With the targets of petroleum exploration transferred to the deep and ancient strata, abundant oil and gas resources have been found in the Lower Paleozoic and older strata in central and western China. Due to complex evolutionary processes including multiple episodes of hydrocarbon accumulation and ubiquitously accompanied by secondary alterations, significant uncertainties were remained for the generation time and accumulation processes of these revealed petroleum. In this paper, relative pure Re and Os elements existing in the asphaltene fractions of Lower Cambrian solid bitumen collected from the Guangyuan area, western Sichuan Basin, SW China and another Middle–Lower Ordovician heavy oils in the Aiding area of the Tahe oilfield in the Tarim Basin, NW China were successfully obtained by the sample pre-treatments, and Re–Os isotopic analysis was subsequently carried out for the dating of these two petroleum. The Re–Os isotopic composition indicates a generation time of Guangyuan bitumen to locate between 572 Ma and 559 Ma, corresponding to the late Sinian period of Neoproterozoic era. By the means of Re–Os isochron ages, initial 187Os/188Os ratios, and carbon isotopic compositions, the Lower Cambrian bitumen is supposed to be originated from source rocks of the Doushantuo Formation in the Sinian strata and subsequently migrated into the reservoirs of Dengying Formation. These previously reserved petroleum had been transformed to present bitumen by the destruction of reservoirs caused by tectonic uplift. The Re–Os dating results of Middle–Lower Ordovician heavy oil of Tarim Basin supposed that it was formed between 450 Ma to 436 Ma, corresponding to the Late Ordovician–Early Silurian system, and the generated petroleum is likely to migrate into the Middle–Lower Ordovician karst reservoirs to form early oil reservoirs. With tectonic uplift, these oil reservoirs were degraded and reformed to be present heavy oil reservoirs.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

Artificial groundwater recharge is commonly used for drinking water supply. The resulting water quality is highly dependent on the raw water quality. In many cases, pre-treatment is required. Pre-treatment improves the drinking water quality, although how and to what extent it affects the subsequent pond water quality and infiltration process, is still unknown. We evaluated two treatment systems by applying different pre-treatment methods for raw water from a eutrophic and temperate lake. An artificial recharge pond was divided into two parts, where one received raw water, only filtered through a micro-screen with 500 µm pores (control treatment), while the other part received pre-treated lake water using chemical flocculation with polyaluminium chloride (PACl) combined with sand filtration, i.e. continuous contact filtration (contact filter treatment). Water quality such as cyanobacterial biomass, microcystin-LR as well as organic matter and nutrients were measured in both treatment processes. We found cyanobacterial biomass and microcystin-LR level after the contact filter treatment was significantly different from the control treatment and also significantly different in the pond water. In addition, with contact filter treatment, total phosphorus (TP) and organic matter removal were significantly improved in the end water, TP was reduced by 96 % (< 20 µg/L) and the total organic carbon (TOC) was reduced by 66 % instead of 55 % (TOC content around 2.1 mg/L instead of 3.0 mg/L). This full-scale onsite experiment demonstrated effective pre-treatment would benefit a more stable water quality system, with less variance and lower cyanotoxin risk. In a broader drinking water management perspective, the presented method is promising to reduce cyanotoxin risk, as well as TP and TOC, which are all predicted to increase with global warming and extreme weather.

Information geometry is a mathematical framework that analyses the structure of statistical models using concepts from differential geometry. It treats families of probability distributions as manifolds, where the parameters of each model determine the coordinate charts. By applying info-geometric tools, we can gain insights into the characteristics of these models. The approach involves characterizing the queueing system's manifold using information geometry and presenting the exponential of the information matrix. This integration of information geometry with queueing theory provides a novel perspective for analyzing the dynamics of queueing systems, incorporating relativistic and Riemannian concepts. Some applications to E-health are highlighted. Finally, closing remarks and the next phase of research.

Aptamers are versatile oligonucleotide ligands used for molecular recognition of diverse targets. However, application of aptamers to the field of amyloid β-protein (Aβ) has been limited so far. Aβ is an intrinsically disordered protein that exists in a dynamic conformational equilibrium, presenting time-dependent ensembles of short-lived, metastable structures and assemblies that have been generally difficult to isolate and characterize. Moreover, despite understanding of potential physiological roles of Aβ, this peptide has been linked to the pathogenesis of Alzheimer disease, and its pathogenic roles remain controversial. Accumulated scientific evidence thus far highlights undesirable or nonspecific interactions between selected aptamers and different Aβ assemblies likely due to metastable nature of Aβ or inherent affinity of RNA oligonucleotides to β-sheet-rich fibrillar structures of amyloidogenic proteins. Accordingly, lessons drawn from Aβ–aptamer studies emphasize that purity and uniformity of the protein target and rigorous characterization of aptamers’ specificity are important for realizing and garnering the full potential of aptamers selected for recongizing Aβ or other intrinsically disordered proteins. This review summarizes studies of aptamers selected for recognizing different Aβ assemblies and highlights controversies, difficulties, and limitations of such studies.

Pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab in immunocompromised patients has reduced the risk of breakthrough infection, disease, hospitalization, and COVID-19 related mortality. However, the advent of the Kraken variant (XBB.1.5) has limited the use of this monoclonal antibody, based on poor efficacy in in vitro studies The objective of the study was to evaluate the risk of breakthrough infection, symptomatic disease, hospitalization, intensive care admission and COVID-19 related death in kidney transplanted recipients receiving pre-exposure prophylaxis with Tixagevimab/Cilgavimab for COVID-19 in the era of the Kraken variant (XBB.1.5). In a prospective, observational study, we enrolled kidney transplant patients undergoing pre-exposure prophylaxis for COVID-19 with Tixagevimab/Cilgavimab at the Division of Infectious Diseases of Federico II University of Naples between February 2023 and August 2023. Each patient subsequently underwent a six-month follow-up with symptom monitoring and surveillance nasopharyngeal swab for SARS-CoV-2 RNA detection every 30 days, regardless of symptoms. Thirty-four kidney transplant patients were enrolled and in the follow-up period, only one tested positive for the nasopharyngeal swab for SARS-CoV-2 research with asymptomatic infection and virological recovery on the eighth day from the diagnosis of infection. Therefore, no patient developed disease and no patient needed hospitalization and no death occurred. No adverse drug reaction to Tixagevimab/Cilgavimab occurred. Our data, although derived from a limited and uncontrolled sample, show the potential of Tixagevimab/Cilgavimab as a valid and viable therapeutic strategy in pre-exposure prophylaxis for immunocompromised patients. These findings highlight the importance of conducting clinical studies on this topic.

Serum albumin (ALB), one of the most important proteins in human physiology, has the main functions of maintaining plasma oncotic pressure and plasma volume, transporting hormones, vitamins, oligominerals and drugs, and exerting a powerful antioxidant-antiinflammatory role. Its prognostic value in liver and malabsorption syndromes is well known. In this narrative review, an analysis of the most important studies evaluating the prognostic significance of low serum ALB levels in hospitalized patients was performed. Specifically, the risk in emergency medicine, cardiovascular diseases, COVID-19 infection, nephrology, oncology and autoimmune rheumatic diseases has been examined to fully explore its clinical value. ALB is a negative acute-phase reactant and the reduction of its serum levels represents a threatening parameter for long-term survival in several clinical settings, and a strong poor prognostic biomarker in most diseases. Therefore, clinicians should consider serum ALB as a valuable tool to assess the efficacy of specific therapies, both in hospitalized patients and in chronic follow-up.

OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Background: There is an exponential increase in caesarean sections with epidemic proportions worldwide, which is considered as a serious public health issue that influences negatively both maternal and childhood health. Aims: This study aimed to evaluate the associations between pre-dominant maternal risk factors and the prevalence of caesarean section in a representative sample of women from Greece. Methods: This is a cross-sectional study conducted on 5182 healthy, re-productive-aged women from 11 geographically diverse Greek areas after applying specific inclu-sion and exclusion criteria. Statistical analysis was applied to evaluate the impact of maternal so-ciodemographic, anthropometric, lifestyle and perinatal factors in the risk of caesarean section de-liveries. Results: A high prevalence of 56.4% of caesarean section deliveries was recorded in the study population. The incidence of caesarean sections reached to 51.5% in private hospitals in which an increasing rate of 47.5% for planned caesarean sections were noticed. Older maternal age, pre-pregnancy overweight and obesity, excess gestational weight gain, high rates of preterm birth, better economic status, smoking habits, and private type of birth hospital were significantly related with enhanced risk of caesarean section, independently of multiple confounding factors. Conclu-sions: Our study revealed that caesarean section rates are continuously increase, while several maternal risk factors, including especially pre-pregnancy overweight/obesity and excess gestational weight gain, cumulatively raise its prevalence which further increase the risk for postnatal adverse outcomes for both the mothers and their children. Emergent public health policies and strategies should be promoted to confront the predominant maternal risk factors-related with caesarean sec-tion and inform future mothers how to be prevented from them by applying healthier nutritional and lifestyle habits and to select caesarean section only due to emergency medical reasons. Alarmingly enough, targeted nutritional intervention strategies are strongly recommended to ef-fectively reduce the prevalence of maternal pre-pregnancy overweight/obesity and the excess gestational weight gain, which in turn may minimize the risk of childhood overweight/obesity.

Background: Pre-pregnancy overweight and obesity of women of childbearing ages, has alarmingly a growing trend in developed countries. This study aimed to evaluate potential associations between maternal pre-pregnancy excess body weight and childhood demographic and anthropometric characteristics, as well as perinatal and postnatal outcomes. Methods: This is a cross-sectional study conducted on 5198 pre-school children aged 2-5 years old and their matched mothers that were enrolled from nine different Greek regions. Maternal and childhood anthropometric and demographic data, perinatal and postnatal outcomes were collected from medical history records or validated questionnaires. Results: A prevalence of 24.4% and 30.6% of overweight/obesity was recorded for the enrolled children and their mothers 2-5 years postpartum. In multivariate analysis, childhood overweight and obesity was significantly more frequently observed when maternal pre-pregnancy BMI status was overweight or obese [OR: 2.11 (1.80-2.42) p=0.0001]. A high prevalence of caesarean section delivery and childhood diabetes type I was also significantly associated with maternal pre-pregnancy overweight/obesity [OR:1.71 (1.33-2.19) p=0.0175) and OR: 1.27 (1.04-1.53) p=0.0014, respectively]. Conclusions: Maternal pre-pregnancy overweight and obesity rates were related with increased childhood weight status at birth and 2-5 years postpartum, highlighting the necessity of encouraging healthy lifestyle promotion, including healthier nutritional habits, and focusing on obesity population policies and nutritional interventions among women of reproducible age.

The rise in prevalence of obesity in women of reproductive age in both developed and developing countries might propagate intergenerational cycles of detrimental effects on metabolic health, contributing to substantial economic burden on society. Placental lipid metabolism might be disrupted by maternal obesity, which possibly affects the life-long health of the offspring. Here, we investigated placental lipid metabolism and handling from women with pre-gestational obesity as a sole pregnancy complication and compared to placental responses of lean women. Open profile and targeted lipidomics were used to assess placental lipids and oxidized products of docosahexahenoic acid (DHA), neuroprostanes, and arachidonic acid (AA), isoprostanes. Placental fatty acid transporters FABP1, FABP3 and endothelial lipase protein were measured. Despite no signs of overall alterations in lipid content, increased contents of DHA, AA, DHA-derived neuroprostanes and AA-derived isoprostanes and decreased content of FABP1 protein were found in placentas from obese women. Multivariate analyses suggested that these oxidised fatty acids are associated with maternal and placental inflammation and also with birth weight. These results might shed light on the molecular mechanisms associated with altered fatty acid metabolism and lipid handling in maternal pre-gestational obesity, placing these oxidized fatty acids as novel mediators of placental function.

In this work we identify combinations of technological activities that signal the presence local capabilities in a country to successfully export a product. We use country-level patent and trade data to generate a multi-layer network, and we apply maximization of entropy to generate synthetic data to effectively divide signal from noise. We show that in several sectors the signal far exceed the noise. Our exercise provides robust evidence of the presence of synergies between technologies to explain trade performances in specific markets. This can be highly useful for policy makers, to inform industrial and innovation policies.

Sources of resistance to powdery mildew in wild barley (Hordeum vulgare subsp. spontaneum) collected in Jordan, Lebanon, and Libya Jerzy H. Czembor 1, *, Elżbieta Czembor 1 1 Plant Breeding and Acclimatization Institute – National Research Institute (IHAR-PIB), Radzikow, 05-870 Błonie, Poland; [email protected] (E.C.) *Correspondence: [email protected]) Abstract Barley powdery mildew (BPM) is caused by the pathogen Blumeria graminis f.sp. hordei (Bgh). It is an economically important disease and plant pathologists are looking for new sources of resistance to BPM. Barley genetic resources present in gene banks are often a rich source of disease resistance to be used by breeders. These new sources of resistance to BPM are often used in combination (pyramiding) with those that are already used in modern cultivars. Barley accessions, including the wild subspecies Hordeum vulgare subsp. spontaneum (Hvs), are stored in many gene banks and often are a valuable source of economically important characteristics. This source of biodiversity should be more efficiently used to improve barley in the process of plant breeding. However, their proper characterization and availability are urgently needed. The resistance to BPM in 81 accessions of wild barley (Hvs) collected in Jordan (47), Lebanon (23), and Libya (11) was investigated. The seed samples of these accessions were obtained from the ICARDA gene bank and collected in 10 expeditions from 1981 to 1995. Twenty European differential isolates of BPM were used to select accessions with efficient resistance. Thirty-one resistant single plant lines were selected from 15 accessions from Jordan and Libya based on tests performed with the most avirulent isolate of Bgh available. These resistant single plant lines were tested for the presence of specific resistance genes using a differential set of Bgh isolates. After analysis of obtained results, it was concluded that all tested 31 single plant lines of wild barley have genes for resistance that are not represented in the Pallas isolines differential s. Twenty-six lines of Hvs selected from accessions originated in Jordan and Libya showed resistance reaction to all isolates used. Identified new sources of effective resistance to BPM in single plant lines of Hvs will be further tested and used in barley pre-breeding programs. Keywords: Blumeria graminis; resistance genes; resistance; germplasm; gene bank;

During the past few decades, hybrid nanoparticles (HNPs) based on a magnetic material and gold have attracted interest for applications in catalysis, diagnostics and nanomedicine. In this paper, magnetic CoFe2O4/Au HNPs with an average particle size of 10–20 nm decorated with 2-nm gold clusters were prepared using methionine as a reducer and an anchor between CoFe2O4 and gold. The obtained nanoparticles were studied by X-Ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared (FT-IR) spectroscopy and X-Ray photoelectron spectroscopy (XPS) and UV-Vis spectroscopy techniques. The TEM images of the HNPs obtained after one, six and ten gold deposition cycles showed that the surface of cobalt ferrite was covered with gold nanoclusters, which slightly increased with an increase in the number of gold deposition cycles (from 2.12 ± 0.15 nm after one cycle to 2.46 ± 0.13 nm after ten cycles) without any change in surface density. The magnetic measurements showed that the obtained HNPs possessed typical ferrimagnetic behaviour, which corresponds to that of CoFe2O4 nanoparticles. The toxicity evaluation of the synthesised HNPs on Chlorella vulgaris indicated that they can be applied to biomedical applications such as magnetic hyperthermia, photothermal therapy, drug delivery, bioimaging and biosensing.

In this paper, the problem of predicting future failure times based on a jointly type-II censored sample from k exponential populations is considered. The Bayesian prediction intervals and point predictors were then obtained. Generalized Bayes is a Bayesian study based on a learning-rate parameter. This study investigated the effects of the learning rate parameters on the prediction results. The loss functions of squared error, Linex, and general entropy were used as point predictors. Monte Carlo simulations were performed to show the effectiveness of the learning rate parameter in improving the results of prediction intervals and point predictors.

The main objective of this paper is to continue the development of activities of basic and applied research related to wind energy and to develop methods of optimal control to improve the performance and production of electrical energy from wind. A new control technique of Double fed induction generator for wind turbine is undertaken through a robust approach tagged nonlinear sliding mode control (SMC) with exponential reaching law control (ERL). The SMC with ERL proves to be capable of reducing the system chattering phenomenon as well as accelerating the approaching process. A nonlinear case numerical simulation test is employed to verify the superior performance of the ERL method over traditional power rate reaching strategy. Results obtained in Matlab/Simulink environment show that the SMC with ERL is more robust, prove excellent performance for the control unit by improving power quality and stability of wind turbine.

In this paper described numerical expansion of natural-valued power function x^n, in point x = x₀ where n, x₀ - natural numbers. Apply- ing numerical methods, that is calculus of finite differences, namely, discrete case of Binomial expansion is reached. Received results were compared with solutions according to Newton’s Binomial theorem and MacMillan Double Bi- nomial sum. Additionally, in section 4 exponential function’s e^x representation is shown.

The recent demand for reducing pollutant and CO2 emissions from internal combustion engines has created an urgent need for the development of ultra-lean burn engines that ensure combus-tion stability and reducing knocking tendency. One of the most promising methods is the Pre-chamber Spark Ignition (PCSI) system, where a jet of high-energy reactive gases, produced by pilot combustion in a pre-chamber, ignites the main combustion event in the cylinder. Given the intricate phenomena inherent to PCSI systems, conducting 3D CFD studies is imperative for a comprehensive analysis and optimal design. Furthermore, the detailed CFD model, combined with the calibrated 0-D/1-D model, is anticipated to yield a significant amount of new data that would be difficult to obtain through experimental approaches, making it essential for advancing our understanding and optimization of these systems. However, recently published papers re-port that some state-of-the-art models developed for conventional SI operation may not be pre-dictive under the challenging conditions of TJI systems, particularly under lean conditions. This review elucidates the reasons behind the widespread adoption of CFD in the optimal design of PCSI engines. It presents significant examples and delves into the potential and challenges of employing CFD not just as a predictive tool but also as a design instrument for enhancing PCSI engine performance.

To handle the challenges and uncertainties of the actual labor market, the concept of employability becomes increasingly relevant, especially for graduates facing the transition from university to work. Graduate employability is a complex concept comprising different dimensions that may trigger effective job search and career management. This study introduces a new multifaceted tool, labeled the Orientamentis Questionnaire, to assess graduate employability from an agentic perspective, testing its psychometric properties. The Orientamentis questionnaire measures 14 dimensions merged into four 4 areas, namely graduate psychological capital, graduate agentic capabilities, graduate career perspective, and graduate proactive strategies. The tool was administered to a sample of 1238 graduate students (66% females) examining its factorial structure and reliability. To test its criterion validity, associations with three occupational outcomes collected one year later were explored on a subsample of 273 participants (66% females). Results attested the factorial structure of the Orientamentis questionnaire and the reliability of all dimensions. Moreover, the four areas and most of the dimensions were differently related to at least one subsequent occupational outcome. The Orientamentis Questionnaire represents a sound and reliable tool providing a multifaceted profile of graduate employability strengths and weaknesses, that could be used in academic placement programs to foster graduates’ self-awareness and proactive job search.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

Kaposi’s sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi’s sarcoma, primary effusion lymphoma (PEL) and multicentric Castleman’s disease. During KSHV lytic infection, lytic-related genes, categorized as immediate-early, early, and late genes, are expressed in a temporal manner. The transcription of late genes requires the virus-specific pre-initiation complex (vPIC), which consists of viral transcription factors. However, the characterization of the protein-protein interactions of the vPIC factors has not been completely elucidated. KSHV ORF18 is one of the vPIC factors, and its interaction with other viral factors has not been sufficiently revealed. Here, we analyzed the interaction between ORF18 and another vPIC factor, ORF30, in living cells using the bimolecular fluorescence complementation (BiFC) assay. We identified four amino-acid residues (Leu29, Gln36, His41 and Trp170) on ORF18 that were responsible for its interaction with ORF30. The artificial intelligence (AI) system AlphaFold2 predicted that identified four residues are exposed to the surface of ORF18 and located in proximity to each other in the surface of ORF18. Thus, AI-predicted model supports the importance of four residues for binding of ORF18 to ORF30. Our results indicated that wet experiments in combination with AI may enhance the structural characterization of vPIC protein-protein interactions.

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors influence COVID-19 by applying the Adverse Outcome Pathway (AOP) framework well-established in regulatory toxicology. This framework aims to model the sequence of events starting from an initial interaction of a stressor with the organism and the progress through key biological events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 patients have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

Background Hokkaido is the northernmost, least populous, and coldest of the Japanese islands. It was the first prefecture to be affected by COVID-19, while Kanagawa is home to one of the most populous areas of Japan, namely the Tokyo metro area. The Japanese government responded early during the pandemic by identifying infected patients, contact tracing, and performing PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. The government has also been publishing information about each individual who tested positive for the virus. Both Hokkaido and Kanagawa started recording COVID-19 cases in the winter of 2020 and have detailed records of thousands of patients, thus providing an invaluable resource for the transmission and behavior of the virus. Methods The current study analyzed the COVID-19 registry data from the Hokkaido and Kanagawa prefectures. The Hokkaido registry contained 1,269 cases (674 (53%) females and 595 (47%) males) recorded between February 14 and July 22, 2020. The Kanagawa registry had 3,123 cases (1,346 (43%) females and 1,777 (57%) males. The final data contained a total of 4,392 cases (2,020 (46%) females and 2,372 (54%) males). By leveraging the information on viral transmission paths available in the registry data, we performed exponential random graph model (ERGM) network analysis to examine demographic and symptomological homophilies of the SARS-CoV-2 viral transmission networks. Results We observed age, symptomatic, and asymptomatic homophilies in both prefectures. Furthermore, those patients who contracted the virus through secondary or tertiary contacts were more likely to be asymptomatic than those who contracted it from primary infection cases. The transmission networks showed that transmission occurred significantly in healthcare settings, as well as in families, although the size of the networks was small in the latter. Most of the transmissions stopped at the primary and secondary levels and no transmission beyond quaternary was observed. We also observed a higher level of asymptomatic transmission in Kanagawa than in Hokkaido. Conclusions Symptom homophilies are an important component of COVID-19 and suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can thus result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Moreover, environmental variables such as temperature and humidity may also be playing an important role in the overall pathogenesis of the virus.

(1) Background: Artificial Intelligence (AI) models have shown potential in various educational contexts. However, their utility in explaining complex biological phenomena, such as Intrinsically Disordered Proteins (IDPs), requires further exploration. This study empirically evaluated the performance of various Large Language Models (LLMs) in the educational domain of IDPs. (2) Methods: Four LLMs, GPT-3.5, GPT-4, GPT-4 with Browsing, and Google Bard (PaLM 2), were assessed using a set of IDP-related questions. An expert evaluated their responses across five categories: accuracy, relevance, depth of understanding, clarity, and overall quality. Descriptive statistics, ANOVA, and Tukey's honesty significant difference tests were utilized for analysis. (3) Results: The GPT-4 model consistently outperformed the others across all evaluation categories. Although GPT-4 and GPT-3.5 were not statistically significantly different in performance (p&gt;0.05), GPT-4 was preferred as the best response in 13 out of 15 instances. The AI models with browsing capabilities, GPT-4 with Browsing and Google Bard (PaLM 2) displayed lower performance metrics across the board with statistically significant differences (p&lt;0.0001). (4) Conclusion: Our findings underscore the potential of AI models, particularly LLMs such as GPT-4, in enhancing scientific education, especially in complex domains such as IDPs. Continued innovation and collaboration among AI developers, educators, and researchers are essential to fully harness the potential of AI for enriching scientific education.

Carbonate reservoirs, influenced by depositional and diagenetic processes and characterized by features like faults and vugs that impact storage capacity, require more than traditional Borehole Imaging logs (BHI) for accurate porosity data. This data is essential for geological assessments, production forecasting, and reservoir simulations. This work aims to address this limitation by developing methods to measure and monitor the sustainability of carbonate reservoirs and exploring the application of sustainability principles to their management. The study integrates BHI and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation (BVF) in the Santos Basin. The methodology involves four steps: (i) analyzing conventional logs; (ii) segmenting BHI logs; (iii) integrating conventional and segmented BHI logs using Self-Organizing Maps (SOM); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies (A to D). Facies A has high non-matrix porosity with 14,560 small megapores, 5,419 large megapores, and 271 gigapores (71.9%, 26.76%, and 1.34% of the 20,250 pores, respectively). Facies B shows moderate non-matrix porosity with 8,669 small megapores, 2,642 large megapores, and 33 gigapores (76.42%, 23.29%, and 0.29% of the 11,344 pores, respectively) and medium matrix porosity. Facies C exhibits low non-matrix porosity with 7,749 small megapores, 2,132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9,901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9,355 small megapores, 2,346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The novelty of this work lies in integrating data from two sources to classify porosity across the Pre-Salt reservoir interval, serving as a proxy for preliminary litho-facies identification without core data. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes.

Aptamers have a well-earned place in therapeutic, diagnostic, and sensor applications, and we now show that they provide an excellent foundation for education, as well. Within the context of the Freshman Research Initiative (FRI) at The University of Texas at Austin, students have used aptamer selection and development technologies in a teaching laboratory to build technical and 21st century skills appropriate for research scientists. One of the unique aspects of this course-based undergraduate research experience is that students develop their own projects, and take ownership of their own science in what would otherwise be a traditional teaching lab setting. Of the many successes, this work includes the isolation and characterization of novel calf intestinal alkaline phosphatase (anti-CIAP) RNA aptamers by an undergraduate researcher. Further, preliminary survey data suggest that students who participate in the aptamer research experience express significant gains in their self-efficacy to conduct research, and their perceived ability to communicate scientific results, as well as organize and interpret data. This work will describe the use of aptamers in an educational setting, highlight the positive student outcomes of the aptamer research experience, and more particularly present the research findings relative to the anti-CIAP aptamer.

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV- 2). The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as, deep learning, in (i) rapid disease detection from x-ray/computerized tomography (CT)/ high-resolution computed tomography (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) identification of the epicenter in each country/state and forecasting the disease from social networking data, (iv) prediction of drug-protein interactions for repurposing the drugs, and (v) socio-economic impact and prediction of future relapses, has attracted much attention. In the present manuscript, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real time polymerase chain reaction (qRT-PCR) and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, possibility of future relapses and socio-economic impact are also discussed. We inspect how the virus transmits depending on different factors, such as, population density and mobility among others. We depict how AI-based mobile app for contact tracing and surveys can prevent the transmission. A modified deep learning technique can assess affinity of the most probable drugs to treat COVID-19. Here a few effective antiviral drugs, such as, Geneticin, Avermectin B1, and Ancriviroc among others, have been reported with their appropriate validation from previous investigations.

In 1937,German mathematician L.Collatz proposed a conjeture:for any positive integer,if it is even,divide it by 2;if it is odd,multiply it by 3 and 1 to get an even number;keep going on and on, the final result is 1.This paper gives a sequence classification of odd numbers,and gives 1-1 mapping of odd numbers and odd number sequences,and uses these results to give a proof of this conjecture.

Recent advancements of computer and electronic systems have motivated the extensive use of intelligent systems for automation of agricultural industries. In this study, the temperature variation of the mushroom growing room is modeled through using a multi-layered perceptron (MLP) and radial basis function networks. Modeling has been done based on the independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP is found to be the second repetition with 12 neurons in the hidden layer and 20 neurons in the hidden layer for radial basis function networks. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural networks with radial basis function was selected as the optimal predictor for the behavior of the system.

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

This paper explores the challenges faced by blue-collared international migrants in adapting to new and unfamiliar climates while pursuing job opportunities, with a focus on poverty alleviation. Drawing on existing research and empirical evidence, the study investigates the trials encountered by these migrants as they navigate climate shocks and assesses the potential impacts on poverty reduction efforts. The findings underscore the significant difficulties blue-collared migrants experience when confronted with abrupt changes in climate conditions, adversely affecting their physical and mental well-being, as well as their employment prospects. The process of adjustment involves not only acclimating to new climate patterns but also navigating cultural nuances and social dynamics, exacerbating stress and feelings of isolation. Consequently, these challenges can perpetuate poverty by disrupting employment stability and limiting opportunities for career advancement. To address these issues, the paper proposes comprehensive support measures, including targeted pre-departure training programs, access to retraining opportunities, and assistance in securing sustainable livelihoods. By prioritizing climate resilience and empowering blue-collared migrants to adapt effectively to new climates, it becomes possible to enhance their livelihood prospects, reduce poverty levels, and foster sustainable development. The paper concludes by highlighting the significance of integrating climate adaptation strategies with poverty alleviation efforts to create a more inclusive and resilient future for blue-collared migrants in their pursuit of sustainable livelihoods.

Background: Open AI developed ChatGPT, a language model based on the GPT architecture, designed for text-based communication. Trained on diverse internet texts, ChatGPT generates contextually appropriate responses using machine learning. It understands input through analysis and context interpretation, generating coherent and contextually relevant responses. Interaction is possible through messaging platforms. Materials and Methods: In November 2023, we utilized ChatGPT 3.5, the default version at that time, to answer questions from the Italian National Residency Program Admission Tests (SSMs) of 2021, 2022, and 2023. The questions cover clinical, diagnostic, analytical, therapeutic, and epidemiological scenarios, sometimes accompanied by images. The study compared ChatGPT's answers to the official corrections on the Italian Ministry of University and Research (MUR) website. The scoring method used for evaluation was 1 point for correct answers, 0 points for unanswered questions, and -0.25 points for incorrect answers, reflecting the SSM test scoring system. Results: In summary, ChatGPT was tested with a total of 420 questions, 140 for each test. It achieved an overall accuracy of 80.48%, providing correct answers for 338 questions and incorrect answers for 82 questions. When faced with questions containing both text and images, it answered 55% correctly and 45% incorrectly. The model's performance varied over time, with an 82.14% accuracy rate in 2021 and 2022 (115 correct out of 140) and a 77.14% accuracy rate in 2023 (108 correct out of 140). Applying thisscoring method to the SSM test, ChatGPT would have scored 105 points in 2021 and 2022, and 100 points in 2023. Conclusions: ChatGPT has exhibited above-average performance in the last three SSM tests, highlighting its robust capability to interpret clinical scenarios and offer precise diagnostic and therapeutic guidance. Despite this, some limitations persist, notably the software's inability to interpret non-textual information.

The transition from the Peptide/RNA world to the Protein/RNA world in the hydrothermal vent environment was a major event in the history of life. The advent of proteins utterly changed the conditions of emerging life, representing a watershed in its development. During subsequent translation various protein enzymes emerged driving protocells into a more complex and interconnected system. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Four major events followed after availability of various kinds of protein molecules during prebiotic synthesis. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) the beginnings of the virus world; and (4) the advent of DNA. The first innovation mediated by proteins was the improvement of the cell membrane. The phospholipid membrane was initially evolved in a vent environment from the gradual modification of a fatty acid membrane via an intermediate phosphatidate acid by non-enzymatic reactions. The phospholipid is then synthesized from phosphatidate acid by a series of enzymes. To make the phospholipid membrane more permeable, various protein molecules interacted with the cell membrane. Proteins not only stabilized the wall membrane, but also acted as pumps, preventing some molecules from the protocells from crossing the membrane barriers, while permitting other selected molecules and ions to enter and leave the protocell. The second modification led by proteins is the gradual conversion of the interior of the protocell from a water-like medium into a gel-like cytoplasm, which became the storehouse of a wide range of biomolecules including amino acids, proteins, nucleic acids, ribosomes, as well as salt and water. The third innovation utilizing the newly synthesized proteins was the emergence of the ancient virus world. In the milieu of different kinds of mRNAs in the prebiotic soup, jelly-roll capsid genes originated de novo within genomes of nonviral mRNAs by overprinting. These fragile capsid genes were possibly coated by proteins on the mineral substrate for stability and durability, transforming them into ancient viral particles. These protein coats were random and were not encoded by encased genes. Some protocells might have engulfed these viral particles, when the capsid genes utilized the ribosomes of the host to translate into the appropriate capsid proteins. These capsid proteins then coated the viral genes to make new copies of primordial viruses inside the protocell. Since then, viruses became capsid-encoding organisms. These primordial mRNA viruses parasitized RNA-based protocells, manipulating them to make new copies of themselves. This was the beginning of a relentless war between viruses and their protocellular hosts. The next stage in viral evolution was the emergence of a primitive retrovirus (pre-retrovirus) with a new kind of replicative strategy in a sense that it could turn its RNA into DNA using its own reverse transcriptase enzyme. This is the beginning of the Retro world that facilitated the transition from RNA to DNA genomes. The infection of RNA protocells with pre-retroviruses progressively transferred the RNA genome to a viral DNA genome by retro-transcription. The advent of DNA by the pre-retrovirus marks the fourth innovation, when a number of enzymes had already developed and were utilized by pre-retroviruses. With continued infection, DNA viruses slowly transferred not only their core replication enzymes, such as helicase, primase, and DNA polymerase, to RNA protocells, but also to their DNAs as well. Thus, began the DNA world, when DNA replaced RNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. The new protocell with the DNA genome will diversify into large populations of DNA protocells that will outcompete populations of RNA protocells. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. It was carried out by a set of enzymes that formed a Z-ring at the site of replication. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification. These primordial hyperthermophiles, presumably the first life, obtained food and energy directly from the vent environment. However, such a situation was self-limiting, so the early cells evolved their own mechanisms for generating metabolic energy and synthesizing the molecules necessary for their reproduction. The earliest fossil record (≥ 3.5 Ga) of biotic activity is preserved in the Archean hydrothermal and sedimentary rocks of the Nuvvuagittuq Craton of Canada, the Isua Craton of Greenland, the Pilbara Craton of Australia, the Kaapvaal Craton of South Africa, and the Singhbhum Craton of India in the form of the carbonaceous remains of microbial cells, cellular microfossils, and stromatolites. These microscopic fossils provide crucial evidence of the origin and early evolution of prokaryotic cells, beginning with hyperthermophiles. Molecular phylogenetic analysis suggests that both domains of life ¬– Bacteria and Archaea probably split from the last universal common ancestor (LUCA), a hyperthermophilic organism. In the younger sequences of these Archean cratons, two kinds of photosynthetic bacteria, anoxygenic green sulfur bacteria, and oxygenic cyanobacteria, appeared in quick succession from the thermophilic ancestor, indicating a shift of niche from a benthic to a planktonic, with reduced thermotolerance. The development of anoxygenic and oxygenic photosynthesis would have allowed life to escape the hydrothermal setting and invade a newly evolved habitat—broad continental shelves to tap solar energy. Cyanobacteria invaded the global ocean, turned it into blue and green, produced oxygen for the first time, and left their signatures in the carbonates and stromatolites.

The Van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Because of its chaotic nature, controlling, or forcing, the behavior of a Van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two Van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the Van der Pol oscillator. Deterministic AI (DAI) is an adaptive feedback control method that leverages the known physics of the Van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of DAI employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error (MAE) between the target trajectory and the response oscillator trajectory over time. RLS with exponential forgetting (RLS-EF) had the lowest MAE overall, with a 2.46% reduction in error. However, another method was notable. Least Mean Squares with normalized gradient adaptation (LMS-NG) had worse initial error in the first 10% of the simulation, but after that point had consistently better performance. We found that over the last 90% of the simulation, DAI with LMS-NG had a 48.7% reduction in MAE compared to feedforward alone.

The emergence of proteins in the prebiotic world was a watershed event at the origin of life. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Five major biochemical innovations followed in succession after availability of various kinds of protein molecules during decoding and translation of mRNAs. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) primitive gene regulation; (4) the beginnings of the virus world; and (5) the advent of DNA. The creative role of viruses during prebiotic synthesis led to the origin of the DNA world, when DNA replaced mRNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification by mutation. The habitat of the earliest fossil record (≥ 3.5 Ga) from the Archean sedimentary rocks of Canada, Greenland, Australia, South Africa, and India offers a new window on the early radiation of microbial life. The development of anoxygenic and then oxygenic photosynthesis from early hyperthermophiles would have allowed life to escape the hydrothermal setting to the mesophilic global ocean.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of a stable M/G/1queue is characterised from the viewpoint of IG. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of stable M/G/1 queue manifold are devised. In addition to that, new results that uncovered the significant impact of stability of M/G/1 queue manifold on the existence of (IFIM) are obtained. The Kullback’s divergence (KD), and J-divergence (JD).New result has been devised on the significant impact of both server utilization and squared coefficient of variation of the underlying M/G/1 queue manifold on both (KD) and (KD) are devised. Also, it is revealed that stable M/G/1 QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of M/G/1 queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability and the instability phases of the underlying M/G/1 queue manifold. Furthermore, a new discovery that presents the significant impact of stability of M/G/1 queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. It is also shown that the obtained (IME) is unstable. Also, it is shown that stability of the devised (IME) enforces the instability of M/G/1 queue manifold. Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometric (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).Extending the study over two new additional divergence measures, namely and together with a complete illustrative numerical results for all these measures including KD, JD. This links Queueing theory, IG with deep machine learning and metric learning. Furthermore, this reveals the revolutionary approach of queue learning. Full analytic study of Gaussian curvatures subject to both Angular and Monge techniques together with the overall stability dynamics impact on these curvatures. Full analytic study of Einestein Tensor and Stress Energy Tensor together with the overall stability dynamics impact on these curvatures. The inclusion of the definitions of Gaussian and Ricci, Ricci scalar curvatures and Einstein Tensor together with their physical interpretations; The proposed novel approach for the pioneer visualization of queueing systems via computational information geometry. The determination of new important links between classical queueing theory and other mathematical disciplines, such as IG, matrix theory Riemannian geometry and the THEORY OF RELATIVITY by providing for first time i) The full detailed derivations of the Gaussian curvature ii) The Ricci curvature tensor and iii) The full physical as well as the geometric interpretation of these new results. The provision of a novel link between Ricci Curvature (RCT) and the stability analysis of the stable M/G/1 QM. The full investigation of the newly introduced QT-IG unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them. The full investigation of the newly introduced (QIGU) unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them.