In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in the 5th Generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs) such as user experience, quality of service (QoS), or other important aspects of the network. To address this challenge, advanced wireless technologies have been incorporated into the design of 5G networks across various network layers. Given the rapid advancements in Artificial Intelligence (AI) and emerging technology trends such as Machine Learning (ML), which is considered a subset of AI, the integration of such trends into 5G networks has become a significant area of research. The primary objective of this survey is to analyze AI integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

The accompanying mechanical behavior during the flow of ore-rock dispersion is an important factor leading to the instability and failure of the orepass in the ore storage section. It is of great significance to accurately understand the instability characteristics of the well wall in the ore section, during the ore drawing process by understanding the flow characteristics and internal mechanical transfer mechanism of ore-rock dispersion. Using the discrete element method to analyze the flow characteristics, contact compactness, stress distribution characteristics and contact force probability distribution of the ore-rock dispersion, it realizes the quantitative characterization of the damage degree of ore-rock flow, and reveals the damage mechanism of mine storage wall. The results show that: (1) The friction between ore-rock particles, between ore-rock and well wall is an important reason for the change of the macroscopic flow form of ore-rock from “one” to “V” type. (2) The compactness and stress concentration of ores are increasing with the increase of storage depth, which is shown that with the increase of storage depth, the greater the probability of well wall instability. (3) The probability distribution of contact force strength between particles decreases exponentially in the whole process of ore drawing, in which the strong chain plays a major role in the stability of the dispersion system. (4) The number of overpressure and the overpressure coefficient can be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic side pressure decreases periodically in the form of exponential fluctuations, and the dynamic load formed by the ore-rock flow mainly played a role in the lower area of the ore storage section.

Alzheimer’s disease manifests itself as a complex pathological condition with neuroinflammation, oxidative stress and cholinergic dysfunction being a few of the many pathological changes. Due to the complexity of the disease current therapeutic strategies aim at a multitargeted approach often relying on a combination of sub-stances with versatile and complementary effects. In the present study, unique combination of α-lipoic acid, citicoline, extracts of leaves from olive tree and green tea, Vitamin D3, selenium and an immune supporting complex was tested in a scopolamine-induced dementia in rats. Using behavioral and biochemical methods we assessed the effects of this combination on learning and memory, and elucidated the mechanisms under-lying its effects. Our results showed that as compared to the components, experimental combination was most efficient in improving short- and long-term memory assessed by the step-through method as well as spatial memory, assessed by T-maze and Barnes maze underlying decrease in AChE activity and LPO, in-creases in SOD activity in cortex; activities of catalase and GPx, levels of BDNF and pCREB in the hippocam-pus. No significant histopathological changes or blood parameter changes were detected, making the experi-mental combination an effective and safe candidate in a multitargeted treatment of AD.

Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.

Triangles are an ever present feature in nature, which the building construction industry duly echoes. However, an exact expression intended to offer the radiant field due to any triangle in an upright or inclined position had not been identified by previous research. In this case, the author has been able to solve by direct integration, the canonical expression of radiative transfer. This result alone confers a myriad of possibilities that had been inconceivable before, for studying in detail the three-dimensional heat transfer behavior of volumes and figures in which triangles do manifest, such as fins, windows, roof-gables and louvers of various kinds. Typically, shading devices, when tilted, give rise in their extremes to rhomboidal shapes which were difficult to take into account or had to be subject to discretization and subsequent Monte Carlo methods in order to perform an approximate calculation of their emission. This implied a lengthy and inexact procedure that induced many errors and consumed computing time. We can now avoid all the former downsides due to the advances hereby presented. As the novel expression can be converted into an algorithm it will be advantageously employed for simulation. This significant finding dovetails in the intricate puzzle of radiated heat and we believe that their consequences will greatly affect the conception and design of HVAC devices, aircraft manufacturing and specifically building or lighting industries among others.

Abstract: Background: The practice of menstrual hygiene(MH) is a public health issue and depends on various factors. Taboos and incorrect MH practices can lead to health hazards. This study aimed at assessing the Knowledge, Attitude, and Practice of MH among the female students and faculty of Medical and Health Sciences University. Materials and methods: This cross-sectional questionnaire survey between February and June 2023 using consecutive sampling. Univariate analysis and comparisons were done with a significant p-value <.05. Results: A total of 253 women participated in the study. The knowledge and practices of MH were good. There were significantly more irregular and painful periods in younger females (p=.005,.0001). 89% of women used disposable pads and least used menstrual cups. Younger women were unaware of menstrual cloth. The attitude towards menses varied, with younger females feeling annoyed (p=.002), and older females embarrassed (p=.0001) at menarche. About 1/3rd of participants perceived menstrual blood as unhygienic. Younger women were more open about talking about menses (p=.005). Conclusion: There was good knowledge and practices of MH among participants. There were differences in menstrual history and attitude among older and younger women. The attitude and beliefs vary among the participants, due to sociocultural factors and age differences.

Optical “fingerprints” are widely used in chemometrics-assited recognition of samples of different nature. An emerging trend in this area is the transition from obtaining "static" spectral data to reactions occurring over time. The indicator reactions are usually carried out in aqueous solutions; in this study we have developed the reactions that occur in an organic solvent, which makes it possible to recognize fat-soluble samples. In this capacity, we used 5W40, 10W40 and 5W30 motor oils of 4 manufacturers, totally 6 samples. The procedure involved mixing of the dye, sample, and reagents (HNO3, HCl, or t-butyl hydroperoxide) in ethanolic solution in a 96-well plate and measuring absorbance or near-IR fluorescence intensity every several minutes during 20–55 min. The obtained photographic images were processed by linear discriminant analysis (LDA) and k-nearest neighbors algorithm (kNN). The discrimination accuracy was evaluated by using the validation procedure. Reaction of oxidation of a dye with nitric acid allowed to recognize all 6 samples with 100% accuracy by LDA. Merging data of 4 reactions that did not provide complete discrimination ensured an accuracy of 93% by kNN technique. The developed indicator systems have good prospects for the discrimination of other fat-soluble samples. Overall, the results confirm the viability of the kinetic-based discrimination strategy.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.

There are close dynamic relationships among the livelihood, well-being, and ecological environment of farm households. It is of great significance to scientifically clarify the impact of the grain for green policy on the livelihoods and well-being of farm households in mountainous areas. Based on data from a survey of 392 farm households in Zhangbei County, a system of indicators for farm household livelihood assets and farm household well-being was constructed, drawing on a sustainable livelihood framework (SLF). The livelihood assets and well-being levels of different types of farm households were measured, and a multiple linear regression model was used to analyze the impact of the grain for green policy implementation on the well-being levels of farm households. The three main results are as follows: (1) The level of natural assets among the total average livelihood assets of farm households in Zhangbei County is the highest at 0.374, while the level of physical assets is the lowest at 0.018. The level of livelihood assets of returned farmland households (0.948) is lower than that of nonreturned farmland households (1.117). (2) The level of well-being of all farm households in Zhangbei County is 0.517, with the level of wealth contributing the most to the well-being of farm households at 40.20% and the quality of the ecological environment contributing the least at 11.99%. The level of well-being of returned farmland households (0.518) was slightly higher than that of nonreturned farmland households (0.514). (3) The degree of influence of each influence factor on the level of well-being of farm households varies significantly. Household size was the strongest driver, at 0.366, while educational attainment of household members, household labor capacity, annual household expenditure, livelihood diversity, number of large production tools, and total value of livestock were also important drivers of household well-being, and area of arable land is negatively associated with household well-being. There are also differences in the factors influencing the level of well-being of different types of farming households.

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social internet of vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into an intra-cluster and an inter-cluster emergency as well as general safety message dissemination technique. In a vehicle to infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.

This article analyzes 6 probability distributions from the Generalized Pareto family, with 3, 4 and 5 parameters, with main purpose to identify other distributions from this family with applicability in flood frequency analysis compared to the distribution already used in the literature from this family such as Generalized Pareto Type II and Wakeby. This analysis is part of a larger and more complex research carried out in the Faculty of Hydrotechnics regarding the elaboration of a norm for flood frequency analysis using the linear moments method. In Romania, the standard method of parameter estimation is the method of ordinary moments, thus the transition from this method to the method of linear moments is desired. All the necessary elements for the distributions use are presented like, the probability density functions, the complementary cumulative distribution functions, the quantile functions, the exact and approximate relations for estimating parameters, for both methods of parameters estimation. All these elements are necessary for a proper transition between the two methods, especially since the use of the method of ordinary moments is done by choosing the skewness of the observed data depending on the origin of the maximum flows. A flood frequency analysis case study, using annual maximum and annual exceedance series, was carried out for the Prigor river, to numerically present the analyzed distributions. The performance of this distributions is evaluated using relative mean error, relative absolute error and linear moments diagram.

This research is completed in development of researches devoted to relations between elastic modulus (MoE) and thermal expansivity (CTe) of different materials. This study experimentally confirmed the relation of MoE and CTe for thermosetting polymers and FRPs both under normal and heating temperatures. As the thermosetting polymers, the epoxy compositions were used and glass fiber and mineral additives for FRP. The experiment was based on dilatometric and elastic deformations’ testing. Also, the relaxation model of MoE/CTe was proposed, that is based on physical properties of polymers’ supramolecular structures. The comparative analyzing of MoE/CTes’ different models is done. The most accurate models are determined, and the relaxation model has demonstrated the high accuracy.

Physical roots, exemplifications and consequences of periodic and aperiodic ordering (represented by Fibonacci series) in biological systems are discussed. The role and physical and biological roots of symmetry and asymmetry appearing in biological patterns is addressed. Generalization of the Curie-Neumann Principle as applied to biological objects is presented, briefly summarized as: “asymmetry is what creates a biological phenomenon”. The “up-bottom approach” and “bottom up” approaches to the explanation of symmetry in organisms are presented in detail. The “up-bottom approach”, implies that the symmetry of the biological structure follows the symmetry of media in which this structure is functioning; the “bottom-up” approach, in turn, adopt that the symmetry of biological structures emerges from the symmetry of molecules constituting the structure. A diversity of mathematical measures applicable for quantification of ordering in biological patterns is introduced. The continuous, Shannon and Voronoi measures of symmetry/ordering and their application to biology objects are addressed. The fine structure of the notion of “ordering” is discussed. Informational/algorithmic roots of ordering inherent for the biological systems are considered. Ordered/symmetrical patterns provide economy of biological information, necessary for algorithmic description of a biological entity. Application of the Landauer principle bridging physics and theory of information to the biological systems is discussed.

Background: The link between nocturia and aging male symptoms (AMS) has not been scientifically established. This study aimed to measure the degree of severity of AMS that impact on the health-related quality-of-life (HRQoL) in adult males living with nocturia, and to determine the predictive values of nocturnal factors on AMS. Methods: It is an extended analysis of new data collected by using the Hong Kong Traditional AMS (HK-AMS) scale and Cantonese version of the Pittsburgh Sleep Quality Index (PSQI) in a recently published cross-sectional population-based survey. Results: Of the 781 respondents that have completed the set of questionnaires, 68% and 61% of men living with nocturia reported clinically significant (at moderate-to-severe levels) somato-vegetative and sexual AMS, whereas the prevalence and severity were increased with advancing nighttime voiding frequency. The nocturia-specific QoL (NQoL) score and nocturnal frequency were found to be significant predictive factors for composite, somato-vegetative and sexual AMS, in addition to age, global OSQI score, and certain metabolic diseases. Conclusions: Current findings suggested the inclusion of nocturia when measuring the male-specific HRQoL related to aging.

In recent years, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns, policy decision support, etc. because the information on alighting stops of users is missing. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. Various studies to overcome this limitation have been conducted in South Korea and other countries to develop es-timation methodologies of alighting stops. Even existing studies introduce an advanced method, we found the margin for better accuracy by combining various estimation methodologies for estimating alighting stops. This study reviewed previously conducted studies to classify data with missing alighting stop information into trip types and then applied an appropriate alighting stop estimation methodology for the characteristics of each trip type by stage. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checked the accuracy for each standard of allowable error for sensitivity analysis. Furthermore, the number of trips, accuracy, and valid tag rate were checked for each type to examine the need for classifying the trip types. Finally, our evaluation also examines the impact of classifying trip types on estimation accuracy. The evaluation criteria are accuracy of the number of trips and valid tag rate. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can es-timate users’ destinations more accurately than previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies.

A brand-new approach to the understanding of breast cancer (BC) is urgently needed. In this contribution, the etiology, pathogenesis, and treatment of this disease is approached from the new pH-centric anticancer paradigm. Only this Unitarian perspective based upon that the hydrogen ion (H+) dynamics of cancer, allows understanding and integrating the many dualisms, confusions, and paradoxes of the disease. The new H+-related wide range model can embrace under a unique frame of mind the many aspects of the disease and at the same time therapeutically interfere with most, if not with all, the hallmarks of cancer known to date. The pH-related armamentarium available for the treatment of BC here reviewed may be beneficial for all types and stages of the disease. In this vein, we have attempted a mega synthesis of traditional and new knowledge in the different areas of breast cancer research and treatment based upon the wide range approach afforded by the hydrogen ion dynamics of cancer. The concerted utilization of a pH-related drugs nowadays available for the treatment of breast cancer is advanced.

How, if at all, consciousness can be part of the physical universe remains a baffling problem. This article outlines a new, developing philosophical theory of how it could do so, and offers a preliminary mathematical formulation of a physical grounding for key aspects of the theory. Because the philosophical side has radical elements, so does the physical-theory side. The philosophical side is radical, first, in proposing that the productivity or dynamism in the universe that many believe to be responsible for its systematic regularities is actually itself a physical constituent of the universe, along with more familiar entities. Indeed, it proposes that instances of dynamism can themselves take part in physical interactions with other entities, this interaction then being “meta-dynamism” (a type of meta-causation). Secondly, the theory is radical, and unique, in arguing that consciousness is necessarily partly constituted of meta-dynamic auto-sensitivity, in other words it must react via meta-dynamism to its own dynamism, and also in conjecturing that some specific form of this sensitivity is sufficient for and indeed constitutive of consciousness. The article proposes a way for physical laws to be modified to accommodate meta-dynamism, via the radical step of including elements that explicitly refer to dynamism itself. Additionally, laws become, explicitly, temporally non-local in referring directly to quantity values holding at times prior to a given instant of application of the law. The approach therefore implicitly brings in considerations about what information determines states. Because of the temporal non-locality, and also because of the deep connections between dynamism and time-flow, the approach also implicitly connects to the topic of entropy insofar as this is related to time.

The introduction of electrical vehicle charging infrastructure including EV charger, renewable energy resource at secondary feeder in distribution system has been increased as one of countermeasure for global environmental issues. However, the Electric Vehicle Charging (EVC) infrastructure may act as the peak load in distribution system, which can adversely impact on the voltage stability when the electric vehicle is quickly charged. Therefore, to keep within the limit capacity of secondary feeder and allowable limit for feeder voltage, this paper proposes a stabilization method by the Energy Storage System (ESS) control strategy at secondary feeder in order to be not violated over than lower and upper limit. Also, this paper presents the estimation method to keep the proper standard value of State of Charge (SOC). From the simulation results, the voltage stabilization operation by ESS should make the feeder voltages of the distribution system(secondary feeder) introduced EVC Infra keep better voltage conditions, also estimation method to keep the proper standard value is confirmed that the SOC of ESS when is the standby condition could be exactly kept within the proper reference range.

This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.

Coatings of specific materials in few atoms of thickness to several microns on certain substrate is the basic need of society and they attend the regular attention of scientific community working in different domains; decorative and protective coatings, transparent and insulating coatings, coating medical implants and surgical instruments, coatings for drug delivery and security purposes, ultra-precision machine coatings, coating cutting tools, coatings for MEMS and NEMS, and so on. Different coatings develop under significant composition of atoms where certain force-energy behaviors provide the provision for electrons (of gas atoms) to deal double clamping of energy knots of unfilled states (of solid atoms). Under certain process conditions, different nature atoms oppositely-switch force-energy behaviors to the ones originally owned where they locate common mid-points of their ground points at accommodating level resulting into deal binding. Because of adjusting contraction-expansion of clamping energy knots under varying potential energy of electrons, they develop structure of their atoms termed as hard coating, which is known since antiquity. Different properties and characteristics of hard coatings like hardness, adhesion, roughness and friction coefficient, etc. are emerged under attained mid-points of transformed atoms in evolving structure. This work describes the science of depositing hard coating opening several new areas. Keywords: Hard coating; TiN; Atomic nature; Expansion and contraction of clamping energy knot; Potential energy of electron; Ground point; Structure evolution

Traditionally, palliative care (PC) systems focused on the needs of advanced cancer patients. But, most patients needing PC have end-stage organ diseases. Similarly, PC models focused on the needs of patients in hospice or at home; however, in most cases PC is provided in acute hospitals. Indeed, the symptom burden that these patients experience in the last year of life frequently forces them to seek care in Emergency Department. The majority of them are admitted to the hospital and many die. This issue poses important concerns. Despite the efforts of attending healthcare professionals, inhospital patients do not receive optimal care near the end-of-life. Also, evidence is emerging that delay in identifying patients needing PC have a detrimental impact on their quality of life (QoL). Therefore, there is an urgent need to early and properly identify these patients among those hospitalized. Several trials reported the efficacy of PC in improving the QoL in these patients. Each hospital should ensure that a multidisciplinary PC team is available to support attending physicians to achieve the best QoL for both PC patients and their families. This review discusses the role and the impact of inhospital PC in patients with end-stage disease or advanced cancer.

The main goal of the present paper is to construct some families of the Carlitz’s q-Bernoulli polynomials and numbers. We firstly introduce the modified Carlitz’s q-Bernoulli polynomials and numbers with weight (α, β) and investigate their some explicit properties and identities arising from the bosonic q-Volkenborn integral on ℤ_p. We then define the modified degenerate Carlitz’s q-Bernoulli polynomials and numbers with weight (α, β) and obtain some recurrence relations and other identities. Moreover, we derive some correlations with the modified Carlitz’s q-Bernoulli polynomials with weight (α, β), the modified degenerate Carlitz’s q-Bernoulli polynomials with weight (α, β), the Stirling numbers of the first kind and second kind.

Recent evidence indicates that the risk of being diagnosed with cancer in a tissue is strongly correlated (0.80) with the number of stem cell divisions accumulated by the tissue. Since cell division can generate random mutations during DNA replication, this correlation has been used to propose that cancer is largely caused by the accumulation of unavoidable mutations in driver genes. However, no correlation between the number of gene mutations and cancer risk across tissues has been reported. Because many somatic mutations in cancers originate prior to tumor initiation and the number of cell divisions occurring during tumor growth is similar among tissues, here I use whole genome sequencing information from 22,086 cancer samples and incidence data from the largest cancer registry in each continent to study the relationship between the number of gene mutations and the risk of cancer across 33 tissue types. Results show a weak positive correlation (mean = 0.14) between these two parameters in each of the five cancer registries. The correlation became stronger (mean = 0.50) when gender-related cancers were excluded. Results also show that 1,003 samples from 29 cancer types have zero mutations in genes. These data suggest that cancer etiology can be better explained by the accumulation of stem cell divisions than by the accumulation of gene mutations. Possible mechanisms by which the accumulation of cell divisions in stem cells increases the risk of cancer are discussed.

We propose a novel framework for understanding quantum phenomena through the lens of a modified electromagnetic spacetime governed by Einstein field equations. By assuming the existence of an electromagnetic spacetime that adheres to these modified geodesic and Einstein field equations, provided the field equations satisfy the Coulomb force law in weak-field approximations, we explore the derivation of Maxwell’s equations and highlight the necessity of incorporating spin to validate these equations. We derive the wave equation for the normalized transverse metric and establish its equivalence with the de Broglie wave proposition, suggesting a relationship between this metric and the particle's wavefunction. This approach offers a classical explanation for several quantum phenomena, including the double-slit experiment, entanglement, tunneling, non-locality, and wavefunction collapse. Additionally, it posits a method to visualize atomic orbital structures in line with quantum mechanical predictions. These findings suggest that the foundational principles of quantum mechanics can be interpreted as linearized approximations of general relativity applied to electromagnetic spacetime. While this theory does not yet extend to the correlations between general relativity, quantum electrodynamics (QED), and quantum chromodynamics (QCD), it provides a valuable visualization tool for physicists and advances our understanding of the unresolved challenges linking quantum mechanics with general relativity.

INTRODUCTION: The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients. METHODS: The machine learning study population consisted of 2173 patients (1587 mild and non symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. RESULTS: The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that in the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management

After proving, in a purely categorial way, that the inclusion functor InAlg(Σ) from Alg(Σ), the category of many-sorted Σ-algebras, to PAlg(Σ), the category of many-sorted partial Σ-algebras, has a left adjoint FΣ, the (absolutely) free completion functor, we recall, in connection with the functor FΣ, the generalized recursion theorem of Schmidt, which we will also call the Schmidt construction. Next we define a category Cmpl(Σ), of Σ-completions, and prove that FΣ, labeled with its domain category and the unit of the adjunction of which it is a part, is a weakly initial object in it. Following this we associate to an ordered pair (α,f), where α=(K,γ,α) is a morphism of Σ-completions from F=(C,F,η) to G=(D,G,ρ) and f a homomorphism in D from the partial Σ-algebra A to the partial Σ-algebra B, a homomorphism ΥαG,0(f):Schα(f)B. We then prove that there exists an endofunctor, ΥαG,0, of Mortw(D), the twisted morphism category of D, thus showing the naturalness of the previous construction. Afterwards we prove that, for every Σ-completion G=(D,G,ρ), there exists a functor ΥG from the comma category (Cmpl(Σ)↓G) to End(Mortw(D)), the category of endofunctors of Mortw(D), such that ΥG,0, the object mapping of ΥG, sends a morphism of Σ-completion in Cmpl(Σ) with codomain G, to the endofunctor ΥαG,0.

Let {$\Gamma(k)$} denote {$(k-1)!$}, and let {$\Gamma_{n}(k)$} denote {$(k-1)!$}, where {$n \in \{3,\ldots,16\}$} and {$k \in \{2\} \cup [2^{\textstyle 2^{n-3}}+1,\infty) \cap \mathbb N$}. For an integer {$n \in \{3,\ldots,16\}$}, let $\Sigma_n$ denote the following statement: if a system of equations {${\mathcal S} \subseteq \{\Gamma_{n}(x_i)=x_k:~i,k \in \{1,\ldots,n\}\} \cup \{x_i \cdot x_j=x_k:~i,j,k \in \{1,\ldots,n\}\}$} with $\Gamma$ instead of $\Gamma_{n}$ has only finitely many solutions in positive integers {$x_1,\ldots,x_n$}, then every tuple {$(x_1,\ldots,x_n) \in (\mathbb N \setminus \{0\})^n$} that solves the original system ${\mathcal S}$ satisfies {$x_1,\ldots,x_n \leqslant 2^{\textstyle 2^{n-2}}$}. Our hypothesis claims that the statements {$\Sigma_{3},\ldots,\Sigma_{16}$} are true. The statement {$\Sigma_6$} proves the following implication: if the equation {$x(x+1)=y!$} has only finitely many solutions in positive integers $x$ and $y$, then each such solution {$(x,y)$} belongs to the set {$\{(1,2),(2,3)\}$}. The statement {$\Sigma_6$} proves the following implication: if the equation {$x!+1=y^2$} has only finitely many solutions in positive integers $x$ and $y$, then each such solution {$(x,y)$} belongs to the set {$\{(4,5),(5,11),(7,71)\}$}. The statement {$\Sigma_9$} implies the infinitude of primes of the form {$n^2+1$}. The statement {$\Sigma_9$} implies that any prime of the form {$n!+1$} with {$n \geqslant 2^{\textstyle 2^{9-3}}$} proves the infinitude of primes of the form {$n!+1$}. The statement {$\Sigma_{14}$} implies the infinitude of twin primes. The statement {$\Sigma_{16}$} implies the infinitude of Sophie Germain primes.

Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. Due to the technological and econonomic limitations of energy storage solutions, using other, mosty conventional, sources to cover energy shortfalls and at the same time utilising solar surpluses production becomes necessary. The need to optimize energy systems, ensuring power continuity, and balancing energy supply and demand has led to the development of various forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasizes the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses the emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. This includes defining research questions, selecting eligibility criteria, literature search, data extraction, synthesis, and assessing bias and quality. The article contributes to the solar forecasting field by providing up-to-date knowledge, along with insights on the emerging trends, future research directions, and anticipating implications for the theory and practicce.

The lack of application of management models that allow strategies to be converted into action and then measure their results in order to apply improvement actions to meet the objectives in small and medium-sized companies (SMEs); they generate problems in their management causing the closure of their operations unexpectedly; To mitigate the risks of closure and bankruptcy with the support of ICT, an alternative was presented considering that risk management is important in decision making at a strategic, tactical and operational level. The objective is to design a prototype to mitigate risks and prevent threats to avoid the closure of this type of company. Through the analysis of related articles, applying sequences of methodological steps; It was determined that the proposed results are aimed at how to identify and mitigate the risks and threats that cause their bankruptcy and closure. Prototypes resulted that allow mitigating risks, preventing threats and minimizing vulnerabilities in the management of SMEs. It was concluded that the proposed prototype to detect threats and vulnerabilities provides an optimal security model for small and medium-sized companies; As a result of the simulations, a security percentage of 77.92% was established, which means that the greater the number of risks and threats, the greater the probability that a small and medium-sized company will close.

Functional health literacy is the ability to read, write, and speak about health. Functional health literacy events can nudge the development of children’s health identity when using valid and reliable reading materials that portray the “what, where, when, why, and with whom” to eat nutritious foods and be physically active. Children who lack social role models for healthy eating and physical activity may also lack development in language and motor skills. Therefore, literacy-rich environments are needed when developing cognitive-motor language skills with children. Children with obesogenic body frames can gain from the production of childcentric health narratives that draw upon the Habits of Mind and Habits of Health© Model to produce Electronic Texts for Health Literacy©. When health professionals plan and implement functional health literacy events so children can read, write, and speak about their own health, new behavioral patterns and routines emerge into performances of understanding. Constructivist pedagogical approaches can help children to create health narratives using multimodal language while practicing health literacy skills as a foundational impetus to health behaviors. Children who struggle with obesity can also gain social agency when writing with peer partners who serve as supportive collaborators when developing interactive health literacy.

Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy

Higher traffic density in Russian cities and less uniform traffic due to congestions are reasons for increased emissions of pollutants contained in exhaust gases of vehicles. The paper presents the modeling results of pollutant dispersion in Magnitogorsk. The analysis of these results shows that now many blocks of flats and social infrastructure of the city are in a zone with an excess of maximum allowable concentration of pollutants. Having summarized the methodological framework formulated by Russian and foreign researchers, the authors systemized the existing methods used to decrease pollutants produced by vehicles. It is proposed to choose an optimal combination of such methods applying a developed mathematical model. By adopting an optimal combination of the methods, it is possible to ensure minimum air pollutant emissions from vehicles, allocating limited financial resources. The mathematical modeling of the solutions on changing traffic density and structure and implementing methods used to decrease pollutants on one of the Magnitogorsk crossroads with the heaviest traffic is used to draw a conclusion about efficiency of the designed model used as a tool for strategic planning of development and transformation of street networks.

The Suter curves for Wh and Wm characteristics of collected and the first time presented Four-quadrant (4Q) diagrams of 11 radial pump-turbine models with different specific speeds (nq=24.34, nq=24.8, nq=27, nq=28.6, nq=38, nq=41.6, nq=41.9, nq=43.83, nq=50, nq=56, and nq=64.04) are given in the paper, as well as the Suter curves for 2 pump models (nq=25, and nq=41.8) which were already published in the literature. These 11+2 curves were analyzed to establish a certain universal law of behavior in dependence on the specific speed. For the determination of existing laws in behavior, the fitting procedure using the Regression and Spline methods was used. The paper presents a research plan and structures that include: data collection of four-quadrant diagrams for models of pump-turbines and pumps with different specific speeds (nq), the procedure of recalculation of Four-quadrant diagrams of the models into Suter curves for Wh and Wm characteristics, the definition of the optimal point for the pump and turbine operating mode in pump-turbine models for different specific speeds, and developing numerical models in the MATLAB program to obtain the Universal Equation for the Wh and Wm characteristics. The scientific contribution of this paper is the firstly published original mathematical curve named Universal Equation for the Wh and Wm characteristics for radial pumps and pump-turbines. Application of the equation has been demonstrated at the pumping station where two radial pumps are installed. The calculation of transient processes was performed using the numerical model developed by the authors in the mathematical program MATLAB. Comparison of the transition process results has been made for the two cases - when input data in the numerical model have been firstly used with the values of Suter curves for Wh and Wm characteristics obtained by recalculation from the Four-quadrant operating characteristics (Q11, n11, M11) of model’s specific speed, and then secondly with the values of the Suter curves for Wh and Wm characteristics obtained from the Universal Equation.

During the construction of buildings, dangerous chemicals such as benzene, formaldehyde and others are used, which accumulate during the operation of the building. High concentrations of these substances can harm human health. In this article, we have analyzed the possible impact of modular green wall systems on improving of the air. During the research, four different modular green wall systems and various plant species were considered. The result showed that a modular green wall system can improve the air quality by removing harmful pollutants from the air in the buildings. Urban design used to be turned greener and more sustainable using the modern technologies of modular green wall systems. This study focuses on the air-cleaning benefits provided by modular green wall systems and their assessment. This research contributes to fill existing literature gaps on the green wall systems on the buildings and biophilic architecture.

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively

unlike the volume, the expression for the surface area of a regular conoid has not yet been obtained by means of direct integration or a differential geometry procedure. As this non-developable form is relatively used in engineering, the difficulty to determine its surface, represents a serious shortcoming for several problems which arise in radiative transfer, lighting and construction, to cite just a few. In order to solve the problem, I conceived the surface as a set of linearly dwindling ellipses which remain parallel to a circular directrix, a typical problem appears when searching the length of such ellipses. I employed a new procedure which, in principle, consists in dividing the surface into infinitesimal elliptic strips to which we have successively applied Ramanujan’s second approximation for the longitude of the ellipse. In this manner, we can obtain the perimeter of any transversal curve pertaining to the said form as a function of the radius of the directrix and the position of the ellipse’s center on the X-axis. Integrating the so-found perimeters of the differential strips for the whole span of the conoid, an unexpected solution emerges through the newly found number psi (ψ) which seems like a refined approximation to the third decimal of Pi but derived from a definite integral equation. As the strips are in truth slanted in the symmetry axis, their width is not uniform and we need to perform some adjustments in order to complete the problem with sufficient precision, but this is discussed separately in the annexes. Relevant implications for mathematical symmetry applied to multifarious architectural and engineering forms can be derived from this finding

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

The concept of spiritual intelligence is currently at the focus of scientific debate. One of the most important problems is if it is a valid intelligence that meets the criteria developed by scholars. In this paper, I look at this idea in the context of Howard Gardner's theory of multiple intelligences and show some evidence from biology, neurology, and other scientific disciplines

Background: Only few studies have studied the link between risk perception and sleep in the context of the COVID-19 pandemic. This study investigates the effect of two distinct risk appraisals—risk perception and perception of collective coordinated defense (PCCD) on Chinese adults’ sleep quality during the COVID-19 pandemic, and tested COVID-19-related fear and rumination as potential mediators of the relationships. Methods: Data were collected using a self-report online questionnaire from a sample of 224 Chinese adults during the fourth wave of the COVID-19 pandemic in Hong Kong. Results: COVID-19 risk perception and PCCD were related to poor sleep quality. Mediation analysis showed that both fear and rumination mediated the relationship between risk perception and sleep quality, whereas only fear mediated the relationship between PCCD and sleep quality. The model showed an excellent fit to the data and accounted for 44% of the variance in sleep quality in Chinese adults. Conclusions: This study demonstrates the distinct perceptual processes—risk appraisals in particular—contributed to poor sleep quality in Chinese adults during the COVID-19 public emergencies. These findings would be helpful for policy makers to address the sleep problems induced by psychological consequences of the pandemic.

We discuss what we believe could be an improvement in future discussions of the ever-changing brain. We do so by distinguishing different types of brain variability and outlining methods suitable to analyse them. We argue that, when studying brain and behaviour data, classical methods such as regression analysis and more advanced approaches both aim to decompose the total variance into sensible variance components. In parallel, we argue that a distinction needs to be made between innate and acquired brain variability. For varying high-dimensional brain data, we present methods useful to extract their low-dimensional representations. Finally, to trace potential causes and predict plausible consequences of brain variability, we discuss how to combine statistical principles and neurobiological insights to make associative, explanatory, predictive, and causal enquires; but cautions are needed to raise association- or prediction-based neurobiological findings to causal claims.

The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

Financial Payment has evolved into mobile payment for easy access and convenience. Despite the benefits of mobile payment, the adoption rate is unsatisfactory worldwide. Therefore, understanding the customer's adoption intention factors is essential for both researchers and practitioners. This study examines mobile payment intention and actual use by adapting the integrated framework, which combines the Mobile Technology Acceptance Model and the Theory of Planned Behaviour with additional new constructs found to be related. The new model consists of 12 variables moderated by the individual's experience and is examined via a structured PLS equation mixed-mode methodology. Knowing mobile payment influence will shape the industry strategic decision and socially contribute to transforming society into a cashless society. This paper provides an opportunity to prove the relationship between technology variables and human behaviour concerning mobile payment adoption. In contrast, this study novelty adopted an integrated model that combines MTAM with the TPB.

We investigate new graph characteristics namely total (vertex, edge) face irregularity strength of gen- eralized plane grid graphs Gmn under k-labeling Phi of type (Alpha, Beta, Gamma). The minimum integer k for which a vertex-edge labelled graph has distinct face weights is called the total face irregularity strength of the graph and is denoted by tfs(Gmn). In this article, the graphs G = (V;E; F) under consideration are simple, fi nite, undirected and planar. We will estimate the exact tight lower bounds for the total face irregularity strength of some families of generalized plane grid graphs.

Static standing postural stability has been measured by multiscale entropy (MSE), which is used to measure complexity. In this study, we used the average entropy (AE) to measure the static standing postural stability, as AE is a good measure of disorder. The center of pressure (COP) trajectories were collected from 11 subjects under four kinds of balance situation, from stable to unstable: bipedal with open eyes, bipedal with closed eyes, unipedal with open eyes, and unipedal with closed eyes. The AE, entropy of entropy (EoE), and MSE methods were used to analyze these COP data, and EoE was found to be a good measure of complexity. The AE of the 11 subjects sequentially increased by 100%as the balance situations progressed from stable to unstable, but the results of EoE and MSE did not follow this trend. Therefore, AE, rather than EoE or MSE, is a good measure of static standing postural stability. Furthermore, the comparison of EoE and AE plots exhibited an inverted U curve, which is another example of a complexity versus disorder inverted U curve.

Understanding the evolutionary role of environmentally-induced phenotypic variation (i.e., environmental plasticity) is an important issue in developmental evolution. One of the major physiological responses to environmental changes is cellular stress, which is counteracted by a generic stress reaction that detoxifies the cell, refolds proteins, and repairs DNA damage. In this paper, we elaborate on a previous finding suggesting that the cell differentiation cascade of human decidual stromal cells, a cell type critical for embryo implantation and the maintenance of pregnancy, evolved from a cellular stress reaction. We hypothesize that the stress reaction in these cells was elicited ancestrally through the inflammation caused by embryo attachment and invasion. We describe a model, Stress-Induced Evolutionary Innovation (SIEI), whereby ancestral stress reactions and their corresponding pathways can be transformed into novel structural components of body plans, such as new cell types. After reviewing similarities and differences between SIEI and the “plasticity first hypothesis” (PFH) of evolution, we argue that SIEI is a distinct form of plasticity-based evolutionary change because it leads to the origin of novel structures rather than the adaptive transformation of a pre-existing character.

Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.

In previous studies we found that generalized functions can be smooth, discrete, periodic or discrete periodic and they can either be local or global and they are regular or generalized functions. We also saw that these properties were related to Poisson’s summation formula on one hand and to Heisenberg’s uncertainty principle on the other. In this paper, we interlink these studies and show that scalars (real or complex numbers) considered as trivial functions are discrete and periodic, local and global as well as regular and generalized, simultaneously. However, this is also a paradox because it means that Dirac’s δ and 1 (its Fourier transform) coincide. They both are unity. We show that δ and 1 coincide in the sense of scalars (real or complex numbers) but they differ in the sense of (generalized) functions. This result can moreover be related to Max Born’s principle of reciprocity. It also answers an open question in present-day quantum mechanics because it means that the Dirac delta squared is simply delta.

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

The main foundations of the standard $\Lambda $CDM model of cosmology are that: 1) The redshifts of the galaxies are due to the expansion of the Universe plus peculiar motions; 2) The cosmic microwave background radiation and its anisotropies derive from the high energy primordial Universe when matter and radiation became decoupled; 3) The abundance pattern of the light elements is explained in terms of primordial nucleosynthesis; and 4) The formation and evolution of galaxies can be explained only in terms of gravitation within a inflation+dark matter+dark energy scenario. Numerous tests have been carried out on these ideas and, although the standard model works pretty well in fitting many observations, there are also many data that present apparent caveats to be understood with it. In this paper, I offer a review of these tests and problems, as well as some examples of alternative models.

There is scientific consensus that organismal aging did not evolve by natural selection (NS) because it lacks individual benefit. Nonetheless it exists, leading to much speculation about its origins, and when the diminishing force of selection begins. Both concepts are based upon two misconceptions; that aging occurs in and of itself and is caused by the declining strength of NS during the reproductive lifespan. Although lacking individual benefit, aging evolved by NS as a tradeoff of survival for reproduction. Based upon regulatory dynamics that participate in this tradeoff, aging begins once reproductive success has been achieved through offspring nurturing. Thereafter, the strength of NS wanes to exponentially accelerate aging, leading to death. Assumptions of the theory are that: (1) a life-long, “holistic” regulatory mechanism whose genic expression is modified epigenetically, originates in ontogenesis; (2) the regulatory mechanism of the last developmental stage becomes redundantly expressed during “morphostasis”, a non-aging, life interval of peak vitality to ensure completion of reproduction through nurturing, and (3) thereafter, loss of regulatory redundancy causes aging which reduces the strength of natural selection and allows accumulation of randomly occurring somatic damage.

The Internet of Things (IoT) signifies a revolutionary technological advancement, enhancing various applications through device interconnectivity while introducing significant challenges due to these devices' limited hardware and communication capabilities. To navigate these complexities, the Internet Engineering Task Force (IETF) has tailored the Routing Protocol for Low-Power and Lossy Networks (RPL) to meet the unique demands of IoT environments. However, RPL struggles with traffic congestion and load distribution issues, negatively impacting network performance and reliability. This paper presents a novel enhancement to RPL by integrating learning automata designed to optimize network traffic distribution. This enhanced protocol, the Learning Automata-based Load-Aware RPL (LALARPL), dynamically adjusts routing decisions based on real-time network conditions, achieving more effective load balancing and significantly reducing network congestion. Extensive simulations reveal that this approach outperforms existing methodologies, leading to notable improvements in packet delivery rates, end-to-end delay, and energy efficiency. The findings highlight the potential of our approach to enhance IoT network operations and extend the lifespan of network components. The effectiveness of learning automata in refining routing processes within RPL offers valuable insights that may drive future advancements in IoT networking, aiming for more robust, efficient, and sustainable network architectures.

The artificial general intelligence (AGI) industry, represented by ChatGPT, has impacted social order during its development, and also brought various risks and challenges such as ethical concerns in science and technology, attribution of liability, intellectual property monopolies, data security, and algorithm manipulation. The development of AI is currently facing a crisis of trust. Therefore, the governance of AGI industry must be prioritized and the opportunity of the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken. It is necessary to enhance the norms for the supervision and management of scientific and technological ethics within the framework of the rule of law. Additionally, it is also essential to continuously improve the regulatory system for liability, balance the dual values of fair competition and innovation encouragement, and strengthen data security protection systems in the field of AI. All of these will enable coordinated governance across multiple domains, stakeholders, systems, and tools.

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes, although the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab outperformed all other methods. Finally, we suggest new strategies and opportu-nities to improve computational predictions of conformational B-cell epitopes.

According to Quantum Mechanics a narrow wave-package of the center of mass of macroscopic objects, due to their heavy mass, remains stable over astronomical times. However Quantum Mechanics allows and energetically even prefers largely smeared out c.m. states that never been seen. Why is this the real state of the world we know? Does it suggest a micro-macro threshold with different theoretical descriptions?

The rapid proliferation of Internet of Things (IoT) technology has ushered in a new era of connectivity, allowing billions of devices to produce extensive data. Significant challenges in software engineering need to be addressed for full utilization of Internet of Things (IoT) applications. This article examines the challenges in Internet of Things, software engineering and discusses current developments aimed at addressing these issues and seizing new opportunities. We examine the challenges associated with Internet of Things software development, including resource constraints, device diversity, security vulnerabilities, and data management concerns. New studies have indicated the use of advanced techniques like as containerization, model-driven development, AI and ML integration, and robust security frameworks. Moreover, the significance of system architecture is explained, including the device, network, and cloud layers, with an emphasis on the principles of reusability, scalability, and modularity. Results and conversations explore the complexities of security measures, emerging opportunities, and scaling solutions made possible by simulation tools, penetration testing, and cross-domain development platforms. The study expects IoT system evolution and emphasises the need for continual software and firmware changes, testing, and deployment.

In the field of quality control, the critical challenge of analyzing microdefects in steel filament holds significant importance. This is particularly vital as steel filaments serve as reinforced fibers in the use and applications within various component manufacturing industries. This paper addresses the crucial requirement of identifying and investigating microdefects on steel filaments. Eddy current signals are used for the identification of microdefects, and an in-depth investigation is conducted. The core objective is to establish the relationship between the depth of defects and the signals detected through the eddy current sensing principle. The threshold of eddy current instrument was set at 10%, corresponding to a created depth of 20 µm, to identify defective specimens. A total of 30 defective samples were analyzed, and the phase angles between the experimental and theoretical results were compared. The depths of defects ranged from 20 to 60 µm, with one sample having a depth exceeding 75 µm. The calculated threshold of 10.18% closely aligns with the set threshold of 10%, with a difference of only 1.77%. The resulting root mean square error (RMSE) was found to be 10.53 degrees, equivalent to 3.49 µm for the difference in depth and phase between measured results and estimated results. This underscores the methodology's accuracy and its applicability across diverse manufacturing industries.

Investigation into a modified deterministic rumour spread model using mathematical epidemiology approach as an extension of Daley and Kendall (D-K) model was carried out. It is proven that the spread of a rumour is fairly comparable to a contagious disease transmission, except that the spread depends solely on the fraction of individuals who are aware of the rumour at the early stage as well as the intrinsic rumour spread rate . Model simulation reveals a rumour diminishing strategy so far as is maintained.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula river. Using image processing procedures, 2D and 3D shape descriptors were determined. The Principal Components Analysis (PCA) was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed 3 or 4 main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (CSF, Compactness, Dimensionality). Considering the scraps, PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

TThe locality condition of probabilities underpinning the derivation of Bell inequalities can be violated classically. The wave function collapse of an entangled state of single photons results in the factorization of quantum probabilities. It is possible to differentiate, locally, between ensemble probabilities of single detections with and without wave function collapse for the alleged quantum nonlocality. The theoretical concept of photonic quantum nonlocality cannot be implemented physically because of the quantum Rayleigh scattering of single photons. A distinction needs to be made between the correlation of individual, single measurements of pure states and the correlation of the measurement ensemble of the mixed states. The correlation operator of Pauli vector operators delivers the same probabilities of correlated detections of photons for both independent and multi-photon states as for ‘entangled’ states of photons. As single-photon sources are not needed, the design and implementation of quantum computing operations and other devices will be significantly streamlined. © 2024 The Author

The paper reconstructs Merleau-Ponty’s philosophical project in terms of a phenomenology of sensible transcendence. According to this framework, i) any given data is correlative to a subjec-tive apprehension, ii) but it cannot be fully captured by this same experience. Therefore, subjec-tive apprehension must remain open to a type of absence or radical indeterminacy. This notion of transcendence must be grounded in bodily experience, and the challenge is to develop a notion of logos that can account for its sensible donation. We describe that the critical apparatus mobilized to achieve this goal, primarily through the notions of “field of presence” and “presentation”, re-stores a logic of consciousness in these analyses that focus on the body. Keywords

MiR-204 is a small noncoding RNA with diverse regulatory roles in breast cancer (BC) development and progression. miR-204 is implicated in the instauration of fundamental traits acquired during the multistep development of BC known as hallmarks of cancer. It may act as a potent tumor suppressor by inhibiting key cellular processes like angiogenesis, vasculogenic mimicry, invasion, migration, and metastasis. It achieves this by targeting multiple master genes involved in these processes, including HIF-1α, β-catenin, VEGFA, FAK, FOXA1, among others. Additionally, miR-204 modulates signaling pathways like PI3K/AKT and interacts with HOTAIR and DSCAM-AS1 lncRNAs, further influencing tumor progression. Beyond its direct effects on tumor cells, miR-204 shapes the tumor microenvironment by regulating immune cell infiltration, suppressing pro-tumorigenic cytokine production, and potentially influencing immunotherapy response. Moreover, miR-204 plays a crucial role in metabolic reprogramming by directly suppressing metabolic genes within tumor cells, indirectly affecting metabolism through exosome signaling, and remodeling metabolic flux within the tumor microenvironment. This review aims to present an update on the current knowledge regarding the role of miR-204 in the hallmarks of BC. In conclusion, miR-204 is a potential therapeutic target and prognostic marker in BC, emphasizing the need for further research to fully elucidate its complex roles in orchestrating aggressive BC behavior.

Ulcerative colitis (UC) is a chronic inflammatory disease, the incidence of which is increasing worldwide. However, the etiology and pathogenesis of UC remains unclear. The n-butanol extract of pulsatilla decoction (BEPD) of the traditional Chinese medicine herbal formula has been shown to be effective in treating UC. We aimed to explore the molecular mechanism underlying the effects of BEPD on UC, in particular its effects on neutrophil extracellular trap (NET) formation by neutrophils. High-performance liquid chromatography was used to determine the principal compounds of BEPD. UC was induced in mice using dextran sodium sulfate, and mice were treated with 20, 40, or 80 mg/kg BEPD daily for seven days. Colonic inflammation was determined by assessing the disease activity index, histopathology, colonic mucosal damage index, colonic mucosal permeability, and pro- and anti-inflammatory cytokine levels. The infiltration and activation status of neutrophils in the colon were determined by analyzing the levels of chemokine (C-X-C motif) ligand (CXCL)1 and CXCL2, reactive oxygen species, Ly6G, and numerous NET proteins. Our results showed that BEPD improved the disease activity index, histopathology, and colonic mucosal damage index scores of mice with UC, and restored colonic mucosal permeability compared with untreated mice. The expression levels of the pro-inflammatory cytokines interleukin-1β, interleukin-6, and tumor necrosis factor-α in colon tissues were significantly decreased, while the expression levels of anti-inflammatory cytokines in colon tissues were significantly increased, exceeding those of control mice. In addition, BEPD reduce the expression of the neutrophil chemokines CXCL1 and CXCL2 in the colon tissue of mice with UC, reduced neutrophil infiltration, reduced reactive oxygen species levels, and significantly reduced the expression of NET proteins. BEPD also significantly reduced NET formation. The results of this study suggest that BEPD exerts therapeutic effects in a murine model of UC by inhibiting neutrophil infiltration and activation in the colon, as well as by inhibiting the expression of key proteins involved in NET formation and reducing NET formation, thereby alleviating local tissue damage and disease manifestations.

We report on the phosphorescence of singlet oxygen photogenerated through a stimulated Raman process. Nanosecond radiation in the green spectral region focused on hexane induces a Raman transition of the dissolved in the solvent oxygen molecules toward the singlet oxygen state producing a Stokes signal in the near infrared. The excited oxygen relaxes to ground emitting an infrared photon at 1269 nm. While the Stokes signal’s wavelength changes with the pumping light’s wavelength, the wavelength of the phosphorescent photon remains unaltered. The result confirms previously reports on stimulated Raman excitation of singlet oxygen.

Background Oral anticoagulation (OAC) for atrial fibrillation (AF) patients is administered frequently. Vitamin K antagonist (VKA) and direct oral anticoagulants (DOACs) prevent thromboembolic events, but information about the quality of life (QoL) and patient satisfaction, related to both anticoagulants, is limited. Methods REGUEIFA is an observational and multicentre study that included patients with AF diagnosis treated by cardiologist. Those on OAC, VKA, and DOACs were selected. The EuroQol-5D (EQ-5D) questionnaire evaluated QoL, and the ACTS (Anti-Clot Treatment Scale) questionnaire investigated patient satisfaction with OAC. Results From a total of 1007 patients, 904 patients were on OAC, 532 on VKA and 372 on DOACs. In total, 846 patients completed the EQ-5D questionnaire, with results significantly worse in patients on VKA than on DOACs: (1) mobility limitations (37.6% vs. 24.2%, p

Chronic lymphocytic leukemia (CLL) is a distinct category of lymphoproliferative disorder characterized by the clonal expansion of mature B cells, followed by their accumulation in primary and secondary lymphoid organs. Cluster of differentiation (CD) markers such as CD20, CD22, CD23, CD5, and CD81 serve as reliable prognostic indicators and disease progression factors in CLL. The human leukocyte antigen (HLA) system is a critical genetic variable in many diseases, with well-documented associations between HLA and various cancers. This study aims to investigate, for the first time, potential connections between HLA typing and CD marker expression in CLL. Despite being one of the most prevalent neoplasms in developed regions, there is a need for biomarkers that can improve survival predictions and identify possible genetic determinants. Methods: This study included 66 CLL patients and 100 controls, with all samples analyzed using biochemical methods, flow cytometry, and cytomorphology. Next-generation sequencing (NGS) was performed for HLA typing. Results: The results indicate that several CD markers are statistically associated with different HLA alleles. Specifically, CD20 was associated with HLA-DRB1*11:04:01; CD45 with HLA-C*07:01:01; CD79b with HLA-DPA1*02:01:02; CD23 with HLA-B*39:01:01; CD43 with HLA-DRB1*15:01:01; CD22 with HLA-B*49:01:01, HLA-C*07:01:01, HLA-DPB1*02:01:02, and HLA-DRB1*07:01:01; and CD81 with HLA-DPB1*04:02:01, HLA-DQA1*01:04:01, and HLA-DQB1*05:03:01.Conclusion: In conclusion, this research demonstrates significant statistical links between HLA genes and immunophenotypic markers in CLL patients, shedding new light on the immunological context of CLL.

We hypothesis that large earthquakes generate Horndeski-like Gravitational Wave (GW). We find that such a Horndeski-like GW propagates with the speed of sound. The sound waves generated by an earthquake make a local and temporal change to the Earth’s diagravitational medium; therefore, they modify the GW speed in a standard, Alternative-Theory-of-Gravity (ATG) sense. The quantum of the Horndeski-like GW is a massless scalar quasiparticle and cannot exist outside of the propagation region of the P-field. The Horndeski-like GW may be detectable by future GW detectors with a sensitivity of 10−15 Hz^−1/2 in the region of 0.1–1 Hz.

This paper investigates the relationship between public spaces and quality of life (QoL) for older adults, aiming to identify knowledge gaps within the context of population ageing and urbanization. Recognizing the growing importance of sustainable urban development, the research explores how cities can foster active ageing and improve QoL through accessible and inclusive public spaces. A scoping literature review identifies five key QoL domains for older adults: autonomy, meaningful activities, positive social relationships, leisure opportunities, and health. The review highlights areas requiring further investigation, including the interplay between environmental design and social connections, intergenerational perspectives on public space liveliness, the influence of context on QoL and the built environment, and the value of qualitative research in this field. By contributing to the understanding of QoL in relation to public spaces through the lens of Person-Environment fit theory, this study aims to inform urban design, landscape architecture, and policymaking in the creation of age-friendly communities with inclusive public spaces.

Sleep and stress significantly affect emotions, productivity, and overall well-being, crucial for optimal health and performance. However, limited research examines how Nepalese nurses' quality of life relates to their sleep quality and stress levels. This study aims to assess this relationship among nurses in private hospitals in Kathmandu. Three private hospitals were selected for this cross-sectional analytical study. Eligible nurses were chosen through purposive sampling, and data were analysed using IBM SPSS v25, employing Chi-square tests and logistic regression analysis. Research revealed a high rate of poor sleep quality (83.9%) among individuals with a mean age of 24.33 ± 3.08 years. Initially, significant associations were found with the factors like bachelor's degree, no tobacco use, absence of insomnia, good sleep quality, and low stress with good quality of life. After adjustment, the association with bachelor's degree [AOR:2.025, CI at 95%: 1.021-4.016, p

Local food has continued to become more popular in both the home and as featured ingredients in all segments of food service. Numerous studies have explored the motivating factors for consumer purchasing motivators. However, no known study has explored if there is a correlation between ego and intent to buy local foods. This study adopted a modified version of the Theory of Trying and surveyed a sample of 237 food service buying agents in South Carolina to examine their perceptions of buying local. Results suggest that ego is a predictor of trying to purchase local foods for restaurants. It was also found that the modified model is an acceptable model for predicting buyer intent. These findings support the majority of the studies looking at ego-centric behaviors with green purchases and could potentially help local food suppliers to better market their products to foodservice establishments. This study could also act as the foundation for future studies to explore the full extent of ego’s motivational effect on purchasing intentions in the food service industry.

Epidemiological data show that the use of alcohol and other psychoactive substances is higher among university students when compared with the general population and high school students. The use of psychotropic drugs by students in health courses deserve special attention due to because they will be responsible for the health education and care of the population. The present study aims to describe, through a cross-sectional study, the risks and the profile of consumption of psychoactive substances by students of the Biological and Health Sciences courses at the Federal University of Bahia (UFBA). An online and anonymous questionnaire was applied from September 20, 2019 to June 15, 2021. 514 responses were obtained, of which 502 were considered according to the inclusion criteria. For recreational use of psychoactive substances, both for lifetime use and for use in the last 3 months, the prevalence of alcohol, cannabis and tobacco were the highest ones in that order. Only alcohol, cannabis and inhalants showed a percentage of individuals at high risk of developing problems. These results indicate the need for local intervention, in order to prevent risky behavior, damage to mental health and major consequences for society and university performance.

Objectives: To assess the short-term impact of a single global stretching session on plantar pressures and lumbar range of motion (RoM) over one week in sedentary university students. Design: A randomised controlled trial with two parallel groups. Participants: Sixty-four university students were randomly assigned to a stretching group or control group. The stretching group received a single session of global stretching and the control group remained seated for the same length of time as the stretching session. Main outcome measures: Lumbar RoM and plantar pressures were assessed before, immediately after, 48 hours after, and 7 days after intervention. Results: Group-by-time interaction was significant for lumbar lateral flexion (p = 0.044), right maximal pressure (p = 0.003), left maximal pressure (p = 0.008), right mean pressure (p = 0.025), left mean pressure (p = 0.002), and maximal pressure minus mean pressure in right foot (p = 0.007). The model with plantar pressures showed a significant difference for group-by-time interaction (p = 0.010).Conclusion: Global stretching seems to counteract the ill-effects of prolonged sitting up to seven days with respect to plantar pressure in standing without improving lumbar ROM

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

Keywords: green spaces, NDVI, level of greenness, urban environment, Aktobe

The two-parameter distribution introduced by Kumaraswamy (1980) is a very flexible alternative to the Beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper we apply the “biased transformation” methodology proposed by Raschke (2009) to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both low size distortion, and respectable power. In particular, the “biased transformation” Anderson-Darling test dominates the other tests that are considered.

Microplastics, which are plastic particles smaller than 5 mm in size, are a growing environmental concern since they have been found in oceans, rivers, lakes, and even on land. These tiny particles can escape water filtration systems and end up in water bodies as well as through the degradation of the larger plastic products causing serious environmental and food safety concerns. This research represents the magnitude of this pollution, characterization of microplastic contamination, its type of polymer, and possible sources of this pollution in the rivers around Dhaka, Bangladesh. Quantitative analyses were done following the method suggested by the NOAA and other conventional methods. Among the rivers, Buriganga has the highest microplastic pollution with 3.654 items/L in surface water and 43.125 items/kg in sediment. The concentration in Shitalakshya and Turag rivers are 3.011 items/L and 2.051 items/L in surface water and 34.875 and 17.875 items/kg in sediment respectively. ANOVA test revealed a significant difference between the concentration level of microplastic in the surface water of the Burigana and Turag rivers. A positive correlation between the concentration level of microplastic in the surface water and the sediment was also found. On average 58% of the microplastics were found to be fibrous in surface water. Qualitative analyses were done to find the characteristics and chemical composition of the microplastics. Through the use of FTIR spectroscopy, the study identified different kinds of synthetic polymers in the sample including polyamide (Nylon), polyethylene (PE), polystyrene (PS), polypropylene (PP), polyethersulfone (PES), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) and polyethylene were the most abundant polymer in the samples. This study involved the investigation of three distinct rivers located in the vicinity of Dhaka city

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Alhagi pseudalhagi in the arid zone of the Atyrau region and an analysis of their current state with an assessment of raw material reserves were studied. Botanical characteristics, ontogenetic spectrum, morphological indicators, productivity, areas of thickets and reserves of raw materials of above-ground organs were assessed. The structural parameters of these coenopopulations in the Zhangyr and Coneu Rivers valleys, the vicinity of Imankara Mountain, and the Taisoigan sands were studied. It was established that the species composition of the populations includes 63 species from 54 genera and 30 families. The most common species include 49 taxa, category II – 17 taxa, category III – 7 species, category IV–V – 2 species. The maximum similarity in species composition was noted between populations in the Zhangyr and Coneu Rivers valleys. The maximum morphometric indicators were observed among the population of the river valley. Coneu and the minima are located on the Taisoigan sands. Analysis of the age spectrum made it possible to determine that the populations in the Zhangyr and Coneu Rivers valleys are characterized as young, in the area of Imankara Mountain and on the Taisoigan sands - as stable and middle-aged.

According to published descriptions, one of the most incapacitating forms of orofacial pain is trigeminal neuralgia (TN). At its core, trigeminal neuropathic pain (TN) is still a clinical diagnostic that has to be differentiated from other forms of TN and face pain linked to other neuralgias or headache disorders. Imaging can only help with the cause of the pain, whether it’s a vascular origin or not. It is becoming more and more evident that there is no one-size-fits-all medication or surgical procedure that can effectively treat every patient with trigeminal neuralgia (TN). This is probably because TN is a diverse collection of conditions that all present with face discomfort. Medical therapy using anticonvulsants like carbamazepine is still the first-line treatment for TN. If this doesn't work for the patient, surgical treatments are available. Microvascular decompression is often a safe, efficient operation with results that are both rapid and long-lasting. Patients who may benefit from percutaneous techniques such as radiofrequency ablation, glycerol ablation, balloon compression, or a combination, include those who cannot undergo general anesthesia or whose medical conditions prohibit a suboccipital craniectomy. Radiosurgery may be a great option for patients with bleeding diathesis brought on by blood-thinning medications who are not eligible for invasive procedures or who do not want to undergo open surgical procedures, as long as the patient is aware that achieving maximum pain relief may take several months. In conclusion, peripheral neurectomies persist in offering a low-cost and resource-saving substitute for pain management to those residing in areas with restricted financial and healthcare resources. In the end, developing a better understanding of the molecular processes behind trigeminal neuralgia will lead to new, less intrusive, and more effective treatments.

Continuous cropping of Platycodon produces problems that significantly impact its yield and quality making it a pressing issue that requires immediate attention. In this study, the sandwich method is employed to evaluate the allelopathic activity of different plant parts (leaves, stems, and roots) of Platycodon. The inhibitory effects of the various Platycodon plant parts were quantified based on their effects on lettuce seedlings growth by Sandwich method, with the following average inhibition percentages at 1 mg/ml (agar) of : leaves (79.4%, 61.8%), stems (58.0%, 45.7%), and roots (53.4%, 49.3%) for lettuce radicals and hypocotyls, respectively. At a concentration of 5 mg/ml, the inhibitory effects were as follow: leaves (91.9%, 72.2%), stems (79.5%, 60.3%), and roots (71.4%, 65.2%). Also, by employing the Plant Box method, it was determined that Platycodon root exudation. The growth inhibition rates of lettuce seedling roots and hypocotyls were 45.5% and 18.9%, respectively. The effect of activated carbon on the adsorption of allelopathic substances was investigated, and the results of the sandwich method with a concentration of 1 mg/ml showed the following growth inhibitory effects on lettuce seedlings and hypocotyls: roots (27.8%, 25.7%), leaves (13.3%, 25.7%), and stem (9.07%, 13.6%). The Plant Box method revealed average rates of inhibition of -16.7% and -4.70% on the growth of lettuce seedlings and hypocotyls, respectively. The results of this study demonstrated that activated carbon has a mitigating effect on the allelopathic inhibition associated with the different plant parts and root exudation of Platycodon, thus may provide a potential solution for overcoming obstacles associated with continuous cropping of Platycodon.

Persons who inject drugs (PWIDs) represent an important reservoir of hepatitis C virus (HCV) infection, hence therapeutic communities (TCs) are promising points of care for the identification and treatment of HCV-infected PWIDs. We evaluated the effectiveness and efficacy of an HCV micro-elimination program targeting PWIDs in the context of a drug-free TC applying Cascade of Care (CoC) evaluation by calculating frequencies of infection diagnosis, confirmation, treatment and achievement of a sustained virological response (SVR). We also evaluated the risk of reinfection of PWIDs achieving HCV eradication, by collecting follow-up virologic information of previously recovered individuals and eventual relapse in drug use, assuming the latter as a potential source of reinfection. We considered all PWIDs (aged 18+ years) residing in the San Patrignano TC at the beginning of the observation period (Jan/2018–Mar/2022) or admitted thereafter, assessing for HCV and HIV serology and viral load by standard laboratory procedures. Ongoing infections were treated with direct acting antivirals (DAA), according to the current national guidelines. Out of the 792 individuals tested on admission, 503 (63.5%) were found to be seropositive for antibodies against HCV. 481 of these 503 individuals (95.6%) underwent HCV-RNA testing. Out of the 331 participants positive for HCV-RNA, 225 were ultimately prescribed a DAA treatment with a Sustained Viral Response (SVR), which was achieved by 222 PWIDs (98.7%). 186 of 222 PWIDs (83.8%) with SVR remained HCV-free on follow-up (with a median follow-up of 2.73 years after SVR ascertainment). The CoC model in our TC proved efficient in implementing HCV micro-elimination, as well as in preventing reinfection and promoting retention in care of individuals, which aligns with the therapeutic goals of addiction treatment.

Infant hepatitis B vaccine coverage in China is high, with over 95% of infants immunized; however, high vaccine coverage can often mask low timeliness. The vaccination interval between the second and third dose is not clearly defined by immunization guidelines in China. This retrospective cohort study assessed the time interval distribution of hepatitis B vaccination among a cohort of randomly selected live births from Centers for Disease Control and Prevention across four provinces or municipalities in China between January 2017 and December 2021. Among the infants analyzed, 163,224 received the first dose of hepatitis B vaccine with 146,905 (90.0%) and 135,757 (83.2%) infants receiving the second and third doses, respectively. A total of 132,577 (90.2%) infants received the second dose between 28–61 days after the first dose. Of the 119,437 (88.0%) infants that completed the hepatitis B series between 61–214 days after the second dose, 87,067 (64.1%) infants were vaccinated with the third dose between 151–180 days after the second dose. The time interval distribution for the three doses of hepatitis B vaccine varied across the four provinces or municipalities (P&lt;0.001). Of the 58,077 infants who completed the hepatitis B vaccine series, 36,377 (62.6%) infants used the same type of hepatitis B vaccine for all three doses. Overall, the timeliness of hepatitis B vaccination for infants was lower than expected, with regional disparities observed. This highlights the need for improved timeliness through the introduction of a defined timeframe for the last two doses of vaccine, and training for obstetricians and related personnel.

The paper proposes a traceability framework based on methodological approach for the deployment of IOTA-Based Distributed Ledger in the mining industry, as a first step of certification and labelling of the sustainable material production. The methodology is evaluated in real-world implementation in scope of the DIG_IT project. The implementation provides significant amount of heterogeneous data originating from different processes and sources during the operation in mines, such as vehicles operations, mining and raw material extraction, workers physical parameters, environmental parameters, sensors (field, biometric, assets), market (supply chains, commodity prices), weather data, etc. Most of these datasets, especially the data about the emission, needs integrity and the ability to be audited by the public community, government and the stakeholders. The methodology for establishing the transparency for the mining operations, the requirements, technical architecture, General Data Protection Regulation (GDPR) assessment, Distributed Ledger Technology (DLT) infrastructure, as well as end-to-end traceability, are proposed and evaluated with the actual deployment. The approach advocates end to end security for the increased traceability and data integrity based on Distributed Ledger Technology. Namely, W3C Decentralised Identities and Public Key Infrastructure (PKI) should be deployed from the data source to the cloud and anchored onto the Blockchain. The approach showed promising results with the regulations compliance made possible, making the data available and advertised publicly for the emission compliance.

Primary failure of eruption (PFE) is a rare non-syndromic condition involving total or partial non-eruption of posterior teeth, in the absence of mechanical obstruction. This paper presents the case of a healthy girl referred at age 4 years for asymmetry of upper arch. Clinical examination confirmed a slight shift of the upper midline to the left, with no erupted teeth distal to the upper left canine and left posterior open bite. Panoramic X-ray showed delayed intra-osseous development of lateral left upper teeth compared to the right side. Clinic and radiographic follow-up during the next 9 years showed that 26 has not erupted by almost 13 years of age, 27 has delayed development and an unusual shape and there is agenesis of 24, 25. Genetic analysis using the PTH1R single gene sequencing did not detect any known disease-causing or rare pathogenic variants that could explain the patient's phenotype. Even when detected early, PFE rises difficult problems with regard to diagnosis and ortho-surgical treatment planning due to the impossibility to accurately predict the evolution. Tooth agenesis on the same arch worsens the prognosis and adds challenges to planning the treatment. Close long-term follow-up and timely adjustment of treatment plan in accordance to the evolution of the case are needed.

To facilitate wireless communication, ocean exploration, and various environmental and research applications, the underwater Internet of Things has been developed with an infrastructure of acoustic sensor networks. There has been a significant increase in failures and a decrease in the efficiency of the network due to the fluid and challenging underwater environment. Consequently, achieving quality of service (QoS) in underwater networks remains a research area. Supporting QoS in underwater sensor networks is challenging due to resource limitations such as processing power, memory, bandwidth, and power sources. The purpose of this study was to examine the service quality requirements as well as protocols that have been implemented in recent years. Finally, based on quality criteria, we have presented a routing solution based on fuzzy logic, which is more efficient than other recent methods regarding energy consumption rate, JFI, efficiency, and reliability.

Studies on the domestication, genetic differentiation, and crop evolution of foxtail millet are re-viewed. Several genetic studies were carried out to elucidate the genetic relationships among foxtail millet accessions originating mainly from Eurasia, based on intraspecific hybrid pollen semi-sterility, isozymes, DNA markers, and single-nucleotide polymorphisms. Most studies suggest that China is the center of diversity of foxtail millet, and landraces were categorized into geographical groups. Furthermore, the evolution of genes was reviewed (i.e., the Waxy gene conferring amylose content in the endosperm, the Si7PPO gene controlling polyphenol oxidase, the HD1 and SiPRR37 genes controlling heading time, the Sh1 and SvLes1 genes involved in grain shattering, and the C gene controlling leaf sheath pigmentation), and the variation and distribu-tion of these genes suggested complex patterns of evolution under human and/or natural selec-tion.

Recent theory of tachyon neutrinos, providing an explanation for Dark Matter, leads to two sets of experiments for testing that theory. One predicts an enhanced signal in detection of the Cosmic Neutrino Background. The other compares models of Dark Matter used to fit data on galaxy rotation curves and also gravitational lensing. In addition, an apparent contradiction within the new theory, related to Dark Energy, is identified and addressed.

: Background: The aim of this study was to evaluate Phenylketonuria (PKU) patients’ oral health habits and the efficiency of tailored recommendations for PKU patients in Latvia on the correct management of oral hygiene. The-free amino acid formula can negatively impact PKU patient’s oral health as it contains a high amount of complex carbohydrates. Materials and methods: In this study with a one-month interval pre and post implementation of oral hygiene recommendations, two questionnaires regarding oral hygiene habits and knowledge were prepared and provided by trained medical personnel to be filled in for all diagnosed PKU patients in Latvia who were not lost to follow-up. Results: Based on the questionnaires among the PKU patients under 18 years of age, 27% cleaned their teeth once a day. However, 6% of patients did not engage in regular teeth brushing at all. Among PKU patients over and 18 years of age, 26% brushed their teeth only once a day. Additionally, a mere 39% of PKU patients ≥18 years of age and 17% &lt; 18 years of age incorporated flossing into their oral hygiene routine. In total, 45% of PKU patients ≥18 years of age and 44% &lt; 18 years of age visited a dentist once a year, but 40% ≥18 years of age and 39% &lt;18 years of age visited a dentist less often than once per year. After reading the recommendations, a remarkable number of PKU patients &lt; 18 years of age had improved their daily oral hygiene routine, including toothbrushing (25%) and flossing (23%). Conclusion: Given the unique dietary requirements and the implementation of these daily at-home oral health practices, motivation and regular reminders should result in better oral health of PKU patients and cooperation with clinicians.

Caregiver burden, a response to the challenges faced by those caring for sick loved ones, combines objective caregiving aspects with subjective experiences. This study aimed to describe the caregiver burden in guardians of ailing animals with dermatological and oncological pathologies. Additionally, the study aimed to correlate the degree of caregiver burden with the duration of the animal's treatment, disease stability, family income, owner's age, and the number of people living in the same household responsible for the animal's treatment. Using a cross-sectional approach, questionnaires were administered to guardians at a veterinary hospital. Out of 182 valid responses, 50.55% related to oncological patients and 49.45% to dermatological patients. Notably, 36.9% of oncological and 37.8% of dermatological guardians exhibited clinically significant burden, with no statistical differences between the groups. Of the respondents, 84.6% were dog guardians, with 34.4% showing a significant burden. Feline guardians (15.4% of respondents) exhibited a higher burden (53.5%) compared to dog guardians (p = 0.02). Disease stability, time since diagnosis, and guardian age significantly influenced the burden, while household composition, income, and treatment duration showed no substantial impact. These findings underscore the psychosocial impact of caring for animals, emphasizing the need for a comprehensive understanding of the caregiver's perspective.

The recycling of wastes to obtain value-added products is a great challenge in the sustainable economy. In our studies, a mixed precursor containing aluminium scrap from end – of – life vehicles and TiO2 Degussa P25 was used to prepare TiO2 modified Al photocatalyst through solid state reaction. Al scrap and TiO2 Degussa P25 in the ration of n(Ti) / n(Al) = x (x = 0.0 - 2.5%), and acetylacetonate and Tween 80 was mixed under magnetic stirring and sintered using a con-ventional furnance at 550°C. DTA – TG – DTG, X – ray diffraction, Fourier – transform infrared, UV – Vis and photoluminescence spectroscopies were assed to investigate the thermal, structural, optical properties and photocatalytic activity. The effect of aluminium concentration on the characteristics of TiO2 Degussa P25 depending on the above instruments are studied. XRD revealed that the materials have crystal structure of anatase TiO2, rutile TiO2 and aluminium. The opto-electronic properties show that Al addition shifted the absorption spectra toward the visible range. The photocatalytic performance of Al – TiO2 composites was quantified by the degradation of Allura Red solution under a visible condition. The highest photocatalytic activity (79.99%) was achieved for the sample containing 0.5% Al and a higher concentration of Al particles shields the active centres of the semiconductor, thus reducing its photocatalytic activity. Aluminium scrap from end-of-life vehicles can be a source of raw material to be considered in semiconductor production. The spin-trapping technique evidenced that •O2- was the main species generated responsible for the Allura Red degradation.

Urbanization triggers the emergence of slums in urban areas. Palembang is one of the Indonesian cities with slum settlements. This research aimed to analyze the interpolation of slum positions in Palembang and their distribution pattern using kernel density analysis and by modeling spatial data on accuracy values. It employed a quantitative method with a survey approach. Samples were selected using proportional random sampling from families in 64 slum areas across the 13 districts in the city, and their positions were recorded with a GPS device and then processed and mapped in ArcGIS. The data were analyzed with inverse distance weighted, kernel density, and spatial data modeling. Results showed that slum areas near the riverbanks had high population density, while those located further were lower. The values obtained from the kernel density analysis varied from 0 to 58.1123, while the inverse distance weighted showed a value range of 2.26745–380.991. The spatial data modelling analysis demonstrated that the distribution of slum areas was widely spread along the Musi River, with an accuracy value of 96.8%, which falls within the range of >0.9-1.

The emergence of the Internet of Things (IoT) has led to the deployment of various types of sensors in many application fields, including environment monitoring, smart cities, health, industries, and others. The increasing number of connected devices has led to the creation of massive quantities of data that need to be analyzed. Typically, this data is ordered by time, as a time series. In this context, this paper presents a time series prediction model based on Recurrent Neural Networks in order to predict one step ahead. Result obtained through five Internet of Things monitoring datasets, showed that the Recurrent Neural Network obtained better performance that the prediction methods, ARIMA and SVM.

Although first described in the final decade of the 19th century, the axis responsible for atrioventricular conduction has long been the source of multiple controversies. Some of these continue to reverberate. When first described by His, for example, many doubted the existence of the bundle we now name in his honour, while Kent suggested that multiple pathways crossed the atrioventricular junctions in the normal heart. It was Tawara who clarified the situation, although many of his key definitions have not universally been accepted. In key studies in the third decade of the 20th century, Mahaim then suggested the presence of ubiquitous connections that provided "paraspecific" pathways for atrioventricular conduction. In this review, we show the validity of these original investigations, based on our own experience with a large number of datasets from human hearts prepared by serial histological sectioning. Using our own reconstructions, we show how the atrioventricular conduction axis can be placed back within the heart. We emphasise that newly emerging techniques will be key in providing the resolution to map cellular detail to the gross evidence provided by the serial sections

First described in 1760 by the anatomist Morgagni, CoA is a congenital heart defect characterized by narrowing the aorta, typically distal to the left subclavian artery. It accounts for approximately 5-8% of all congenital heart diseases, with an incidence estimated at 4 per 10,000 live births. In 1944, the Swedish surgeon Clarence Crafoord achieved the first successful surgical CoA repair by performing an aortic end-to-end anastomosis on two patients aged 12 and 27 years old. Presently, the most prevalent techniques for surgical repair, particularly in infants and neonates with isolated coarctation, involve resection with end-to-end anastomosis (EEA) and the modified Crafoord technique (extended resection with end-to-end anastomosis (EEEA)). Subclavian flap aortoplasty (SCAP) is an alternative surgical option for CoA repair in patients under two years of age. In cases where the stenosis extends beyond resection and end-to-end anastomosis feasibility, patch aortoplasty (PP) employing a prosthetic patch can augment the stenotic region, especially for older patients. Despite advances in pediatric cardiology and cardiac surgery, recoarctation remains a significant concern after surgical or interventional repair. This comprehensive review aims to provide a thorough analysis of coarctation management, covering the pioneering techniques introduced by Crafoord using head-to-tail anastomosis and now extending to the contemporary era marked by percutaneous interventions as well as the recoarctation rate associated with each type.

BACKGROUND: Cervical cancer is the second most frequent cancer among women worldwide between 14 and 49 years of age including Nigeria. Figures have greatly reduced in developed countries after the introduction and implementation of effective screening and vaccination programs which is greatly undeveloped and inefficient in Nigeria and other developing countries at large. OBJECTIVES: The study assessed the knowledge and attitude of reproductive age women towards cervical cancer prevention in selected tertiary institutions in Osun State. METHODOLOGY: The study was a descriptive cross-sectional study carried out among reproductive age women in selected tertiary institutions in Osun State, Nigeria. A probability based multistage sampling technique was adopted as the sampling technique for the study. Data was collected using a semi-structured, self-administered and interviewer guided questionnaire. RESULTS: Age of respondents was 25.305±8.195. 313(79.0%) of the total respondents were Christians, and 83(21.0%) of the respondents were Muslims. For the overall knowledge score, only 52.0% of the respondents had good knowledge while 48.0% of the respondents had poor knowledge. 52.0% exhibited negative attitude towards cervical cancer prevention while 48% exhibited positive attitude towards cervical cancer prevention. Only 23% of the respondents had taken part in screening and vaccination towards cervical cancer prevention while 77% of the respondents had not. CONCLUSION: The knowledge of reproductive age women towards cervical cancer prevention was above average while their attitude towards cervical cancer prevention was low. This issue could be addressed by increasing the awareness of the effects of cervical cancer among reproductive age women in the country.

Ankylosing spondylitis (AS) is a chronic inflammatory arthritis that affects the axial skeleton, causing intense pain, progressive joint destruction, and a gradual reduction in physical function. It has been recognized not only as affecting the axial skeleton but as a systemic disease. Since it also causes an increase in the rates of inflammatory bowel disease, psoriasis, acute anterior uveitis, and mental disorders affecting the quality of life. Furthermore, an association with neurological and cardiovascular events is documented. With the advent of biologics, treating AS dramatically changed due to its high efficacy and tolerable safety. AS can be treated with various biological treatments. Nevertheless, there are differences in traits, including rapidity of onset, long-term efficacy, safety profile, and influence on comorbidities. A better understanding of such traits enables clinicians to make the best decision for each patient, increasing persistence, extending medication survival, enhancing patient satisfaction, and reducing the disease effect of ankylosing spondylitis. In this article, we emphasize biologics' efficacy and safety profile in patients with ankylosing spondylitis. In addition, we discuss the impact of biologics on comorbidities and health-related quality of life.

To describe the main health surveillance strategies for the control of COVID-19 in Brazil, with emphasis on decisions and actions taken at the federal level to prevent the infection caused by the new SARS-CoV-2. This is a scoping review study carried out from November 2019 to January 2021 using the DECS and MeSH health descriptors in Portuguese and English in four databases, in addition to the legislation found on the Brazilian Government's legislation portal. Based on the analysis of the documents, the actions describe non-pharmacological and pharmacological measures, measures related to decisions on closing and opening international borders, the acquisition of supplies and essential services. Although some strategies for containing the pandemic were identified, discrete and unnoticed surveillance measures are observed in view of the number of cases and number of deaths during the period of research analysis. The results show the absence of the federal level as support for the states and municipalities and imply decision-making and planning of actions by the Unified Health System to control the pandemic.

Background: Myasthenia Gravis (MG) is a rare autoimmune disease presenting with au-to-antibodies that affect the neuromuscular junction. Aside from symptomatic treatment options, novel therapeutics include monoclonal antibodies (mAbs). IMGT®, the international ImMunoGe-neTics information system® (https://www.imgt.org), extends the characterization of therapeutic antibodies with a systematic description of their mechanisms of action (MOA) and makes them available through its database for mAbs and fusion proteins, IMGT/mAb-DB. Methods: Using the available literature data combined with the amino acid sequence analyses from mAbs managed in IMGT/2Dstructure-DB, the IMGT® protein database, biocuration allowed to define in a standardized way descriptions of MOA of mAbs that target molecules towards MG treatment. Results: New therapeutic targets include FcRn and molecules such as CD38, CD40, CD19, MS4A1 and interleukin-6 receptor. A standardized graphical representation of the MOA of selected mAbs was created and integrated within IMGT/mAb-DB. The main mechanisms involved in these mAbs are either blocking or neutralizing. Therapies directed to B cell depletion and plasma cells have a blocking MOA with an immunosuppressant effect along with Fc-effector function (MS4A1, CD38) or FcγRIIb engager effect (CD19). Monoclonal antibodies targeting the complement also have blocking MOA with a complement inhibitor effect and treatments targeting T cells have a blocking MOA with an immunosuppressant effect (CD40) and Fc-effector function (IL6R). On the other hand, FcRn antagonists present a neutralizing MOA with an FcRn inhibitor effect. Conclusion: The MOA of each new mAb needs to be considered in association with the immuno-pathogenesis of each of the subtypes of MG in order to integrate the new mAbs as a viable and safe option in the therapy decision process. In IMGT/mAb-DB, mAbs for MG are characterized by their sequence, domains, chains and their MOA is described.

This study intricately explores the evolving tapestry of Christmas celebrations in a post-pandemic era, where tradition delicately intertwines with technology. The literature review presents a global panorama of Christmas observances, delving into varied religious perspectives. Within this exploration, a Southeast Asian country discreetly initiates its festivities as early as September—a nuanced response to the profound significance of Christmas. Navigating the digital age, the paper dissects the impact of virtual gatherings, the surge in e-commerce, and digital church services on festive traditions. Employing diverse research methods, including thematic analysis, nuanced insights are synthesized from extensive literature reviews, interviews, and observations. Scrutinizing challenges posed by the digital divide, the erosion of personal connections in virtual gatherings, and sustainability concerns in online celebrations, the qualitative research methodology reveals the interplay between tradition and technology. The paper contemplates the future of Christmas in this transformative era, offering implications for future celebrations, technology developers, policymakers, and beyond. This endeavor invites contemplation on the dynamic interplay shaping the festive landscape and prompting reflection on future celebrations.

Hospitals are perpetually challenged by the dual requirements of concurrently improving the quality of healthcare and maintaining financial solvency. Both issues are among the top concerns for hospital executives across the United States, yet some have questioned if the efforts to enhance quality are financially sustainable. led us to examine if improving quality in the hospital setting impacts revenue. Using multivariate regression, we assessed if numerous quality measures were associated with our targeted measure of hospital profitability: the net patient revenue per adjusted discharge. The independent variables included the HCAHPS Summary Star Rating, Hospital Compare Overall Rating, All-Cause hospital-wide Readmission Rate, Total Performance Score, Clinical Outcomes Domain Score, Safety Domain Score, Person and Community Engagement Domain Score, and the Efficiency and Cost Reduction Score. Our results indicated that improving quality was significantly associated with improved net patient revenue per adjusted discharge for seven of the eight of these quality measures at the hospital level. It is clear that failing to address quality and patient safety issues is costly for US hospitals, thus we believe our findings support the premise that increased attention to the quality of care delivered as well as patients’ perceptions of care may allow hospitals to accentuate profitability and advance a hospital’s financial position.

Abstract: The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.