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.

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%.

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.

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.

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)

The impact of the climate crisis on urban environments and life within them is evident. In the face of challenges like heatwaves, floodings and other extreme events, pollution, and declining quality of life in cities, there is a growing demand for the preservation and expansion of urban green spaces, often driven by citizen-led transformations. This paper examines 100 urban greening projects initiated or supported by citizens globally, categorizing them according to the type of greenery, the stakeholders involved, the mode of implementation and the use of smart technologies incorporated. We notice variations in green endeavors based on the stakeholders spearheading them, however most of entirely citizen-led initiatives aimed at the creation of urban farms and food growing, demonstrating the pressuring needs for food securing and self-determination in communities. More than half of the assessed initiatives that managed to scale up and multiply had public authorities providing a framework or a type of support for their development or an NGO or other organization providing expertise and mobilize citizens at various stages. In terms of technological use, we find mostly websites and social media platforms easing participatory endeavors and knowledge sharing of best practices, accelerating scaling efforts, while there is a low integration of more advanced digital technologies, which if used, could enable real-time monitoring of green spaces, inform evidence-based decision-making, and streamline processes in scaling up green initiatives.

Aerobic glycolysis in cancer cells, originally observed by Warburg 100 years ago, which involves production of lactate as the end product of glucose breakdown even in the presence of adequate oxygen, is the foundation for the current interest in cancer cell-specific reprograming of metabolic pathways. The renewed interest in cancer cell metabolism has now gone well beyond the original Warburg effect related to glycolysis to other metabolic pathways to include amino acid metabolism, one-carbon metabolism, pentose phosphate pathway, nucleotide synthesis, antioxidant machinery, etc. Since glucose and amino acids constitute the primary nutrients that fuel the altered metabolic pathways in cancer cells, the transporters that mediate the transfer of these nutrients and their metabolites not only across the plasma membrane but also across the mitochondrial and lysosomal membranes have become an integral component of expansion of the Warburg effect. In this review, we focus on the interplay between these transporters and metabolic pathways that facilitates the metabolic reprogramming which has become the hallmark of cancer cells. The beneficial outcome of this recent understanding of the unique metabolic signature surrounding the Warburg effect is the identification of novel drug targets for development of a new generation of therapeutics to treat cancer.

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

In this article, we investigate both the Schrödinger and continuity equations in the presence of nonlocal potential that arising from electron-electron interaction and local one in the context of minimal uncertainty in momentum within commutative and noncommutative frameworks. Furthermore, the Frahn–Lemmer potential type is used. Interestingly, the combined effects of both the phase-space noncommutativity and nonlocality on the current density while considering a minimal uncertainty in momentum are examined. We find that the current density, as conventionally defined, does not fulfil the condition of current conservation, thus, a new definition of the current density that encompasses the aforementioned contributions is given. Thereafter, it is shown that the computed current using the new definition of the current density satisfies the current conservation. Noting that using both the $★$product and linear Bopp-Shift, the noncommutativity is inserted.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to use a single type of task, and the exploration on its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifac-eted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have a relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks, with a sin-gle-channel prefrontal electroencephalography (EEG) recording. Significant positive correlations were observed between the subjective flow scores of individual’s best-flow-experience task and the EEG activities at delta, gamma, and theta bands, peaking at latencies around 2 minutes after task onset. The outcomes of our multiple regression analysis yield a maximum coefficient of de-termination (R²) of 0.279. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.

Due to the large interest and need, there has been much recent work in epileptic seizure detection using machine learning models. Using un-intrusive measurements of brain activity such as electroencephalograms (EEG) has allowed for large datasets to be constructed and used for computational intelligence to identify seizure events within EEG data. In this paper, we use a publicly avaibale EEG dataset to develop a lightweight Machine learning supervised model (simple Decision Tree) to classify seizure events from brain waves. The performance of this developed model was compared with a complex ML model (Support Vector Machine). The cross-validated Decision Tree model performed better for seizure event classification with an overall accuracy of 91.17%. This lightweight model will allow for developing mobile applications and user comfort

Executive functioning is a key component involved in many of the processes necessary for effective weight management behavior change (e.g., setting goals). Cognitive behavioral therapy (CBT) and third-wave CBT (e.g., mindfulness) are considered first-line treatments for obesity, but it is unknown to what extent they can improve or sustain executive functioning. This pilot randomized controlled trial examined if a CBT-based generalized weight management intervention would affect executive functioning and executive function-related brain activity in individuals with obesity or overweight. Participants were randomized to an intervention condition (N=24) that received the Noom Weight program or to a control group (N=26) receiving weekly educational newsletters. EEG measurements were taken during Flanker, Stroop, and N-back tasks at baseline and months 1 through 4. After 4 months, the intervention condition evidenced greater accuracy over time and, to some extent, neural markers of executive function (error-related negativity and beta and gamma band powers) compared to the control group on the Flanker and Stroop tasks. The intervention condition also lost more weight than controls (-7.1 pounds vs. +1.0 pounds). Given mixed evidence on whether CBT-based interventions can change markers of executive function, this study contributes preliminary evidence that a multicomponent CBT-based weight management intervention (i.e., that provide both support for weight management and is based on CBT) can help individuals sustain executive function compared to controls.

Cannabis is the most widely cultivated, trafficked and abused illicit drug (“WHO | Cannabis,” n.d.; “World Drug Report 2020,” n.d.). In 2018, an estimated 192 million people aged 15-64 years used cannabis for nonmedical purposes globally (Degenhardt et al., 2013). The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated that, across the globe, there were more than 22·1 million people with cannabis dependence (Degenhardt et al., 2018). Moreover, the same study calculated that cannabis dependence could be accounted for 646 thousand Disability Adjusted Life Years, globally. Importantly, cannabis dependence mostly affects young adults (20-24 years), and thus has significant negative impact on the growth and productivity of not only these individuals but also to the societies and nations (Degenhardt et al., 2013). In addition to the dependence syndrome, cannabis use is associated with increased risk of psychosis, cognitive dysfunction, academic problems, and road side accidents (Volkow et al., 2014). A review showed a fairly consistent associations between cannabis use and both lower educational attainment and increased reported use of other illicit drugs (Macleod et al., 2004). In the United States, Cannabis Use Disorder (CUD) is an escalating problem in young adults by legalization (Cerdá et al., 2020) where National Survey on Drug Use and Health reported increased prevalence from 5.1% in 2015 to 5.9% in 2018 in 18-25 year olds (“2019 NSDUH Detailed Tables | CBHSQ Data,” n.d.). The psychoactive effects are due to type 1 cannabinoid receptor (CB1), the cannabinoid binding protein, that are highly expressed in the cerebellar cortex (Marcaggi, 2015). CB1 is primarily found in the molecular layer at the most abundant synapse type in the cerebellum (Marcaggi, 2015) that can shape the spike activity of cerebellar Purkinje cell (Brown et al., 2019). Moreover, granule cell to Purkinje cell synaptic transmission can trigger endocannabinoid release (Alger and Kim, 2011), which may be important for information processing by cerebellar molecular layer interneurons (Dorgans et al., 2019). This suggests that endocannabinoids could be essential to neurocognitive aspects of cerebellar function (Di Marzo et al., 2015)^,(Marcaggi, 2015)^,(Alger and Kim, 2011). Accumulating evidence also suggests cerebellar modulation of the reward circuitry and social behaviour, via direct cerebellar innervation of the ventral tegmental area (VTA) including dopamine cell bodies (A1) in the VTA (Carta et al., 2019). The VTA-dopamine (DA) signalling in the nucleus accumbens (NAc) and the medial prefrontal cortex (mPFC) (Lohani et al., 2019) play a key role in motivatedbehaviours and cognition. Cerebellar neuropathological changes can result in aberrant dopaminergic activity in the NAc and mPFC (ROGERS et al., 2011)^,(Lohani et al., 2019). Therefore, there is a critical need to determine how cerebellum modulate limbic VTA-DA signalling. Cerebellar Non-Invasive Brain Stimulation (NIBS) is postulated to be most relevant in CUD since endocannabinoids are essential to cerebellar function that includes reward-related behaviours, information processing, and cognitive control. (Di Marzo et al., 2015)^,(Marcaggi, 2015)^,(Alger and Kim, 2011). Furthermore, cerebellar NIBS can facilitate training of cognitive control in CUD during a during visual cue reactivity paradigm using a mobile virtual reality (VR) interface that can also allow remote delivery of cerebellar NIBS in conjunction with VR-based cognitive training for home-based intervention. Specifically, transcranial electrical stimulation (tES) can be translatable to low-cost (<$150) mobile devices that can be used in a low resource home-based setting (Carvalho et al., 2018).

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

India has pledged ambitious initiatives to mitigate its growing economies’ impact on the climate, including a goal of net zero emissions by 2070. The transportation sector is one of the key sources of emissions. Therefore, fostering e-mobility within this sector is a key priority for the Indian government. The lifecycle of LiBs raises numerous concerns regarding its sustainability in production, use of rare materials, and most importantly waste management. Furthermore, this paper will highlight the case for EV battery recycling as a growing sector that also has the potential to foster circularity in the EV sector. Through the introductory section, this paper lays out rationale for battery recycling in a circular EV economy. The subsequent sections discuss the current and emerging battery technologies and their link to recycling methods whilst overviewing domestic and foreign battery recycling companies. Finally, the paper concludes with a gap assessment of the battery recycling ecosystem in India and makes recommendations to further integrate this industry into the EV supply chain. As India steers towards a sustainable future, it is imperative that the country integrates battery recycling to enhance circularity in the EV ecosystem, closing the gap between a growing number of EVs and waste management.

This paper provides an overview of modelling and simulation methods for complex systems, including queuing and life support systems. To realize and capture every aspect of a system or situation, a model is used in place of the actual one. Using a model and simulation, we may create scenarios and make decisions. Computer experiments with the model can save costs, time, and effort once its validity has been shown. A model of the thing under study is created by computer simulation, which then runs the model on a digital computer and examines the results. It's interesting to note that a knowledge base of data includes information about predicted behaviour of the model as well as coordinated determination of external variables and parameters. Selecting the precise model creation technique is the first and most difficult task in simulation. A review of techniques used in simulation model execution (serial and parallel discrete-event simulation), simulation model analysis (calibration, validation, verification, goal-seeking), and conceptual, declarative, functional, constraint, and multimodal design are presented in this paper.

_{he present study was conducted to quantify the heavy metals cadmium, lead, copper, chromium, nickel, and zinc in the bivalve mollusk concha prieta (Anadara tuberculosa) using in- ductively coupled plasma optical emission spectroscopy (ICP-OES). This research aims to identify whether the bioaccumulate content of heavy metals exceeds the maximum limits established by various public health bodies such as the ONU, FAO, Codex Alimentarius, EEC, and NHI. Samples of the species were collected randomly from three locations in Puerto El Morro, Playas Municipal Market, and La Libertad Seafood Market in the Gulf of Guayaquil. Thirty-three soft tissue samples of (Anadara tuberculosa) were evaluated, and the values quantified in mg/kg were in the following order: Zn>Cu>Cd>Ni>Cr>Pb.}

This study focused on the investigation of myeloperoxidase through a Molecular Docking approach with various natural compounds to identify potential substances for cardiovascular research. Recent research has linked elevated myeloperoxidase levels to the severity of coronary artery disease. The results of the docking analysis revealed Polydatin, Daidzin, Astringin, Ginkgetin, and Amentoflavone as promising compounds with excellent binding capacity. To complement this, toxicity prediction studies were conducted using pkCSM. Overall, all four investigated molecules (Amentoflavone, Daidzin, Ginkgetin, and Astringin) were found to be non-toxic, exhibiting high maximum tolerated doses (human) and favorable values for Oral Rat Acute Toxicity and Oral Rat Chronic Toxicity. Moreover, they showed non-AMES toxicity and non-hepatotoxicity. Astringin, in particular, stood out with the highest positive effects, including a high maximum tolerated dose (human) and favorable values for Oral Rat Chronic Toxicity, along with dual inhibition of hERG II and hERG I. These findings suggest the potential utility of these compounds in cardiovascular research with minimized unwanted effects.

“Energy” is probably the most important term in science and technology. The reason is energy can be converted while it is overall conserved, and thereby correlates changes of a wide variety. Despite its successful use in many areas over more than a century, an analysis of current literature shows that definitions of what energy is and explanations why it is conserved are inconsistent. With regard to energy forms not even a definition was found, and examples given are often wrong. Looking at two interacting objects in the most simple situations, with energy conservation but without conversion, a consistent understanding of energy and its conservation can be derived. Based on it, the systematic set of energy forms with a clear definition of each is then presented.

Passive marine listening, involving the acoustic monitoring of underwater environments, has become increasingly vital for scientific research, environmental monitoring, and defense applications. However, the success of such applications critically depends on the ability to extract meaningful information from the often noisy and dynamic underwater acoustic environment. In this context, Mel-frequency cepstral coefficients (MFCCs) have emerged as an essential tool for feature extraction, capturing the spectral characteristics of marine sounds and making them ideal for species identification and sound event detection. This research presents an innovative approach to enhance the robustness of MFCC-based passive marine listening through adaptive noise reduction techniques. The proposed approach utilizes dynamic spectral subtraction to counter underwater noise, resulting in enhanced signal-to-noise ratios for desired sounds. This adaptive system adjusts to dynamic underwater noise, allowing it to concentrate on target sounds and suppress interference effectively. The experimentation further validates the effectiveness of the proposed approach, with results reaching 99% on the full dataset of 570 mammals and vessels, demonstrating the effectiveness of SVM and Random Forests in the classification of underwater audio.

This theoretical study presents a novel perspective on the potential anti-inflammatory properties of Bromelain, a natural agent extracted from pineapple stems. The investigation explores the binding capabilities of Bromelain to Apoptosis-associated speck-like containing CARD (ASC), a crucial adapter molecule known for its involvement in inflammatory processes and inflammasome formation. For the first time, these findings suggest that Bromelain exhibits a notable affinity for ASC, indicating its promising role as a natural anti-inflammatory agent. This study sheds light on the molecular interactions that may contribute to Bromelain's therapeutic potential in modulating inflammatory responses.

We use the Stefan-Boltzmann law to derive and investigate what we will call the black hole CMB temperature luminosity. Interestingly, the CMB luminosity is always the same for every black hole at a luminosity distance equal to the Schwarzschild radius, which is fully valid in black hole cosmology, a topic that is actively discussed to this day. The CMB black hole luminosity is always the same , regardless of the mass of the black hole. This is in contrast to the Bekenstein-Hawking luminosity, which is linked to the Hawking temperature, that is mass dependent.

Developmental stability (DS) is indicated by the ability of individuals to resist random environmental and/or genetic perturbations during their development. Fluctuating Asymmetry (FA) is the commonest mean of assessing DS in bilateral traits. FA is defined as small random deviations from an ideal morphological symmetry. To date, little attention has been paid to sexual differences in FA among tortoises. With this issue in mind, we assessed levels of carapace scutation symmetries in a sample of 45 wild adult red-footed tortoise, Chelonoidis carbonarius (13 males and 32 females) from Arauca plains, East Colombia. Procrustes ANOVA reflected statistically significative sex-related FA, with males tending to exhibit a more pronounced asymmetry. Although we cannot identify the potential sources responsible for detected developmental instability, our results suggest a high degree of stress and highlight that human intervention in Arauca are affecting wildlife. Similar studies in the future, correlated with an estimate of human impact, could provide irrefutable proof of causality between FA and environmental stressors. The results of this study can represent an estimate of the fitness status of the studied population at present.

The use of artificial intelligence technologies may help companies obtain the optimal use of resources and accomplish more activities with an increase in productivity by 40%. With access to real-time data and the ability to learn from patterns and behaviors, AI can perform these routine tasks more efficiently. Ambient intelligence, Ami, is one of the applications of artificial intelligence, which is called artificial environmental intelligence, which is a concept that focuses on using technology to create an environment that is intelligent and responsive to the needs of its users. This technology has the potential to revolutionize disaster response and management, which in turn facilitates access to necessary information and resources. necessary vitality in the event of a crisis. It may use ambient intelligence to create a network of sensors that can detect and respond to changes in the environment. So that this network can be used to monitor the environment for potential disasters, such as floods, fires and earthquakes. Sensors can also be used to detect changes in temperature, humidity, and other environmental factors that may indicate a potential disaster. This information can then be used to alert emergency responders and provide them with the information needed to respond quickly and effectively. Ambient intelligence can also be used to create a comprehensive database of resources and information that can be accessed in times of crisis. This database may include information about the location of shelters, medical facilities, and other resources that can be used to assist those affected by a disaster. It may also provide information about the condition of roads, bridges, and other infrastructure that may be affected by a disaster. This information can be used to inform response and management decisions, helping to ensure that resources are used in the most efficient way possible. Ambient intelligence can be used to create a communication platform between emergency responders and those affected by a disaster. This platform can also be used to provide updates on the emergency situation, in addition to providing information about available resources to those affected. This can help ensure that those affected by a disaster can access the resources they need in a timely manner.

The COVID-19 pandemic caused by the RNA virus SARS-CoV-2, has had a significant impact not only on bodily metabolism but also at the genomic level. This study utilizes a bibliometric approach to map and identify the relationship between COVID-19 and DNA damage, genomic instability, and cell cycle deregulation. The research began with the development of in-depth research questions and conducted two rounds of literature search in the Scopus database with stringent filters to ensure relevance and accuracy. From an initial 1098 documents, 576 relevant documents were selected for further analysis. The collected data was then visualized using bibliometric methods, enabling the identification of important patterns, trends, and relationships among the research variables. Key findings indicate that DNA damage, which can induce apoptosis, is the worst impact of the infection on cells. Term frequency analysis and science mapping revealed a close relationship between "DNA damage" and "apoptosis". Furthermore, discussions on genomic instability highlight telomere regulation issues, while cell cycle deregulation during virus replication in mammalian cells indicates the importance of maintaining cell membrane integrity.This study confirms the importance of bibliometric analysis in detecting and understanding the impact of COVID-19 at the genomic level, proposing several potential research targets and enhancing our understanding of the biological response to viral infection.

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Seismic vulnerability assessment in urban areas would in principle require the detailed modeling of each single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at urban scale where many buildings have to be considered and therefore it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns the application of machine learning algorithms, able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper machine learning algorithms will be applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity occurred in 2009. A combination of a trained neural network and a random forest algorithm allows to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.

This paper studies the dividend optimization problem in the entropy regularization framework by following the same continuous-time reinforcement learning setting as in Wang et al. (2020). The exploratory HJB is established and the optimal exploratory dividend policy is a truncated exponential distribution. We show that, for suitable choices of the maximal dividend paying rate and the temperature parameter, the value function of the exploratory dividend optimization problem could be significantly different from the value function in the classical dividend optimization problem. In particular, the value function of the exploratory dividend optimization problem could be classified into three cases based on its monotonicity. Numerical examples are also presented to show the impact of temperature parameter on the solution.

Glial cells provide physical and chemical support and protection for neurons. Secreted neurotrophic factors, scaffolds and vesicles regulate in addition to normal homeostasis, repair following neural tissue injury or degeneration. We previously demonstrated that human CNS glia have revascularization capacity by inducing endothelial cell sprouting and microchanneling driven by glial cell secreted components. The clinical potential of using secreted factors and vesicles generated by glial cells in conditioned media was shown by their re-normalization of pathological blood vessels and 3D tissue wound healing and remodeling capacity. To identify secreted components of glial cells and how CNS and PNS glial cells differentially regulate neural tissue homeostasis, disease and injury response, we developed a novel culture method for various types of glial cells that allow for isolating and characterizing secreted glial components, free of animal factors and contaminants normally associated with serum-based culture conditions. Our culture method will enable to identify secreted components using ultrasensitive RNA, glycoconjugate and protein analysis technologies. The characterization of glial secreted factors involved microchanneling may help in development of new clinical therapies for glial and neural cells or axons to migrate and reform connections.

Adamantinomatous Craniopharyngioma (ACP) is a rare epithelial tumor located in the craniopharyngeal duct, often associated with high morbidity and a high long-term recurrence rate. ACP is predominantly diagnosed in children below the age of 15 and accounts for a majority of Craniopharyngioma cases in the United States. It is most commonly caused by a mutation in exon 3 of the CTNNB1 gene, which promotes overaccumulation of β-Catenin in the WNT pathway. As β-Catenin accumulates, it over-activates the transcription factor, causing an over-transcription of WNT proteins and uncontrolled cell growth. Current methods of treatment for ACP are centered around surgical intervention and radiotherapy, including gross-total resection (GTR), subtotal resection (STR), and a combination of subtotal resection and adjuvant radiotherapy (STR+XTR). These methods of treatment are associated with high risk due to the proximity of ACP to critical structures near the craniopharyngeal duct. High recurrence rates and morbidity are closely associated with current methods of treatment and other methods have yet to be attempted in craniopharyngioma. This review will explore two possible methods of non-surgical and non-radiological therapeutic interventions. The use of RNAi therapy and CRISPR may provide insight and be a potential way to treat ACP with increased quality of life and decreased recurrence rates.

Building defects are very prevalent and contribute significantly to a building's economic value. There are numerous information sources on building defects that have a lot potential for learning more about building defects. This study aimed to identify the information sources used in previous building defect studies and to identify the motivation for carrying out such studies. To fulfil this aim a scoping study was carried out. The information sources identified included insurance companies, private databases, questionnaire surveys, lawsuits, building surveys, client complaint forms, and maintenance reports. This study found that insurance company and client complaint forms include the largest collection of real building defect cases, but such databases may lack detailed descriptions of the causes of the defects. The main purposes of building defects studies identified here included design challenges, identification of defects, building maintenance management, quality management, systematization in data collection, providing an overview of typical defects, and classifying defects. Identification was found to be the most common purpose, indicating that the industry wants to learn more. This study identified research gaps in the climate perspective in relation to building defects. Most of the studies focused on the economical perspective, and none focused on the carbon footprint perspective.

The escalating levels of atmospheric carbon dioxide (CO₂) due to anthropogenic activities have raised significant concerns regarding their far-reaching consequences, including potential impacts on human brain function. This study employs Agent-Based Modeling (ABM) and a comprehensive literature review to comprehensively examine the effects of varying atmospheric CO₂ concentrations on human brain activity. Historical and projected CO₂concentration data, along with neuroscientific insights, were integrated into the ABM framework to simulate brain activity responses under different CO₂ scenarios. The simulations revealed that elevated CO₂ levels disrupt neural activity and alter neurotransmitter profiles, indicating potential cognitive consequences. Sensitivity analysis emphasized the importance of accurate parameterization for robust simulations. The findings underscore the urgent need to address global CO₂ emissions to preserve cognitive health. Further research is essential to validate and refine the model, incorporating additional factors for a comprehensive understanding of the cognitive repercussions of rising CO₂ levels, crucial for informed policy-making and public awareness campaigns.

Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.

Polydatin, a polyphenolic compound sourced from various natural origins, has garnered attention due to its potential anti-tumor properties and low toxicity. This research utilized Molecular Docking methods to investigate the molecular interactions between Polydatin and the Crystal Structure of Human UDP-glucuronic acid decarboxylase (UXS1), aiming to uncover the mechanisms contributing to its anti-tumor effects. The study revealed a substantial binding energy value of approximately -10 kcal/mol for the Polydatin-UXS1 interaction, indicating a robust and favorable binding. This suggests that Polydatin holds significant promise in cancer research, offering insights into its ability to interact with and potentially modulate the activity of UXS1. Moreover, the additional step of predicting toxicity properties using the pKCSM server provides valuable insights into the safety profiles of polydatin ( or piceid). From these results, the parameters for polydatin: —Max. tolerated dose (human),- Oral Rat Acute Toxicity (LD50), and - Oral Rat Chronic Toxicity (LOAEL)—suggest a favorable safety profile.These findings position Polydatin as a compelling candidate for further exploration in cancer research, highlighting both its efficacy and safety considerations.

Subacute sclerosing panencephalitis (SSPE) is a progressive neurological complication that affects the central nervous system. It occurs as a late complication of the measles infection the young children and adolescents who are commonly affected present with personality changes and neurological deficits. The diagnosis can be done via clinical features, electroencephalography, and cerebrospinal fluid analysis. At present, there is no cure for SSPE. The immunisation against measles is the main strategy in preventing this condition. Here, we present a case of a 22-year-old Indian male diagnosed with Subacute sclerosing panencephalitis.

The Swiss Personalized Health Network has developed a national framework for enabling the semantic representation of health data within a Knowledge Graph. This framework has been implemented in all Swiss university hospitals, promoting seamless sharing and integration of clinical routine data with other health-related data, including omics and clinical research data. While research projects often have flexibility in selecting terminologies and specific versions, historical clinical routine data are typically coded for billing or administrative purposes using predefined terminologies in various (sometimes even unknown) versions over time. Some of these terminologies do not adhere to best practices for ontology design, presenting significant challenges to the retrospective re-use of such coded data for research. Common issues with these terminologies include the lack of machine-readable traceability across versions and non-adherence to FAIR principles. Terms from older versions frequently disappear in newer ones, making it challenging to distinguish outdated from invalid terms. Additionally, 'semantic drift' occurs, where the meaning of terms changes across versions. To address these challenges, we have implemented FAIR and historized versions of ATC, CHOP, and ICD-10-GM. We represent each version in RDF using versioned URIs and track meaning changes between versions in a machine-readable way using OWL and RDFS. The integration of these historized terminologies into our quality control framework, based on SHACLs, enables comprehensive data quality control in hospitals and empowers researchers to effectively utilize this data. Our work aims to bridge the gap between health data coded in different terminology versions, ensuring a consistent and reliable semantic representation.

(1) Background: Breast cancer (BC) is the second largest cause of mortality in female worldwide. Besides from basic risk factors, there are several hereditary variables that contribute to BC. Breast cancer has been linked to a variety of nuclear DNA changes as well as mitochondrial DNA alterations. The aim of this study was to analysis the association of mitochondrial tRNA leucine2 gene with BC. (2) Material and methods: In the current study, 24 samples have been collected from various families in Peshawar. DNA was extracted from Blood. PCR was used to amplify the mitochondrial tRNA MT-TL2 gene, and 22 samples were sequenced. The sequence was compared with accession #NC-012920.1 of the revised Cambridge Reference Sequence (rCRS). (3) Results: The results (Chromatograph, nucleotide sequence and rCRS alignment) shows mutations in mitochondrial tRNA MT-TL2 gene in our participants is not the cause of Breast cancer. (4) Conclusion: Yet, a significant number of BC patients must be studied, and their full mitochondrial DNA must be analyzed. This will provide an indication of the potential DNA marker that might be used to prevent BC deaths at the earliest stages.

Patients with bipolar disorder (BD) experience more frequent and intense emotions in response to environmental conditions relative to healthy subjects, which leads to mood instability. The aim of this study was to investigate the time-course of emotion regulation strategies, distraction, and reappraisal, in euthymic BD patients using electroencephalography (EEG). Fourteen BD patients and 13 matched healthy controls took part in an experiment constituted of three conditions, i.e., a passive viewing of positive, negative, and neutral pictures and two regulation conditions, one with reappraisal strategy and the other with distraction strategy. Critically, ERP results indicated that during passive viewing, the Late Positive Potential (LPP) was larger in BD patients compared with healthy controls, but only for neutral pictures. During emotion regulation, LPP amplitude was reduced in distraction condition compared with viewing one, especially for negative emotions in both patients and controls. Importantly, LPP was reduced in reappraisal condition compared with passive viewing in an early time window for negative emotions and in a later time window for positive emotions in controls but not in patients. Our findings showed that the neurochronometry of emotion regulation is faster for negative than for positive emotion in controls but not in BD patients.

Construction safety is especially important because the construction industry is so important to a country's development. Significant research and practice have been conducted to mitigate potential risks during construction and improve worker efficiency. With the rapid advancement of cognitive neuroscience and the incorporation of medical technology in recent years, various wearable monitoring devices have been widely used in the construction field for real-time monitoring of workers' physical and mental status. Among these, the use of EEG (Electroencephalogram) in construction environment research allows researchers to gain insight into the physical and mental states of construction workers while performing construction tasks. This review introduces EEG technology and portable EEG devices, followed by an overview of their use in both monitoring workers' adverse reactions and identifying hazards on construction sites, providing an effective guide for EEG research in the construction field and on-site safety management.

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

Background: Body dysmorphic disorder (BDD) is a psychiatric disorder characterized by excessive preoccupation with imagined defects in appearance. Optical illusions induce illusory effects that distort the presented stimulus thus leading to ambiguous percepts. Using electroencephalography (EEG), we investigated whether BDD is related to differentiated perception during illusory percepts. Methods: 18 BDD patients and 18 controls were presented with 39 optical illusions together with a statement testing whether or not they perceived the illusion. After a delay period, they were prompted to answer whether the statement is right/wrong and their degree of confidence for their answer. We investigated differences of BDD on task performance and self-reported confidence and analysed the brain oscillations during decision-making using nonparametric cluster statistics. Results: Behaviorally, the BDD group exhibited reduced confidence when responding incorrectly, potentially attributed to higher levels of doubt. Electrophysiologically, the BDD group showed significantly reduced alpha power at mid-central scalp areas, suggesting impaired allocation of attention. Interestingly, the lower the alpha power of the identified cluster, the higher the BDD severity, as assessed by BDD psychometrics. Conclusions: Results evidenced that alpha power during illusory processing might serve as a quantitative EEG biomarker of BDD, potentially associated with reduced inhibition of task-irrelevant areas.

The study of the brain networks using analysis of electroencephalography (EEG) data based on statistical dependencies (functional connectivity) and mathematical graph theory concepts is common in neuroscience and cognitive sciences for examinations of patient and healthy individuals. The Consciousness Fields according to Taheri theory and applications in the optimization of system under study have been investigated in various studies. In this study, we examine the results of working with Faradarmani Consciousness Field (FCF) in the brain of Faradarmangars. Faradarmangars are one of the necessary components in mind mediation of the function of Faradarmani Consciousness Fields according to Taheri. For this purpose, the functional and effective connectivity and the corresponding brain graphs of EEG from the brain of Faradarmangars is compared with that of non Faradarmangar groups during FCF connection. According to the results of the present study, the brain of the Faradarmangars shows significant decreased activity in delta (BA8), beta2 (BA4/6/8/9/10/11/32/44/47) and beta3 (in 34 of 52 BA) frequency bands mainly in frontal lobe and after that in parietal and temporal lobes in the comparison with the non Faradarmangars. Moreover, the functional and effective connectivity analysis in the frontal network shows dominant multiple decreased connectivity mainly in the case of beta3 frequency band in all parts of the frontal network. On the other hand, the graph theory analysis of the Faradarmangar brain shows an increase in the activity of the O2-T5-F4-F3-FP2-F8 areas and significant decrease in the characteristic path length and increases in global efficiency, clustering coefficient and transitivity. In conclusion, the unique higher graph function efficiency and the reduction in the brain activity and connectivity during the Faradarmani Consciousness Field mind mediation, shown the passive and detector like function of the human brain in this task.

The interest in dry EEG electrodes has increased in recent years and especially as everyday suitability earplugs for measuring drowsiness or focus of auditory attention. However, the challenge is still the need for a good electrode material, which is reliable and can be easily processed for highly personalized applications. Laser processing as used here is a fast and very precise method to produce personalized electrode configurations that meet the high requirements of in-ear EEG electrodes, for example. The arrangement of the electrodes on the very flexible and compressible mats allows an exact alignment of the electrodes to the ear mold and contributes to a high wearing comfort, as no edges or metal protrusions are present. For better transmission properties, an adapted coating process for surface enlargement of platinum electrodes is used, which allows easy control of the thickness and growth form of the porous layer. The porous platinum-copper alloy is chemically very stable, shows no exposed copper residues and enlarges the effective surface area by 40. In a proof-of-principle experiment, these porous platinum electrodes could be used to measure the Berger effect in a dry state using just one ear of a test person. Their signal-to-noise ration and frequency transfer function is comparable to gel-based silver/silver chloride electrodes.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

Though nowadays germanium do not reach the range of popularity than other metals, i.e. rare earth elements, its utility in target industries makes of it also a strategic metal. Though germanium can be find in a series of raw materials, the principal source for its recovery is from secondary wastes of the zinc industry, and also, the recycle of germanium-bearing waste materials is becoming of interest. In this recovery, Hydrometallurgy is performing a key role to the successful achievement of this goal. The present work reviews the most recent applications (2023 and 2024 years) of hydrometallurgical operations on the recovery of germanium from different solid and liquid sources.

The liver diseases are posing serious threat to human health associated with abusive use of allopathy medicines, especially in developing economies like Pakistan. The strengthening of detoxification system using natural products might be a suitable option to minimize the liver problems. In this current research, Zingiber officinale and Azadirachta indica were evaluated for their hepatoprotective role. For the purpose, 20 Sprague Dawley rats were equally divided into four groups i.e., negative control, paracetamol positive control, 2.5% ginger and 2% neem powder, respectively. During 42 days study, the levels of bilirubin and liver enzymes significantly elevated due to liver damage. Although, the values dropped (20-25%) in treatment group. Serum glucose and serum lipid profile increased in positive control group, but reduced (5-10%) in both ginger and neem groups. The serum protein profile declined in liver damage group, however, improved (~5%) in treatment groups. The hematological parameters slightly declined in liver damage group, but increased in ginger and neem groups. Moreover, the white blood cells differential count and renal parameters showed abnormal values in liver damage group, but improved after treatment. This study is promising and highlighted the hepato-protective role of neem and ginger that may open several avenues of research.

A difficult open problem that has never been fully resolved in the literature to date is the behaviour of the (TDCDP) third approximation. As a giant step towards modern control theory, this cutting-edge paper will advance control theory and other relevant inter-disciplinary subjects. Because control theory is important in industry and engineering, this study will be especially beneficial to anyone working in these disciplines who wish to understand more about recent advances in control theory settings. In contrast, the wealth of applications of Fokker Planck Kolmogorov (FPK) equations numerous disciplines As a result, the study instantly gains more taste and credibility. At the end of the paper, there are several challenging open problems, some concluding remarks, and recommendations for further investigation.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

Introduction: Parameters used for neonatal mortality calculation vary among publications. Mortality in < 33 weeks gestational age at birth display global variations across different healthcare units. Objective: This study aims to explore trends in neonatal mortality among infants born preterm in the different units in Latin America and the Caribbean utilizing the EpicLatino Network Database in comparison to Canadian Neonatal Network (CNN) 2022. Materials and Methods: This study focused on an eight-year period, with a particular attention to mortality rates during the pre-pandemic (2015-2019) and pandemic/post pandemic (2020-2022) periods. Survival rates from CNN 2022 were included in the comparison analysis. Logistic regression analysis was confined to the 2020-2022 period. Adjustments were made for factors including gestational age, small for gestational age (SGA), Snape II score, inborn/outborn status, and center. As major malformations could account for mortality differences among units. The incidence of major congenital malformations, as defined by CNN, was compared among deceased patients. Results: A total of 15,454 records from 2015-2019 and 10,711 records from 2020-2022 were scrutinized. Overall, there were no significant differences in mortality rates between the two time periods (p=0.22). Moreover, the originating unit during the 2020-2022 period significantly influenced all statistical computations. Conclusions: Survival rates among infants < 26 weeks of gestation in Latin America and the Caribbean are on an upward trajectory, with the healthcare unit playing a pivotal role in this outcome within the EpicLatino database. Major malformations do not seem to be a significant contributing factor. These findings underscore the imperative of implementing quality improvement initiatives to elevate neonatal care standards in Latin America and the Caribbean.

Graph databases (GDBs) are becoming increasingly popular across scientific disciplines, being highly suitable to store and connect complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph (KG) databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available GDB technologies. Relevant publications are then categorised according to their domain and application. We detail different approaches and highlight advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardise and harmonise KG creation and the maintenance of integrated resources. Outlining prospects, including the use of GDBs as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of GDBs will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

The emerging industry of offshore wind turbines mounted on floating bases has garnered significant attention from both academia and industry. The desire to understand the complex physics of these floating structures has led to the development of numerical and physical modelling techniques. While physical testing has traditionally been employed, there is a growing focus on cost-effective and accurate high-fidelity numerical modelling as a potential alternative or supplement. However, commonly used numerical engineering tools in the offshore industry are considered mid- to low-fidelity and may lack the desired precision for floating offshore wind turbines (FOWTs). Given the complexity of these simulation codes, it is crucial to validate their accuracy. To address this, the International Energy Agency (IEA) Wind Technology Collaboration Programme initiated various research endeavors, including the Offshore Code Comparison Collaboration (OC3), Offshore Code Comparison Collaboration Continuation (OC4), Offshore Code Comparison Collaboration Continuation with Correlation (OC5), and the recent Offshore Code Comparison Collaboration Continued with Correlation and Uncertainty (OC6) projects. This study offers a comprehensive survey of the simulation tools available for FOWTs which were part of OC projects, focusing particularly on horizontal axis wind turbines (HAWTs) and highlighting their capabilities and fundamental theories.

Nanoindentation load-depth (P-h) plots is the basis for determination of mechanical properties of the samples and is especially vital for thin coatings deposited on substrates of various mechanical makeup from amorphous-brittle (borosilicate glass) to Silicon (having different crystallographic texture) to ductile substrates like stainless steel as well other aspects like indenter tip shape. The elastic-plastic response by simulated P-h plots as per modified empirical relations have been presented. The outcomes of this computational studies enlighten the database on the P-h plots for various coating/substrate systems and shall aid in the analysis of experimental P-h plots obtained,

We show succinctly a natural origin of the beautiful Gamma function in light of the WJ distribution, linking to quite distinct fields.

Modern research applies the Open Science approach that fosters the production and sharing of Open Data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In the geospatial context this is generally achieved through the setup of OGC Web services that implements open standards that satisfies the FAIR requirements. Nevertheless, the requirement of Findability is not fully satisfied by those services since there’s no use of persistent identifiers and no guarantee that the same dataset used for a study can be immutably accessed in a later period: a fact that hinders the replicability of research. This is particularly true in recent years where data-driven research and technological advances have boosted frequent updates of datasets. Here, we review needs and practices, supported by some real case examples, on frequent data or metadata updates in geo-datasets of different data types. Additionally we assess the currently available tools that support data versioning for databases, files and log-structured tables. Finally we discuss challenges and opportunities to enable geospatial web services that are fully FAIR: a fact that would provide, due to the massive use and increasing availability of geospatial data, a great push toward open science compliance with ultimately impacts on the science transparency and credibility.

Star copolymer films were studied for confirming the role of film morphology in antimicrobial activity previously reported. With this scope, films of a two-branches copolymer, namely m-PEG-(MMA-ran-DMAEMA)2, containing ≈ 40 % in mol of non-quaternized 2-(dimethylamino)ethyl methacrylate, were produced by using spin-coating, drop-casting and casting deposition techniques, thus obtaining ultra-thin, thin and thick films, respectively. The morphology is generally flat for thin and thick films, but it becomes substrate dependent for ultra-thin films where the film planarization effect is not efficient. Mechanical properties of such films were investigated by Force Volume Maps in both air and liquid. In air, ultra-thin films are in the substrate-dominated zone and thus the elastic modulus E is overestimated, while E reaches its bulk value for thin and thick films. In liquid (water), E follows an exponential decay for all films with a minimum soaked time t0 of 0.37 and 2.65 h for ultra-thin and thin and thick films, respectively. After this time, E saturates to a value in average 92 % smaller than that measured in air due to films swelling. Such results confirm the role of morphology envisaged in literature, suggesting also an additional role of mechanical properties in the antimicrobial activity.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

Coral reefs are one of the most productive yet vulnerable ecosystems on Earth. An important step in understanding the functioning of coral communities is studying the environmental impact on the state of reefs. The present study aimed to assess the impacts of the water temperature, precipitation, wave action and population density on the seasonal bleaching, partial and complete mortality of shallow-water branching corals of Pocillopora verrucosa species complex. The present study was conducted at the Dam Bay research station (Nha Trang Bay) from 26 April 2020 to 2 April 2021. Throughout the study the percentage of bleached coral colonies varied from 1 to 41.5% with two peaks, in autumn (October) and spring (April). Rates of the development of partial mortality varied from 0 to 10%, with a pronounced peak in December. At the end of the experiment, the percentage of coral colonies suffering from partial mortality reached 47%. Only 4,5% of experimental colonies died by the end of the study. Analysis revealed that water temperature and partial mortality are the drivers of bleaching, while wave action, temperature and bleaching are the factors with a significant impact on partial mortality. In the present study, the main cause of coral death was the development of algal fouling at the sites of colony lesions, which gradually spread over the entire surface of the colony. We showed also that the impact of individual stress on mortality prevailed over the impact of seasonal bleaching and partial mortality events.

The possibility of a shock wave recovery at a discrete closed interface with a heated gas has been investigated. A two-dimensional model applied to conditions of optical discharges featuring spherical, elliptical, and drop-like configurations demonstrated that non-symmetry in the shock refraction contributes to the specific mechanism of recovery other than simply its compensation. Even though the full restoration of the hypersonic flow state doesn’t occur in a strict sense of it, clear reverse changes toward the initial shape of the shock front eventually take place, thus creating an appearance of a full recovery seen in experiments. From analysis for different interfaces symmetries, the factors determining the recovery dynamics are identified. The results are directly applicable to the problem of energy deposition into a hypersonic flow, however it can be useful anywhere else where the flow modifications following the interaction are important. The dimensionless form of the equations allows applications on any scale other than that demonstrated for the optical discharges.

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong com-pound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the Electromagnetic Interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, common mode rejection ratio , gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain Computer Interfaces and neurofeedback training systems.

With the trend of public infrastructure becoming more sustainable, large multi-purpose sports venues (LMPSVs) have become one of the world's most complex architectural projects and one of the landmark buildings of an international city. LMPSVs will lead the world in sustainable design, construction and operational innovations, and play a crucial role in raising awareness of sustainability around the world. System Thinking (ST), System Dynamics (SD) and Sustainable System Development (SSD) theories are three interrelated concepts that together form a methodological framework for solving complex system problems and driving strategies. The purpose of this study is to examine how LMPSVs can implement environmental, social, governance, and innovation (ESGI) enterprise transformation strategy (ETS) based on a sustainable systems development (SSD) framework. In view of the current global environmental challenges and social responsibility requirements, the motivation of this study is to provide an integrated transformation pathway for LMPSVs to achieve the goal of sustainable development and to contribute to the field of related disciplines. The methodology of this research adopts a combination of literature review and case study analysis, and the theoretical basis is mainly drawn from cutting-edge research in the fields of sustainable development, corporate social responsibility (CSR) and environmental management. Then, by analyzing successful transformation cases in China, we extract data and information to explore the application and effectiveness of these strategies in practice. The results of the study show that by implementing ESGI-based transformation strategy, LMPSVs can not only make significant progress in terms of environmental protection, social responsibility, and governance structure, but also drive long-term corporate growth and competitiveness through innovation. Specifically, these results are realized in terms of increased efficiency in energy use, improved waste management, community involvement and increased stakeholder satisfaction. The study concludes by emphasizing that the SSD-based ESGI Transformation Strategic framework provides a sustainable pathway for LMPSVs. In the future, the extension of this transformation strategy will help a wider range of industry sectors to achieve sustainable development goals, and has wide application potential and far-reaching social significance.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string.

Objective To investigate nailfold capillary parameters in community-dwelling individuals aged over 60 years who have hypertension and do not exercise regularly. Furthermore, the study examined the correlations between capillary function and other health-related indicators. Design A single- center pilot trial Setting Faculty of Health, Tsukuba University of Technology, Japan Participants hypertensive community-dwelling elderly people Intervention Microcirculation was observed before and 1 min after arm-curl exercise by means microscopy of capillary with non-exercised limb. Additionally, we examined other health-related indicators. Outcome measures: Acute effects of reperfusion on nailfold density, flow, and diameters. Secondary outcomes included the correlations between microvascular parameters and other health-related indicators. We hypothesized that brief exercise could enhance microcirculation reperfusion and correlate with other health-related parameters.　 Results: There were 20 participants with a mean (SD) age of 67.1 (5.8) years. The capillary flow rate changed from 2.3±6.7 to 2.7±0.2 log µm/s (P

In genetic conflicts, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables a selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

Understanding clinical significance of variants of unknown significance (VUS) reported in next-generation sequencing (NGS) has become essential in cancer treatment. Our study examined six widely used in silico tools: PolyPhen-2, Align-GVGD, MutationTaster2, CADD, REVEL, and Chat-GPT. We utilized a dataset of gene variants known to potentially affect immune therapy. No single tool could comprehensively determine mutation variant pathogenicity. MutationTaster2021 showed the highest overall accuracy and MCC among the tools. Notably, REVEL and Chat-GPT exhibited 100% specificity, suggesting their proficiency in accurately identifying pathogenic variants and minimizing false positives. In contrast, CADD displayed optimal sensitivity, making it suitable for effectively ruling out benign variants.

Based on recent progress in cosmological thermodynamics, we present a new thermody- namic formulation of the Friedmann equations in the new Haug-Tatum cosmology model, as well as for other similar models put forward in the literature from different solutions to Einstein’s field equation. Since the CMB temperature has been measured much more accu- rately than the Hubble constant, we demonstrate that our thermodynamic formulation of the Friedmann equation dramatically narrows the acceptable range for the critical density of the universe. Furthermore, we show how our thermodynamic formulation leads to a more precise cosmological constant estimate.

We are losing love. By studying porn movies to understand love, I discovered that love originally comes from the body, from positive emotions and vibrations of the wonderful body, from the heart and all other parts of the body, such as sexual organs. António Damásio made Descartes’ error by postulating that feelings increase their power from homeostasis (mechanisms of life regu-lation from bacteria to humans). He made this mistake unconsciously because many people un-consciously have a very bad opinion of sexual organs. We consider them ugly because they come from the prehistoric period. I have combined António and Hanna Damásio’s work (body emotions, thinking and language). I discovered that porn actors do not talk during sex, and this is striking. Porn actors mirror what happens in society. This means that people come from prehistory, do not talk during sex, and still unconsciously behave like primitive animals during sexual intercourse; we do not respect the body sufficiently during sex. Therefore, love, which comes from the body, is smothered and buried. We must very delicately talk to the wonderful, delicious and respectful body to wake up love feelings, body emotions and vibrations during sex. We must invent talking movies with porn films. Language of positive emotions associated with the language of wonderful and delicious body will warm love feelings and will give envy to love through sexual intercourse because António Damásio discovered that emotions are social.

In many countries, increasing concern for animal welfare is driving retailer commitments and government legislation that aim to improve the lives of farmed fish. One aspect of fish welfare involves stunning fish prior to slaughter. The feasibility of stunning depends on the species of fish and physical farm characteristics. In this article, we provide an overview of stunning before slaughter in European sea bass and sea bream aquaculture, one of the largest finfish farming industries in the developed world that does not yet stun most of its production. Sea bass and sea bream stunning necessitates the use of electrical stunning equipment aboard harvest vessels, often a significant distance from the shoreline; this presents an interesting engineering and policy challenge. Together, Türkiye, Greece, Spain, and Italy produced over 400,000 t of sea bass and sea bream in 2020. In Türkiye and Greece, farms are numerous and located very close to the shoreline. In Spain and Italy, farms are few and located far from the shoreline. The highest average production is found in farms from Türkiye (1,000 t) and Spain (1,300 t, and lower average production is found in Greece (300 t) and Italy (350 t). Producer progress towards the installation of electrical stunning appears comparatively well-developed for Türkiye, Spain, and Greece, though we emphasise that producers and other stakeholders require continued support to realise this opportunity. Producers in Italy appear slower to make progress on this aspect of animal welfare and may require additional support.

The Casimir effect can either work over very short distances or at very low temperatures. At the cosmic scale, we naturally deal with long distances, but at the same time, we encounter extraordinarily low temperatures, namely the CMB (Cosmic Microwave Background) tem- perature. Recently, there has been increased interest in the Casimir effect and its implications in cosmology. Here, we briefly demonstrate that the luminosity and radiation pressure of the CMB is mathematical identical to a theoretical Casimir effect within the Hubble sphere, or actually our solution seems to be valid for any Schwarzschild black hole. The Casimir effect forevery Schwarzschild black hole is identical. This also include the Hubble sphere if we treat it as a Schwarzschild black hole that is related to increased interest in black hole cosmology.

Caused by age or trauma, collapsed connective tissue can cause nerve entrapment and damage within the tarsal tunnel. Tarsal tunnel syndrome is relatively underdiagnosed. This study presents an intervention targeting damaged tissues surrounding the nerves and replacing the structural cushioning with a Wharton’s jelly tissue allograft. The eight selected patients from four clinical sites had tarsal tunnel-related defects. Patient outcomes were tracked on a 90-day calendar utilizing the Numeric Pain Rating Scale (NPRS) and the Western Ontario and McMaster University Arthritis Index (WOMAC). All patients had failed standard care practices for at least six weeks. Each patient received a Wharton’s jelly tissue allograft to sites around the affected tarsal tunnel. No patients experienced adverse reactions. Percent change calculated from the initial application to the 90-day follow-up showed an improvement of 59.43% in NPRS and a 37.58% improvement in WOMAC. This study provides evidence that WJ allograft applications are safe, minimally invasive, and efficacious for patients who have failed standard care treatments for tissue defects associated with Tarsal Tunnel syndrome. Limitations include the small cohort size and nonblinded nature. The results of this study warrant further research to confirm the efficacy, optimal dose, protocol, and durability of Wharton's jelly.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

The dermatological manifestations of Long Covid (LC) have languished in the shadows of chronic fatigue and brain fog. Yet they are all linked by gut dysbiosis and the cytokine triad of TNF-α, IL-1β, and IL-6. The gut microbiome common not only to LC, psoriasis, AA, and vitiligo but also to neurodegenerative disease has been recently described. This gut microbiome induces an altered tryptophan metabolism linked to autoimmune disease. SARS CoV2 invades enterochromaffin cells rich in ACE2 receptors and curtails absorption of the essential amino acid tryptophan and subsequent synthesis of serotonin and melatonin. This review suggests that an etiologic prebiotic (d-mannose)/probiotic (lactobacilli, bifidobacteria)/postbiotic (butyrate) approach to autoimmune skin disease that improves intestinal barrier integrity and that suppresses the triad of TNF-α, IL-6, and IL-1β may enhance or even eliminate the traditional immunotherapy of targeted monoclonal antibodies, Janus kinase inhibitors, and steroids. Health benefits of this approach extend well beyond suppression of autoimmune skin disease.

Upon the outbreak of the COVID-19, countries worldwide developed policies with the goal to introduce restrictive measures geared towards mitigating the spread of the virus. Although these health initiatives were put in place through top-down public directives, they relied heavily on the cooperation of citizens who had to be willing to carry them out. As a consequence, we could observe differences in compliance with governmental restrictions within and between countries. Increasing policy stringency was the method adopted overall to promote people’s compliance. This article used survey data from 10308 participants in Brazil to examine whether there was a positive correlation between policy stringency and compliance with restrictive measures to reduce the spread of COVID-19. The results revealed, however, that there was no direct influence of policy stringency on people’s health behaviors. Accordingly, I hypothesized that individual differences in personality traits were one of the drivers behind policy compliance, as they would support self-interests such as the duty to support authorities as well as the perceived health risks of certain behaviors and ultimately motivate compliance. The findings’ implications for both compliance research and for authorities wanting to nurture voluntary compliance with public health orders are discussed and suggestions are provided.

This work introduces a novel facial image capture system that utilizes computer vision technology and artificial intelligence for real-time detecting, tracking, and capturing of human faces. The objective of this study is to address the challenges posed by poor-quality facial images in biometric authentication, especially in passport photo acquisition and recognition. By combining face-aware capture technology with AES encryption for secure image storage, we present a completely open-source hardware solution that consists of a Jetson processor, a 16MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. Pilot data collection demonstrates the system's ability to capture high-quality images, achieving a 98.98% accuracy in storing images of acceptable quality. The integration of AES encryption ensures data security, making the proposed system suitable for real-time applications in other domains beyond identity verification in passport applications, such as security systems, video conferencing, etc

Albert Einstein, shortly after publishing his General Relativity Theory (GRT) applied it to the entire universe in a paper of 1917. Einstein assumed that such a universe was static in time and possessed a uniform distribution of matter on the largest scales, obtaining a static and finite universe of spherical spatial curvature of radius R. Although this model was refuted by Edwin Hubble's discovery of the expansion of the universe, it is still often studied as a theoretical framework for some problems, such as the twin paradox. This paper will show that Einstein's static universe model leads to a privileged frame of reference, so that any observer moving at the speed v will be able to measure speeds of light other than c depending on the direction of beam transmission. The above will refute the validity of the application to this closed and static universe of the Special Relativity Theory (SRT) proposed by Einstein in 1905 in his article . In addition, a new synchronization convention and new coordinate transformations will be deduced, the "generic" transformations, which will replace those of Lorentz. Finally, the twin paradox in this universe will be solved by making use of them.

Exosomes, a subset of extracellular vesicles, have emerged as pivotal mediators of intercellular communication, revolutionizing our comprehension of cellular interactions. Their diverse cargo, comprising proteins, nucleic acids, and bioactive molecules, plays a crucial role in various physiological and pathological processes. In the intricate landscape of adult neurogenesis, exosomes wield significant influence, actively participating in the generation of new neurons within the adult mammalian brain. Originating predominantly from non-niche cells, these vesicles contribute substantially to the regulation of neuroinflammation, thereby impacting neurogenesis and offering therapeutic promise for neurological disorders. In the specific context of Parkinson's disease (PD), exosomes assume a multifaceted role, prominently implicated in the propagation of α-synuclein oligomers and the orchestration of neuroinflammation. Stem cell-derived exosomes present themselves as promising therapeutic agents, capable of reshaping the microenvironment, repairing neuronal damage, and mitigating apoptosis. The ongoing exploration of exosomes continues to unravel intricate biological processes, paving the way for innovative medical interventions and treatments, particularly within the challenging landscape of neurodegenerative diseases such as PD. The comprehensive understanding of exosomal functions holds the potential to advance our therapeutic approaches, offering new avenues for intervention and treatment strategies in the realm of neurodegenerative disorders.

Among the number of hurdles that the vaccines based in modRNA platforms have been facing, there is now another critical one: immune imprinting which decreases even more the effectiveness of the modRNA Covid vaccines.

The initiation of micro-cracks in concrete is primarily due to its brittleness, which causes severe impacts on the overall behavior of the concrete. However, incorporating fibers in concrete could significantly improve its mechanical properties. The current study demonstrates the influence of two types of fibers on the mechanical properties of concrete. Nylon Fiber Threads (NFT) and Nylon Fiber Strands (NFS) at a fiber content of 2.5 kg/m³ were incorporated in the concrete mix. Concrete with a compressive strength of 30 MPa and a water-cement ratio of 0.4 was produced to investigate the fresh and mechanical properties of conventional and nylon-fiber reinforced concrete (NFRC). The concrete prims and cylinders were tested for compressive, splitting, and flexural strength using ASTM standards (ASTM C-39, C-496, and D-790, respectively). It was found that NFT-reinforced samples achieved higher mechanical properties of concrete (13.05%) compared to NFS-reinforced samples. Test results showed that incorporation of nylon fibers (NF) in concrete decreased the compressive strength of cylindrical and cubical specimens by 0–10% and 5–15%, respectively. However, it increased the splitting tensile and flexural strengths by 0-15%, and 5-15% respectively. Furthermore, the incorporation of NF in concrete significantly improved the ductility and delayed the crack initiation in concrete.

The Power Theory of Exchange and Money (PTEM) refines the economic neoclassical two-equation model of exchange. PTEM replaces the statistical average condition of exchange, which represents the equality of supply and demand for all transactions in the market, with a more specific equation of the equality of the generalized power of the parties in relation to their objects of exchange. The purpose of the article is to show the application of PTEM for the study of various social phenomena. Along with a brief summary of the main statements of this theory, its integration into the existing corpus of theories and ideas in economics and sociology is considered. The conclusions of power theory are then applied to consider the following topics: the objective motivation of exchange by the desire to increase power, the power nature of market equilibrium, the power nature of money, the accumulation of power through profit, the creation of money as a power act, the role of the state in the creation of money, the measure of power as a possible tool for social and political research.

In the domain of financial trading, where accuracy and swift response to data discrep- ancies are paramount for maintaining clienttrust, our research paper delves into the development of a real-time alert system utilizing SpringBoot, a framework renowned for its robustness and adaptability in enterprise applications. We embark on a comprehensive journey, beginning with addressing thefoundational challenges inherent in monitoring trade inquiries - the necessity for real-time processing, unwavering data accuracy,and ensuring unwavering system reliability. The study then methodically transitions to the architectural design, where we explore howSpringBoot’s capabilities are optimally utilized to meet these challenges. Following this, we delve into the im- -plementationphase, detailing the strategic selection of compatible technologies and methods for effective data management and discrepancydetection. The paper culminates in a thorough evalu- ation of the system, where we assess performance across various operationalscenarios, scrutinize the accuracy of the discrepancy detection mechanisms, and integrate real-world feedback from users in thetrading sector. This holistic approach not only highlights the versatility and efficiency of SpringBoot in creating reliable alert systems in the high-stakes world of financial trading but also serves as a detailed guide for similar endeavors in this field

In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD – Post-traumatic stress disorder – cases. For this aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models to find the feature weights to detect the PTSD cases. We classified 32 channels of 12 subjects including 6 PTSD and 6 healthy controls using a 6-mean classifier. The linear SVM found the weights of distinguished channels within each subject for each cluster. It was found that the significant SVM weights of F4, F8, and Pz are smaller in PTSD than in healthy subjects. This new method can be used as a tool to better understand the interaction of EEG signals and diagnosis.

Electroencephalography (EEG) signals provide valuable insights into various activities of the human brain, including the detection of mental stress, which is a complex physiological and psychological response. However, the challenge lies in identifying mental stress accurately while mitigating the limitations associated with a large number of EEG channels. This includes issues such as computational complexity, the risk of overfitting, and the increased setup time for electrode placement, which can be cumbersome for real-life applications. Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. Our approach, known as the Correlation Coefficient of Hjorth Parameters (CCHP), assesses the correlation between activity, mobility, and complexity in the time domain to nominate the most relevant channels. By selecting channels that exhibit high correlation with the stress task while being uncorrelated with each other, CCHP significantly reduces the number of EEG channels required, without compromising accuracy or performance. To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. Furthermore, CCHP outperformed existing channel selection methods by an impressive 8%. These findings strongly indicate that the CCHP algorithm shows great promise in the design of a wearable application for mental stress detection, utilizing a minimal number of EEG channels.

Background: There is an increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community. Methods: We performed an electronic search in Scopus looking for studies reporting on rehabilitation in patients with neurological disabilities. The most influential papers outlined the knowledge base, while a word co-occurrence analysis imprinted the research hotspots. Likewise, co-citation analyses highlighted collaboration networks between Universities, authors, and countries. The results were presented in summary tables, burst detection plots, and geospatial maps. Finally, a content review based on the top-20 most cited articles completed our study. Results: Our current bibliometric study was based on 874 records from 420 sources. There was a vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by Nicolas-Alfonso LF and Gomez-Gill J, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by Daly J. and Wolpaw JR (708 citations). The USA, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of “functional magnetic imaging” to EEG-based “brain-machine interface”, “motor imagery”, and “deep learning”. Conclusions: EEG constitutes the most significant input in brain-computer interfaces (BCI) and can be successfully used in the neurorehabilitation of patients with stroke, amyotrophic lateral sclerosis, and traumatic brain and spinal injury. EEG-based BCI facilitates training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG filtering algorithms.

Background: Modern computational solutions enabling evaluation of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, a vast number of algorithms used in these analyzes may be an uncontrolled source of results inconsistency. Objective: Our study aimed to verify whether the comparison of schizophrenia patients with healthy controls, in terms of indexes describing the organization of the neural network, will give analogous results when these parameters are calculated using two different functional connectivity measures. Methods: Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, Minimum Spanning Tree (MST) graphs were computed two times using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Results: Two series of be-tween-group comparisons regarding MST parameters calculated based on PLI or PLV gave contradictory results, in many cases the values of a given MST index based on PLI were higher in patients, and the results based on PLV were lower in patients than in the controls. Additionally, within the patients' group, selected network measures were significantly different when calculated from PLI or PLV. Conclusions: The selection of FC measures significantly affects the parameters of MST-based neural networks and might be a source of disagreement between the results of network studies on schizophrenia.

Fundamentals of Laparoscopic Surgery (FLS) is a training module designed to provide basic surgical skills. During skill training of the FLS "suturing and intracorporeal knot-tying" task – the most difficult among the five psychomotor FLS tasks, learning from errors is one of the basic principles of motor skill acquisition where appropriate contextual switching of the brain state on error is postulated. This study investigated changes in the brain state following an error event based on the fusion of simultaneously acquired functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. Here, human error processing is postulated to differentiate experts from novices based on the differences in the error-related chain of mental processes. Thirteen right-handed novice medical students and nine expert surgeons participated in this study. Error-related microstate analysis was performed using 32-channel EEG data at a high temporal resolution. Six microstate prototypes were identified from combined EEG data from experts and novices during the FLS task. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10 sec error epoch was significantly affected by the skill level (p<0.01), microstate type (p<0.01), and the interaction between the skill level and the microstate type (p<0.01). Then, the EEG band power (1-40Hz) related to slower oxyhemoglobin (HbO) changes were found using regularized temporally embedded Canonical Correlation Analysis of the fNIRS-EEG signals. The HbO signal from the fNIRS channel overlying ‘Frontal_Inf_Oper_L’, ‘Frontal_Mid_Orb_L’, ‘Postcentral_L’, ‘Temporal_Sup_L’, ‘Frontal_Mid_Orb_R’ cortical areas from Automatic Anatomical Labelling showed significant (p<0.05) difference between experts and novices in the 10-sec error epoch. Here, the frontal/prefrontal cortical areas are postulated to be related to the perception and the activation of the primary somatosensory cortex at the postcentral cortical area is hypothesized to be related to the action underpinning perception-action coupling model for the error-related chain of mental processes. Therefore, our study highlighted the importance of error-related brain states from portable brain imaging when comparing complex surgical skill levels.

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.