In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

Not a lot is known about either death or the dying process. Politicians and many in the medical profession in the UK tend to shy away from interfering with it by not allowing euthanasia as an end of life option for the patient. This is the first paper in a series of two, comparing the situation in human medicine and veterinary medicine, in which euthanasia is well practiced for relieving suffering at the end of an animal’s life. This first part takes the form of a literature review including best practice around end of life care, its deficiencies and the need for assisted dying. Veterinary surgeons are well trained in the ethics of euthanasia and put it to good use in the best interest of their animal patients. In countries which have legalized physician assisted suicide for the terminally ill reporting indicates that it works well, without increases in involuntary euthanasia and most importantly without intimidation of the vulnerable. However, there is still an ever increasing tendency to overuse sedation and opioids at the end of life, which merits further investigation. With advances in medical science able to significantly prolong the dying process, patient autonomy demands a review of the law in the UK.

This is the second of a series of two papers comparing the end of life issues in human and veterinary medicine. We outline the main differences between human and animal patients such as patient communication, finance and ‘conflicts of interest’ between animal, owner and veterinarian. We discuss striking similarities between human and veterinary issues such as assessing quality of life and the primary role of the attending veterinarian or doctor being the welfare and care of the patient. This paper takes the form of an ethical argument in favour of allowing euthanasia in human medicine, by providing insights into end of life issues for humans from an independent veterinary perspective. Veterinary surgeons are well trained in the ethics of euthanasia and put it to good use in the best interest of their animal patients. Doctors in the UK are limited and unwilling to put forward a case for the option of euthanasia for those patients who face a slow and agonizing death. With advances in medical science being able to significantly prolong the dying process, autonomy for the patient demands a review of the law regarding patient choice in the UK.

Partially state-owned firms are common in both developed and developing countries and contribute around 10% of the world's GDP. This has led to a rich literature exploring various aspects of competition between such “mixed”-ownership firms. This paper contributes to the existing literature by studying the effects of mixed ownership on the intensity of competition in oligopoly markets. Our findings indicate that state ownership exerts an anti-competitive influence, with a higher state ownership share resulting in less aggressive behavior by firms in the product market. This result implies that industries with a higher proportion of state ownership yield lower total surplus. Further, when considering factors such as taxes, soft budget constraints, and weak property rights, the anti-competitive effects of state ownership are accentuated.

Objective: Health/wellness applications on mobile devices (Apps) may positively affect the health of seniors (persons age 65+). But for an App to promote health in its target audience, it must achieve meaningful use. Method: For one and a half years, residents at a Life Plan Community (mean age 86) used a wellness App running on iPad. In a digital survey, residents rated their overall satisfaction with the App on a scale from 1 (strongly dislike) to 10 (strongly enjoy). Results: User satisfaction (96 respondents of 252; 38% response rate) was distributed with maximum 8, median 7, mean 6.6, and standard deviation 2.3. Discussion: The results suggest that it is feasible to inspire seniors to pursue whole-person wellness using an App: technology may promote “active aging.”

Schistosomiasis, acquired by skin-penetrating cercariae of dioecious digenean schistosomes during freshwater contact, afflicts nearly 260 and 440 million people with active infections and residual morbidity, respectively; about 10 million women at reproductive age contract schistosomiasis during gestation every year. Acute schistosomiasis is characterized by pre-patent pro-inflammatory CD4+ T-helper 1 or CD4+ Th1/T-helper 17 reactivity against immature schistosomulae. Chronic schistosomiasis is dominated by post-patent anti-inflammatory CD4+ T-helper 2 reactivity against ova epitopes; flukes co-exist in immunocompetent definitive hosts as they are capable of evading their defense mechanisms. Preventive measures should be complemented by vaccination, inducing long-term protection against transmission, infection, and disease recurrence, given the latest advancements in schistosomal vaccines. Transmission models incorporating vaccination available in PubMed, Embase and Web of Science up to December 31, 2023 are presented. Besides conceptual model differences, predictions meant to guide decision- and policymaking reveal continued worm harboring facilitating transmission besides residual infections, and increased susceptibility to re-infection and rebound morbidity, both shifted to later life stages following the intervention. Consequently, a vaccination schedule is pivotal considering the optimal age for initial immunization, i.e., pre-schoolchildren or schoolchildren, in a cohort-based or population-based manner while incorporating potential non-adherers promoting ongoing transmission; longevity over magnitude of vaccine protection to antigenic schistosomal moieties is crucial including accounting for existing pre-acquired immunity from natural exposure, in utero priming besides herd immunity, and induced by chemotherapy. Combining as one multi-component approach long-term effects of vaccination with short-term effects of chemotherapy as regular repeated vaccine-linked therapy in contrast to a single-component intervention seems most promising for achieving WHO’s endpoints of transmission elimination and morbidity control.

This paper considers the Hermitian solutions of a new system of commutative quaternion matrix equations, we establish both necessary and sufficient conditions for the existence of solutions. Furthermore, we derive an explicit general expression when it is solvable. To illustrate our main findings, we present two numerical algorithms and examples in this paper.

This prospective observational study aimed to describe voriconazole administrations and trough concentrations in patients with Child-pugh class C clinically, and to investigate the variability of trough concentration. A total of 144 voriconazole trough concentrations from 43 Child-pugh class C patients were analyzed. The majority of patients (62.8%) received adjustments. The repeated measured trough concentration was higher than the first and final ones generally (median, 4.33 vs. 2.99, 3.90 mg/L). Eight patients with ideal initial concentration later got supratherapeutic with no adjusted daily dose, implying accumulation. There was a significant difference in concentrations among the six groups by daily dose (P=0.006). The bivariate correlation analysis showed that sex, CYP2C19 genotyping, daily dose, prothrombin time activity, international normalized ratio, platelet, and Model for end-stage liver disease score were significant factors for concentration. Subsequently, the first four factors mentioned above entered into a stepwise multiple linear regression model (variance inflation factor < 5), implying that CYP2C19 testing makes sense for precision medicine of Child-pugh class C cirrhosis patients. The equation fits well and explains the 34.8% variety of concentrations (R^2 = 0.348). In conclusion, it needs more cautious administration clinically due to no recommendation for Child-pugh class C patients in the medication label. The adjustment of the administration regimen should be mainly based on the results of repeated therapeutic drug monitoring.

The release of the active substance from the matrix modified-release tablets based on Kollidon®SR and chitosan depends on its solubility in the dissolution medium and on the composition of the matrix-forming polymers. The study is based on new oral matrix tablets based on Kollidon®SR and chitosan, formulated to optimize the low oral bioavailability of chlorzoxazone, a non-steroidal anti-inflammatory drug, a class II Biopharmaceutical Classification System. Nine types of chlorzoxazone matrix tablets were obtained using the direct compression method, and their combination at 1:1, 1:2, 1:3 chlorzoxazone/ excipients, 20%-40% (w/w) Kollidon®SR, 3-7% (w/w) chitosan and the auxiliary substances were formulated in constant concentrations. Pharmaco-technical characterization included the analysis of flowability and compressibility properties (flow time, friction coefficient, angle of repose, Hausner ratio, Carr Index), and of pharmaco-chemical characteristics (mass and dose uniformity, thickness, diameter, mechanical strength, Friability, degree of softening, in vitro release profile) of the tablets. Based on the initial evaluation of the pharmaco-technical analysis, pharmaco-chemical characteristics, and in vitro release profile, only three matrix tablet formulations (F1b, F2b, and F3b) were further tested. They were evaluated through Fourier-transform infrared spectrometry, X-ray Diffraction, thermogravimetry, differential scanning calorimetry, and in vitro dissolution tests. The three formulations were comparatively studied regarding the release kinetics of active substances using in vitro release testing. The results were analyzed by fitting into four representative mathematical models for the modified release oral formulations. In vitro kinetic study reveals a complex mechanism of release occurring in the first (0 - 2 hours) and second (2.0 hours – 36 hours) steps of drug release. Two factors have been calculated to assess the release profile of chlorzoxazone: f1 - the similarity factor and f2 - the factor difference. The results have shown that both Kollidon®SR and chitosan may be used as matrix-forming agents when combined with chlorzoxazone. In vitro dissolution tests revealed the F2b sample, data suggesting a faster drug release. The three formulations showed optimum pharmaco-technical properties and in vitro kinetic study, and they had tremendous potential to be used in oral pharmaceutical products for the controlled delivery of chlorzoxazone.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

This investigation mainly focused on the microstructural analysis of Al based hybrid metal matrix composites. Aluminium as base metal and reinforced with 2% B4 C, ZrO2 (2%,4%,6%), SiC (4%,8%,12%). Specimens were manufactured by using conventional stir casting technique. Hybrid metal matrix composites were examined by using Scanning electron microscope. This research explained XRD, EDS and Fractography analysis. This investigation shows the reinforced material has given better results in all aspects than pure Al6061 alloy and homogeneous dispersion of particles inside the matrix. Porosity, delamination, cracks, particle distributions were observed through microstructural analysis. Additionally determined the crystal size of pure Aluminium 6061 alloy and hybrid composites.

This note reconsiders a matrix equality $A_1A_2^{-}A_3A_4^{-}A_5 = A$ composed by six matrices of appropriate sizes, where $A_2^{-}$ and $A_4^{-}$ are generalized inverses of $A_2$ and $A_4$, respectively, and solves a selection of matrix set inclusion problems associated with various mixed reverse order laws for generalized inverses of products of two, three, and four matrices by means of this equality and its variations.

Aiming at the limitations of single-functionality, limited applicability, and complex designs prevalent in current metasurfaces, we propose a terahertz multifunctional and multiband tunable metasurface utilizing a VO2-metal hybrid structure. This metasurface structure comprises a top VO2-metal resonance layer, a middle polyimide dielectric layer, and a gold film reflective layer at the bottom. This metasurface exhibits multifunctionality, operating independently of polarization and incident angle. The varying conductivity states of the VO2 layers, enable the metasurface to achieve various terahertz functionalities, including single-band absorption, broadband THz absorption, and multiband perfect polarization conversion for linear (LP) and circularly polarized (CP) incident waves. Finally, we believe that the functional adaptability of the proposed metasurface expands the repertoire of options available for future terahertz device designs.

Biochar is a carbonaceous material derived from the thermochemical conversion of biomass under oxygen-lean conditions. Its properties render it many applications in agriculture, environmental remediation, and catalysis. Commonly produced by pyrolysis under a nitrogen atmosphere, the biochar composition is influenced mainly by residence time, heating rate, and process temperature. The existing literature primarily assesses biochar properties, such as ultimate, proximate, calorific, porosity, and combustibility, generated under various thermo-conversion conditions. However, there is a notable lack of sufficient investigation and attention given to the practical application of biochar. This article seeks to bridge that gap by enhancing the comprehension of the necessary experimental assessment for specific biochar applications and proposing process optimization methods using Multi-Criteria Decision Analysis (MCDA). By delving into these aspects, the study offers a concise overview of biochar production and presents a comprehensive panorama based on articles and patents. Additionally, the research explores the physicochemical characterization techniques associated with biochar and its various applications in conjunction with MCDA. This comprehensive approach aims to uncover valuable insights that could further advance the practical and sustainable utilization of biochar in a wide range of fields.

Thin people who undergo implant insertion into the chest wall face a high risk of implant exposure to the external environment. Five months after an 8-year-old boy underwent implantable cardioverter-defibrillator (ICD) implantation in a subcutaneous pocket in the left anterolateral chest wall to manage long QT syndrome, ICD replacement became necessary owing to exposure risk from distal and lateral thinning of the ICD pocket. Pocket rupture and exposure would increase the risk of infection; therefore, we performed ICD removal and primary pocket closure. Two weeks later, a new suprafascial pocket was created, acellular dermal matrix (ADM) was attached to the inner wall to prevent ICD protrusion, and a new ICD was inserted. One year postoperatively, the ADM was engrafted, and no complications were observed. A thin subcutaneous layer increases the risk of ICD implantation complications. Inner wall strengthening with ADM can help prevent pocket rupture.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties, Stroke and Epilepsy in particular, clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence (AI) can predict the expected neurological impairment from an AIS (Acute Ischemic Stroke), the possibility of ICH (IntraCranial Hemorrhage) expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at project level impact the emergence of systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analyzing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system, and to that end, mitigate the impacts of climate change. This paper demonstrates a workflow using drone-based photogrammetry, 3D modeling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modeled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Rapid SARS-CoV-2 self-tests have the potential to expand access to COVID-19 testing and improve community-level case detection, particularly in resource-constrained countries such as Kenya. However, prior to their introduction, their acceptability must be assessed. This qualitative study explored key decision-takers’ values towards SARS-CoV-2 self-testing in Kenya. Healthcare workers, representatives of civil society, and potential implementors from Mombasa and Taita-Taveta were selected as decision-takers. Semi-structured interviews and focus group discussions were used to collect data on their values towards self-testing. A thematic analysis approach was applied. Most informants considered that the Kenyan public is equipped to accept and use self-testing safely as an approach to help to reduce workload at public healthcare facilities, and know one’s COVID-19 status in a private manner. The informants emphasized the need to provide counselling to end-users, to support those needing to self-isolate, and to engage different civil society stakeholders in information provision on self-testing. Fear of stigma and of forced isolation were noted as potential deterrents to self-testing uptake for some individuals. In conclusion, there is high acceptability of self-testing in Kenya among decision-takers. However, enhanced education, counselling, and addressing deterrents to testing would be helpful to ensure effective use of SARS-CoV-2 self-testing in Kenya.

The transformation of the states of polarization of electromagnetic waves that interact with polarimetrically linear media can be represented by means of the associated Mueller matrix. A global measure of the ability of a linear medium to modify the states of polarization of incident waves, due to any combination of enpolarizing, depolarizing and retarding properties, is introduced as a distance of the Mueller matrix to the identity matrix. This new descriptor, called the polarizing power, is applicable to any Mueller matrix and can be expressed as a function of the degree of polarimetric purity and the trace of the Mueller matrix. The graphical representation of the feasible values of the polarizing power provides a general view of its main peculiarities and features. The values of the polarizing power for several typical devices are analyzed.

In this paper after introducing a model of binary data matrix (BDM) for physical parameters of an evolving system (of particles), we develop a Hilbert space as an ambient space to derive induced metric tensor on embedded parametric manifold identified by associated joint probabilities of particles observables (parameters). Parameter manifold assumed as space-like hypersurface evolving along time axis, an approach that resembles 3+1 formalism of ADM and numerical relativity. We show the relation of endowed metric with related density matrix. Identification of system density matrix by this metric tensor, leads to the equivalence of quantum Liouville equation and metric compatibility condition while covariant derivative of metric tensor has been calculated respect to Wick rotated time or spatial coordinates. After deriving a formula for expected energy per particles, we prove the equality of this expected energy with local scalar curvature of related manifold. We show the compatibility of BDM model with Hamilton-Jacobi formalism and canonical forms. On the basis of the model, I derive the Ricci flow like dynamics as the governing dynamics and subsequently derive the action of BDM model and Einstein field equations. Given examples clarify the compatibility of the results with well-known principles such as equipartition energy principle and Landauer’s principle. This model provides a background for geometrization of quantum mechanics compatible with curved manifolds and information geometry. Finally, we conclude a “bit density principle” which predicts the Planck equation, De Broglie wave particle relation, , Beckenstein bound and Bremermann limit.

The present paper discusses a study of a class of Charlier matrix polynomials and its generalized analogue. Certain generating matrix functions, recurrence matrix relations, matrix differential equation, summation formulas and many new results have been discussed for these matrix polynomials. Weisner's group theoretic method is used to obtain matrix generating relations for Charlier matrix polynomials and the details of this method were given in this paper. Finally, we will discuss only briefly the procedure followed.

The purposes of this study were to identify clinician’s characteristics associated with higher prescription rates of long-acting injectable (LAI) antipsychotics, as well as the information sources influencing medical decision-making about treatment of schizophrenia. We surveyed 202 psychiatrists during 6 regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, Strasbourg). Data on the characteristics of practice, prescription rates of antipsychotic and information sources about their clinical decisions were collected. Most of psychiatrists used second-generation antipsychotic (SGA), and preferentially an oral formulation, in the treatment of schizophrenia. SGA LAI was prescribed to 30.4% of schizophrenic patients. The duration and the type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with the higher use of LAI antipsychotics and the lower use of oral SGA. Personal experience, government regulatory approval and guidelines for the treatment of schizophrenia were the 3 main contributing factors guiding the clinical decision-making of clinicians about treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotic. The development of specialised programmes with top specialists should lead to better use of LAI in the treatment of schizophrenia.

Data is the most valuable asset of a company or organisation nowadays, for the management of such data there are several techniques used for the storage and analysis of these data, if the organisation chooses wrongly among the alternatives it could face a very costly problem. Based on the above information we will study a very important issue today in the business world, with the global economic growth has also grown the world of technology and therefore organizations or companies also need to move forward with firm and fast pace how business evolves; Its weaknesses, strengths and the need to always be at the forefront of technological tools we will dive into the subject of business intelligence or also known as Business Intelligence (BI), Datawarehouse and Data Mining which is nothing more than a group of applications and tools that allow you to extract, transform and load some data to get to get information and knowledge in order to make a quick decision, accurate and efficient in the organization to achieve the objectives outlined. Due to the fact that there are still companies or organisations that make blind decisions in their customer or strategic processes. In order to solve this problem we rely on data mining as an alternative to minimise risks, which can lead to major and valuable losses within an organisation. In the case of a private company where data mining is applied to study patterns of customer behaviour on their own parameters of location, consumption, etc.. And third party data. The search for information is profitable for the business administration. Data mining is applied as a tool for the development of marketing tactics in competitive production and industrial sectors. This technology attempts to help perceive the attachment of databases. Data mining works on a preferential level looking for patterns, behaviours, orders or groupings that can create a model that allows us to better understand the concept and help in decision making so organisations rely on different systems such as CRM, ERP and many others, but to move from just information to generate business intelligence must be centralised in a single place where you can run data analysis of all types to discover trends that help decision making that place can be mainly a Lake or a Warehouse.

Background: Although transcatheter arterial chemoembolisation (TACE) is the standard of care for intermediate-stage hepatocellular carcinoma (HCC), this is a largely heterogeneous disease that includes a subgroup of patients who do not benefit from TACE. The treatment strategy for this subgroup of patients currently remains an unmet need in clinical practice. Here, we performed a proof-of-concept study that lenvatinib may be more favourable treatment option over TACE as an initial treatment in intermediate-stage HCC patients with large or multinodular tumours exceeding the up-to-seven criteria. Methods: This proof-of-concept study included 642 consecutive patients with HCC initially treated with lenvatinib or conventional TACE (cTACE) between January 2006 and December 2018. Of these patients, 176 who received lenvatinib or cTACE as an initial treatment and met the eligibility criteria [unresectable, beyond the up-to-seven criteria, no prior TACE/systemic therapy, no vascular invasion, no extrahepatic spread and Child-Pugh A liver function] were selected for the study. Propensity score matching was used to adjust for patient demographics. Results: After propensity-score matching, outcome of 30 patients prospectively treated with lenvatinib (14 in clinical trials, 1 in early access program and 15 in real world setting) and 60 patients treated with cTACE as the initial treatment was compared. The change of ALBI score from baseline to the end of treatment were -2.61 to -2.61 for 30 patients in lenvatinib group (p = 0.254) and -2.66 to -2.09 in cTACE group (p < 0.01), respectively. The lenvatinib group showed a significantly higher objective response rate (73.3% vs. 33.3%; p < 0.001) and significantly longer median progression-free survival than the cTACE group (16.0 vs. 3.0 months; p < 0.001). Overall survival was significantly longer in the lenvatinib group than in the cTACE group (37.9 vs. 21.3 months; hazard ratio: 0.48, p < 0.01). Conclusion: In patients with large or multinodular intermediate-stage HCC exceeding the up-to-seven criteria with Child-Pugh A liver function, who usually do not benefit from TACE, lenvatinib provides more favorable outcome than TACE.

In this paper, we define Tetranacci and Tetranacci-Lucas matrix sequences and investigate their properties.

Hydrogels can be considered as mimics of extracellular matrix (ECM).Cytoskeleton through integrins is connected with ECM and cytoskeleton tension depends on ECM stiffness.A number of age-related diseases depend on cellular processes related with cytoskeleton function.Some examples of cancer initiation and progression and heart disease in relationship with ECM stiffness has been analysed.Incorporation of rigid particles in ECM can increase ECM stifness and to promote formation of internal residual stresses. Water migration, changes of water binding energy to biomactomolecules, changes of the state of water from tightly bound water to free and loosely bound water changes stiffness of ECM. Risks related with rigid particles incorporation into ECM have been also iscussed.

Cells actively sense differences in topology, matrix elasticity and protein composition of the extracellular microenvironment and adapt their function and morphology. In this study, we focus on the cross-talk between matrix stiffness and protein coating density that regulate morphology and proliferation dynamics of single myocytes. For this, C2C12 myocytes were monitored on L-DOPA functionalized hydrogels of 22 different elasticity and fibronectin density compositions. Static images were recorded and statistically analyzed to determine morphological differences and to identify the optimized extracellular matrix (ECM). Using that information, selected ECMs were used to study the dynamics before and after cell proliferation by statistical comparison of distinct cell states. We observed a fibronectin density independent increase of the projected cell area until 12 kPa. Additionally, changes in the fibronectin density led to an area optimum at about 2.6 μg/cm, which was confirmed by an independent F-actin analysis, revealing a maximum actin filament to cell area ratio of 7.5 %. The proliferation evaluation showed an opposite correlation between cell spreading duration and speed to the matrix elasticity and protein density, which did not affect the cell cycle duration. In summary, we identified an optimized ECM composition, while independent matrix properties regulate distinct cell characteristics.

Bonchev and Trinajstic defined two distance based entropy measures to measure the molecular branching of molecular graphs in 1977 [Information theory, distance matrix, and molecular branching, J. Chem. Phys., 38 (1977), 4517–4533]. In this paper we use these entropy measures which are based on distance matrices of graphs. The first one is based on distribution of distances in distance matrix and the second one is based on distribution of distances in upper triangular submatrix. We obtain the two entropy measures of paths, stars, complete graphs, cycles and complete bipartite graphs. Finally we obtain the minimal trees with respect to these entropy measures with fixed diameter.

High maternal mortality in the developing countries, particularly in Nigeria, poses serious challenge to achieving the maternal mortality target of the Sustainable Development Goals (SDGs) in the countries. Hence, there is need for multifaceted approach to curtailing the scourge. Women being the victims of maternal mortality, this study finds the effect of their household decision making power in reducing maternal mortality. The study used data from the 2013 Nigeria Health and Demographic Survey (NDHS) and logistic regression model to explore the relationship between women household decision making power and maternal mortality in Nigeria. The finding shows that women who decide and participate in household decision on own health, major purchases and visit to family and relatives were 35% (OR = 0.65, 95% CI: 0.52, 0.83), 27% (OR = 0.73, 95% CI: 0.57, 0.92), and 37% (OR = 0.63, 95% CI: 0.50, 0.80) less likely to experience maternal mortality, respectively, compared to those whose husbands alone decide. Women household decision making power is therefore instrumental to reducing maternal mortality. It is thus important for policy makers, particularly in Nigeria, to pay more attention to social and cultural factors that surround women household decision making ability for speedy reduction in maternal deaths.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Matrix metalloproteinases (MMPs) have been demonstrated to have both detrimental and protective functions in inflammatory diseases. Several MMP inhibitors, with the exception of Periostat®, have failed in Phase III clinical trials. As an alternative strategy, recent efforts have been focussed on the development of more selective inhibitors or targeting other domains than their active sites (e.g., exosites, ectosites) through specific small molecule inhibitors or monoclonal antibodies. Here, we present some examples that aim to better understand the mechanisms of conformational changes/allosteric control of MMPs functions. In addition to MMP inhibitors, we discuss unbiased global approaches such as proteomics and N-terminomics to identify new MMP substrates and achieve a better understanding of the roles of these proteases in diseases.

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.

Introduced in 1967 by Lov\'{a}sz, graph exponentiation has received little attention over the past several decades. Since graph exponentiation produces large graphs and any graph can be constructed utilizing its adjacency matrix, we provide a four step linear construction algorithm that generates the adjacency matrix for the graph exponential.

Biofilm matrix is akin to bacterial abodes, primarily composed of proteins, polysaccharides, extracellular DNA, and lipids, providing essential structural support to bacteria. While some biofilm matrix offers significant benefits, such as engineered biofilm reactors that assist in water pollutant removal, others pose risks to human health, potentially leading to conditions like lung infections. Realizing the potential of advantageous biofilm matrix and mitigating the harmful effects of detrimental ones necessitates a comprehensive and systematic exploration of this intricate domain. To facilitate this examination, our study leveraged a dataset of the 1000 most pivotal papers in the field and conducted an exhaustive bibliographic review. This paper serves as a valuable resource for understanding the current progress in biofilm matrix research and delving into vital avenues for future exploration in this dynamic field.

We report a theoretical derivation of the Cabibbo-Kobayashi-Maskawa (CKM) matrix parameters and the accompanying mixing angles. These results are arrived at from the exceptional Jordan algebra applied to quark states, and from expressing flavor eigenstates (i.e. left-chiral states) as superposition of mass eigenstates (i.e. the right-chiral states) weighted by square-root of mass. Flavor mixing for quarks is mediated by the square-root mass eigenstates, and the mass ratios used have been derived in earlier work from a left-right symmetric extension of the standard model. This permits a construction of the CKM matrix from first principles. There exist only four normed division algebras, they can be listed as follows - the real numbers $\mathbb{R}$, the complex numbers $\mathbb{C}$, the quaternions $\mathbb{H}$ and the octonions $\mathbb{O}$. The first three algebras are fairly well known; however, octonions as algebra are less studied. Recent research has pointed towards the importance of octonions in the study of high energy physics. Clifford algebras and the standard model are being studied closely. The main advantage of this approach is that the spinor representations of the fundamental fermions can be constructed easily here as the left ideals of the algebra. Also the action of various Spin Groups on these representations too can be studied easily. In this work, we build on some recent advances in the field and try to determine the CKM angles from an algebraic framework. We obtain the mixing angle values as $\theta_{12}=11.093^o, \theta_{13}=0.172^o, \theta_{23}=4.054^o$. In comparison, the corresponding experimentally measured values for these angles are $13.04^o \pm 0.05^o, 0.201^o \pm 0.011^o, 2.38^o \pm 0.06^o $. The agreement of theory with experiment is likely to improve when running of quark masses is taken into account.

Metal matrix composite are used in such fields of technology, such as: aerospace, electronics, energy, industry, defense, automotive, aviation, shipbuilding, and more. Composite coatings of ceramic - metals is used primarily to enhance the durability of machine parts. Therefore, new materials are permanently looked for, what has resulted in the past in development of composite materials. The coatings dispersed are consisting of metallic matrix (metals and their alloys) and small non-metallic particles. The deposition of ceramic particles simultaneously with metallic matrix leads often to composite coatings possessing properties much better than those of metallic coating. The nickel and less often, other iron group elements are usually used as a matrix and Al2O3 as tough particles. The welding technology of applying alloy and composite coatings is widely used. The technology of infrasound thermal spraying of metal matrix composite coatings was presented. It is a simple technology and a very useful one in ship machinery regeneration during the cruise craft (e.g. internal combustion engines, torque pumps, separators). The metal matrix composite coatings must undergo finishing due to high surface roughness after application. In the article to used finishing by plastic working and machining of coatings nickel matrix composite was proposed.

Outcomes of repeated decision--making processes may be affected by adversarial actors, without being noticed. Adversaries may try to gain knowledge about a particular decision--making process, identify its decision--makers, and guess which underlying decision support model is used. Then they can simulate the process, and craft different scenarios to affect its decision outcomes. Therefore, designers of decision support systems need to incorporate this in the decision modeling phase. The purpose of this study is to demonstrate this for the repeated decision--making in a patent application process. In this process, two sequential decision outcomes can be affected by adversarial actors: a company's decision to which type of patent office to send a patent request to, and the decision of a specialized patent officer to grant an application, or not. It is motivated that the company's decision--maker is \emph{bounded} rational. A theory for information--theoretic bounded rational decision--making under uncertainty proposed by Ortega et al.\ is adopted to model this type of decision--maker. A framework is provided to simulate a number of scenarios that adversaries may deploy to affect decision outcomes of a repeated patent application decision--making process. The framework is also utilized for statistically testing the presence of the scenarios, and to demonstrate how to discourage adversaries from deploying them.

Gastric inflammation-related disorders are commonly observed digestive system illnesses char-acterized by the activation of proinflammatory cytokines, particularly tumor necrosis factor-α (TNF-α). This results in the induction of cyclooxygenase-2 (COX-2)/prostaglandin E2 (PEG2) and matrix metallopeptidase-9 (MMP-9). These factors contribute to the pathogenesis of gastric in-flammation disorders. We examined the preventive effects of Lonicera japonica Thunb. ethanol ex-tract (Lj-EtOH) on gastric inflammation induced by TNF-α in normal GES-1 cells. The GES-1 cell line was used to establish a model that simulated the overexpression of COX-2/PGE2 and MMP-9 proteins induced by TNF-α, to examine the anti-inflammatory properties of Lj extracts. The re-sults indicated that Lj-EtOH exhibits significant inhibitory effects on COX-2/PEG2 and MMP-9 ac-tivity, attenuates cell migration, and provides protection against TNF-α-induced gastric inflam-mation. The protective effects of Lj-EtOH are associated with the modulation of COX-2/PEG2 and MMP-9 through activation of TNFR–ERK 1/2 signaling pathways as well as the involvement of c-Fos and nuclear factor kappa B (NF-κB) signaling pathways. Based on our findings, Lj-EtOH ex-hibits a preventive effect on human gastric epithelial cells. Consequently, it may represent a novel treatment for the management of gastric inflammation.

Novel tetragonal matrix Ba0.5Gd0.5-xNaxF2.5-x, where x = 0.05, doped by Yb3+, Ho3+, Er3+, was synthesized by molten salt synthesis (MSS) from nitrate flux. XRD data show that tetragonal phase with a = 4.122(1) Å, c = 17.672(1) Å is stable in argon atmosphere up to 960 °C. Luminescence spectra recorded in 500-900 nm and 1050-1700 nm upon 974 nm pumping demonstrated the characteristic luminescence at 1550 nm (4I13/2 → 4I15/2) for Er3+ and 1150 nm (5I6 → 5I8) for Ho3+. The relative thermal sensitivity (Sr) at 296-316 K were 0.3 %*K-1 and 5.5%*К-1 in shortwave infrared (SWIR) and visible range, respectively. Synthesized luminophores can be used as dual-range optical temperature sensors, which simultaneously operating in visible and SWIR ranges.

It is shown that a Mueller quaternion can be formulated as a quaternion with matrix coefficients, and this Mueller quaternion transforms the Stokes quaternion for depolarizing process as well as for nondepolarizing process. Mueller quaternion keeps track of the phase acquired by the Stokes quaternion and, in general, quaternion states of optical media comprises all properties of Jones, Mueller-Jones and Mueller matrices.

In the dynamic and complex environment of industrial control rooms, operators are often inundated with a multitude of tasks and alerts, which can lead to an overwhelming situation known as task overload. This state can precipitate decision fatigue and a heavier reliance on cognitive biases, potentially compromising the decision-making process. To mitigate such risks, the implementation of decision support systems becomes crucial. These systems are designed to assist operators in making swift and well-informed decisions, particularly when they sense their own judgment may be faltering. Our research introduces an AI-based framework that leverages dynamic influence diagrams and reinforcement learning to construct an effective decision support system. The cornerstone of this framework is the creation of a robust, interpretable, and efficient tool that supports control room operators during critical process disturbances. By integrating expert knowledge, the dynamic influence diagram forms a comprehensive model that captures the uncertainties inherent in complex industrial processes. It is adept at anomaly detection and recommending optimal actions. Moreover, this model is enhanced through a strategic partnership with reinforcement learning algorithms, which fine-tune the recommendations to be more context-specific and precise. The ultimate goal of our framework is to provide operators with a live, reliable decision support system that can significantly improve their response during process upsets. This paper outlines the development of our AI framework and its application within a simulated control room environment. Our findings indicate that the use of decision support systems can lead to improved operator performance and reduced cognitive workload. However, it also reveals a trade-off with situation awareness, which tends to diminish as operators may become overly reliant on the system’s guidance. Trust emerges as a critical factor in the adoption and effectiveness of decision support systems. Our research underscores the importance of balancing the benefits of decision support with the need for maintaining operator engagement and comprehension during process operations.

The reliance on cloud computing services and related technologies has increased due to their many benefits, including resource sharing and the need for organizations to maintain their competitive advantage in the digital market. Also, the pace of cloud growth has significantly increased because of COVID-19. Many companies and businesses have shifted their core processes to the cloud for seamless operations during the pandemic. This migration of legacy systems to the cloud creates many opportunities for businesses. However, on the other hand, it may also bring substantial challenges. Hence, a careful decision is needed to align with company goals. In this paper, we demonstrate the need and advantages of cloud computing, followed by highlighting AWS's characteristics as one of the leading cloud service providers. We also discuss the merger of AWS with SDN to improve cloud data security and performance. The outcomes of this study can provide a data migration strategy and guide decision-makers toward a secure and sustainable migration to a cloud computing environment.

Abstract: Congenital cytomegalovirus (cCMV) infection poses significant risks to fetal development, particularly affecting the nervous system. This study reports a fetal autopsy case, examining cCMV infection and focusing on CMV DNA measurements in various fetal organs before formalin fixation, a novel approach for comprehensive CMV DNA evaluations in fetal organs affected by cCMV. A 20-week-old male fetus was diagnosed with cCMV following the detection of CMV DNA in ascites obtained via abdominocentesis in utero. After termination of pregnancy, multiple organs of the fetus, including the cerebrum, thyroid gland, heart, lungs, liver, spleen, kidneys, and adrenal glands, were extracted and examined for CMV DNA loads using real-time polymerase chain reaction. Histopathological examination involved hematoxylin-eosin and CMV-specific immunostaining. A correlation was found between CMV DNA loads and pathology, with higher CMV-infected cell numbers observed in organs positively identified with both staining methods, exhibiting CMV DNA levels of ≥1.0 × 104 copies/mL, compared to those detected solely by CMV-specific immunostaining, where CMV DNA levels ranged from 1.0 x 103 to 1.0 x 104 copies/mL. These results highlight a quantifiable relationship between organ infection extent and CMV DNA concentration, providing insights into cCMV pathogenesis and potentially informing future diagnostic and therapeutic strategies for cCMV infection.

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N=192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human-AI decision-making and ameliorate the understanding the interaction dynamics at the confluence of human factors and information features.

Iyer Markoulakis Helmholtz Hamiltonian mechanics formalisms mathematically modeled physics with vortex rotational fields acting with gradient fields, typically in zero-point microblackhole general fields. Here, Helmholtz metrics have been gaged to Coulombic Hilbert metrics, representing Gilbertian and Amperian natures of electromagnetic fields out of the mechanical fields from Helmholtz Hamiltonian mechanics. This ansatz general gaging helps to properly isolate field effects – mechanical, electric, magnetic components within the analytical processes. Vacuum gravitational fields and the flavor Higgs-Boson matter inertial gravitational fields have been thus quantified extending to stringmetrics constructs matrix showing charge asymmetry gage metrics, having the power to analyze dark energy superluminal phase, dark matter luminal phase, and light matter subluminal phase. Interpreting particle physics gage matrix pointing to Dirac seas electrons, monopoles with positrons, electron-positron annihilation leading to energy production, relativistic energy generating matter, and both monopoles – vacuum and compressed out of vortex Helmholtz decomposition fields have been interpolated. Quantum ASTROPHYSICS gage metrix posits superluminal profile of signals velocity generating electron-positron chain like “curdling” action that is consistent with physics literature reporting nature electron photon observed oscillatory fields configurations. This helps proposing creation of neutrino antineutrino pair orthogonal to electron positron “curdling” planes, that may lead to formation of protonic hydrogen of stars or orthogonally muon. These aspects will explain receding or fast expanding universe with dark matter in terms of flavor metrics versus gage associating metrics flavor. Additional interpretations of virtual gravitational dipoles that have gravitational charge of opposite signs, reported per physics literature at Planck dimensions, that may originate at the center of supermassive blackhole. On the other hands, vacuum monopoles occur probably at infinity with cosmos extent.

We work with reference to a well-known semiclassical model, in which quantum degrees of freedom interact with classical ones. We show that, in the classical limit, it is possible to represent classical results (e.g., classical chaos) by means a pure-state density matrix.

Portfolio weights solely based on risk avoid estimation error from the sample mean, but they are still affected from the misspecification in the sample covariance matrix. To solve this problem, we shrink the covariance matrix towards the Identity, the Variance Identity, the Single-index model, the Common Covariance, the Constant Correlation and the Exponential Weighted Moving Average target matrices. By an extensive Monte Carlo simulation, we offer a comparative study of these target estimators, testing their ability in reproducing the true portfolio weights. We control for the dataset dimensionality and the shrinkage intensity in the Minimum Variance, Inverse Volatility, Equal-risk-contribution and Maximum Diversification portfolios. We find out that the Identity and Variance Identity have very good statistical properties, being well-conditioned also in high-dimensional dataset. In addition, the these two models are the best target towards to shrink: they minimise the misspecification in risk-based portfolio weights, generating estimates very close to the population values. Overall, shrinking the sample covariance matrix helps reducing weights misspecification, especially in the Minimum Variance and the Maximum Diversification portfolios. The Inverse Volatility and the Equal-Risk-Contribution portfolios are less sensitive to covariance misspecification, hence they benefit less from shrinkage.

This article is devoted to the results of in-depth analysis of the system of binary-oppositional structures in DNA n-plet alphabets and their algebraic-matrix representations. These results show that the molecular complementary replication of DNA strands is accompanied by the presence of an algebraic version of the principle "like begets like" in matrix representations of DNA alphabets having internal structures. This algebraic version is based on binary-oppositional structures in the genetic molecular system, which can be represented by binary numbers and corresponding matrices of DNA alphabets. The received results allow thinking that the phenomenon "like begets like" (or a complementary replication in a wide sense) is systemic in the genetic organization and is connected with algebraic features of biological organization. Correspondingly, the biological principle "like begets like" can be additionally modeled by algebraic-matrix methods and approaches. Such algebra-matrix modeling of the genetic coding system gives new ways for studying and understanding the key role of the named principle in genetic and other inherited physiological complexes. On this way, the author discovered general rules of stochastic organization of information binary sequences of genomic DNAs of eukaryotes and prokaryotes. The presented rules are connected with information dichotomies of probabilities and corresponding fractal-like trees of probabilities, which fundamentally differ from constructional dichotomies in biological bodies. The received phenomenological data and rules lead to new biological ideas.

The extracellular matrix (ECM) is a ubiquitous member of the body and is key to the maintenance of tissues and organs integrity. Initially thought to be a by-stander in many cellular processes, the extracellular matrix has been shown to have diverse components that regulate and activate many cellular processes and ultimately influence cell phenotype. Importantly, the ECM composition, architecture, and stiffness/elasticity influence cellular phenotypes. Under normal conditions and during development, the synthesized ECM is constantly undergoing degradation and remodeling processes via the action of matrix proteases that maintain tissue homeostasis. In many pathological conditions including fibrosis and cancer, the ECM synthesis, remodeling, and degradation is dysregulated causing its integrity to be altered. Both physical and chemical cues from the ECM are sensed via receptors including integrins and play key roles in driving cellular proliferation and differentiation and in various disease progression such as cancers. Advances in ‘omics’ technologies have seen an increase in studies focussing on bi-directional cell-matrix interactions and here we highlight emerging knowledge on the role played by the ECM during normal development and in pathological conditions. This review summarizes current ECM-targeted-therapies that can modify tumor ECM to overcome drug resistance and better cancer treatment.

By constructing a static map, the library robot can navigate and access documents autonomously, which greatly improves efficiency; however, for libraries where shelves can be moved, map changes do not allow for direct navigation, and also sensors such as radar and camera are relatively expensive, so we propose a low-cost navigation algorithm based on real-time ranging data and map matrices, and designed a mobile archive access robot. In order to obtain real-time distance data, nine near/long range sensors were mounted on the chassis and a composite digital filtering algorithm was designed based on different moving area characteristics; then the access task matrix and map matrix were designed based on the archive access task and the location characteristics of the archive shelf placement, the robot can rely on the range data to update the map matrix during the moving process, complete its own positioning, and use the task matrix The robot can use the task matrix to complete autonomous navigation and access to multiple files. Experiments show that the filtered positioning accuracy can reach 1cm, while the robot can move to the target shelf autonomously, which is more practical and less costly.

The paper summarizes in a step by step ‘recipe’ format a model of developing new ideas (Ideation) that can be used to form a new venture or develop a new product. The proposed ‘recipes’ are widely used and can be applied in tandem with other prevalently available models or tools. The intention of the paper is also to serve as a resource for entrepreneurs and innovators, and the links and references munificently used in the paper are serving this purpose. The author has used the outline and the tools described here in numerous Ideation classes taught for over a decade in several countries around the world. The paper’s intention is not to conduct a systematic review on the subject of new venture creation or propose new research agenda (for that, see for example Shepherd et al., 2021). The purpose is to provide an effective and efficient set of tools, models and techniques that can assist the entrepreneur in her journey to create and achieve her dreams. One such tool is the matrix of Reasonings-Outcomes introduced here for the first time.

The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher Information Matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs since the FIM only accounts for the linear variation of the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, where model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of Baker's yeast.

This paper focuses on enhancing the object detection and decision-making capabilities of Autonomous Emergency Medical Response Systems (AEMRS). Using advanced sensors, Convolutional Neural Networks (CNN), and reinforcement learning, we propose a model that processes environmental data to identify obstacles and make optimal navigation decisions. The integration of these technologies aims to minimize response time and improve the efficiency of emergency medical services. The perception system utilizes radar, LiDAR, and camera inputs to create a comprehensive understanding of the environment. These data are processed through a series of CNN layers to detect and classify objects such as vehicles, pedestrians, and road signs. On the decision-making front, a Q-learning algorithm is employed to enable the ambulance to learn from its interactions with the environment, continuously improving its route planning and collision avoidance strategies. By combining these advanced AI techniques, the proposed AEMRS can significantly enhance the speed and reliability of emergency responses, ultimately saving more lives. This paper presents the design, implementation, and simulation results of the proposed system, demonstrating its potential to revolutionize emergency medical services.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, particularly susceptible to instability. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By contrasting empirical correlations with those derived from random matrices, we uncover non-random properties and underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data.

Matrix-variate Gaussian type or Wishart type distributions in the real domain are widely used in the literature. When the exponential trace has an arbitrary power and when a factor involving a determinant enters into the model or a matrix-variate gamma type or Wishart type model with exponential trace having an arbitrary power, is extremely difficult to handle. Evaluation of the normalizing constant in such a model is the most important part because when studying the properties of such a model, the method used in the evaluation of the normalizing constant will be the relevant steps in all the computations involved. One such model with a factor involving a trace and the exponential trace having an arbitrary power, in the real domain, is known in the literature as Kotz' model. No explicit evaluation of the normalizing constant in the model involving trace with an exponent and determinant with an exponent entering into the model and at the same time the exponential trace having an arbitrary exponent seems to be available in the literature. The normalizing constant widely used in the literature and interpreted as the normalizing constant in the general model and refers to as a Kotz' model does not seem to be correct. Corresponding model in the complex domain, with the correct normalizing constant, does not seem to be available in the literature. One of the main contributions in this paper is the matrix-variate distributions in the complex domain belonging to Gaussian type, gamma type, type-1 and type-2 beta type when the exponential trace has an arbitrary power. All these models are believed to be new. A second main contribution is the explicit evaluation of the the normalizing constants, in the real and complex domains especially in the complex domain, in a matrix-variate model involving a determinant and a trace as multiplicative factors and at the same time the exponential trace having an arbitrary power. Another main contribution is the introduction of matrix-variate models with exponential trace having an arbitrary exponent, in the categories of type-1 beta, type-2 beta and gamma distributions or in the family of Mathai's pathway models [1], both in the real and complex domains. Another new contribution is the logistic-based extensions of models in the real and complex domains with exponential trace having an arbitrary exponent and connecting to extended zeta functions introduced by this author recently. Some properties of such models are indicated but not derived in detail in order to limit the size of the paper. The techniques and steps used at various stages in this paper will be highly useful for people working in multivariate statistical analysis as well as people applying such models in engineering problems, communication theory, quantum physics and related areas, apart from statistical applications.

Matrix metalloproteinases are involved in extracellular matrix remodeling through the degradation of extracellular matrix components and are involved in the inflammatory response by regulating the activities of TNF-alpha and IL-1beta, which are pro-inflammatory cytokines, in addition to extracellular matrix components. Since the regulation of inflammatory response and changes in the extracellular matrix by MMPs are related to the development of various diseases including lung and cardiovascular diseases, many studies have been conducted on the role of MMPs in pathogenesis. In addition, various studies have demonstrated that MMPs are involved in the pathogenesis of infectious diseases by regulating the expression and activity of MMPs by infection with pathogens. In this review, we discuss the role of MMPs in infectious diseases and the role of MMPs in inflammatory responses and present their potential as therapeutic targets in infectious diseases.

The theory of one-photon ionization and two-photon above-threshold ionization is formulated for applications to heavy atoms in attosecond science using the Dirac-Fock formalism. A direct comparison of the Wigner-Smith-Eisenbud delays for photoionization is made with delays from the Reconstruction of Attosecond Beating By Interference of Two-photon Transitions (RABBIT) method. Photoionization by an attosecond pulse train, consisting of monochromatic fields in the extreme ultraviolet range, is computed with many-body effects at the level of the Relativistic Random Phase Approximation (RRPA). Subsequent absorption and emission processes of infrared laser photons in RABBIT are evaluated using static ionic potentials as well as asymptotic properties of relativistic Coulomb functions. As expected, light elements, such as Argon, show negligible relativistic effects, while heavier elements, such a Krypton and Xenon, exhibit delays that depend on the fine-structure of the ionic target. The relativistic effects are notable close to ionization thresholds and Cooper minima with differences in fine-structure delays predicted to be as large as tens of attoseconds. The separability of relativistic RABBIT delays into a Wigner-Smith-Eisenbud delay and a universal continuum--continuum delay is studied with reasonable separability found for photoelectrons emitted along the laser polarization axis in agreement with prior non-relativistic results.

Formalism proofing general derivation, applying matrix properties operations, showing fundamental relationships with inner product to outer product has been advanced here. This general proof formalism has direct application with physics to quantify quantum density at micro scale level to time commutator at macro scale level. System of operator algebraic equations have been rigorously derived to obtain analytic solutions which are physically acceptable. Extended physics application will include metricizing towards unitarization to achieve gaging Hamiltonian mechanics to electromagnetic gravitational strong theory, towards grand unifying physics atomistic to astrophysics or vice versa via quantum relativistic general physics thereby patching to classical physics fields energy.

The matrix converter converts the input line voltage into a variable voltage with an unrestricted output frequency without using an intermediate circuit, dc link circuit. A pure sine in and pure sine out is the unique feature of the matrix converter. This research paper also analyzes the basic operating principle and the simulation modeling of the direct matrix converter, which is controlled by the Space Vector Pulse Width Modulation technique by using the software which is known as MATLAB/Simulink. The most desirable features in the power frequency changes can be fulfilled by using the matrix converters, and this is the reason for the tremendous interest in the topology. Since the power electronic circuits which is known as the motor drives are used to operate the AC motors at the frequencies other than that of the supply.

An oil and vinegar scheme is a signature scheme based on multivariate quadratic polynomials over finite fields. The system of polynomials contains $n$ variables, divided into two groups: $v$ vinegar variables and $o$ oil variables. The scheme is called balanced (OV) or unbalanced (UOV), depending on whether $v = 0$ or not, respectively. These schemes are very fast and require modest computational resources, which make them ideal for low-cost devices such as smart cards. However, the OV scheme has been already proven to be insecure and the UOV scheme has been proven to be very vulnerable for many parameter choices. In this paper, we propose a new multivariate public key signature whose central map consists of a set of polynomials obtained from the multiplication of block matrices. Our construction is motivated by the design of the Simple Matrix Scheme for Encryption and the UOV scheme. We show that it is secure against the Separation Method, which can be used to attack the UOV scheme, and against the Rank Attack, which is one of the deadliest attacks against multivariate public-key cryptosystems. Some theoretical results on matrices with polynomial entries are also given, to support the construction of the scheme.

This paper attempts to extend the Riemann Zeta function of a complex number to a function of a matrix and/or a tensor $A$, namely $$\zeta (A)=\sum _{n=1}^{\infty} \frac{1}{n^{A}}= \sum _{n=1}^{\infty} \sum_{k=1}^n\lambda_k A^k$$ and inverse $$A=\sum _{n=1}^{\infty} \sum_{k=1}^n \mu(n)\lambda_k \zeta(A^k)$$ where $\mu(n)$ is the M\"obius function, $A$ is a complex matrix or tensor with any order, and $\lambda_k$ is eigenvalue of the matri/tensor $A$. This kind of calculations on the Riemann Zeta function has never been seen in the literature. Some examples are provided.

Epoxy matrix composites reinforced with high-performance fibers such as carbon, Kevlar, and glass, exhibit excellent specific stiffness and strength in many mechanical applications. However, these composites are disappointingly non-recyclable and are usually disposed of in landfill sites, with no realistic prospect for biodegradation in reasonable time. In contrast, moldable composites with carbonized elastomeric matrices developed in the last decades possess attractive mechanical properties in final net-shape products and can also be incinerated or recycled. Many carbon and inorganic fillers have recently been evaluated to adjust the properties of carbonized elastomeric composites. Renewable organic fillers such as human or animal hair offer an attractive fibrous material with substantial potential for reinforcing composites with elastomeric matrices. Samples of unidirectional fiber composites (with hair volume fraction up to 7 %) and quasi-isotropic short fiber composites (with hair volume fraction up to 20 %) of human hair-reinforced nitrile butadiene rubbers (HH-NBR) were produced in the peroxide-cured and carbonized states. The samples were characterized using SEM, Raman spectroscopy and photoacoustic microscopy. Mechanical tests were performed in tension using a miniature universal testing machine. The expected effect of fiber reinforcement on the overall mechanical performance has been demonstrated for both cured and carbonized composites. Considerable enhancement of the elastic modulus (up to 10 times), ultimate tensile strength (up to 3 times) and damage tolerance was achieved. The evidence of satisfactory interfacial bonding between hair and rubber was confirmed by SEM imaging of fracture surfaces. The suitability of photoacoustic microscopy was assessed for 3D reconstruction of the fiber sub-system spatial distribution and non-destructive testing.

(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in woman and is a high cause of mortality of them in reason to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy and inhibit distant metastases so potentially acts on tumor micro-environment; (2) Methods: Ferroptosis/Extracellular matrix remodeling literature text-mining results were integrated in breast cancer transcriptome cohort according their distant relapse free survival (DRFS) under adjuvant therapy (anthracyclin+taxanes) and also in MDA-MB-231 transcriptome functional experiments with ferroptosis activations (GSE173905); (3) Results: Ferroptosis/Extracellular matrix remodeling text-mining identified 910 associated genes in at list 10 articles. Univariate Cox analyses censored on breast cancer (GSE25066) selected 252 individual significant genes and 171 of them found with an adverse expression. Functional enrichment of these 171 adverse genes predicted basal breast cancer signatures. By text-mining some ferroptosis significant adverse selected genes shared citations in domain of ECM remodeling such as: TNF, IL6, SET, CDKN2A, EGFR, HMGB1, KRAS, MET, LCN2, HIF1A, TLR4. A molecular score based on expression the eleven genes was found predictive of worst prognosis breast cancer at univariate level: basal subtype, short DRFS, high grade values 3 and 4, estrogen and progesterone receptors negative and nodal stages 2 and 3. This eleven gene signature was validated as regulated by ferroptosis inductors (erastin and RSL3) in triple negative breast cancer cellular model MDA-MB-231.; (4) Conclusions: Crosstalk between ECM remodeling-Ferroptosis functionalities allowed to define a molecular score which have been characterized as an independent adverse parameter in prognosis of breast cancer patients. Gene signature of this molecular score have been validated to be regulated by erastin/RSL3 ferroptosis activators. This molecular score could be promising to evaluate ECM impact of ferroptosis target therapies in breast cancer.

This paper seeks to optimize the mechanical and durability properties of ultra-high performance concrete (UHPC). To meet this objective, concrete specimens were manufactured by using 1,100 kg/m3 of binder, water/binder ratio 0.20, silica sand and last generation of superplasticizer. Silica fume, metakaolin and two types of nano silica were used for improving the performances of the concrete. Additional mixtures included 13mm long OL steel fibers. Compressive strength, electrical resistivity, mercury intrusion porosimetry tests and differential and thermogravimetric thermal analysis were carried out. The binary combination of nano silica and metakaolin, and the ternary combination of nano silica with metakaolin and silica fume, led to the best performances of the UHPC, both mechanical and durable performances.

Export participation and restricted access to external formal credit are two factors attracting meticulous attention from researchers and policymakers, especially in developing countries. Exploring the interactive relationship of these factors in both the static and dynamic models is the purpose of this study. The study uses data sets from small and medium-sized manufacturing enterprises (SMEs) in Vietnam for the period 2009 - 2015. The instrumental variable approach is implemented to deal with the endogenous variable problem in the model. The results show an effect of credit constraint on the firms’ exporting status, and continuous exports are likely to reduce the limit of credit constraint.

The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DT) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents ("oracles") and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.

Finding a suitable support is a key process in the life history of climbing plants. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Numerous studies on climbing plant behavior have elucidated the mechanistic details of support searching and attachment. Far fewer studies have addressed the ecological significance of support-searching behavior and the factors that affect it. Among these, the diameter of supports influences their suitability for twining plants. When support diameter increases beyond some point climbing plants are unable to maintain tensional forces and therefore lose attachment to the trellis. Here we further investigate this issue by putting pea plants in the situation to choose between supports of different diameters while their movement was recorded by means of a three-dimensional motion analysis system. The results indicate that the way climbing plants move can vary depending on whether they are presented with one or two potential supports. Furthermore, when presented with a choice between a thin and a thick support, the plants showed a distinct preference for the former than the latter. The present findings shed further light on how climbers make decisions as far as support search is concerned, and provide evidence that plants adopt one of several alternative plastic responses in a way that optimally corresponds to environmental scenarios.

Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. We present a two-part data-driven methodology for objectively determining the best EEG bands for a given dataset in this paper. First, a decision tree is used to estimate the optimal frequency band boundaries for reproducing the signal’s power spectrum for a predetermined number of bands. The optimal number of bands is then determined using an Akaike Information Criterion (AIC)-inspired quality score that balances goodness-of-fit with a small band count. Data-driven EEG band discovery may aid in objectively capturing key signal components and uncovering new indices of brain activity.

The problem of scheduling is an area that has attracted a lot of attention from researchers for many years. Its goal is to optimize resources in the system. The assigning task to the lecturer is an example of the timetabling problem, a class of scheduling. This study introduces a mathematical model to assign fixed tasks (the time and required skills to be fixed) to university lecturers. Our model is capable of generating a calendar that maximizes faculty expectations. The formulated problem is in the form of a multi-objective problem that optimal makes decisions require the trade-off presence of trade-offs between two or more conflicting objectives. To solve this, we use the Compromise Programming approach to multi-objective programming. We then proposed the new version of the Genetic Algorithm to solve the introduced model. Finally, the model and algorithm tested with real scheduling data collected at the Computing Fundamental Department, FPT University, Hanoi, Vietnam.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes financial damage to the environment. In the case of air pollution, harmful and foreign substances in the air reach an amount and density above normal values. Air pollution also causes climate change, which affects global warming. Particulate matter is a harmful substance that harms the health of humans and other living things, causes air pollution, increases radiation, and negatively affects cloud formation. Particulate matter is extremely small solid particles and liquid droplets suspended in the air. PM10, which is less than 10 microns in diameter, is defined as small particles that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. Many criteria affect PM10 density and changes in density. The main criteria are vehicle traffic and fuel consumption, industrialization, coal, fuel oil and wood-burning heaters, greenhouse gas impact, and unregulated urbanization. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data between 2019 and 2023 obtained from the Air Monitoring Station in Kocaeli, Türkiye are used. After determining the criteria affecting PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking expert decision-maker opinions. The proposed decision-making model aims to guide obtaining the important causes of PM10 emission in terms of danger and focusing on these causes. Additionally, PM10 data is analyzed in the context of Covid-19 and a statistical analysis is made. One-way ANOVA is used to evaluate whether there is a significant difference in the average monthly data over the years. Games-Howell test, one of the post-hoc tests, is used for differences between groups (years). The study is important in that it provides a focus on the criteria affecting PM10 with an intuitive fuzzy perspective, along with statistical analysis.

This paper proposes a model that combines multi-criteria and multi-faceted risk assessment. Using two data sources, a fuzzy Technique for Order of Preference by Similarity to Ideal Solution FTOPSIS method combined with FMEA assessment. The FMEA method presented in this paper combines the technique of prioritising preferences according to FTOPSIS similarity to the ideal solution and conviction structure to overcome the defects of traditional FMEA indicators. The paper describes the exact process and tool selection, and the results obtained in the study verified accurate data. Finally, a numerical case study was presented on hospital reorganization services and case adaptation to prioritize surgical abandonment in Poland. The problem considered in the publication is the priority of patients’ operations in hospitals. The selection of relevant criteria, their importance and patient preferences are presented. The results obtained from the method provide a viable action plan for the proposed research problem. The proposed method is multi-faceted and can be part of an information system supporting reorganization, restructuring, and modification of the operational process.

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.

Let us assume that defence mechanisms are so strong that the average outcome of a hacking attack is unsuccessful. How to calculate the costs arising from false positives and false negatives in intruder detection? Is it better for the hacker to make fewer but more effective attacks rather than several but less effective attacks? How to calculate the difference between these alternative strategies?

Business school curriculums are designed to improve business skills and a student’s eventual workplace performance. In addition to these business skill sets the emerging business environment demands softer skills associated with ethical decision-making and sustainable business practices. Understanding the key influencers of ethical orientation and attitudes towards the environment is the first critical step for curriculum planning designed to develop both ethical decision-making and environmental sensibilities of students in business schools. Using a bivariate regression analysis (OLS) that compared the established New Ecological Paradigm (NEP) scale and the newly introduced Ethical Orientation Scale (EOS), this study assesses environmental eco-consciousness and ethical orientation over time and across varying socio-demographic variables. The study shows first, that in addition to socio-cultural variables, situational factors influence ethical decision-making. Secondly, it illuminates that ethical orientations as measured by the EOS predicts beliefs about the environment as measured by the NEP scale. It further provides evidence of the ethical underpinnings of the New Ecological Paradigm as well as provides initial validation for the new EOS. These outcomes provide additional levers to assist business educators in the creation of high impact teaching strategies to measure and encourage ethical decision-making and sustainable business practices that protect the environment.

Background Cesarean scar ectopic pregnancies are a rare form of extrauterine pregnancies, yet their incidence has increased along with the rise in the number of cesarean deliveries. As with other ectopic pregnancies, cesarean scar ectopic pregnancies pose a greater risk for maternal hemorrhage and ultimately maternal mortality. Case presentation We present a series of clinical cases of cesarean scar ectopic pregnancy diagnosed by transvaginal ultrasonography. Each patient received an individualized treatment: the rate of success depended on the particular maternal condition in each case. Due to the low frequency of this entity, there are no clear protocols for its treatment and thus there are numerous options for treatment and follow up: expectant management, medical therapy, surgical intervention, uterine artery embolization or a combined approach. Each method has different levels of success and is dependent on the surgeon’s skill and patient presentation. The transvaginal ultrasound is necessary to obtain the fine details of the gestation sac and its relation to the scar and must be followed by a meticulous abdominal scan with a full bladder. Conclusion Herein, we present a rare pathological phenomenon whose frequency is on the rise, and for which transvaginal ultrasound and flow doppler provide high diagnostic accuracy. Early diagnosis of cesarean scar ectopic pregnancies offers treatment options to that may help avoid uterine rupture and bleeding, thus preserving the uterus and future fertility.

The systems ESTE are running in nuclear crisis centers at various levels of emergency preparedness and response in Slovakia, the Czech Republic, Austria, Bulgaria, and Iran (at NPP monitored by International Atomic Energy Agency, IAEA). ESTE is a decision support system, running 24/7, and serves the crisis staff to propose actions to protect inhabitants against radiation in case of a nuclear accident. ESTE is also applicable as decision support system in case of a malicious act with radioactive dispersal device in an urban or industrial environment. Dispersion models implemented in ESTE are Lagrangean particle model (LPM) and Puff trajectory model (PTM). Described are models approaches as implemented in ESTE. PTM is applied in ESTE for the dispersion calculation near the point of release, up to 100 km from the point of nuclear accident. LPM for general atmospheric transport is applied for short-range, meso-scale and large-scale dispersion, up to dispersion on the global scale. Additionally, a specific micro-scale implementation of LPM is applied for urban scale dispersion modelling too. Dispersion models of ESTE are joined with radiological consequences models to calculate a complete spectrum of radiological parameters - effective doses, committed doses and dose rates by various irradiation pathways and by various radionuclides. Finally, radiation protective measures, like sheltering, iodine prophylaxis, or evacuation, evaluated on the base of predicted radiological impacts are proposed. Dispersion and radiological models of the state-of-the-art ESTE systems are described. Results of specific analyses, like number of particles applied, initial spatial distribution of the source, height of the bottom reference layer, are presented and discussed.

A complex square matrix $A$ is said to be Hermitian if $A= A^{\ast}$, the conjugate transpose of $A$. The topic of the present note is concerned with the characterization of Hermitian matrix. In this note, the we show that each of the two triple matrix product equalities $AA^{\ast}A = A^{\ast}AA^{\ast}$ and $A^3 = AA^{\ast}A$ implies that $A$ is Hermitian by means of decompositions and determinants of matrices, which are named the two-sided removal and cancellation laws associated with Hermitian matrix, respectively. We also present several general removal and cancellation laws as the extensions of the preceding two facts about Hermitian matrix.

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics and others. Due to its application in control theory, the matrix equation AXB=C has been extensively studied. However, there is currently limited information on matrix equation AXB=C over the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of the dual quaternion matrix equation AXB=C, and present the expression for the general solution when it is solvable. As an application, we derive the ϕ-Hermitian solutions for the dual quaternion matrix equation AXAϕ=C, where the ϕ-Hermitian extends the concepts of Hermiticity and η-Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper.

In this paper, we introduce ResNetMF, a groundbreaking approach that harnesses the power of residual network matrix factorization to revolutionize recommendation systems. ResNetMF integrates residual networks, renowned for their ability to capture intricate patterns and features, with matrix factorization techniques that excel in modelling user-item interactions. This fusion presents a novel solution that surpasses the limitations of traditional recommendation systems. Through comprehensive experimentation and evaluation of diverse datasets, ResNetMF demonstrates remarkable enhancements in recommendation accuracy and efficiency. By effectively capturing both linear and nonlinear relationships in user-item interactions, ResNetMF provides superior recommendation quality. The outcomes from experiments unequivocally highlight the superiority of ResNetMF over existing state-of-the-art recommendation approaches, thereby validating its innovative nature and underscoring its potential to shape the future of recommendation systems. Through the integration of the deep residual network, ResNetMF approach facilitates the training of neural networks, enabling them to explore the underlying data layers more comprehensively. Extensive experimentation and evaluation across various datasets provide compelling evidence for the superiority of ResNetMF. Moreover, the proposed method utilized natural language processing (NLP) techniques for targeted information dissemination in recommendation systems, emphasizing the importance of personalized and relevant recommendations for user satisfaction and engagement.

There is need to address the challenges of organ shortage, through development of tissues and organs with alternatives to those of the allograft-kind. This illustrates the quest behind novel biofabrication strategies such as 3D bio-printing, which is necessary to create artificial multi-cellular tissues/organs. Several findings have been reported in this review. First, the role of ECM components in tissue regenerative medicine is presented. Different ECM components such as collagen, gelatin, elastin, fibronectin, laminins and glycosaminoglycans are concisely examined for their tissue regenerative medicine applications. Next, current state of research on extrusion-based 3D bio-printing techniques and their limitations are reviewed. For example, we show that cell viability is still a challenge with extrusion, while the use of natural polymers such as collagen in improving composites’ mechanical properties is limited. Lastly, we examine unresolved research questions necessary to advance the present state of research in the field.

This article shows how to establish expansion formulas for calculating the mixed operations $(A^{\dag})^{\#}$, $(A^{\#})^{\dag}$, $((A^{\dag})^{\#})^{\dag}$, $((A^{\#})^{\dag})^{\#}$, $\ldots$ of generalized inverses, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of a matrix and $(\cdot)^{\#}$ denotes the group inverse of a square matrix. As applications of the formulas obtained, the author constructs and classifies some groups of matrix equalities involving the above mixed operations, and derives necessary and sufficient conditions for them to hold.

In order to meet the diversified rehabilitation needs of people, it is necessary to design a product for active rehabilitation of patients. Existing rehabilitation chairs use intelligent massage, which can cause problems such as large massage area, inability to local massage, large chair size, and inability to meet the continuous use of the damaged parts. In this paper, modular design method and multi-layer evaluation method are used to solve the problems related to rehabilitation chairs. The authors use the questionnaire survey method and the functional technology matrix method to determine the functional requirements of the rehabilitation chair, and then use the multilevel evaluation methods, including the AHP method, entropy weight method main and grey correlation analysis, to optimize the functional solutions of the rehabilitation chair, and finally obtain a chair for the rehabilitation of patients with upper and lower limb disorders. Problems such as generalisation of rehabilitation scope and non-durable use of components were solved, and the purpose of active exercise was achieved. This study verifies that the use of multilevel decision evaluation method can effectively improve the efficiency of programme decision-making, and provides theoretical and practical basis for the design of similar products.

Uterine fibroid (UF) is a tumour in some parts of the uterus, which introduces health challenges or death due to failed surgery among women globally. This study was designed to ascertain the involvement of micro-nutrients, inflammation, and antioxidant enzymes in the UF development to gain further insights and provide a strategy for managing the disease. One hundred ninety reproductive-aged women were recruited and classified equally into case and control subjects. The supernatant obtained from excised tissues from the fibroid and the normal samples from the adjacent myometrium were assessed for the selected biochemical parameters with standard methods. The levels of vitamin A and sodium between 26-35 years; vitamins D, E, zinc, and selenium between 46-55 years; and vitamin E at 56 years and above significantly decreased (p&lt;0.05). Interleukin-2 (IL-2) level significantly increased (P &lt; 0.05) among the case between 36-45 years. An increase in the activity of glutathione-s-transferase and the reduction in glutathione peroxidase activity and vitamin A level in the uterus between 26-45 years were the most pronounced significant findings (p&lt;0.05) recorded. Prolonged vitamin A deficiency coupled with excess sodium salts facilitating inflammation induced by IL-2 are critical factors for UF development.

Computation of Discrete Fourier Transform (DFT) is a challenging task. Especially, on computational machines/embedded systems where resources are limited. The importance of Fourier Transform (FT) cannot be denied in the field of signal processing. This paper proposes a technique that can compute Discrete Fourier Transforms for a matrice or vector with the help of matrix multiplication. Moreover, this paper discusses the trivial methods used for computation of DFT along with methods based on matrix multiplication used to compute discrete Fourier Transform in addition to the shortcomings. The proposed method can help in the calculation of a Discrete Fourier Transformation matrix by truncation of values from the proposed generic method which can help in computing DFT of varying lengths of vectors. On legacy computing machines and programming environments, having support for matrix multiplication, the proposed methodology can be implemented.

Tenascin C (TNC) is an element of the extracellular matrix (ECM) of various tissues, including the skin, and is involved in modulating ECM integrity and cell physiology. Although skin aging is apparently associated with changes in the ECM, little is known about the role of TNC in skin aging. Here we found that Tnc mRNA level was significantly reduced in the skin tissues of aged mice compared with young mice, consistent with reduced TNC protein expression in aged human skin. TNC-large (TNC-L; 330-kDa) and -small (TNC-S; 240-kDa) polypeptides were observed in conditional media from primary dermal fibroblasts. Both recombinant TNC polypeptides, corresponding to TNC-L and TNC-S, increased the expression of type I collagen and reduced the expression of matrix metalloproteinase-1 in fibroblasts. Treatment of fibroblasts with a recombinant TNC polypeptide, corresponding to TNC-L, induced phosphorylation of SMAD2 and SMAD3. TNC increased the level of TGF-β1 mRNA and upregulated the expression of type I collagen by activating the TGF-β signaling pathway. In addition, TNC also promoted the expression of type I collagen in fibroblasts embedded in a three-dimensional collagen matrix. Our findings suggest that TNC contributes to the integrity of ECM in young skin and to prevention of skin aging.

The use of biodegradable materials has a growing field of application due to environmental concerns, however, scientific research on incremental forming using biomaterials is scarce. Thus, this study focuses on the single point incremental forming (SPIF) process applied to a composite sheet that combines a biodegradable thermoplastic matrix (Solanyl) reinforced with natural fibres (flax). The influence of the process parameters on the final geometry is determined, evaluating the effect of the following factors: step depth, wall angle and temperature reached during the process. Additionally, a heated aqueous medium is incorporated which facilitates the formability of the composite sheets. This method is especially useful for materials that have poor formability at room temperature. The benefits of using controlled heat include the reduction of formation forces applied to the plate, improved accuracy due to the reduction of elastic recovery, and the manipulation of the samples remarkably close to the glass transition temperatures. Through this experimental study with the variables analysed, a maximum shaping depth of 310 mm is obtained. These results confirm that the single point shaping used with bioplastic materials is possible and has positive outcomes for incremental forming.

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

Heparanase (HPSE) has been defined as a multitasking protein that exhibits a peculiar enzymatic activity towards HS chains but which simultaneously performs other non-enzymatic functions. Through its enzymatic activity, HPSE catalyzes the cutting of the side chains of heparan sulfate (HS) proteoglycans, thus contributing to the remodeling of the extracellular matrix and of the basal membranes. Furthermore, thanks to this activity, HPSE also promotes the release and diffusion of various HS-linked molecules as growth factors, cytokines and enzymes. In addition to being an enzyme HPSE has been shown to possess the ability to trigger different signaling pathways by interacting with transmembrane proteins. In normal tissue and in physiological conditions, HPSE exhibits only low levels of expression restricted only to keratinocytes, trophoblast, platelets and mast cells and leukocytes. On the contrary, in pathological conditions, such as in tumor progression and metastasis, inflammation and fibrosis, it is overexpressed. With this brief review, we intend to provide an update on current knowledge about the different role of HPSE protein exerted by its enzymatic and not-enzymatic activity.

Heparanase (HPSE) has been defined as a multitasking protein that exhibits a peculiar enzymatic activity towards HS chains but which simultaneously performs other non-enzymatic functions. Through its enzymatic activity, HPSE catalyzes the cutting of the side chains of heparan sulfate (HS) proteoglycans, thus contributing to the remodeling of the extracellular matrix and of the basal membranes. Furthermore, thanks to this activity, HPSE also promotes the release and diffusion of various HS-linked molecules as growth factors, cytokines and enzymes. In addition to being an enzyme HPSE has been shown to possess the ability to trigger different signaling pathways by interacting with transmembrane proteins. In normal tissue and in physiological conditions, HPSE exhibits only low levels of expression restricted only to keratinocytes, trophoblast, platelets and mast cells and leukocytes. On the contrary, in pathological conditions, such as in tumor progression and metastasis, inflammation and fibrosis, it is overexpressed. With this brief review, we intend to provide an update on current knowledge about the different role of HPSE protein exerted by its enzymatic and not-enzymatic activity.

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces their evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSS in the future.

According to the cognitive radio (CR) paradigm, the efficient use of the radio spectrum is achieved by performing four main functions: spectral perception, spectral mobility, decision-making, and cooperation. In this work, a multivariable algorithm that considers geographic mobility (GM) is presented. The decision-making process includes a feedback mechanism for the dynamic selection of reservation channels in a wireless network with cognitive capabilities. Geographic mobility plays a crucial role in how radio takes advantage of its environment; however, it has been scarcely explored in the literature. In the present work, geographic mobility tests were carried out involving the interaction of CR and primary users (PU), where a measurement of the latency time of the proposed algorithm called: Algorithm for Decision-Making with Geographic Mobility (ATDeMoGeo) was evaluated and compared with other algorithms such as: Dijkstra's Algorithm, Analytic Hierarchical Process (AHP), Fuzzy Analytic Hierarchical Process (FAHP) and Decision-Making Algorithm with Modified Dijkstra (ATDDiM). It was found that the proposed algorithm is robust and that it considerably reduces the latency time to accurately determine the best communication channels, thanks to the decision-making function implemented to establish which channels the CRs can occupy. For the selection of the best reserve channels, a series of criteria were considered, such as: signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), estimated channel time of availability (ETA) using the random waypoint mobility model (RWPM). These criteria are evaluated for two types of service, i.e., real time (RT) and best effort (BE). For the evaluation and validation of the ATDeMoGeo algorithm, a network with cognitive characteristics in NS-3 was simulated. The simulation scenarios consisted of a base station (BS) with mobile CRs and PUs. Based on the previous criteria, i.e., BW, SINR, AP and ETA, the BS assesses the spectrum occupancy information obtained by the CRs. The results show that the proposed algorithm can be used without problem for the proposed scenarios with higher speed and accuracy for the selection of better communication channels.

This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consists of five integrated system components: 1) data preparation system, 2) data exploration system, 3) data visualization system, 4) data analysis system, and 5) knowledge extraction system. The functional requirements of the integrated systems are elucidated. In addition, JAVA- and spreadsheet-based systems are developed to realize the proposed integrated system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry electrical discharge machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In synopsis, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.