This Special Issue of Humanities comes at a time when the viability of the humanities are challenged on numerous fronts. On the one hand, the humanities face material threats as the politics of austerity continues throughout Europe and the United States, diminishing public support and making profit margin and “job creation” the primary measure of value or the basis of state university funding decisions. On the other, the humanities face conceptual, theoretical and ethical challenges, as the emergence of post-racial and post-humanist discourses signal what Foucault called “a change in the fundamental arrangements of knowledge.” The defining boundaries of constructs such as “race” and “human” have been radically called into question, challenging us to rethink the classificatory systems that found hierarchical relationships between, for example, the “fully human” and sub-human or non-human others. Despite dominant nations’ professed commitment to a universal human rights paradigm, racialized identities are still often the targets of disenfranchisement and dehumanization, while the exploitation and destruction of the natural world continues in the name of “progress” and profits.

Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content. The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model. We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength. CVWI was able to differentiate two groups of dataset when regressed against the absorption feature. CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.

Extrauterine growth restriction (EUGR) is a common complication and a known risk factor for impaired development in very low birth weight (VLBW) preterms. We report a population of 288 patients with no or with low grade MRI lesions scanned at term equivalent age (TEA) born between 2012 and 2018. Griffiths mental development scale II (GMDS II) at 2 and 3 years, preterm complications and weight growth were retrospectively analysed. EUGR was defined for weigth z-score ˂10th percentile at TEA, 6 and 12 months of correct age or as z-score decreased by 1-point SDS from birth to TEA and from TEA to 6 months. Multivariate analysis showed that a higher weight z-score at 6 months is protective for global developmental quotient (DQ) at 2 years (OR 0.74; CI 95% 0.59-0.93; p=0,01). EUGR at 6 months was associated with worse locomotor, personal-social, language and performance DQ at 2 years and worse language and practical reasoning DQ at 3 years. In conclusion a worse weight z-score at 6 months of age seems to be an independent risk factor for significantly reduced GMDS in many areas. These results suggest to better invest on post-discharge nutritional, optimizing family nutritional education.

Primary care pediatricians encounter a wide range of complex conditions in their daily clinical practice. It is not uncommon for their patients to get exposed to medical errors. Diagnostic errors, defined as missed, delayed, or wrong diagnosis, are of great importance as they may cause significant harm to the pediatric patient with severe disease. These errors often occur when the symptoms and signs of a disease are atypical or missing. Data on the most frequent misdiagnosed situations in pediatrics is scarce. One of these groups of conditions is that regarding cardiac diseases. The pediatric cardiology field has met rapid development over the past few decades. Primary care providers play a key role in diagnosis, management, and referral of children with heart disease, both congenital and acquired, because many common cardiac complaints are first addressed in the primary care setting. The scope of this review is to investigate the cardiac entities that are often missed by the general pediatricians. In particular, it aims to spot the most common of them, to describe their clinical presentation, their diagnostic options, red flags, and tips, and to provide the ways to avoid erroneous, delayed, or missed diagnosis.

The associations among increased pain sensitivity, obesity, and systemic inflammation have not been described as a direct link between BBB dysfunctions. To analyze the metabolic, behavioral, and inflammatory effects of a HFD and ultrastructural modifications in brain regions, we used an experimental model. Adult male Wistar rats were randomly assigned to one of two conditions, an ad libitum control group, or a high-fat-diet (60%) group (HFD-fed), for eight weeks. At the end of the protocol, glucose, and insulin tolerance tests were performed. Additionally, we analyzed the response to a normally innocuous mechanical stimulus and changes in motor coordination. At the end of the protocol, HFD-fed individuals presented increased HOMA-IR and metabolic syndrome (MetS) prevalence. HFD-fed rats also developed an increased nociceptive response to mechanical stimuli and neurological injury, resulting in impaired motor function. Hypothalamus and cerebellum neurons from HFD-fed presented with nuclear swelling, an absence of nucleoli, and karyolysis. These results reveal that HFD consumption affecting vital brain structures such as the cerebellum, hippocampus, and hypothalamus. This, in turn, causes neuronal damage, impairs cellular communication, and alters motricity and pain sensitivity. Understanding the link between BBB impairment and sensory and motor changes could lead to targeted therapeutic strategies.

This paper aims to introduce an overview of several aspects of the so-called Artificial Intelligence, their potential impacts on economic and social dimen- sions, and suggestions for possible approaches of investiment based upon effective and mature techniques. In this fashion, it is important to address from educational and academic issues to industrial densities and profiles, rel- atively to a given region, country or continent. Even etymological adequacy and psychological consequences of the denomination ”Artificial Intelligence” need some reflection, and suggestions for a lucid replacement are presented. In addition, suggestions about how can specific firms choose the right type of technique in order to improve profit and organizational efficiency. After all, what are the main transformations needed to amplify gains and structural improvements from the use of higher level technological mecha- nisms? Which connections can be established between the pillars of evolu- tionary economics and this field of knowledge? Which institutional contexts are able to benefit from AI tools, inducing constructive externalities to firms in terms of education, technical and scientific skills upgrading, so as to reach higher levels of employment in the long term and limit unemployment in the short one? Which branches of the so-called Artificial Intelligence are best suited to which types of activities? This work aims to contribute in the search for answers to these questions.

The association between human and cat (F. catus) is well known. This domestic animal is also known for its malodorous urine and feces. The complexity of the odorous urine and feces impacts human life by triggering the human sensory organ in a negative way. The objective of this research was to identify the volatile organic chemicals (VOCs) and associated odors in cat urine and feces using gas chromatography-mass spectrometry and simultaneous sensory analysis of fresh and aged samples. The solid-phase microextraction (SPME) technique was used to pre-concentrate the VOCs emitted from urine or feces samples. Twenty-one compounds were identified as emitted from fresh urine, whereas 64 compounds were emitted from fresh feces. A contrasting temporal impact was observed on the emission of VOCs for urine and feces. On aging, the emission increased to 36 detected chemicals for stale urine, whereas only 17 chemicals were detected in stale feces. Not all compounds were malodorous; some compounds had a pleasant hedonic smell to the human nose. Although trimethylamine, low molecular weight organic acids, and ketones were contributors to the odor to some extent, phenolic compounds and aromatic heterocyclic organic N compounds generated the most intense odors and substantially contributed to the overall malodor, as observed by this study. This work might be useful to formulate cat urine and feces odor remediation approaches to reduce odor impacts.

Background: Lower limb length discrepancy (LLD) in children and adolescents, often due to congenital or acquired conditions, is treated to achieve limb equality and alignment, optimizing function and minimizing cosmetic concerns for an active adulthood. This study evaluated the Health-Related Quality of Life (HRQoL) and physical functioning of adults who underwent unilateral limb lengthening with circular external fixators (EFs) in childhood. Methods: Fifty patients treated at a median age of 14.9 years completed the Short Form 36 (SF-36) and Stanmore Limb Reconstruction Score (SLRS) questionnaires in adulthood, with a median follow-up of 8.9 years. Results: Among the 50 patients, 38 underwent a single limb lengthening (21 tibia, 12 femur, 5 both), while 12 required multiple cycles. The median residual LLD was 0.4 cm, with 12 patients (24%) having over 2 cm. Complications occurred in 67% of procedures, mainly due to prolonged healing. Physical and mental health scores were significantly lower than normative data. The mean Physical Component Summary was 52.2 ± 7.2 (p = 0.20). The mean Mental Component Summary was 43.9 ± 8.6 (p = 0.001), notably lower in congenital LLD cases. Many SLRS items (Pain, Social, Physical Function, Work, and Emotions) strongly correlated with SF-36 items. Conclusions: The study highlights the challenges in treating LLD, emphasizing the significant mental health impact, especially in congenital cases, and the necessity for comprehensive long-term care strategies. These findings suggest that future therapeutic strategies should focus on both physical and psychological outcomes to improve overall recovery in LLD treatment.

Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

The intestinal microbial community of aquatic animals is closely related to the environment and thus affects the health of aquatic animals. Here we investigated annual variation of intestinal bacteria community structures of Ruditapes philippinarum cultured using two different methods, bottom sown and raft hanging. Bacterial diversity of seawater and sediment in different seasons was examined as well. Correlation between R. philippinarum intestinal bacteria community structures and environmental factors was analyzed. The results showed that in the two aquculture areas where R. philippinarum was differentially cultured, bacteria community structure of seawater and sediment both altered with seasons. However, neither seasonal changes nor difference in culture methods could significantly affect the intestinal bacteria community structures of R. philippinarum. Additionally, in terms of the alpha diversity of intestinal bacteria communities, there was no significant difference in R. philippinarum cultured using the two methods. For benthic cultured clams, alpha diversity of the intestinal bacterial communities was often higher than that of the environmental bacterial communities. For raft hanging cultured calms, Alpha diversity of the intestinal bacterial communities was normally comparable with that of the environmental counterparts. Beta diversity analysis showed that the intestinal bacterial community structure of R. philippinarum was mainly determined by both deterministic and stochastic processes. Correlation analysis showed that the intestinal bacterial community structures of R. philippinarum were determined by a variety of environmental factors. For clams collected from the benthic culture area, the intestinal bacterial structure was more correlated with the physicochemical properties of sediment in comparing with those of seawater. For clams collected from the raft-culture area, the intestinal bacterial structure was mainly determined by the phsicochemical characteristics of seawater. This study helps us to better understand the bacterial community structure in complex aquaculture ecosystems, providing a reference for healthy shellfish aquaculture and ecosystem protection in aquaculture area.

The German government's ambitious goal of achieving CO2-neutrality by 2045 has prompted a focus on improving building insulation as a vital step toward energy efficiency. However, in this process, existing radiators and boilers are often left unchanged, leading to inefficiencies due to oversized heating systems. This paper presents various approaches to reduce the required radiator inlet temperature, drawing on data from 100 multifamily buildings. The approaches include heat reserve reduction, heat power shifting, uniform radiator usage, and heat requirement analysis. These approaches collectively aim to lower flow temperatures and decrease peak heat loads, reducing energy consumption, and minimizing waste in compliance with the user comfort temperature. The findings reveal that the application of these methods can lead to substantial temperature reductions, with an average decrease of 20.82 K and 7.28 K for the highest necessary flow temperature in the provided example. Furthermore, peak load reductions of 21.07% and 13.65% were achieved, demonstrating the potential to enhance energy efficiency. While each approach can be applied individually, their synergistic implementation can highly increase the efficiency of water to x heat pumps, due to the lower required flow temperatures.

The search for active cellulolytic consortia among soil microorganisms is of significant applied interest, while the dynamics of the formation of such communities remain insufficiently studied. To gain insight into the formation of an active cellulolytic community, the experiment was designed to study the colonization of a sterile substrate (cellulose) by microorganisms from two soil types: sod-podzolic and chernozem. To achieve this, the substrate was placed in the soil and incubated for six months. To assess microbiome dynamics, the experiment employed sequencing of 16S rRNA and ITS2 amplicon libraries at four-time points. It was demonstrated that from the second month of the experiment, the prokaryotic component of the communities reached a state of stability, with a community composition that was specific to each soil type. The results demonstrated that there was no relationship between changes in community diversity and soil respiration. There also was no significant shift in community diversity throughout the chronosequence. Furthermore, the taxonomic composition of the community shifted towards a decrease in the proportion of Proteobacteria and an increase in representatives of the Bacteroidota, Bacillota, and Verrucomicrobiota phyla. The network analysis of the community demonstrated that, in contrast to sod-podzolic soil, chernozem is distinguished by a higher modularity, with the formation of taxon-specific groups of microorganisms at each stage of the chronoseries. These differences are attributed to alterations in the eukaryotic component of the community, particularly in the prevalence of nematodes and predatory fungi, which in turn influenced the cellulolytic community.

The aim of the present study was to predict the response of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitors (ICIs) by leveraging computed tomography (CT) images using deep learning techniques. Retrospectively, 624 sequential CT images were gathered from 156 patients at Jiangsu Province Hospital, along with their clinical data. The dataset was subsequently partitioned into three groups: training (n=547), validation (n=64), and test (n=64). Moreover, an external validation cohort included 37 CT images from patients at Nanjing Pukou Peoples' Hospital, accompanied by comprehensive clinical data. An advanced Video Vision Transformer (ViViT) model incorporating global self-attention was utilized to analyse patients treated with ICIs and predict their response. The ViViT model's efficacy was evaluated using a confusion matrix and a receiver operating characteristic curve (ROC). Notably, the ViViT model demonstrated predictive prowess for ICIs response, yielding respective areas under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.69-0.78), 0.74 (95% CI: 0.61-0.86), 0.76 (95% CI: 0.62-0.88), and 0.69 (95% CI: 0.5-0.87) in the training, validation, test, and external validation cohorts. The present study illustrates how a deep learning model can provide a non-invasive means to predict clinical outcomes in NSCLC patients undergoing ICIs, potentially transforming personalized treatment approaches for individuals with NSCLC.

The present paper discusses an elliptic equation with Steklov boundary conditions and $(p(x),q(x))$-Laplacian. Using mountain pass theorem together with Ekeland's variational principle, we prove, under appropriate conditions on the functions involved, that the problem admits at least two solutions.

Sepsis is a pervasive condition that affects individuals of all ages, with significant social and economic consequences. Early diagnosis of sepsis is fundamental for establishing appropriate treatment and is based on warning scores and clinical characteristics, with positive microbiological cultures being the gold standard. Research has yet to identify a single biomarker to meet this diagnostic demand. Presepsin is a molecule that has the potential as a biomarker for diagnosing sepsis. In this paper, we present a narrative review of the diagnostic and prognostic performance of presepsin in different age groups. Given its particularities, it is identified that presepsin is a potential biomarker for sepsis at all stages of life.

In Africa, the urban population and informal settlements are increasing, resulting in additional public health and environmental risks and challenges related to the need of basic and sustainable infrastructures and services. In Maputo, the capital of Mozambique, at least 80% of the city is composed of informal settlements, and although some areas are regularized and have approved urbanization plans, the municipality has major deficiencies in meeting current demands in terms of basic infrastructures, such as water supply, sewerage, solid wastes, energy, roads and communications. Most of the peri-urban areas is occupied with small but overcrowding houses, mostly self-built, served by unpaved roads, and with access limitations. The water network is almost non-existent, and the same occurs for stormwater drainage, sanitation, waste collection and public illumination. Despite the improvements made in the last few years, some households still do not have safe sanitation, and in some neighborhoods, open defecation still prevails. In this study, the authors try to understand and explore the interdependences among the infrastructures of the different sectors and how they may jointly contribute for adequate services and a better life of the urban poor, and how to identify the combination of infrastructures to be implemented at short term in face of the limited budgets for investments.

Escherichia coli is a gram-negative commensal bacterium living in human and animal intestines. Its pathogenic strains are cause of high morbidity which can adversely affect people by causing urinary tract infections, food poisoning, septic shock, or meningitis. Humans can contract E. coli by eating contaminated food—such as raw or undercooked raw milk, meat products, and fresh produce sold in open markets—as well as by coming into contact with contaminated settings like wastewater, municipal water, soil, and faeces. Some pathogenic strains of E. coli have been identified in Nigeria such as enterohemorrhagic (EHEC), as well as enterotoxigenic (ETEC), enteropathogenic (EPEC), enteroinvasive (EIEC), enteroaggregative (EAEC), and verotoxigenic (VTEC). This causes acute watery or bloody diarrhoea, stomach cramps, and vomiting. Apart from the virulence profile of E. coli, antibiotic resistance mechanisms such as the presence of blaCTX-M found in humans, animals and environmental isolates which are of great importance and require surveillance and monitoring for emerging threats in resource-limited countries. This review is aimed at understanding the underlying mechanisms of evolution and antibiotic resistance in E. coli in Nigeria and highlighting the use of improving One Health approaches to combat the problem of emerging infectious diseases.

The aim of this study was to quantify methane (CH4) emissions from dairy cows during milking by maximum amplitude of eructation peaks and compare estimates with other metrics for CH4 emissions. Spot measurements of CH4 and carbon dioxide (CO2) emissions were recorded for at least 7 days of continuous sampling using an infrared gas analyser in a robotic milking station whilst cows were milked. Concentrations of CH4 and CO2 were measured at 1 second intervals. A total of 105,701 CH4 spot measurements were obtained from 2,206 dairy cows on 18 farms. Methane emissions per individual cow were quantified by maximum amplitude of eructation peaks, average area under eructation peaks (integral of eructation), average concentration during milking and ratio of CH4 to CO2 concentration. There was a high correlation (r = 0.71) between CH4 emissions quantified by maximum amplitude and integral of eructation peaks. The correlation between maximum amplitude and average CH4 concentration was low, and there was no correlation between maximum amplitude and ratio of CH4 to CO2. As both approaches identify and extract eructation peaks to quantify enteric CH4 emissions, the substantial association between maximum amplitude and eructation integral was expected.

Creatine has been linked to the development of kidney damage since 1998. However, uncertainties persist regarding the actual impact of creatine supplementation on individuals with kidney disease before supplementation or with pathological conditions that have an increased risk of developing kidney dysfunction. This study performed a comprehensive review of the literature and utilized bioinformatics tools to investigate the effects of creatine on renal tissue and function. Moreover, we identified 44 genes modulated explicitly in response to creatine exposure using the Comparative Toxicogenomics Database, including IGF1, SLC2A4, and various creatine kinase genes. The analysis revealed associations with metabolic processes such as amino acid metabolism, indicating a connection between creatine and tissue physiology. Gene expression analysis using the Genotype-Tissue Expression Portal highlighted tissue-specific expression patterns in kidney and pancreas tissues. Furthermore, the study explored creatine metabolism pathways and kinase regulation through biological databases and Gene Expression Omnibus data, shedding light on the potential regulatory roles of creatine in cellular processes during kidney diseases. Understanding the impact of the balance between the endogenous creatine synthesis and the creatine uptake, particularly the genes GATM, GAMT, SLC6A8 and IGF1, insights into molecular mechanisms underlying creatine's physiological effects on renal tissues. Overall, this work provides new insights into the molecular interactions and pathways influenced by creatine in renal function.

Objectives: In this retrospective observational study, it is intended to establish how the implementation of the use of antimicrobial optimization programs at the hospital and ICU level in a Sincelejo Sucre Clinic has significantly impacted the improvement of the rational use of antibiotics1, due to the alarming situation of increasing antibiotic resistance. Materials and methods: The methodology used was to analyze the Excel database of the Clinic, in such a way that it would allow comparing the pre-PROA and post-PROA data of 4 antibiotics, ceftriaxone3, ciprofloxacin4, meropenem5 and vancomycin6, measured in Daily Dose. defined. Results: According to the DDD values obtained for the 4 antibiotics, a reduction in DDD was identified in the post-Proa period. On the other hand, considering the DDD reported by the WHO for each of the antibiotics, significant differences were verified compared to the DDD obtained in the clinic in the hospitalization and ICU services. Conclusions: In conclusion, in the clinic there was evidence of a reduction in DDD in the post-ASP2 period compared to the pre-PROA period, both in hospitalization and in the ICU, as well as a slight to large effect with Cohen's D.

Lindernia dubia (L.) Pennell is a species with invasive behavior outside of its native range of distribution (North America), linked mainly to aquatic habitats. This annual species has been acknowledged as a weed in rice paddies in Europe and Asia. Due to the impacts of this invasive plant, some authors have even listed this species as a global invader. The present work focused on spontaneous plant species occurring in cattail seedlings grown in central Spain for the establishment of constructed wetlands. Weed inventory revealed the presence of L. dubia as a dominant spontaneous species in this crop environment. A suite of mesocosm experiments were designed to study the distribution of L.dubia versus the distribution of the other dominant plant species, and to determine traits associated with its weedy potential. Results showed that L.dubia presents competitive attributes such as morphological variability, early flowering, long seeding time, short growth cycle, small and light seeds and high seed production and germination rate (25 ⁰C), meaning a high reproductive capacity in a cycle of about three months, in non-limiting conditions for plant growth. Data from this work provide a basis for understanding the weedy potential of L.dubia, and for management decisions of a potentially invasive species, little investigated in Europe

Remote work as a new working pattern is considered of high importance in determining the competitiveness of the institutions that adopt this form of work. This trend toward remote working has benefited workers greatly in terms of flexibility and work-life balance and has had unanticipatedly positive effects on sustainability. The issue has grown in importance considering recent development of digitalization and high concern of countries to achieve the sustainable goals. This study attempts to examine the effect of three explanatory variables: remote work, institutional efficiency, and work management on the competitiveness of finance institutions in Saudi Arabia. To collect the primary data, a survey was conducted on a stratified random sample that comprised 1270 respondents. The sample covers the three finance sector segments: banks, insurance, and finance companies. Correlation analyses, one-sample t-tests, and multi-regression analyses are applied to analyze the data. The results of the study showed that there is a positive significant relationship between the three explanatory variables of the study and institutional competitiveness. Besides, the regression model revealed that the two explanatory variables; remote work and institution efficiency are statistically significant at 95% confidence intervals, while work management is not statistically significant. The study recommends promoting remote work to increase the finance institution's competitiveness by restructuring the work policies, designing work strategies, and introducing a new management system compatible with remote work.

Aβ peptides are known to bind neural plasma membranes in a process leading to the deposit of Aβ-enriched plaques. These extracellular structures are characteristic of Alzheimer’s disease, the major cause of late-age dementia. The mechanisms of Aβ plaque formation and deposition are far from being understood. A vast amount of literature describes the efforts to analyze those mechanisms, using a variety of tools. The present review focuses on biophysical studies, mostly carried out with model membranes, or with computational tools. The review starts by describing basic physical aspects of lipid phases, and commonly used model membranes (monolayers and bilayers). This is followed by a discussion of biophysical techniques applied to these systems, mainly but not exclusively Langmuir monolayers, isothermal calorimetry, density-gradient ultracentrifugation, and molecular dynamics. The methodological section is followed by the core of the review, a summary of important results obtained with each technique. The last section is devoted to an overall reflection and an effort in understanding Aβ-bilayer binding. Concepts such as Aβ peptide membrane binding, adsorption, and insertion are defined and differentiated. The roles of membrane lipid order, nanodomain formation, and electrostatic forces in Aβ-membrane interaction are separately identified and discussed.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

Pomegranate juice possesses therapeutic properties against metabolic syndrome. Microencapsulation of pomegranate juice (MPJ) may extend its shelf life; however, further research is necessary to ascertain its comparative efficacy with fresh juice. This work evaluated the effect of (MPJ) in an animal model with induced metabolic syndrome (MetS). Male Wistar rats were induced-MetS by administering 30% sucrose orally for 22 weeks. Once MetS was verified in the animals, 7 groups were formed as follows: control (Ctrl); MetS, MetS+Placebo; Ctrl+MPJ; MetS+MPJ1 600, MetS+MPJ2 1800, and MetS+MPJ3 2900 mg/kg. Systolic and diastolic blood pressure, glucose, triglycerides, and total HDL-c levels in the MetS+MPJ groups improved significantly (p &lt; 0.05) vs. the MetS group at the end of the study. Microencapsulation maintained the anti-metabolic syndrome properties of pomegranate juice and could be used to control metabolic syndrome.

Accelerating, exact, explicit and simple solutions of relativistic hydrodynamics allow for a simple and natural description of highly relativistic p+p collisions. These solutions yield a finite rapidity distribution, thus they lead to an advanced estimate of the initial energy density of high energy collisions. We show that such an advanced estimate yields an initial energy density in $\sqrt{s}=7$ and 8 TeV p+p collisions at LHC around or above the critical energy density from lattice QCD, and a corresponding initial temperature above the critical temperature from QCD and the Hagedorn temperature. This suggests that the collision energy of the LHC corresponds to a large enough initial energy density to create a non-hadronic perfect fluid even in pp collisions. %We also show, that several times the %critical energy density may have been reached in high multiplicity events, hinting at a non-hadronic medium created in %high multiplicity $\sqrt{s}=7$ and 8 TeV p+p collisions.

In the pathogenesis of endometriosis, the differences between the eutopic and ectopic endometrium as well as between the eutopic endometrium with and without endometriosis are repeatedly pointed out. Various mechanisms have been suggested to explain these changes among them epithelial-mesenchymal transition (EMT). Recently, we suggested based on immunohistochemical data that most of the changes occur after and not before implantation of endometrial cells into ectopic locations. Furthermore, the subtle changes between eutopic endometrium with and without endometriosis and maintenance of epithelial cell-to cell contacts only suggest a partial EMT. In this study, we have re-analyzed the mRNA expression array data of eutopic and ectopic endometrium with respect to expression changes and EMT. Especially, we found that the similarity between eutopic endometrium with and without endometriosis is extremely high (~99.1%). In contrast, eutopic endometrium compared to ectopic endometrium only shows an overall similarity of ~95.3%. Analysis of some EMT-associated genes revealed small differences in the mRNA expression levels of some members of the claudin family. The array data suggest that the changes in eutopic endometrium at the beginning of the disease are quite subtle and that the majority of differences occur after implantation into ectopic locations.

Isothermal Nucleic Acid Amplification based-Lateral Flow Testing (INAA-LFT) has emerged as a robust technique for on-site pathogen detection, providing a visible indication of pathogen nucleic acid amplification that rivals or even surpasses the sensitivity of real-time quantitative PCR. The isothermal nature of INAA-LFT ensures consistent conditions for nucleic acid amplification, establishing it as a crucial technology for rapid on-site pathogen detection. However, despite its considerable promise, the widespread application of isothermal INAA amplification-based lateral flow testing faces several challenges. This review provides an overview of the INAA-LFT procedure, highlighting its advancements in detecting plant viruses. Moreover, the review underscores the imperative to address existing limitations and emphasizes ongoing research efforts dedicated to enhancing the applicability and performance of this technology in the realm of rapid on-site testing.

In vitro testing is the first important step in the development of new biomaterials. The human fetal osteoblast cell line hFOB 1.19 is a very promising cell model; however, there are vast discrepancies in cultivation protocols, especially in the cultivation temperature and the presence of selection reagent, geneticin (G418). We intended to use hFOB 1.19 for the testing of Zn-based degradable metallic materials. However, the sensitivity of hFOB 1.19 to zinc ions has not yet been studied. Therefore, we compared the toxicity of zinc towards hFOB 1.19 under different conditions and compared it with that of the L929 mouse fibroblast cell line. We also tested the cytotoxicity of three types of Zn-based biomaterials in two types of media. The presence of G418 used as a selection reagent decreased the sensitivity of hFOB 1.19 to Zn2+. hFOB 1.19 were more sensitive to Zn2+ at elevated (restrictive) temperatures. hFOB 1.19 were less sensitive to Zn2+ than L929 (both as ZnCl2 and extracts of alloys). Therefore, the appropriate cultivation conditions of hFOB 1.19 during biomaterial testing should be chosen with caution.

Dementia is a major issue for healthcare systems worldwide, necessitating the development of creative and effective strategies for its management. This paper examines the use of Artificial Intelligence (AI) technologies in caring for and managing people with dementia. AI-driven solutions can improve diagnosis, personalize care, optimize medication management, and reduce the burden on caregivers. This paper discusses the implementation of an AI-based framework for creating videos related to people’s memories to support train-therapy or travel- therapy, a non-pharmacological intervention for Alzheimer’s disease patients

Automatic generation control (AGC) plays a vital role in creating an equilibrium between generated output power and load demand, in order to maintain frequency at desired value. This study focuses on performance analysis of simple-multi attribute rating technique (SMART) assisted proportional-integral-derivative (PID) controller design for AGC of two interconnected power systems. PID controller is designed by minimizing frequency variations, area control errors for area-1 and area-2, and tie-line power deviation. An objective function is framed considering error minimization of aforementioned factors as sub-objectives. The construction of objective function involves using the integral of time-multiplied absolute error (ITAE) for frequency deviations for area-1 and area-2, ITAE for deviation in tie-line power, and ITAEs for area control errors for area-1 and area-2 as sub-objective functions. By assigning appropriate weights to these sub-objective functions, an overall objective function is formed. This study determines these weights in an organized manner using SMART method, rather than randomly/equally assigning them. Overall objective function is minimized using Jaya algorithm. To demonstrate effectiveness of the proposed Jaya-based PID controller, its performance is analysed and compared with controllers tuned using other optimization algorithms, including sine cosine, Luus-Jaakola, teacher-learner based optimization, Nelder-Mead simplex, and elephant herding optimization. Considering six different case studies that consider a range of load variations, responses for fluctuations in frequency and tie-line exchange are plotted. Statistical and non-parametric analysis performed further provide additional insights into the performance of Jaya-based PID controller.

Decarbonization of air mobility requires the decarbonization of its energy. While biofuels will play an important role, other low carbon energy carriers based on electricity are considered: battery electrification, liquid hydrogen (LH₂) or eFuel, a hydrogen-based energy carrier. Each energy carrier has its own conversion steps and losses and its own integration effects with the aircraft. These combinations lead to different energy requirements and must be understood to compare their cost and CO₂ emissions. Since they are all electricity-based, this study compares these energy carriers using the well to rotor methodology when applied to a standard Vertical Take-Off and Landing (VTOL) air mobility mission. This novel approach allows one to understand that the choice of the energy carrier dictates the propulsive system architecture, leading to integration effects with the aircraft which can significantly change the energy required, from 400 to 2665 kWh for the same mission. These deviations lead significant differences in CO₂ emissions and costs. Battery electrification is impacted by the battery manufacturing but has the lowest electricity consumption. It is an optimum solution but only until the battery weight can be lifted. In all scenarios eFuel is more efficient than LH₂. We conclude that carrying the most efficient molecule in an aircraft pays the extra energy cost spent on the ground. Finally, we found that for each of these energy carriers, it is the electricity carbon intensity and price which will dictate the cost and CO₂ emissions of an air mobility mission.

Olive fruits at different ripening stages give rise to various table olive products and oil qualities. Therefore, developing an efficient method for recognizing and sorting olive fruits based on their ripening stages can greatly facilitate postharvest processing. This study introduces an automatic computer vision system that utilizes deep learning technology to classify the `Roghani` Iranian olive cultivar into five ripening stages using color images. The developed model employs convolutional neural networks (CNN) and transfer learning based on the Xception architecture and ImageNet weights as the base network. The model was fine-tuned by testing multiple configurations of well-known CNN layers. To minimize overfitting and enhance model generality, data augmentation techniques were employed. By considering different optimizers and two image sizes, four final candidate models were generated. These models were then compared in terms of loss and accuracy on the test dataset, classification performance (classification report and confusion matrix), and generality. All four candidates exhibited high accuracies ranging from 86.93% to 93.46% and comparable classification performance. In all models, at least one class was recognized with 100% accuracy. However, by taking into account the risk of overfitting, two models were discarded. Finally, a model with an image size of 224 × 224 and an SGD optimizer, which had a loss of 1.23 and an accuracy of 86.93%, was selected as the preferred option. The results of this study offer robust tools for automatic olive sorting systems, simplifying the differentiation of olives at various ripening levels for different post-harvest products.

Cellulose-based carbon (CBC) has been widely concerned with its porous structure, high specific surface area, and is liable to adsorb gas molecules and macromolecular pollutants. However, the application of CBC in gas sensing has been little studied. In this paper, ZnO/CBC heterojunction was formed by means of simple co-precipitation and high temperature carbonization. As a new ammonia sensor, the prepared ZnO/CBC sensor can detect ammonia compared with that of the previous pure ZnO ammonia sensor can not at room temperature. It has great gas sensing response, stability and selectivity to ammonia concentration at 200 ppm. This study provides a new idea for the design and synthesis of biomass carbon-metal oxide composites.

The increading emission of carbon dioxide to the atmosphere has urged the scientific community to investigate alternatives to alleviate such emissions being the principal contributor to the greenhouse gas effect. One major alternative is carbon capture and utilisation (CCU) towards the production of value-added chemicals using diverse technologies. This work aims at the study of the catalytic potential of different cobalt-derived nanoparticels for methanol syntheis from carbon dioxide hydrogenation. Thanks to its abundance and cost-efficacy, cobalt can serve as an economical catalyst compared to noble-metal-based catalysts. In this work, we present a systematic comparison among different cobalt and cobalt oxide nanocomposites in terms of their efficiency as catalysts for carbon dioxide hydrogenation to methanol as well as how different supports can enhance their catalytic capacity. The oxygen vacancies in the cerium oxide act as carbon dioxide adsorption and activation sites, which facilitates a higher methanol production yield.

In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum

Continuous glucose monitoring devices allow diabetes condition management. However, when limited data is available, one option is to increase their size by generating synthetic samples. From a homemade wearable prototype was created a real dataset with 18 instances and 53 attributes that capture characteristics of capillary and venous blood glucose, oxygen concentration, pulse rate, skin temperature, and 24 modules and 24 phases related to bio-impedance. The objective of this article is to generate synthetic datasets, and also it investigates the ideal features subset and optimal model for non-invasive diabetes prediction. Gaussian-Copulas (GC), conditional generative adversarial networks (CG), variational autoencoders, and Copula-GAN techniques' were used to generate five synthetic datasets. Experiments show that GC1 and GC2 datasets follow min/max boundaries and are not copies of the original data. Multilayer perceptron regressor outperformed (train and test) with 2.17, 2.51 in MAE; 9.29, 13.59 in MSE; 3.05, 3.69 in RMSE, and 0.95, 0.92 in R2 in GC1, and 2.64, 3.02 in MAE; 11.43, 15.11 in MSE; 3.38, 3.89 in RMSE, and 0.94, 0.92 in R2 in GC2 with eight features. Future work is necessary to explore autoencoder and generative architectures, datasets with diverse characteristics, and the effect of the number of features.

In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems and increase the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30% to 100%, and methods can be expanded to other fields.

This work aimed to develop an easy-to-use smartphone-based electrochemical biosensor to quickly assess coffee blend's total polyphenol (Phs) content at industrial and individual level. The device is based on a commercial carbon-based screen-printed electrode (SPE) modified with multi-walled carbon nanotubes (CNTs) and gold nanoparticles (GNPs). At the same time, the biotransducer, Laccase from Trametes versicolor, TvLac, was immobilized on the sensor surface by using glutaraldehyde (GA) as cross-linking agent. The platform was electrochemically characterized to ascertain the influence of the SPE surface modification on its performance. The working electrode (WE) surface morphology characterization was obtained by scanning electron microscopy (SEM) and Fourier-transform infrared (FT-IR) imaging. All the measurements were carried out with a micro-potentiostat Sensit Smart by PalmSens connected to a smartphone. The developed biosensor provided a sensitivity of 0.12 μA/μM, a linear response ranging from 5 to 70 μM, and a lower detection limit (LOD) of 2.99 μM. Afterwards, the biosensor was tested for quantifying the total Phs content in coffee blends, evaluating the influence of both the variety and the roasting degree. The smartphone-based electrochemical biosensor performance has been validated though the Folin-Ciocâlteu standard method.

For an enterprise, the most critical aspect of development is resource management, which has a significant impact on all aspects of the enterprise. Therefore, enterprises must pay attention to resource management allocation, which can better promote the sustainable development of the enterprise and obtain optimal benefits. In the production and development of modern enterprises, the acquisition of benefits also involves the allocation of resources in enterprise management. This paper proposed a benefit optimization scheduling algorithm based on a macroeconomic prediction model under auction mechanism and a grid resource scheduling algorithm driven by a benefit function to allocate resources reasonably. This article used macroeconomic forecasting models to fully understand the resource needs of various departments and the resources held by enterprises, and rationally allocate various resources in various departments. This can improve the work efficiency of various departments, thereby reducing the cost of the enterprise, and achieving optimal benefits for the enterprise. The experimental results in this paper showed that the cost of resource management allocation for the benefit optimization scheduling algorithm and the grid resource scheduling algorithm based on the benefit function driven under the auction mechanism was 105.6 yuan and 46.8 yuan respectively when the task volume was 125 under the multi user environment. The time allocated for resource management was 36.6s and 18.9s respectively. It can be seen that the efficiency function driven grid resource scheduling algorithm had a lower cost and time for resource management allocation, so the efficiency function driven grid resource scheduling algorithm can achieve enterprise efficiency optimization.

The method of micro-arc oxidation has been utilized to synthesize a protective biocompatible coating for a bioresorbable orthopedic Mg implant. This paper presents the results of a comprehensive research of micro-arc coatings based on diatomite – a biogenic material consisting of shells of diatom microalgae. The main focus of this study was the functionalization of diatomite-based micro-arc coatings by incorporating the particles of titania (TiO2) into them. Various properties of the resulting coatings were examined and evaluated. The XRD analysis has revealed the formation of a new magnesium orthosilicate phase – forsterite (Mg2SiO4). It has been established that the corrosion current density of the coatings has decreased by 2-4 orders of magnitude after the inclusion of TiO2 particles, depending on the coating process voltage. The adhesion strength of the coatings has increased following the particle incorporation. The processes of dissolution of both coated and uncoated samples in a sodium chloride solution were studied. The in vitro cell viability was assessed, which showed that the coatings significantly reduce the cytotoxicity of Mg samples.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

The optical properties and physiology of biological tissue, as well as how near-infrared (NIR) light interacts with the tissue, both play a significant role in interpreting the tissue probing optical measurements, and in solving the inverse problem of near-infrared spectroscopy (NIRS)-based medical imaging modalities such as diffuse optical tomography and functional near-infrared spectroscopy. This paper discusses the optical properties of tissue, specifically in the NIR wavelength range, which influence NIRS measurements in NIRS-based medical imaging. There is an easy-to-understand explanation given in this paper of the NIR light-tissue interaction phenomenon. The mathematical explanation, the processes involved in the interaction, and the rationale for a few approximations are described. Various types of chromophores present in the tissue, their composition in the tissue, and how these chromophores overall affect the scattering and absorption of NIR light are presented. The absorption spectra of these chromophores are shown. Finally, the paper concludes with the author’s perspective on two NIRS-based medical imaging modalities, diffuse optical tomography, and functional near-infrared spectroscopy.

The purpose of this review is to present the current role of ultrasound-based techniques in the diagnostic pathway of prostate cancer (PCa). With overdiagnosis and overtreatment of a clinically insignificant PCa over the past years, multiparametric magnetic resonance imaging (mpMRI) became recommended for every patient suspected of PCa before performing a biopsy. It enabled targeted sampling of the suspicious prostate regions, improving the accuracy of the traditional systematic biopsy. However, mpMRI is associated with high costs, relatively low availability, long and separate procedure or exposure to the contrast agent. The novel ultrasound modalities such as shear wave elastography (SWE), contrast-enhanced ultrasound (CEUS) or high frequency micro-ultrasound (MicroUS) may be capable of maintaining the performance of mpMRI without its limitations. Moreover, the real-time lesion visualization during biopsy would significantly simplify the diagnostic process. Another value of these new techniques is the ability to enhance the performance of mpMRI by creating the image fusion of multiple modalities. Such models might be further analyzed by artificial intelligence to mark the regions of interest for investigators and help to decide about the biopsy indications. The dynamic development and promising results of new ultrasound-based techniques should encourage researchers to thoroughly study their utilization in prostate imaging.

Drilling vibrations has been identified as a key performance limiter that prevents successful energy transfer from the surface to the bit. The resultant irregular bit rotation speed causes drilling challenges like poor hole quality and reduction in rate of penetration to name a few. Amongst other vibration suppression techniques, active control of vibrations is widely used and recommended. Active control system utilizes a closed loop feedback control to continuously adapt to downhole vibrations detected at the surface. The system requires regular tuning and upgrades which is done experimentally before field deployment. The paper presents a PID based experimental tuning of horizontal and vertical strings for irregular RPM. In doing so, the paper highlights the need of having horizontal experimental setups for state-of-the-art PID tuning for downhole RPM. The settling time of irregular RPM for horizontal setup is found to be lower than the vertical configuration due to multiple contact points and increased friction coefficient.

For any molecule, network, or process of interest, to keep up with new publications on these, is becoming increasingly difficult. For many cellular processes, molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large scale molecular interaction maps and database curation. Text mining and Natural Language Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and machine learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention based models, a special type of neural network (NN)-based architectures that have recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at a sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conduct a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.

The application of signal-to-noise ratio (SNR) observations from ground-based GNSS Reflectometry is becoming an operational tool for coastal sea-level altimetry. As in all data analyses, systematic influences must be reduced here too, to achieve reliable results. A prominent influence results from atmospheric refraction. Different approaches exist to describe or to correct for this influence. In our contribution we will revise the latest developments and suggest a simple atmospheric interferometric delay model that takes into account ray bending as well as along-path propagation delay. The model takes into account a spherical reflector and can therefore be applied for data from very low elevation angles, too. The findings are double-checked by numerical experiments based on a step-by-step raytracing procedure.

The Landauer principle asserts that “the information is physical”. In its strict meaning Landauer's principle states that there is a minimum possible amount of energy required to erase one bit of information, known as the Landauer bound W=kBTln2 where T is the temperature of a thermal reservoir used in the process and kB is Boltzmann’s constant. Modern computers use the binary system in which a number expressed in the base-2 numeral system. We demonstrate that the Landauer principle remains valid for the physical computing device based on the ternary and more generally N-based logic. The energy necessary for erasure of one bit of information (the Landauer bound) W=kBTln2 remains untouched for the computing devices exploiting a many-valued logic.

In order to meet the national energy saving goals set in the Vietnam National Energy Efficiency Program in the period of 2019 – 2030 (VNEEP), the Vietnamese government has adopted a series solutions and policies to improve energy efficiency. The Vietnam’s 63 provinces will be as main actor for the national achievement in energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems. In this study, provincial and national data from General Statistic Office are analyzed in terms of the energy efficiency levels. With the trends of economic development and energy consumption in both national and regional levels, the Lorenz curve between Vietnamese energy consumption and GDP is investigated. The Lorenz coefficient shows the energy allocation is nether reasonable nor balanced. By using clustering method, the 63 provinces of Vietnam clustered into 7 groups that the provinces in the cluster has the similar indexes of energy efficiency i.e. ability, responsibility, potential and difficulty. The energy consumption and GDP are predicted in the period of 2019 – 2025. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 are heavy contribution. Among them, the provinces in the cluster 2 and 3 need to focus on the industry sector in their energy saving policy. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Three-phase to single-phase matrix converter (TSMC) is used as the transmitter for transient electromagnetic method (TEM) instrument, instead of an AC-DC converter and an H-bridge inverter. The transmitter produces excitation current for primary field. The output current has a waveform with a bipolar square of half duty cycle, which is required by TEM. The space vector PWM based on input currents is discussed in detail. The control method of input power factor is proposed for TSMC. The simulation model of TSMC is established and the simulations of different conditions are completed. The simulation results show the controllability of the input power factor is illustrated.

A rheological modifier was used to prepare solutions with viscosities of 1, 4, 6 and 8 cp, which exhibited Newtonian behavior. These solutions were later used to prepare suspensions of monosize quartz (53, 45 and 38 microns) at 60 % in solids. Monosize grinding tests in a laboratory ball mill were performed to determine breakage parameters, with different ball diameters. A model was developed to determine the specific rate of breakage in terms of the system rheology. With this model, it was demonstrated that an increase in suspending fluid viscosity until 6 cp, leads to an increased fracture rate and consequently affects the grinding degree. The above test is considered as an alternative for improving and optimizing such operations with excellent quality products with low energy and steel consumption, thereby minimizing environmental impact.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is an infectious disease that seriously affects human life and health. Despite centuries of efforts to control it, in recent years the emergence of multidrug-resistant bacterial pathogens of M. tuberculosis due to various factors has exacerbated the disease, posing a serious threat to global health. Therefore, a new method to control M. tuberculosis is urgently needed. Phages, viruses that specifically infect bacteria, have emerged as potential biocontrol agents for bacterial pathogens due to their host specificity. In this study, a Mycobacterium phage, Henu3, was isolated from soil around a hospital. The particle morphology, biological characteristics, genomics and phylogeny of Henu3 were characterized. Additionally, to explore the balance between phage resistance and stress response, phage Henu3-resistant strains 0G10 and 2E1 were screened by sequence passage and bidirectional validation methods, which significantly improved the sensitivity of phage to antibiotics (cefotaxime and kanamycin). By whole genome resequencing of strains 0G10 and 2E1, 12 genes involved in cell wall synthesis, transporter-encoded genes, two-component regulatory proteins and transcriptional regulatory factor-encoded genes were found to have mutations. These results suggest that phage Henu3 has the potential to control M. tuberculosis pathogens, and phage Henu3 has the potential to be a new potential solution for the treatment of M. tuberculosis infection.

In this paper, we study nonlocal problems for a fractional diffusion equation and a degenerate hyperbolic equation with singular coefficients in the lower terms. The uniqueness of the solution to the problem is proved by the method of energy integrals. The existence of a solution is equivalently reduced to the question of the solvability of Volterra integral equations of the second kind and a fractional differential equation. For a particular solution of the proposed problem, its visualization is carried out for various values of the order of the fractional derivative. It is shown that the order of the derivative affects the intensity of the diffusion process (subdiffusion), as well as the shape of the wave front.

Cardiovascular disease (CVD) is the primary cause of mortality globally with a multifactorial etiology that involves epigenetics. Chromobox 3 (CBX3), the major isoform of heterochromatin protein 1, is involved in intricate epigenetic mechanisms affecting congestive heart failure. In patients with CVD affected by lung cancer risk, CBX3 exerts a sophisticated mechanism of action, suppressing the proliferation, migration, and formation of neointima in vascular smooth muscle cells (VSMCs) by affecting the Notch3 pathway, indicating a potential protective function against vascular remodeling and atherosclerosis. However, the broader impact of CBX3 on endothelial function, as well as its effects on monocyte/macrophage and lymphocyte infiltration and function within the arterial wall, remain poorly understood. Since very little is known so far, more definite research would be needed to reveal the fine mechanisms of CBX3 action, along with its relationship in molecular processes and prospects as a biomarker. Specifically, CBX3 biological features could be examined to gain a greater insight into CVD risks. This review outlines the role of CBX3 in mechanisms associated with CVD and feasibility for optimizing pre-existing therapy and developing new therapeutic strategies based on personalized medi-cine.

Cell-to-cell distant mechanical communication has been demonstrated by using in-vitro and in-vivo models. However, the molecular mechanisms underlying long-range cell mechanoresponsive interactions remain to be more elucidated. This study further examined the roles of α-Catenin and Piezo in traction force-induced rapid branch assembly of airway smooth muscle (ASM) cells on Matrigel hydrogel containing type I collagen. Our findings demonstrate that siRNA-mediated downregulation of α-Catenin or chemical inhibition of Piezo activity significantly reduced both cell directional movement and branching assembly. In regarding the role of N-cadherin in regulating branch assembly but not directional migration, our results further confirmed that siRNA downregulation of α-Catenin caused a remarked reduction of focal adhesion formation, as assessed by focal Paxillin and Integrin α5 localization. These observations implied that mechanosensitive α-Catenin was involved in both cell-cell and cell-matrix adhesions. Additionally, Piezo showed partial localization with Paxillin in focal adhesions, which was inhibited by α-Catenin downregulation with siRNA. This provides a plausible clue for Piezo mechanosensing traction force in the hydrogel. Collectively, our findings highlight the significance of α-Catenin in regulating cell-matrix interactions along with possible interpretation for Piezo mechanosensation at focal adhesions during cell-cell distant mechanical communication.

Drought stress severely affects the normal growth and development of wheat, leading to reduced yields and quality. To address the lag and limitations of traditional drought monitoring methods, this paper proposes a drought stress monitoring model for winter wheat during critical growth stages based on deep learning. By acquiring drought stress images of winter wheat during three critical growth stages: rise-jointing, heading-flowering and flowering-maturity, a dataset correlating these images with soil moisture monitoring data was established. The DenseNet121 network model was selected as the base network to extract phenotypic features of winter wheat under drought stress. Variables such as the model's training approach, changes in learning rate, and whether an attention mechanism was included, were used to conduct eight sets of model training and optimization strategy experiments. A drought stress monitoring model for winter wheat during critical growth stages based on an improved DenseNet-121 was constructed. The results showed that the average recognition accuracy of the eighth group in the test set reached 94.67%, indicating good application prospects in the recognition of drought stress levels during critical growth stages of wheat.

Spectral characteristics of CDOM in water column are a key parameter for bio-optical modeling. Knowledge of CDOM optical properties and spatial discrepancy based on the relationship between water quality and spectral parameters in Yinma River watershed with in situ data collected highly-polluted waters are exhibited in this study. Seasonal field data sets collected over a period of 2 months in 2015 in Yinma River Watershed. Based on the comprehensive index method, the riverine waters showed serious contamination, especially the COD, Fe, Mn, Hg and DO were out of range contamination warning. Dissolved organic carbon (DOC) and total suspended matter (TSM) with prominent non-homogenizing were significantly high in the riverine waters, but chlorophyll-a (Chl-a) was opposite. Ternary phase diagram showed that non-algal paritcles absorption played an important role in total non-water light absorption (>50%) in most sampling locations, and mean contribution of CDOM were 13% and 22% in summer and autumn respectively. Analysis of ratio of absorption at 250-365 nm (E_250:365) and spectral slope (S_275-295) indicated that CDOM had higher aromaticity and molecular weight in autumn than in summer, is consistent with the results of water quality and relative contribution. Redundancy analysis (RDA) indicated that the environmental variables OSM had a strong correlation with CDOM absorption, followed by heavy metal, e.g., Mn, Hg and Cr⁶⁺. However, for the specific UV absorbance (SUVA₂₅₄), the seasonal values showed opposite results compared with the reported literature. The potential reasons were the more UDOM (uncolored Dissolved Organic Matter) from human source (wastewater effluent) existed in waters. Terrigenous inputs simultaneously are in relation to the a_CDOM(440)-DOC relationship with the correlation coefficient was 0.90 in summer (2-tailed, p<0.01), and 0.58 in autumn (2-tailed, p<0.05). Spatial distribution of CDOM parameters exhibited that the downstream regions focused on dry land have high CDOM molecular weight and aromatic hydrocarbon. Partial sampling locations around the cities or countries generally showed abnormal values due to terrigenous inputs. As a bio-optical model parameter, spectral characteristic of CDOM is helpful in adjusting the derived algorithms in highly-polluted environments. The study on organic carbon and pollutants in highly-polluted waters had an important contribution to global carbon balance estimation and water environment protection.

Early reporting of the introduction and establishment of exotic species is of paramount importance for successful management. Here we report the introduction and rapid spread of the Bare-eyed Pigeon, Patagioenas corensis, on the binational island of Saint-Martin, West Indies. This range-restricted species naturally occurs in the arid coastal areas of Columbia and Venezuela and nearby islands. Its introduction on Saint-Martin represents an expansion of about 1000 km beyond its established native range. Using observations recorded in e-bird and results from a recent field survey, we show that since its introduction, most probably between late 2012 and early 2013, the species is expanding fast in Saint-Martin and has recently broadened its habitat to include anthropized, built areas. The expansion of Bare-eyed Pigeon on Saint-Martin and the neighbouring Leeward Islands, possibly facilitated by climate change in the future, could be a threat to both native columbid species and other bird species, through competition for resources. We therefore recommend to local authorities and stakeholders to rapidly eradicate the species or at least prevent its further spread on Saint-Martin, possibly though listing it as a game species, while it is still possible to do so.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Musculoskeletal disorders are a common occurrence in Europe, affecting millions of people and costing billions of euros in social security services. Traditional treatments using drugs like non-steroidal anti-inflammatory drugs (NSAIDs) can cause gastric damage, making prolonged use difficult. To address this issue, the Author has developed NegEnt Spray - Muscles and Joints, a topical medical device that uses liposomal cannabidiol and chamomile hydrolate to reduce inflammation and pain in musculoskeletal disorders. The liposomal technology used in NegEnt Spray allows for better bioavailability and a more targeted action, while chamomile hydrolate provides a natural anti-inflammatory and soothing effect. The spray is non-greasy, absorbs quickly, and can be used under any circumstances. Its pocket-sized design makes it easy to carry and use throughout the day. With NegEnt Spray, patients can alleviate discomfort and pain associated with musculoskeletal disorders without worrying about the side effects of traditional treatments.

Oxygen and hydrogen mobility are among the important characteristics for operation of solid oxide fuel cells, permselective membranes and many other electrochemical devices. This, along with other characteristics, enables reaching a high power density of solid oxide fuel cells and a high oxygen or hydrogen permeation fluxes for membranes. This work focuses on oxygen and hydrogen diffusion of mixed ionic (oxide ionic or/and protonic) – electronic conducting materials for these devices and its role in the performance. Ionic transport properties of conventional and state-of-the-art materials are reviewed.

Skin melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Thus, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis identified two potent drugs (Cialis and Finasteride) that can efficiently inhibit AKT1 and MDM2 protein signatures respectively, and with better therapeutic properties. We proposed that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

Wearable vital signs monitoring and specially the electrocardiogram have taken important role due to the information that provide about high-risk diseases, it has been evidenced by the needed to increase the health service coverage in home care as has been encouraged by WHO. Some wearables devices have been developed to monitor the ECG in which the location of the measurement electrodes is modified respect to the Einthoven model. However, mislocation of the electrodes on the torso can lead to the modification of acquired signals, diagnostic mistakes and misinterpretation of the information in the signal. This work presents a volume conductor evaluation and an ECG signal waveform comparison when the location of electrodes is changed, to find a electrodes’ location that reduces distortion of interest signals. In addition, effect of motion artifacts and electrodes’ location on the signal acquisition are evaluated. A group of volunteers was recorded to obtain ECG signals, the result was compared with a computational model of the heart behavior through the EA ECG, DTW and SNR methods to quantitatively determine the signal distortion. It was found that while the Einthoven method is followed, it is possible to acquire the ECG signal from the patient’s torso or back without a significant difference, and the electrodes position can be moved 6cm at most from the suggested location by the Einthoven triangle in Mason-Likar’s method.

Semiconductor technology is the backbone of modern innovations, propelling technological advancements in industries such as electronics, renewable energy, telecommunications, and health-related fields. This review expatiates the fundamental properties and working principles of semiconductor materials that determine device performances, while also tracing the historical trajectory of these technologies from the inception of the transistor to pioneering developments, such as tunnel gate transistors and 2D materials like graphene. Specific challenges such as material constraints, manufacturing complexities, and environmental impacts are highlighted. In response to such challenges, measures for the improvement of sustainability, energy saving, and waste minimization in production are discussed. The research also responds to the extended effects of globalization, including the disruptions like COVID-19 pandemic, on supply chain and emphasizes on the importance of developing resilient production frameworks for future growth. Additionally, this work highlights emerging technologies, including quantum computing, artificial intelligence integration, and nanotechnology, which are poised to redefine data processing and storage. The research vindicates the necessity of innovation and sustainability as the key motivators for enhancing the capabilities of semiconductors and global technology.

Abstract: This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

Poverty has long been associated intricately with neurological disorders. Majority of the previous studies designed to describe the effects of poverty on neurological ailments have mostly linked the association of hypoxic ischemic encephalopathy and nutritional deficiency disorders with poverty. Authors herein try to delve deeper into this issue. The association of begging with neuropsychiatric disorders is robust. The burden of probable autosomal dominant neurological ailments is very common among beggars; followed by neurological disorders with social stigma like abnormal movements, facial dysmorphism and gait unsteadiness. Dearth of support from family members in autosomal dominant neurological disorders (as family members are likely to be suffering from similar neurodegenerative disorder ) and poor perception about neurological diseases, misconceptions, myths and low educational attainment at large in the society can be implicated as the often overlooked but important factors underneath this association of begging and neurological disorders.

The biological aging, characterized by changing in metabolism and physicochemical properties of cells, has an impact on public health. Environment and lifestyle including factors like diet and physical activity seems to play a key role in healthy ageing. Several studies have shown that regular physical activity can enhance antioxidant defence mechanisms, including the activity of enzymes such as superoxide dismutase (SOD), catalase, and glutathione peroxidase. However, intense or prolonged exercise can also lead to an increase in ROS production temporarily, re-sulting in oxidative stress. This phenomenon is referred as "exercise-induced oxidative stress". The relationship between physical activity and oxidative stress in aging is complex and depends on various factors such as the type, intensity, duration, and frequency of exercise, as well as in-dividual differences in antioxidant capacity and adaptation to exercise. In this review, we ana-lyzed what is reported from several authors regarding the role of physical activity on oxidative stress in aging process as well as the role of hormesis and physical exercise as tools for preven-tion and treatment of sarcopenia, an aging related disease. Finally we reported what has recently been studied in relation to the effect of physical activity and sport on aging in women

In this paper, the neural network is developed for solving the inverse photoacoustic problem aiming to estimate thermo-elastic properties and thickness of a semiconductor sample. The idea was that these sample properties be estimated from the phase characteristic because the phase measurements are more sensitive. The neural network has been trained on a large basis of photoacoustic phases calculated from a theoretical Si n-type model in the range of 20Hz to 20kHz, on which random Gaussian noise has been applied. It is shown that high accuracy and precision could be reached in solving of inverse photoacoustic problem using only phase measurement.

Zero-Knowledge Proofs ZKP provide a reliable option to verify that a claim is true without giving detailed information other than the answer. A classical example is provided by the ZKP based in the Graph Isomorphism problem (GI), where a prover must convince the verifier that he knows an isomorphism between two isomorphic graphs without publishing the bijection. We design a novel ZKP exploiting the NP-hard problem of finding the algebraic ideal of a multivariate polynomial set, and consequently resistant to quantum computer attacks. Since this polynomial set is obtained considering instances of GI, we guarantee that the protocol is at least as secure as the GI based protocol.

DFT calculations on the photoisomerization reactions of pyridazine N-oxide derivatives are presented. The irradiation of 3,6-diphenylpyridazine N-oxide allowed to obtain the first excited singlet state that underwent to a ring opening reaction induced to the oxygen migration to the adjacent carbon atom, with the formation of a diazo derivative. This intermediate can give a ring closure reaction to give a pyrazole derivative, or, with a higher transition energy, it can lose nitrogen allowing the formation of 2,5-diphenylfuran. The irradiation of 3-phenylpyridazine N-oxide allowed the formation only of 2-phenylfuran. In this case, calculations allowed to explain this behavior. The first excited singlet state gave a photoisomerization reaction with the formation of the corresponding diazo intermediate. This intermediate could not be converted into the corresponding pyrazole derivative because it was converted into a N-formyl pyrazole never reported in literature. The only possible reaction is the loss of nitrogen and the formation of 2-phenylfuran.

Objective: The aim of this in vitro study was to evaluate the accuracy of resin-based fixed dental restorations, namely veneers, single crowns, and four-unit fixed partial dental prosthesis (FPDs) using two different 3D printers and polymer-based materials. Materials and Methods: A standard maxillary polyurethane jaw model containing prepared teeth was scanned using an intraoral scanner. The generated STL data were used to design the restorations virtually using CAD software. Two 3D printers were utilized for the provisional digital light processing and stereolithography for the castable resin patterns. Each printer produced 10 specimens of each type of restoration, for a total of 80 restorations. The 3d-printed restorations were then 3D scanned using the same intraoral scanner and evaluated for external and internal dimensional accuracy in terms of trueness and precision. A one-way ANOVA and Two-sample T-Test were implemented to compute the precision (variability between groups) and trueness (with the designed CAD model). A level of statistical significance of p-Value

In August 4 2011, United Nations Environmental Program (UNEP) submitted an unprecedented, scientific, groundbreaking Environmental Assessment Report (EAR) of Ogoniland, to the Nigerian government. This was the outcome of a 14–month intensive evaluation of the extent of pollution. It was intended that UNEP’s recommendations would be implemented to restore the devastated environment, on the one hand, and on the other, counteract the numerous environmental health issues that have for decades, plagued Ogoniland. However, five years post EAR, and, despite the seriousness of the situation, no significant resolution has occurred, both on the part of the government, and on the part of Shell Petroleum Development Company (SPDC) or Shell. To date, millions of Niger Delta residents, particularly those living in the oil-bearing communities, continue to suffer severe consequences. Although, the assessment was conducted in Ogoniland, other communities in the Niger Delta are also affected. This article explores prevailing issues, using Ogoniland (a microcosm of the Niger Delta) as an example. A multidisciplinary approach for sustainable mitigation of environmental health risks in the Niger Delta is paramount, and Environmental Management tools offer valuable strategies. Adopting UNEP’s recommendations for addressing environmental health problems requires implementing the Environmental Management/Environmental Management System (EM/EMS) model.

The Fusarium fujikuroi species complex (FFSC) includes socioeconomically important pathogens that cause disease and/or mycotoxin contamination on numerous crops. Here, we used comparative genomics to elucidate processes underlying the ability of pine-associated and grass-associated FFSC species to colonize tissues of their respective plant hosts. We characterized the identity, possible functions, evolutionary origins, and chromosomal positions of the host-range-associated genes encoded by the two groups of fungi. The 72 and 47 genes identified as unique to the respective genome groups were potentially involved in diverse processes, ranging from transcription, regulation, and substrate transport, through to virulence/pathogenicity. Most emerged early during the evolution of Fusarium/FFSC and were subsequently retained only in some lineages, while some had origins outside Fusarium. Although differences in the densities of these genes were especially noticeable on the conditionally dispensable chromosome of F. temperatum (representing the grass-associates) and F. circinatum (representing the pine-associates), the host-range-associated genes tended to be located towards the subtelomeric regions of chromosomes. Taken together, these results demonstrate that multiple mechanisms drive the emergence of genes in grass- and pine-associated FFSC taxa examined and highlighted the diversity of molecular processes potentially underlying niche-specificity in these and other Fusarium species.

Curcumin, found in the rhizome of turmeric, has extensive therapeutic promises via its antioxidant, anti-inflammatory, and antiproliferative properties. Preclinical in vitro and in vivo data have shown curcumin to be an effective treatment for multiple cancers. These effects are drived by curcumin's ability to induce G2/M cell cycle arrest, induction of autophagy, activation of apoptotic pathways, disruption of molecular signaling, inhibition of invasion and metastasis, and by increasing the efficacy of existing chemotherapeutics. Here we focused on the hormetic behaviour of curcumin. Frequently, low doses of toxins and other stressors not only are harmless but also activate an adaptive stress whereas high dose activates acute responses like autophagy and cell death. This phenomenon is referred to as hormesis. Many molecules that cause cell death elicite an initial autophagic step that is a cytoprotective mechanism relying on elimination of dysfunctional structures intracellular, notably by mitophagy. This phenomenon is considered as a primarily protective mechanism against stressors. At higher doses, cells undergo mitochondrial outer membrane permeabilization due to calcium release from the endoplasmic reticulum and die. Herein, we address the complex crosstalk between the induced mitochondrial biogenesis, mitochondrial destabilization accompanied by mitophagy and cell death that can also be at play.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

(1) Background: Although resistance to pathogens and pests has been researched in many potato cultivars and breeding lines with DNA markers, there is scarce evidence as to the efficiency of the marker-assisted selection (MAS) for these traits when applied at the early stages of breeding. A goal of this study was to estimate the potential of affordable DNA markers to track Rpi disease resistance genes, that are effective against the pathogen Phytophthora infestans, as a practical breeding tool on a progeny of 68 clones derived from a cross between the cultivar Sudarynya and 13/11-09. (2) Methods: this population was studied for four years to elucidate the distribution of LB resistance and other agronomical desirable or simple to phenotype traits such as tuber and flower pigmentation, capacity and structure of yield. LB resistance was phenotypically determined through natural and artificial infection and the presence/absence of nine Rpi genes was assessed via 11 sequence-characterized amplified region (SCAR) markers. To aid this analysis, the profile of Rpi genes in the 13/11-09 parent was established using diagnostic resistance gene enrichment sequencing (dRenSeq) as a gold standard. (3) Results: at the early stages of a breeding program, MAS can halve the workload when screening the segregation of F1 offspring and selected SCAR markers for Rpi-genes provide useful tools.

This survey was conducted on 4 tribes (Ababda, Bisharia, Nubian and Rashayda) live in the south of the Nile and the Eastern Desert of Egypt with the aim to document and compare the traditional herbal medicines and assess the relationships among these tribes. A total of 180 interviews were conducted with the Bedouins and herb healers. Thirty-nine species belonged to 36 genera and 27 families were employed. Fabaceae and Poaceae and Rutaceae were the species-rich families. The used wild species comprised 43.6%, cultivated species (38.5%) and the imported from herbalist shops (17.9%). The leaves were the most used parts (31%), followed by stems and fruits with about 22% each. Distinct species included Acacia nilotica is used in the treatment of dental pain with use value 33.3%, Cymbopogon schoenanthus subsp. proximus in treatment of both cough or headache with use values 35 and 30.6% and a combination of Acacia nilotica with Lawsonia inermis in the treatment of sore throat with use value 22.2%. The highest similarity was recorded between Nubian and Rashayda tribes (55.3%), Ababda and Bisharia (46.8%). Diarrhea and headache were the most popular diseases with 7 different treatments, cough and dental pains with 6 treatments.

Extended Reality (XR) technologies—encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—merge elements of the physical world with virtual environments, revolutionizing various sectors, including healthcare. This paper explores the transformative potential of XR when combined with advanced digital tools to enhance therapeutic practices for individuals with disabilities or social isolation. Emphasizing the pivotal role of healthcare professionals, particularly nurses, the study investigates how these experts can effectively integrate XR technologies to maximize therapeutic benefits. The research includes a pre-implementation phase that assesses the knowledge and acceptance levels of both healthcare professionals and the general population. This foundational study highlights the potential of XR in expanding access to artistic and recreational activities, drawing insights from efforts by the Opera di Santa Maria del Fiore (Florence) to democratize timeless artistic experiences. The findings underscore the im-portance of defining key terms and understanding stakeholder readiness before full-scale implementation.

The rapid development of China’s textile industry leads to consumption and pollution of large volumes of water. Therefore, the textile industry has been the focus of water conservation and waste reduction in China’s 13th Five-Year Plan (2016–2020). The premise of sustainable development is to achieve decoupling of economic growth from water consumption and wastewater discharge. In this work, changes in blue water (water consumption), grey water (water pollutants), and water footprints of the textile industry from 2001 to 2014 were calculated. The relationship between water footprint and economic growth was then examined using the Tapio decoupling model. Furthermore, factors influencing water footprint were determined through logarithmic mean Divisia index (LMDI) method. Results show that the water footprint of China’s textile industry has strongly decoupled for five years (2002, 2006, 2008, 2011, and 2013) and weakly decoupled for four years (2002, 2007, 2009, and 2010). A decoupling trend occurred during 2001–2014, but a steady stage of decoupling has not been achieved yet. Based on the decomposition analysis, the total water footprint is mainly increased by production scale and inhibited by the technology. In addition, the effect of industrial structure adjustment is relatively weak.

Zika virus (ZIKV) has been characterized as one of the potential pathogens and placed under future epidemic outbreaks by the WHO. However, lack of potential therapeutics can result in uncontrolled pandemic like other human pandemic viruses; therefore, prioritized effective therapeutics development has been recommended against ZIKV. In this context, the present study adopted the strategy to explore the lead compounds from Azadirachta indica against ZIKV via concurrent inhibition of the ZIKVpro and ZIKVRdRp proteins using molecular simulations. Initially, structure-based virtual screening of 44 bioflavonoids reported in Azadirachta indica against the crystal structures of targeted ZIKV proteins resulted in the identification of top four common bioflavonoids, viz. rutin, nicotiflorin, isoquercitrin, and hyperoside. These compounds showed substantial docking energy (-7.9 to -11.01 kcal/mol) and intermolecular interactions with essential residues of ZIKVpro (His51, Asp74, and Ser135) and ZIKVRdRp (Asp540, Ile799, and Asp665) by comparison to the reference compounds, O7N inhibitor (ZIKVpro) and Sofosbuvir inhibitor (ZIKVRdRp). Long interval molecular dynamics simulation (500 ns) on the selected docked poses reveals the stability of respective docked complexes contributed by intermolecular hydrogen bonds and hydrophobic interactions. The predicted complex stability was further supported by calculated end-point binding free energy using molecular mechanics generalized born surface area (MM/GBSA) method. Consequently, the identified common bioflavonoids are recommended as promising therapeutic inhibitors of ZIKVpro and ZIKVRdRp for further experimental assessment.

Keywords: Adrenergic signaling in cardiomyocytes; cardiac muscle contraction; Chagas disease; diabetic cardiomyopathy; dilated cardiomyopathy; glycerolipid metabolism; glycolisis/glucogenesis; hypertrophic cardiomyopathy; purine metabolism; steroid hormone biosynthesis.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

Historically, the lack of comprehensive and real-time data is the main challenge in city sustainability assessments. However, with the increase in the availability of crowdsourced data over the last decade, a rich and real-time data source provides detailed images of urban systems. This has opened up new opportunities for practitioners, including geographers, social researchers, and data scientists, to gather and utilize social media data as a core source for studying cities and assessing their sustainability. This paper proposes an assessment model for the sustainability of cities based on crowdsourced data and the principles of General System Theory. The model aims to utilize crowdsourced data for the analysis and decision-making processes to enhance the sustainability and resilience of smart future cities.

To provide carbon steel a long corrosion protection effect in NaCl solution with various pH, a pH-controlled intelligent inhibitor based on poly-acrylamide (PAM) and oleate imidazoline (OIM) was synthesized. SEM, FT-IR and TGA results indicating the OIM inhibitor was successfully loaded into PAM hydrogel with a very high content (39.64 wt%). The OIM release behavior from hydrogel structure have two stages, quickly release and sustained release. The pH of solution could affect the initial release kinetics of OIM inhibitor and the diffusion path in hydrogel structure. Weight loss measurement of L80 steel in different pH solution with OIM@PAM proved the inhibitor responsive release mechanism and an-ti-corrosion performance. The inhibition efficiency of OIM@PAM can maintain over 80% after long term immersion in harsh corrosive environment (pH 3), which is much higher than the inhibition efficiency of inhibitor in moderate corrosive solution.

Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is scarce or inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This "metabolic flux probe" (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

Distributed deformation based on Fiber Bragg Grating sensors or other kinds of strain sensors can be used to evaluate safety in operating periods of bridges. However, most of the published researches about distributed deformation monitoring are focused on solid rectangular beam rather than box girder—a kind of typical hollow beam widely employed in actual bridges. Considering that the entire deformation of a single-cell box girder contains not only bending deflection but also two additional deformations respectively caused by shear lag and shearing action, this paper again revises the improved conjugated beam method (ICBM) based on the LFBG sensors to satisfy the requirements for monitoring two mentioned additional deformations. The best choice for the LFBG sensor placement in box gilder is also proposed in this paper due to strain fluctuation on flange caused by shear lag effect. Results from numerical simulations show that most of the theoretical monitoring errors of the revised ICBM are 0.3%~1.5%, and the maximum error is 2.4%. A loading experiment for a single-cell box gilder monitored by LFBG sensors show that most of the practical monitoring errors are 6%~8%, and the maximum error is 11%.

Corona loss generated from conductors is one of the important design factors on ac transmission lines. Based on helical charge simulation method, the 3-D corona loss calculation model considering the conductors’ outer strands in the corona cage is established. With regard to 2-D corona-loss calculation method, the calculation model proposed in this paper can take the electric field strength’s nonuniformity along the axial direction of stranded conductor into consideration, furthermore, it can also calculate the corona loss when the conductor has sag. The calculation results of the LGJ-300/40 and LGJ-400/35 in the small corona cage well coincide with the measurement results, which indicates that the corona-loss calculation model is appropriate. In addition, the corona loss of the bundle conductors 4 × LGJ720 with sag in UHV corona cage is also analyzed.

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

The three-phase separator was perforated after four years’ service. The perforation position is at the bottom of the vessel under the baffle plate in an oil transfer station. The cause of perforation was investigated by chemical analysis using a direct-reading spectrometer, microstructure examined by optical microscope(OM), and lab simulated corrosion experiments by electrochemical workstation, and the corrosion products examined with scanning electron microscopy(SEM), energy dispersive spectrometer(EDS) in this paper. The analyses indicated that the coating at the base of the pressure vessel failed and this led to the direct exposure of the carbon steel plate to the corrosive medium. The electrical contact between the carbon steel and the stainless steel, sets up a galvanic cell that accelerated the corrosion of the pressure vessel which eventually led leads to the perforation.

Xyleborus beetles, a diverse group of ambrosia beetles, present significant challenges to forestry and agriculture due to their damaging burrowing behavior and symbiotic relationships with fungi. This review synthesizes current knowledge on the biology, ecology, and management of Xyleborus. We explore the beetles' life cycle, reproductive strategies, habitat preferences, and feeding habits, emphasizing their ecological and economic impacts. Control and management strategies, including preventive measures, chemical and biological control, and integrated pest management (IPM), are critically evaluated. Recent advances in molecular genetics and behavioral studies offer insights into genetic diversity, population structure, and host selection mechanisms. Despite progress, managing Xyleborus effectively remains challenging. This review identifies future research needs and highlights innovative control methods, such as biopesticides and pheromone-based trapping systems.

Osteomyelitis is a significant cause of total carcass condemnation in pigs at the slaughterhouse. The decision for total condemnation of a pig carcass for osteomyelitis is often based on traditional perceptions of the risk of pyemia, leading to controversy among Official Veterinarians (OV) in the industry. This review aims to provide a more comprehensive understanding of the etiopatho-genesis of osteomyelitis in pigs, the microorganisms involved, and the risk factors. It also high-lights the urgent need for a more uniform method to evaluate osteomyelitis cases, which could significantly reduce economic losses in the industry. Lesions originating from tail-biting, tail docking, castration, teeth resection, and raw management are described as risk factors for oste-omyelitis. Osteomyelitis is caused by the entry of pathogens into the bloodstream of the animal through an open wound. The most described pathogens are Trueperella monocytogenes, Staphylo-coccus aureus, and Streptococcus spp. At slaughter, OVs condemn carcasses with osteomyelitis due to pyemia. Signs of acute disease are essential to identify pyemia cases. In chronic cases, total carcass condemnation can be avoided depending on the number of lesions and number of vertebrae af-fected. A clear overall image of the problem would help authorities in various countries adopt a more homogenous approach.

Bi1-xSbx crystal is one of the best n-type thermoelectric materials below 200 K, but its weak mechanical strength hinders practical applications for deep refrigeration. Herein, we adopted the mechanical enhancement method of hot extrusion to investigate the comprehensive mechanical and thermoelectric properties of Bi0.905Sb0.095. It revealed that reducing the grain size of the matrix and increasing the extrusion ratio can improve the gain size uniformity and mechanical properties. While the thermoelectric performance depends on the texture, grain size, and local composition. The extruded sample prepared by ingot with the high extrusion ratio of 9:1 generated a high texture degree of (001) plane and small grain sizes between ~4-40 mm, which resulted in the high bending strength of Bi1-xSbx ~130 Mpa and a high-power factor of ~ 68 μW·cm-1·K-2@173 K, as well as the relative high figure of merit of 0.25@173K. This work highlights the importance of the uniform distribution of the grain size and the compositions for Bi1-xSbx, as well as the required universal key parameter for the hot-extrusion method.

Microelectromechanical System (MEMS) vibrating gyroscope design considerations are always intriguing due to their microscale mechanical, electrical, and material behavior. MEMS vibrating ring gyroscopes have become important inertial sensors in inertial measurement units (IMU) for navigation and sensing applications. The design of a MEMS vibrating ring gyroscope incorporates an oscillating ring structure as a proof mass, reflecting unique design challenges and possibilities. This paper presents a comprehensive design analysis of the MEMS vibrating ring gyroscope from the mechanical, electrical, and damping perspectives. The mechanical design of the MEMS vibrating ring gyroscope investigates the various frame designs of the vibrating ring structure, as well as the various beam structures, including curved, rectangular, and semicircular beam structures, which are analyzed using mathematical models and finite element analysis (FEA) simulations that provide an in-depth analysis of the stiffness and deflection of the vibrating structures. The electrical designs of the MEMS vibrating ring gyroscope were analyzed using various electrode configurations, electrostatic actuation, and capacitive detection mechanisms, respectively. The design analysis of various forms of damping, including viscous, structural, thermoelastic, and anchor damping, has been discussed. The variety of design structures investigated for MEMS vibrating ring gyroscopes' mechanical, electrical, and damping performance.