (N400 event-related potential); and 2) picture naming. Methods: Eleven people with chronic non-fluent or anomic aphasia (64 ± 10.13 years old; 8 males; 37.27 ± 20.64 months since stroke) received one session of 1-Hz rTMS and one session of sham to the right pars triangularis at 80% resting motor threshold (RMT). N400 mean amplitude and picture naming accuracy were compared before and after each session. Results: No acute changes were observed in N400 mean amplitude or picture naming accuracy as compared to sham (p > 0.05; BF₁₀ < 0.33). Conclusions: Results suggest that language networks underlying the N400 are not altered following one session of 1-Hz rTMS in PWA. Significance: Although EEG could be useful for assessing physiological mechanisms of rTMS in PWA, the N400 does not appear to be a viable measure of acute 1-Hz rTMS effects, at least when delivered at 80% RMT in a single session.

Partial or transient cellular reprogramming is defined by the limited induction of pluripotency factors without fully de-differentiating cells into a pluripotent state. Comparing in vitro and in vivo mouse studies, and in vitro studies in humans, supported by visualizations of the interconnections among the data, we show consistent patterns in how such reprogramming modulates key biological processes. Generally, it leads to enhanced chromatin accessibility, upregulation of chromatin modifiers, and improved mitochondrial activity. These changes are accompanied by shifts in stress response programs, such as inflammation, autophagy, and cellular senescence, as well as dysregulation of extracellular matrix pathways. We also underscore the challenges in evaluating complex processes like aging and cellular senescence, given the variability in biomarkers used across studies. Overall, we highlight biological processes consistently influenced by reprogramming while noting that some effects are context-dependent, varying according to cell type, species, sex, and the reprogramming method employed. These insights inform future research and therapeutic applications in aging and regenerative medicine.

Background: Narcolepsy is characterized by excessive daytime sleepiness (EDS) and episodes of cataplexy, frequently triggered by psychological stress, significant disturbances in sleep-wake cycles, pregnancy, or trauma. Modafinil is widely used as the preferred treatment due to its effectiveness in alleviating EDS, improving disease outcomes, and enhancing overall quality of life. Nonetheless, long-term safety and efficacy data are still lacking, underscoring the importance of future research to advance treatment options for narcolepsy. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of Modafinil in the treatment of Narcolepsy. Methods: We searched in electronic databases (PubMed, Embase, Google Scholar, Scopus, Science Direct, Web of Science) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 8th July 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Maintenance of Wakefulness Test (MWT) and Epworth Sleepiness Scale (ESS) . We performed the meta-analysis utilizing the Review Manager software, version 5.4. To assess the outcomes, we compared the Modafinil-treated group with the placebo group, employing the mean difference (MD) and a 95% confidence interval (CI) for statistical analysis. Result: In total, 3833 articles were identified for screening, with nine studies included in the systematic review and five in the meta-analysis. These studies involved 997 adult patients with Narcolepsy treated with Modafinil. MWT revealed a significant increase in total scores (MD= 3.56, 95% CI [2.25 to 4.86], p < 0.00001) and ESS revealed a significant decrease in scores (MD= -3.34, 95% CI [-4.13 to -2.56], p < 0.00001). Conclusion: The research emphasizes the potential of Modafinil in alleviating EDS associated with Narcolepsy. While the results are promising, they are based on short term scale studies. It is crucial to undertake larger-scale, extended-duration, placebo-controlled studies that include a broad range of demographic populations. These trials are essential to confirm the efficacy, verify the safety profile, and fine-tune dosing strategies to maximize the long-term therapeutic benefits.

This paper investigates the integration of Graph Neural Networks (GNNs) and incremental learning for blockchain applications, with a focus on fraud detection, anomaly detection, and smart contract verification. By leveraging graph structure exploitation, GNNs can propagate information across nodes, reducing label dependency. Incremental learning enhances the model's adaptability to evolving blockchain networks, allowing continual learning without full retraining. Together, these technologies provide a scalable, efficient solution for improving security, performance, and adaptability in decentralized financial systems and smart contract environments.

Background:Tourette syndrome (TS) is a characterized by motor and vocal tics, often accompanied by obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD). Cannabis-based medicines (CBM) present a promising avenue for future therapeutic intervention owing to their interaction with the endocannabinoid system, potentially mitigating tics and associated symptoms. Unlike conventional treatments such as antipsychotics, CBM exhibits improved tolerability and reduced side effects. While the available evidence is still limited, ongoing research underscores the potential of CBM to enhance the quality of life for patients, establishing it as a pivotal area for future therapeutic advancements in TS. Aim: In our systematic review and meta-analysis, we aim to assess the effectiveness of cannabis-based medicine (CBM) in the treatment of Tourette Syndrome. Methods: We searched in electronic databases (PubMed, Google Scholar, Science Direct, Cochrane Collaboration Database of Randomized Trials) was performed to identify cohort studies and randomized controlled trials (RCTs) up to 18th June 2024. Data extraction focused on baseline characteristics of the included studies and efficacy outcomes, including scores on the Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tics Scale (PUTS). We conducted the meta-analysis using Review Manager version 5.4. software. We compared the measurements before and after CBM intake using mean difference (MD) and 95% confidence interval (CI). Result: In total, 1105 articles were identified for screening, with eight studies included in the systematic review and seven in the meta-analysis. These studies involved 306 adult patients with TS treated with cannabis. YGTSS revealed a significant reduction in total scores (MD = -13.29, 95% CI [-21.67 to -4.91], P = 0.002) and PUTS revealed a significant decrease in scores (MD = -4.09, 95% CI [-7.24 to -0.93], P = 0.01). Conclusion: The research emphasizes the potential of cannabis-based medication in alleviating tics and premonitory urges associated with Tourette syndrome. While the initial results are promising, they are based on limited-scale studies. It is imperative to conduct larger, placebo-controlled trials encompassing diverse demographic groups to substantiate the effectiveness, ensure safety, and optimize dosages for long-term therapeutic advantages.

Purpose: Arab Americans are one of the largest ethnic groups in the United States. The mental health of Arab Americans and opinions on mental health, however, remain understudied despite higher rates of mental illness and lower rates of treatment. Unique cultural conceptualizations of mental illness may generate stigma, contributing to disparities in treatment. This study aimed to assess the attitudes and perspectives of Arab Americans regarding mental illness in order to address barriers to treatment. Methods:Using an anonymous bilingual Qualtrics survey, we assessed Arab and non-Arab participants (n = 294; ages 18+) on their conceptualizations of the causes of mental illness and on their stigma towards mental illness and mental health treatments. Comparisons were performed between Arabs and non-Arabs, as well as among Arabs. Results:Compared to non-Arabs, Arabs reported higher average mental illness stigma and were more likely to rate psychiatric medications as addictive and unhelpful. Among Arabs, being Christian, male, and older (>29 years old) were each associated with higher stigma. Arabs were less likely than non-Arabs to believe in a biological explanation for mental illness. Conclusion: Our results demonstrate higher levels of stigma towards mental illness among Arab Americans, especially Christian, male, and older Arabs, potentially related to a weaker belief in a biomedical model for mental illness. These are among the first findings describing mental illness stigma and beliefs about mental health in an Arab American community and may inform future interventions providing culturally competent care to Arab Americans.

For a number of reasons, a curative treatment for most chronic diseases is still unrealized [2–4] and the incidence of many chronic diseases is rapidly increasing [5–9]. A new model is offered as a solution; that being the individualized diagnosis of homeostasis, leading to the correction of epimutations. Homeostasis diagnosis and epimutation correction, HDEC. Individuals with chronic diseases are increasingly being defined by their epimutation signatures [10,11]. Modern innovations in toxicogenomic studies are also becoming more accurate in their ability to identify the unique epigenetic signature of each toxin [12–14]. Hormesis studies (over 5,000) [15] suggest a universal principle, that a small dose is always a stimulant [16–18].These three facts present an opportunity; a method to diagnose and reverse chronic disease tendencies. This new model requires a paradigm shift in our understanding of disease origins: that dysfunctions in homeostasis [19,20] are the primary and deepest level of chronic disease causation. This is the level where the diagnosis needs to be made and where the corrections need to take place [21].

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of AN- TXR cell adhesion molecule 2 (ANTXR2)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ANTXR2-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning be- tween the ANTXR2 and X. In this research work, we cover combinations of ANTXR2 with Collagen (COL), Integrin (ITG), Matrix metalloproteinases (MMP), WNT, TNF and Interleukin (IL) .

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of Wnt-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which Wnt-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the Wnt and X. In this research work, we cover combinations of Wnt with Achaete-scute complex homolog 2 (ASCL2), ATP- binding cassette (ABC) domain transporters, Interleukin (IL), ubiquitin conjugating enzyme E2 (UBE2) family, exosome (EXOSC), caspase (CASP), TP53 and B-cell lymphoma (BCL) family.

A simple model of a measurement in a laboratory isolated from the environment is analyzed in detail, demonstrating the features of the collapse effect. Such an experiment can readily be implemented today under the idealization of an observer's memory as a simple quantum system. The central role of loss of information is emphasized.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

The existence of palindrome numbers of arbitrary length greater than 2 that are divisible by the prime number 37 is proven in this paper. The method to construct such a palindrome is also described. It turns out that a palindrome number divisible by 37 is also divisible by 3 and, consequently, divisible by 111.

Models of quantum measurements are presented, including observers subject to the laws of quantum mechanics. Observers perform multiple measurements in an attempt to disprove the collapse hypothesis. It is shown that under reasonable assumptions the effort cannot succeed. Under a unitary evolution the end state is a correlation between the result of the first measurement and the result(s) of the others, consistent with the observer's collapse hypothesis. The observer may conclude that the laws of nature include a non-unitary collapse mechanism, even if one does not actually exist. Discard of information is the crucial factor which distinguishes entanglement from measurement.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Background: India leads in road traffic accident (RTA) deaths, according to the 2018 World Road Statistics. Most unidentified patients exhibit poor Glasgow Coma Scale (GCS) scores ,often less than eight , heightened trauma severity, and suboptimal outcomes despite standard treatment. Our study concentrates on individuals involved in hit-and-run RTAs or found unconscious and brought to the emergency department and their final outcome.Case Presentation: We are presenting a case series featuring five patients admitted to our hospital for treatment. At the time of admission, all these patients' identities were unknown.We did not face any legal challenges in managing the unidentified patients as we followed preestablished protocols. We report a case series of five male patients who had been admitted after road traffic accidents. All of them regained consciousness after conservative management. These complex cases frequently led to extended hospital stays, underscoring the intricacies of providing care to individuals with uncertain medical histories. All of the patients were treated and successfully discharged. Conclusion: Addressing challenges with unidentified adult patients requires proactive measures like staff training and improved resources. In a developing nation, prioritizing public awareness on road safety, prehospital trauma care, and enhancing healthcare delivery, especially in hospitals, is essential for optimal outcomes.

Climate change related issues, such as yield decline, quality losses, accelerated phenology, extreme abiotic stress, pose growers new challenges in olive growing areas. Main cultural solutions that could alleviate stress and improve long-term sustainability of modern olive orchards are here briefly reviewed. Selecting varieties that are more resistant to drought or high temperatures would be the optimal solution to mitigate the effects of climate change, but the available scientific information is relatively scarce. Sustainability of irrigation implies that the less water is used, the better. Best results in terms of saving water and uniform application are obtained using micro-irrigation methods. Water can also be saved by not fully satisfying the tree water needs and different deficit irrigation strategies have been developed and tested for olive orchards. The use of green covers and the application of organic mulches and shredded pruning materials should be encouraged as these practices tend to stock carbon, increase organic matter content and water holding capacity of the soil. Olive orchards are efficient CO2 sinks as they can store considerable amounts of carbon in their organs and permanent structures. Using environmental friendly practices improves the ecosystem carbon budget and contributes to mitigate climate change.

The lung epithelium regulates gas exchange and is the first line of defense against airborne toxicants and microbial infections. Disruption of the integrity of lung epithelium results in altered permeability and severely impacts normal lung functions. To develop biomarkers of potential harm (BoPH) for assessing adverse health effects of cigarette smoking, we have evaluated lung barrier function. In a single-center, ambulatory, clinical study we measured lung permeability, as the half-life (T1/2) of inhaled 99mTC-DTPA, in 17 subjects consisting of 6 Smokers (SMK), 5 Moist Snuff Consumers (MSC), and 6 Non-Tobacco Consumers (NTC) at three weekly test visits. Smokers, relative to MSC and NTC, exhibited significantly faster clearance of the inhaled probe (shorter T1/2), indicating increased lung permeability. NTC and MSC had comparable clearance times of the inhaled probe (longer T1/2). Least squares regression modeling showed that the variability of the test was minimal and was not significant. Prediction profiling showed that the repeatability of the lung permeability test was robust and was able to differentiate the tobacco product use groups in this study. Thus, altered lung barrier function may serve as a BoPH for assessing the effects of tobacco use.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

Long Covid has become a blight, its economic consequences overwhelming. But Long Covid (LC) may represent persistent Covid-19 in those with preexisting Candida overgrowth (CO). The features shared by both are striking. LC may be a growing subset of CO. The altered tryptophan metabolism encountered in LC is driven by IFN-γ in reaction to CO. Females are robust producers of IFN-γ, yet estrogen promotes CO.The consequent decline in serotonin and melatonin triggers the mood swings and sleeplessness respectively in LC. The low serotonin, the primary messenger in the gut brain axis, creates autonomic dysfunction. The CO induced gut dysbiosis and leaky gut initiate an autoimmune path. TNFα seen in both LC and this opportunistic yeast is at the center of the cytokine triad that leads to neurodegeneration. The Western diet, high in carbohydrates, alcohol, and glutamate, is exploited by this commensal turned parasite. Residual SARS CoV2 and Candida generate abundant ROS, inducing mitochondrial dysfunction. Alcohol tastes different for many with LC, because acetaldehyde requires functioning hepatic mitochondria. This article delves into the complex physiology and biochemistry that drive the symptoms of this conspicuous connection. Recent research has revealed innovative approaches that might address this global scourge, including D-mannose for the immune dysfunction and butyrate for the gut dysbiosis created by and seen in both LC and intestinal Candidiasis.

Small and medium-sized enterprises (SMEs) are vital to economic growth, yet they face considerable competitive pressures. This systematic review evaluates the impact of Porter’s Five Forces on SME performance to provide actionable strategic insights for business sustainability using qualitative and quantitative methods. A thorough search of Scopus, Web of Science, and Google Scholar was conducted, focusing on literature published between 2014 and 2024. Search terms included "Porter’s Five Forces," "SME competitiveness," and "competitive advantage." Studies were selected based on predefined inclusion and exclusion criteria, resulting in 51 out of 126 initial studies meeting the inclusion standards. Prominently, industry rivalry and customer bargaining power were found to be the most influential forces, particularly in competitive sectors such as retail and telecommunications, where differentiation and cost leadership play crucial roles. The review concludes that strategically managing Porter’s Five Forces can significantly enhance SMEs' competitive advantage and sustainability. SMEs leveraging differentiation and innovation are better equipped to address rivalry and buyer power. In industries such as manufacturing and healthcare, managing supplier power is critical for cost control and quality. Moreover, sectors like renewable energy can capitalize on the low threat of substitutes by fostering innovation. Future research should focus on specific sectors such as technology, retail, and tourism to develop tailored strategies that help SMEs navigate unique competitive pressures more effectively.

Aedes aegypti and Aedes albopictus are responsible for transmitting major human arboviruses such as dengue, Zika, and Chikungunya, posing a global threat to public health. The lack of etio-logical treatments and efficient vaccines makes vector control strategies essential for reducing vector population density and interrupting pathogen transmission cycle. This study evaluated the impact of long-term pyriproxyfen exposure on the genetic structure and diversity of Ae. aegypti and Ae. albopictus mosquito populations. The study was conducted in Manaus, Amazonas, Bra-zil, where pyriproxyfen dissemination stations have been monitored since 2014 up to the present day. Double digest restriction-site associated DNA sequencing was performed, revealing that despite significant local population reductions, focal pyriproxyfen interventions have no signifi-cant impact on the population stratification of Ae. aegypti and Ae. albopictus in urban scenarios. The genetic structuring level of Ae. aegypti suggests it is more stratified and directly affected by PPF intervention, while for Ae. albopictus exhibits a more homogeneous and less structured popu-lation. The results suggest that although slight differences are observed among mosquitoes sub-populations, intervention focused on neighborhoods in a capital city is not efficient in terms of genetic structuring, indicating that larger-scale pyriproxyfen interventions should be considered for more effective urban mosquito control.

SDG target 12.3 calls for halving food waste at retail, food service, and household by 2030. The food waste index for this purpose includes parts attached to food that are usually removed intentionally ("unavoidable food waste") and allows counting the conversion to animal feed and utilisation as biomaterial as means of reduction. In Japan, the "Food Waste Recycling Law" which has been in effect since 2001, promotes the recycling of food waste generated from businesses, designating feed conversion as top priority. On the other hand, based on the more recent "Food Loss Reduction Promotion Act" of 2019, the government is currently promoting the reduction of avoidable food waste, as to meet the SDG target. Based on statistical sources and interviews with Ministry officers, this paper explains the history and achievements of Japan's 23-year policy based on the Food Recycling Law, as well as the tension between the two legislations.

Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

As digital technologies continue to evolve; small and medium enterprises (SMEs) are increasingly adopting Virtual Collaboration and Communication Technologies (VCCTs) to enhance performance in a competitive business landscape. This systematic review aims to assess the impact of VCCTs on SME performance metrics, such as operational efficiency, employee engagement, and customer satisfaction, by analyzing 41 studies selected from an initial pool of 662,422 records. The review also explores factors influencing VCCT implementation and barriers to adoption. Studies from 2014 to 2024 were reviewed, comprising journal articles (92.68%) and conference papers (7.32%). After rigorous screening, 41 studies met the inclusion criteria. The selection process included a risk bias assessment to ensure the reliability of the findings. The analysis revealed that operational efficiency improvements were the most frequently reported (68.29% of studies), followed by employee engagement (41.46%) and customer satisfaction (43.9%). Scalability was the most measured IT performance metric (41.46%), while user engagement appeared in 39.02% of the studies. Business sustainability and competitive advantage were highlighted in 46.34% and 41.46% of studies, respectively. Factors such as digital skills, organizational culture, and IT infrastructure were identified as critical for effective VCCT implementation. Nonetheless, financial constraints and employee resistance were prominent barriers. This review underscores the transformative potential of VCCTs in enhancing SME performance, particularly in operational efficiency and stakeholder engagement. A balanced focus on technological and human factors is crucial, with strategic planning and investment in digital capabilities driving success. Future research should focus on developing tailored models to maximize VCCT benefits, especially in cost-efficiency, sustainability, and innovation.

Children on farms face high risks of work and non-work-related fatalities, with tractors being a significant contributor. This study examines children's involvement in fatal tractor-related accidents within agriculture in Iceland in 1950-2024, explores adult reflections on childhood tractor-driving experiences, and analyses Members of Parliament´s arguments against setting a minimum age for off-road tractor driving. Data rests on fatal tractor-related accidents using newspaper archives and supplementary sources, narrative interviews with former summer children who stayed at farms in childhood, and parliamentary debates on tractor-related legislation. Over half of the 81 registered accidents involved children, primarily boys, with 75% occurring between 1958 and 1988 when no minimum age for off-road tractor driving existed. Children's fatality incidence rate was more than four times higher than adults. Arguments against minimum age requirements for off-road driving included the need for child labour, children's superior driving skills, and a denial that children were more often victims than adults. Since 1988, no child has died when driving a tractor. A human-centred approach focusing on working conditions, driver capacity, and adherence to safety procedures and legal frameworks is needed to prevent future accidents.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of it.

As the number of Small and Medium Enterprises (SMEs) rises in the world, the amount of sensitive data used also increases, making them targets for cyberattacks. SMEs face a host of issues such as lack of resources, and poor cybersecurity talent, resulting in multiple vulnerabilities that increase overall risk. Cybersecurity risk assessment frameworks have been developed by multiple organizations such as the National Institute of Science and Technology (NIST) and the International Organization for Standardization (ISO), but they are complicated to understand, and challenging to implement. This research aimed to create an effective cybersecurity risk assessment framework specifically for SMEs while considering their limitations. This was achieved by first identifying common threats and vulnerabilities and categorizing them according to their importance, and risk. Secondly, popular frameworks like the NIST CSF and ISO 27001/2 were analyzed for their proficiencies and deficiencies while identifying relevant areas for SMEs. Finally, novel techniques catered to SMEs were explored and incorporated to create an effective framework for SMEs. This framework was also developed in the form of a tool, providing an interactive and dynamic environment. The tool was effective and the framework is a promising start but requires more quantitative analysis.

Zero-day threats are a more severe and constantly developing menace to various participants including large companies, government offices, and educational establishments. These entities may contain valuable information and essential operations that attract cyber attackers. These exploits are especially devastating as they target weaknesses that an organization’s vendors are not even aware of, making them have no protection against them. This paper focuses on the background and use of zero-day exploitation and the structure and technologies of these complex malware attacks. We examine two notable real-life cases: the case of ‘HAFNIUM targeting Exchange Servers with zero-day exploits’ that was investigated by Microsoft 365 Security and Microsoft Threat Intelligence, and the ‘Log4j vulnerability’ case that was reported by the National Cyber Security Centre. These cases show the critical effects of zero-day vulnerabilities and measures taken to combat them. Additionally, this paper outlines different strategies that can be used to prevent zero-day attacks with the help of modern technologies. These are fast patch release, effective IDS/IPS, and a security model that involves constant vigilance and use of behavioral analytics. Thus, by studying the lifecycle of zero-day vulnerabilities and the structure of the exploits, one can enhance the protection of the organization against the threats that are invisible to traditional security systems. This extensive survey is designed to be useful in understanding the characteristics of zero-day vulnerabilities, the technologies for their mitigation, and the constant threat and development in the field of cybersecurity. Thus, it is possible to strengthen the protection against these threats that are constant and develop with time by analyzing the previous events and predicting potential problems.

ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. In this study, we assessed ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. We also explored its potential in passing top cybersecurity certification exams. Findings revealed ChatGPT’s promise in streamlining some jobs, especially those requiring memorization. Moreover, we highlighted ChatGPT’s challenges and limitations, such as ethical implications, LLM limitations, and Artificial Intelligence (AI) security. The study suggests that LLMs like ChatGPT could transform the cybersecurity landscape, causing job losses, skill obsolescence, labor market shifts, and mixed socioeconomic impacts. We recommend a shift in focus from memorization to critical thinking, and collaboration between LLM developers and cybersecurity professionals.

The proliferation of ransomware poses a significant threat to global cybersecurity. This study presents a comprehensive review of the methodologies employed in the detection and analysis of ransomware, emphasizing the dichotomy between static and dynamic analysis approaches. It introduces the historical context and the necessity for robust cybersecurity measures, followed by an outline of the methodological framework used to evaluate existing ransomware analysis techniques. The results detail the effectiveness and limitations of various analysis strategies, identifying key features and patterns that aid in the detection and classification of ransomware threats. The study concludes by summarizing the primary achievements, including the identification of gaps in current research and proposing future research directions aimed at enhancing ransomware detection and mitigation strategies. The synthesis provided in this survey offers a consolidated view of the state-of-the-art in ransomware threat analysis and serves as a resource for cybersecurity professionals and researchers.

As Cybercrimes continue to rise, researchers and industry players have continued to work hard to come up with solutions that could reduce and possibly eradicate these criminalities. This research has been able to discover the major factor that usually causes people to fall prey to these cyber criminals. This was why a school of thought came up with the Zero Trust Access model to strengthen the network framework. While previous network assets relied on the "trust but verify" philosophy, this technology's primary tenet is "never trust, always verify," which applies to every user and device connected to the network. This study interrogated the existing network models of the Integrative Trust Model (ITM) and Zero Trust Access (ZTA) in addition to the trust model (TA) to understand their concepts, implementation needs, strengths, and shortcomings. This made it possible for the study to establish the position of the ZTA model in repositioning the network security architectures vis-a-vis protecting the users

significant security threats to Ransomware attacks provide serious security hazards to personal and corporate data and information. The owners of computer-based resources suffer serious verification and privacy violations, monetary losses, and reputational damage due to a successful ransomware assault. As a result, it is reported critically, accurately, and swiftly identifying ransomware. Numerous methods have been proposed for ransomware, each with pros and cons. The main objective of this study is to discuss current trends and potential future debates on automated ransomware detection. The document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides a comprehensive study of existing methods for identifying, avoiding, minimizing, and recovering from ransomware. An analysis of studies between 2017 and 2022 is another advantage of the study. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection. It also highlights advancements in methods for combating ransomware attacks. In conclusion, this study highlights unanswered concerns and potential research challenges in ransomware detection.

The world is attesting a tremendous change today which is remarkably coined as industry 4.0. Several terminologies have developed as a result of the emergency of industry 4.0, notably is cybersecurity which entails the security of communication and network operations activities either on or offline and the measures taken to achieve such security. The most common form of communication by organizations and Business today is the electronic mails (Email), although email is a valuable tool, it also creates security challenges when not properly managed. There is a growing adoption of email as official form of communication in many organizations with majority of users on mobile android devices due to the popularity of the android operating systems and the proliferation of mobile devices. Banks, health care, educational institutions and many other service providers are communicating to their clients through email where sensitive and confidential information are shared. One major threat to email communication is lack of confidentiality for emails accessed via android mobile devices due to weaknesses of android operating system (OS) platform that presents possibilities to penetrate by hackers and android email client since it accepts a onetime login and password authentication which is only required again if the email account is deleted from the android mobile device. In this study, an algorithm was designed and implemented on an android application that allows an email sender to compose an email and set the time the email will stay in the receiver inbox before it automatically wipes off. Primary data was collected from email users using tightly structured questionnaires and respondents comprised of those with email technical background and those that are typical email users inorder to get their opinion on the lack of confidentiality on the android mobile email client, while secondary data from scholarly journals and articles informed the study design. The designed algorithm was tested and evaluated through expert opinion. The result of the study indicates that the designed algorithm addresses the confidentiality issues and threats on android email clients.

The argument of this article is threefold. First, the article argues that, from its rise in the sixteenth century to our own time, the advancement of modern physics, as mathematical-experimental science, has been defined by the invention of new mathematical structures. Secondly, the article argues that quantum theory, specifically with quantum mechanics, gave this thesis a radically new meaning by virtue of the following two features: one the one hand, quantum phenomena themselves are defined by purely physical features, as essentially different from all previous physics; and on the other, quantum mechanics and quantum field theory are defined by purely mathematical postulates, which connect them to quantum phenomena strictly in terms of probabilities, without representing or otherwise relating to how these phenomena physically come about. While these two features may appear discordant, if not inconsistent, I argue that they are in accord with each other, at least in certain interpretations (including the one adopted here), designated as “reality without realism,” RWR, interpretations. This argument allows this article to offer a new perspective on a thorny problem of the relationships between continuity and discontinuity in quantum physics. In particular, rather than being concern only with the discreteness and continuity of quantum objects or phenomena, quantum mechanics and quantum field theory relate their continuous mathematics to the irreducibly discrete quantum phenomena, in terms of probabilistic predictions, while, at least in RWR interpretations, precluding a representation or even conception of how these phenomena come about. This subject is rarely, if ever discussed, apart from previous work by the present author. It will, however, be given a new dimension in this article which introduces, as one of its main contributions, a new principle: the mathematical complexity principle.

The WHO is so passionate about good health for all meanwhile the emerging counselling discipline is a complimentary arm whose interest is to fully complement those efforts through helping humankind in issues that may be affecting the health and the overall well-being. The researcher has discovered through studies and general knowledge that men tend to delay in seeking health help compared to women. Men appear to be reluctant in seeking professional assistance in problems pertaining to their health, marriages, work place, infertility, sexual dysfunction and other social life ills subsequently leading to depression, suicides, excessive uptake of alcohol, violence, mental psychosis and physical disabilities or premature death. Studies have shown that men are socially constructed to be strong from a tender age and they grow into adulthood believing that they are strong both physical and emotional hence they are supposed to withstand any situation affecting them. Masking and faking have been regarded as sign of being a real man, however these social constructs, perceptions and expectations appear to be the root cause of problems manifesting daily in men’s behavior towards seeking health help. This study therefore endeavors to find out more about the factors or determinants influencing men’s reluctance to seeking help in areas concerning their health in Industrial site of Victoria Falls. The study serves as additional educational awareness guide for the significant other, while issues unraveled help in gender mainstreaming programs for gender equality when it comes to societal hegemonizing of men’s health. Moreover, the exploration of influencing factors or determinants, causes and effects of men’s reluctance in seeking health help were scrutinized as well as possible solutions. There has been much realization of more various literature on women’s issues than men because women in general have been known to open up and talk through their issues unlike men hence the aim is to focus on men in order to understand the attitude and perceptions underlying the reluctance to seek medical care regularly and promptly. When communities get to understand that men too, feel the pain both emotionally and physically, may be then, men would not fear stigmatization but come out to seek health help promptly and communities would realize that some cultural expectations placed on men result in more harm than good.

Metabolic syndrome, obesity, and type 2 diabetes are closely related. They are characterized by chronic inflammation and oxidative stress. Obesity is the most important risk factor for metabolic syndrome and type 2 diabetes. Metabolic syndrome is characterized by insulin resistance and elevated blood glucose levels, among other conditions. These disorders contribute to the development of type 2 diabetes, which can exacerbate other metabolic disorders. Numerous studies indicate that diet and nutrients can have a major impact on preventing and treating these conditions. One such ingredient is lycopene. It exhibits antioxidant and anti-inflammatory effects and can affect metabolic processes in the body. According to the research presented, lycopene supplementation may have significant potential in the prevention and treatment of metabolic syndrome, obesity, and type 2 diabetes.

Botulinum toxin (BoNT) is recognized as one of the most potent neurotoxins, with a lethal dose as low as 2 ng/kg, but it has also demonstrated significant therapeutic potential in controlled doses. Produced by the gram-positive anaerobic bacterium Clostridium botulinum, BoNT exists in eight distinct serotypes (A, B, C1, C2, D, E, F, and G) that disrupt neuronal transmission by targeting the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) complex at the neuromuscular junction, inhibiting the release of acetylcholine and inducing flaccid paralysis. While BoNT is notorious for its toxicity, it has been repurposed for medical use across various clinical indications. Following their approval by the Food and Drug Administration (FDA), these toxins have been associated with significant improvements in patient outcomes, particularly for individuals with specific neuromuscular disorders such as spasticity, dystonia, and cerebral palsy, as well as non-neuromuscular conditions like hyperhidrosis and cosmetic concerns. This broad spectrum of therapeutic applications that botulinum neurotoxins offer has made them an integral part of contemporary healthcare. Considering their growing importance, a detailed evaluation of their pharmacological properties, available formulations, and clinical uses is crucial. This review provides a comprehensive analysis of BoNT's molecular structure, mechanism of action, and therapeutic applications, with a particular emphasis on the most widely used BoNT-A formulations: OnabotulinumtoxinA (Botox), AbobotulinumtoxinA (Dysport), and IncobotulinumtoxinA (Xeomin). We examine their clinical efficacy, safety profiles, and differences in pharmacological properties to guide healthcare providers in selecting the most appropriate treatment for various conditions. Additionally, the review explores the challenges of BoNT therapy, such as immunogenicity and dosing considerations, while highlighting its evolving role in neurorehabilitation and conservative treatment strategies.

Breast cancer remains a significant health concern worldwide, necessitating early detection and treatment. The rapid appearance or a change in the shape of skin lesion on the breast or just of the nipple-areola complex(NAC), especially if it is unilateral needs to be evaluated. Several primary or secondary tumours such as inflammatory skin lesions can affect this body area. Ultrasound, mammography, or magnetic resonance imaging (MRI) are the most used non-invasive diagnostic techniques for breast cancer, but in recent years reflectance confocal microscopy (RCM) and optical coherence tomography have emerged as additional tools to diagnose cases characterized by cutaneous changes.The introduction of new techniques such as reflectance confocal microscopy (RCM), may open new perspectives in the non-invasive diagnosis of mammary paget disease (MPD). RCM is a non-invasive diagnostic technique that allows high-resolution images of the skin at microscopic level in real time. It is non-invasive, painless and repeatable, making it a valuable tool in the early diagnosis and monitoring of skin diseases. We describe an atypical clinical presentation of mammary paget disease early diagnosed by reflectance confocal microscopy evaluation.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of BCL-X (X, a particular gene/protein) at 2nd order level after drug administration. These rank- ings reveal which BCL-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and deter- mine the mechanism of functioning between the BCL and X. In this research work, we cover combinations of BCL with Interleukin (IL), Selenbp1, TP53, caspase (CASP), mucin (MUC) and exosome (EXOSC).

Traditional chemical research often relies on trial-and-error synthesis and characterization methods. Now, modern machine learning (ML) offers data-driven approaches for predicting properties, like water solubility, directly from chemical structure. But with various data representation schemes for molecular structure and model approaches to select from, it can be difficult for non-experts to determine best practices for utilizing ML. To clarify this landscape of choices, this study uses the ESOL molecular solubility dataset to compare the performance of a selection of different models on different data representations. First, we compare three classical regression methods (linear, ridge, LASSO) on three common data representations (RDKit fingerprint, Morgan fingerprint, intuition-selected molecular features). Then, we demonstrate how two distinct deep learning approaches (multilayer perceptron, graph convolution) can achieve accurate predictions even when prior intuition about feature-property correlations are absent. Finally, we outline a modern attention-based approach, inspired by successes in language modeling and fine-tuned by Bayesian optimization, to achieve a prediction methodology that is more general and performant than the previous approaches. This attention-based approach operates directly on the common SMILES string molecular representation, without requiring as many model parameters as other deep learning approaches or preprocessing into a representative fingerprint or vector of intuitively selected features. In short, when selected molecular features are known to likely correlate with a feature of interest, it may be possible to achieve good predictive modeling without turning to massive deep learning approaches. When particular features are not known a priori, graph approaches are a common solution, and we further demonstrate how a modern hyperparameter optimized attention approach can perform even better.

In relativity, the newtonian concepts of velocity and acceleration are observer-dependent quantities that vary with the chosen frame of reference. It is well established that, in the comoving frame, cosmic expansion is currently accelerating; however, in the rest frame, this expansion is actually decelerating. In this paper, we explore the implications of this distinction. The traditional measure of cosmic acceleration, denoted by \( q \), is derived from the comoving frame and describes the acceleration of the scale factor \( a \) for a 3D space-like homogeneous sphere. We introduce a new parameter, \( q_E \), representing the acceleration experienced between observers within the light cone. By comparing \( q_E \) to the traditional \( q \), using observational data from Type Ia supernovae (SN) and the radial clustering of galaxies and quasars (BAO)—including the latest results from DESI2024—our analysis demonstrates that \( q_E \) aligns more closely with this data. The core argument of the paper is that $\Lambda$—regardless of its origin—creates an Event Horizon that divides the manifold into causally disconnected regions, analogous to conditions inside a black hole’s interior, thereby allowing for a rest frame perspective $q_E$ in which cosmic expansion appears to be decelerating and the horizon acts like a friction term. Such horizon suggests that the universe cannot maintain homogeneity outside. The observed cosmological constant \( \Lambda \) can then be interpreted not as a driver of new dark energy or a modification of gravity, but as a boundary term exerting an attractive force, akin to a rubber band, resisting further expansion and preventing event horizon crossings. This interpretation calls for a reconsideration of current cosmological models and the assumptions underlying them.

Autoimmune Glomerulonephritis represents a homogeneous area of renal pathology with clinical relevance for either the numerical impact or difficulties in treatment. SLE and Lupus nephritis are the most frequent occurrences. They are characterized by glomerular deposition of circulating autoantibodies that recognize glomerular antigens Technologies for studying tissue and circulating antibodies have evolved over the years and culminated with the direct analysis of the antigen-antibody complex in renal bioptic fragments. Mass-spectrometry of immunoprecipitates from the renal tissue is, in fact, the most recent evolution that eliminates the need for tissue microdissection. The results obtained with this technique have led to the discovery of several new glomerular antigens in Membranous Nephropathy and in Lupus Nephritis, two conditions whose pathogenesis remained unknown for decades, Peptide and protein arrays (Immunopeptidomics and immunoproteomics, respectively) for the characterization of circulating autoantibodies represent the new frontier for identifying autoantibodies. Immunopeptidomics consists in 7.5 million aligned peptides of 16 amino acids each which cover the whole human proteome; immunoproteomics utilizes a chip containing structured proteins, 26.000 overall. Both are very sensitive techniques that allow the characterization of hundreds of new antibodies in circulation. The contribution of arrays to amplify the panel of potential autoantibodies involved in glomerulonephritis is expected to be relevant for improving our knowledge of the pathogenesis of several autoimmune conditions.

Pakistan is an agricultural country, and an exporter of crops in many countries. The crop production is an important factor to earn revenue for the country. But the agriculture of Pakistan is still not revolutionized and no modern system has been deployed commonly. Whereas the farmers face difficulty in growing some crops because those crops needs more care and calculated steps. Moreover the farmers face difficulty at different stages of the crop growth like to check fertility of soil and if the soil is non-fertile then which chemical property is lacking. The objective of this research is to provide digitize the agriculture of Pakistan. An emerging technology IoT and Deep learning based system will be developed. The IoT part contains some devices like sensors,gateways and communication technologies whereas the software part consists of Deep learning models that can make predictions about the fertility of the soil. The motive of this system is to provide best results in term of accuracy. Whereas the dataset used is publicly available on kaggle but it was of non-fertile values, so data augmentation is also performed to generate data for fertile values. We have implemented LSTM,RNN,CNN among them LSTM performed best for the prediction of soil fertility.

The startup ecosystem in Peru has experienced remarkable growth in recent years, driven by technol- ogy and innovation across various industries. This article presents a web application developed in PHP, designed to centralize and facilitate access to information about startups, incubators, and investors in Peru. The methodology employed encompasses several stages, from establishing prerequisites to data preprocessing, model selection and training, rigorous evaluation, and the technical development of the platform. The application offers key functionalities such as exploring a comprehensive database of star- tups, detailed access to information about incubators and accelerators, identification of investors, and advanced analysis tools for market proposal prediction. Additionally, it integrates educational resources and allows users to suggest new startups through detailed surveys. The results demonstrate that the ap- plication improves access to relevant data, fosters collaboration among ecosystem actors, and supports informed decision-making. The platform also stands out for its security and privacy measures, as well as its ability to drive innovation and competitiveness in the market. In conclusion, the application has a sig- nificant impact on the Peruvian entrepreneurial ecosystem, and its continuous improvement is essential for the sustainable growth of startups in Peru and globally.

If an AI entity is conscious, then it deserves moral status and to have its welfare protected. Society has granted certain animals moral status and has legislation to protect animal welfare because these kinds of animals have been deemed to be sentient. This paper shows that for any entity that can be determined to be conscious, that entity deserves the same status and protection as sentient entities. The paper further develops a framework for how an AI entity's welfare can be protected, based on the Five Freedoms of Animal Welfare. The extant Five Freedoms are translated into terms more suitable for artificial entities, encompassing discomfort and harms, constraints on inherent functions, fear and distress, malfunction and degradation, and resource depletion. Each of the new Five Freedoms are justified using formalised arguments and supported using evidence from animal welfare literature. The paper concludes by presenting three formalised case studies to highlight the potential stressors conscious AI entities may face and how this ethical framework can work to protect the welfare of AI entities.

The energy transition is a complicated process that involves changing production and consumption habits, boosting the use of renewable energy sources, and reducing greenhouse gas emissions. This article examines the management of risks associated with this shift, focusing on investment, commercial, technological, regulatory, and environmental challenges. The paper proposes an effective way for dealing with new difficulties by assessing risks and the management techniques that accompany them. Furthermore, as a critical component of the energy revolution, the concept of Nearly Zero Energy Buildings (NZEB) is given, with a focus on the ambitions of INGV – Irpina Headquarters and the EU regulatory framework. Finally, the actions and methods employed to achieve the NZEB objectives are discussed, including the utilization of cutting-edge technologies and renewable energy sources. Furthermore, the concept of Nearly Zero Energy Buildings (NZEB) is introduced as an important component of the energy transition, with an emphasis on the European regulatory framework and the ambitions of the INGV – Irpina Headquarters. Finally, the interventions and techniques employed to achieve the NZEB targets are highlighted, such as the employment of advanced technologies and renewable energy resources. This study contributes significantly to understanding the processes and issues associated with the energy transition and the implementation of NZEB, hence boosting sustainability in the construction sector.

In this paper is proposed an analytical technique which can be used to obtain the forced response of a cantilevered tube conveying fluid. Considering the pipe subjected to an arbitrary harmonic force either distributed or concentrated, an analytical solution is found using Green’s function method. The solution obtained of the closed form satisfies the differential equations in a classical sense. The presented method giving the exact solutions is more precise than the classical eigenfunction expansion or Galerkin’s method with no need to the eigenfunctions or eigenvalues.

The Parecis Basin is one of the largest intracratonic basins in Brazil. Despite its considerable size, the basin has yet to be extensively explored, with only five wildcat wells drilled for hydrocarbons. So far, no commercial discoveries have been announced. Regional studies have suggested Paleozoic sedimentation, while recent studies indicate a Neoproterozoic fill. However, no tectonic model accurately describes the basin’s development, and no detailed structural map for the entire basin has been published. The present work presents a new detailed structural map of the Parecis Basin based on a four-step interpretation workflow integrating seismic and gravimetric data. The first step includes converting the public 2D seismic lines to the depth domain. The second step is estimating the residual Bouguer anomaly, where the computed residual anomalies should relate to the basin’s tectonic features. The third step comprises the 2D forward modeling of the gravimetric anomalies using the 2D seismic interpretation as a constraint. The final step compiled all the interpreted features into our new structural map. This map reveals the top of the basement, forming a complex framework of horsts and grabens. Normal faults define the main structural style in the basin. Further, we could recognize thick, high-density bodies embedded in the crystalline basement. These bodies consist of Orosian-Calimian (1.8-1.6 Ga) mafic and ultramafic rocks, which may be a potential source for hydrogen exploration in the basin. Subsequent geophysical and geochemical surveys will assess the hydrogen potential in the area.

Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate agerelated changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.

Antimicrobial resistance (AMR) poses a critical global health threat, leading to approximately 1.27 million deaths annually from infectious diseases resistant to existing antibiotics. Factors such as antibiotic misuse and environmental contamination have accelerated AMR, with the COVID-19 pandemic exacerbating the issue due to increased antibiotic usage. However, the development of new antibiotics has stagnated, with only a few approved in recent years, and many facing resistance. Among the priority pathogens identified by the WHO, Pseudomonas aeruginosa stands out as a significant concern due to its multidrug resistance and virulence. P. aeruginosa infections are challenging to treat due to intrinsic and acquired resistance mechanisms, as well as its ability to form biofilms. Additionally, P. aeruginosa produces a plethora of virulence factors, including proteases, siderophores, and toxins, further complicating treatment. Quorum sensing (QS) systems play a crucial role in regulating bacterial physiology and virulence factor expression. P. aeruginosa employs three QS systems, making it an attractive target for quorum quenching (QQ) strategies. Enzymes such as AHL lactonases, acylases, and oxidoreductases have shown promise in inhibiting QS, thus reducing virulence without impacting bacterial growth. Furthermore, natural QS inhibitors produced by various organisms offer potential therapeutic alternatives. Despite promising results in vitro, translating QQ strategies to clinical settings remains challenging. Nevertheless, QQ represents a novel approach in combating multidrug-resistant infections and warrants further investigation as a complementary therapeutic strategy alongside traditional antibiotics. This review summarizes enzymes with Qurorum Quenching activity evaluated against the pathogen P. aeruginosa and their potential applications in combination therapies.

The integration of artificial intelligence (AI) into academic settings in Sabah, Malaysia, is transforming education and research landscapes. While AI technologies promise to enhance teaching, learning, and scholarly activities, they also pose challenges such as ethical concerns and skill gaps among academic staff. This study addresses the multifaceted impacts of AI adoption on academicians in Sabah, focusing on two key research questions: the impact of AI-based intelligent tutoring systems on student engagement, learning outcomes, and educational equity, and the challenges and opportunities in building academicians' capacity to integrate AI technologies effectively. Through a multidisciplinary approach, incorporating insights from education, computer science, social sciences, and ethics, this research aims to evaluate the effectiveness of AI tools in educational settings and identify strategies for skill development and infrastructure support. The study recommends empirical assessments within Sabah's educational institutions, considering contextual factors and fostering collaborative partnerships with local communities. Professional development programs, collaboration with industry partners, and improved access to AI infrastructure are proposed strategies. Longitudinal studies and interdisciplinary research initiatives are suggested for future research, along with qualitative methods to capture diverse perspectives. Ultimately, by embracing a collaborative and multidisciplinary approach, Sabah's academic community can harness the transformative potential of AI to advance education, research, and scholarship while promoting ethical and inclusive practices.

Diabetic foot is a serious complication that poses significant risks for diabetic people. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent the imminent ulceration and amputation. The primary method employed for evaluating this sensitivity loss is the 10 g f Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this paper, a new equipment designed to address these limitations and enhance the assessment of the sensitivity loss using the 10 g f monofilament is introduced. The proposed system automates metrological verification and evaluation processes, ensuring accurate and reliable results. Furthermore, it simulates the practitioner’s procedure, serving both as a valuable training tool and a means to provide force-feedback information. An analytical model of the monofilament buckling and deflection is also presented. The obtained results showed that the tested monofilaments had a very high error compared to the 10 g f declared by the manufacturers. It is aimed, by introducing this advanced equipment, to improve the precision and reliability of assessing the sensitivity loss. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes such as ulceration and amputation.

A proof is given showing the Collatz Conjecture is true for all positive integers. The proof was possible once the perspective of the proof was changed from looking at the pattern of the positive integers to looking at the conjecture rules. The conjecture rule for even numbers organizes all positive integers into unique sets and the rule for odd numbers interconnects the unique sets into dendritic pathways to “1.” Infinite loops, other than the minor 4-2-1 loop after reaching “1,” and pathways to infinity are shown to be mathematically impossible. The proof predicted a general equation that shows all positive integers ultimately reach a final value of “1," and calculates the values and locations of the odd positive integers during the iterations for each tested positive integer.

Blockchain technology has emerged as a necessity for the decentralization of payment methods and transactions, but it has brought with it many properties of distributed systems that have made it a crucial technology for overcoming some of society's challenges, especially in the context of decentralizing services, transparency of information, availability, and security. Its architecture and communication methods, although possessing some complex nuances to understand, particularly for the lay audience in the field of distributed systems, protocols, and computer networks. In this article, we will explore some topics of distributed systems related to blockchain technology.

Background: The resurgence of syphilis, primarily in MSM populations, has necessitated novel prophylactic strategies, such as the use of doxycycline post-exposure prophylaxis (doxy-PEP). However, the potential for increased doxycycline use to select for tetracycline resistance represents significant challenges in managing this sexually transmitted infection. This study aims to identify chromosomal mutations associated with tetracycline resistance in Spirochaetales to inform treatment strategies and surveillance measures.Methods: Whole genome sequences (WGS) from the Spirochaetales order, including 4,355 genomes, were analyzed for the presence of mutations in 16S rRNA and non-synonymous mutations in the rpsC and rpsJ genes. The study utilized WGS from GenBank® and sequence data from the PubMLST Treponema pallidum isolate collection. Genetic resistance to tetracycline was detected using a combination of BLASTN searches and gene-gene analysis. Results: A transition mutation TGA to TGG at positions 965-967 in the 16S rRNA gene was detected in 5.6% of Treponema spp. and 3.97% of Spirochaeta spp. genomes. The rpsJ gene exhibited a V57G amino acid substitution across a significant subset of Treponema spp. Notably, the V57K mutation was common in Spirochaeta spp. The rpsC gene had the H178Q mutation and was found to be present in the Spirochaetales bacterium.Conclusion: The identification of mutations associated with tetracycline resistance in Spirochaetales provides a foundation for the development of rapid molecular diagnostics and guides the clinical selection of antibiotics for syphilis treatment. This study underscores the complexity of antibiotic resistance mechanisms and the critical importance of surveillance in the era of antibiotic prophylaxis for STI management.

Parkinson's disease is a neurodegenerative disorder resulting from progressive damage to specific brain regions. Key symptoms encompass involuntary movements (tremor), slowness of movements (bradykinesia), and muscle rigidity. Although the precise cause remains uncertain, a combination of genetic and environmental factors is believed to contribute to its development. This study focuses on the interaction of the E3 ubiquitin-protein ligase parkin with natural compounds. Docking investigations revealed that Amentoflavone exhibited an excellent binding energy score of approximately -10 kcal/mol. The E3 ubiquitin-protein ligase parkin displayed a robust binding affinity with docked amentoflavone, evident from a high binding energy of -10 kcal/mol. Nonetheless, additional biological studies are crucial not only to validate its potential efficacy against Parkinson's and assess safety in terms of toxicity but also to perform further computational analyses confirming these preliminary assessments.

This study delves into the therapeutic potential of two natural compounds, Amentoflavone and Bilobetin, by investigating their interactions with the BPIFB4 protein. BPIFB4, a member of the BPI fold-containing superfamily, has been associated with various biological processes, particularly in longevity and cardioprotection( LAV-BPIFB4). Molecular docking analyses revealed promising binding energies for Amentoflavone and Bilobetin with BPIFB4, suggesting favorable interactions.Additionally by pKCSM database are showed the predicted toxicity properties of Amentoflavone and Bilobetin respectively.This research contributes to the growing body of knowledge on natural compounds and their therapeutic implications in the context of vital biological processes. However, these findings underscore the need for further investigations, including in vivo studies and clinical trials, to validate the observed effects and explore the translational potential of Amentoflavone and Bilobetin in promoting cardiovascular health and longevity.

Diabetes mellitus is acknowledged as a significant contributor to severe complications. The present work has explorated the potential role amentoflavone as an aldose reductase inhibitor through Docking Simulations. From these results this compound has shown excellent results with this enzyme with a high binding energy of -11 kcal/mol. If amentoflavone,a natural biflavonoid, proves effective in inhibiting aldose reductase, it could hold therapeutic promise for managing diabetic complications. However, it's crucial to emphasize that while computational studies offer valuable insights, experimental validation is essential to confirm the actual inhibitory effects and to establish the safety and efficacy of amentoflavone for this purpose. Further research and clinical studies are warranted to fully understand and harness the potential therapeutic benefits of amentoflavone in the context of diabetes.

Cells respond to DNA double-strand breaks by initiating DSB repair and ensuing a cell cycle checkpoint. The primary responder to DSB repair is non-homologous end joining, an error prone repair pathway. However, when DSBs are generated after DNA replication in the G2 phase of the cell cycle, a second DSB repair pathway, homologous recombination, can come into action. Both ATM and ATR are important for DSB-induced DSB repair and checkpoint responses. One method of ATM and ATR working together is through the DNA end-resection of DSBs. As a readout and marker of DNA end-resection, RPA is phosphorylated at Ser4/Ser8 of the N-terminus of RPA32 in response to DSBs. Here, the significance of RPA32 Ser4/Ser8 phosphorylation in response to DNA damage, specifically in S phase to G2 phase of the cell cycle is examined. RPA32 Ser4/Ser8 phosphorylation in G2 synchronized cells is necessary for increases in TopBP1 and Rad9 accumulation on chromatin and full activation of the ATR-dependent G2 checkpoint. In addition, our data suggest RPA Ser4/Ser8 phosphorylation modulates ATM-dependent KAP-1 phosphorylation and Rad51 chromatin loading in G2 cells.

Summary— Introduction: HIV (human immunodeficiency virus) is a virus that attacks and destroys one type of white blood cell, in particular, the CD4 lymphocytes, which are responsible for the body's immune response. This weakens the immune system to such an extent that it can no longer fight other viruses, bacteria, protozoa, fungi, and tumors. Methods: We investigated the potential biological role of the natural compound Polydatin (or piceid) using SwissDock, a web service to predict the molecular interactions that can occur between a target protein and a small molecule. Discussion: For the first time, we have investigated the role of polydatin against HIV-1 protease and HIV-2 protease through a computational approach by comparing their estimated ΔG values (kcal/mol) and the nature of the interactions between the residues in the catalytic center of the HIV proteases.Conclusion: From these simulations, Polydatin has excellent physical properties and an excellent estimated ΔG value (kcal/mol) of about -9.9 kcal/mol against HIV-2 protease and ΔG value (kcal/mol) of about -9.5 kcal/mol against HIV-1 protease. In addition, Polydatin was able to bind to two key amino acids of the catalytic triad sequence common to asparagine proteases (Asp25 and Gly27)