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Optimal pressure regulation to reduce water losses in water distribution systems (WDSs) becomes an important concern due to the increasing water demand and the threat of drought in many areas of the world. The leakage amount in a WDS depends heavily on its operating pressure and thus can be minimized by implementing optimal pressure strategies through pressure reducing valves (PRVs). To achieve this, a model-based optimization is necessary, where an accurate model of the PRVs is required. The PRV models having been used until now for pressure regulations are two-mode models which cannot circumstantiate many situations occurring in WDSs. In this paper, we extend the existing model by a three-mode one for PRVs which is able to describe the required circumstances of pressure regulations in WDSs. The non-smoothness of this model is smoothed by an approximation approach, thus allowing the formulation and solution of a continuous nonlinear optimization problem for optimal pressure regulation. Two benchmark WDSs are used to verify our approach and it can be shown from the results that our PRV model outperforms the existing models in terms of the quality and accuracy of the optimal solutions.

The conventional wisdom is that American universities transfer technologies more rapidly and more effectively than their European counterparts. While this appears to be true, it is important to note that there are substantial cultural, legal, and regulatory differences between the two regions, and even within Europe. We believe that European universities can enhance their effectiveness in technology transfer if this function is given more visibility and prestige, enhanced public support, and formal procedures are implemented to facilitate benchmarking. In this paper, we examine some of the differences in university technology transfer in Europe and the U.S., and discuss these new approaches, which have recently been fully supported by the European Commission.

In this paper, we will consider an arithmetical approach to the Eratosthenes sieve and to the problem of the twin primes.

This paper investigates the dynamical behaviors for a four-dimensional energy resource system with time delay, especially in terms of equilibria analyses and Hopf bifurcation analysis. By setting the time delay as a bifurcation parameter, it is shown that Hopf bifurcation would occur when the time delay exceeds a sequence of critical values. Furthermore, the stability and direction of the Hopf bifurcation are determined via the normal form theory and the center manifold reduction theorem. Numerical examples are given in the end of the paper to verify the theoretical results.

This paper presents the most extensive archaeometallurgical study of iron-smithing debris excavated in East Africa. It presents an integrated methodology, including morphological, chemical, petrographic, and contextual analysis of iron slag excavated from secondary ironworking contexts. Iron slag from three Swahili sites was analysed—Unguja Ukuu located on the southwestern coast of Zanzibar, and Tumbe and Chwaka situated in the north-east of Pemba Island. The results suggest that Unguja Ukuu smithing is associated with oxidising hearth atmospheres and high amounts of CaO, while slag from Tumbe and Chwaka indicates reducing hearth atmospheres and high silica:alumina ratios, potentially pointing to the use of a flux. Distinct technical traditions can be seen at Unguja Ukuu when compared to Tumbe and Chwaka, suggesting a regional rather than chronological pattern. Temporal continuity is evident throughout the occupation of Unguja Ukuu and between sites of different periods in north-western Pemba. The spatial distribution of iron slag at these sites suggests that smithing was taking place across the extent of Unguja Ukuu, while slag scatters were more localised and disassociated from domestic contexts at Tumbe and Chwaka. The wealth of information on technological and organisational aspects of smithing obtained during this study indicates that an integrated methodology can yield valuable data for a variety of smithing sites, irrespective of excavation strategies.

This paper reports a search for Higgs boson pair (hh) production in association with a vector boson ($$W\; {\text {o}r}\; Z$$ W o r Z ) using 139 fb$$^{-1}$$ - 1 of proton–proton collision data at $$\sqrt{s}=13\,\text {TeV}$$ s = 13 TeV recorded with the ATLAS detector at the Large Hadron Collider. The search is performed in final states in which the vector boson decays leptonically ($$W\rightarrow \ell \nu ,\, Z\rightarrow \ell \ell ,\nu \nu $$ W → ℓ ν , Z → ℓ ℓ , ν ν with $$\ell =e, \mu $$ ℓ = e , μ ) and the Higgs bosons each decay into a pair of b-quarks. It targets Vhh signals from both non-resonant hh production, present in the Standard Model (SM), and resonant hh production, as predicted in some SM extensions. A 95% confidence-level upper limit of 183 (87) times the SM cross-section is observed (expected) for non-resonant Vhh production when assuming the kinematics are as expected in the SM. Constraints are also placed on Higgs boson coupling modifiers. For the resonant search, upper limits on the production cross-sections are derived for two specific models: one is the production of a vector boson along with a neutral heavy scalar resonance H, in the mass range 260–1000 GeV, that decays into hh, and the other is the production of a heavier neutral pseudoscalar resonance A that decays into a Z boson and H boson, where the A boson mass is 360–800 GeV and the H boson mass is 260–400 GeV. Constraints are also derived in the parameter space of two-Higgs-doublet models.

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results: We identified two cohort-specific top performing algorithms for GSD and CAD, and a single best for ICD and SL. GSD best algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (PFF). Algorithms’ performances were lower for short WBs; slower gait speeds (Conclusions: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findingsshowed that the choice of algorithm for estimation of GSD and CAD DMOs should be cohort-specific (e.g., slow walkers and with gait impairments). Short WB length and slow walking speed worsened algorithms’ performances. Trial registration: ISRCTN – 12246987.

This paper presents a statistical combination of searches targeting final states with two top quarks and invisible particles, characterised by the presence of zero, one or two leptons, at least one jet originating from a b-quark and missing transverse momentum. The analyses are searches for phenomena beyond the Standard Model consistent with the direct production of dark matter in pp collisions at the LHC, using 139 fb$$^{-\text {1}}$$ - 1 of data collected with the ATLAS detector at a centre-of-mass energy of 13 TeV. The results are interpreted in terms of simplified dark matter models with a spin-0 scalar or pseudoscalar mediator particle. In addition, the results are interpreted in terms of upper limits on the Higgs boson invisible branching ratio, where the Higgs boson is produced according to the Standard Model in association with a pair of top quarks. For scalar (pseudoscalar) dark matter models, with all couplings set to unity, the statistical combination extends the mass range excluded by the best of the individual channels by 50 (25) GeV, excluding mediator masses up to 370 GeV. In addition, the statistical combination improves the expected coupling exclusion reach by 14% (24%), assuming a scalar (pseudoscalar) mediator mass of 10 GeV. An upper limit on the Higgs boson invisible branching ratio of 0.38 ($$\text {0.30}^{+\text {0.13}}_{-\text {0.09}}$$ 0.30 - 0.09 + 0.13 ) is observed (expected) at 95% confidence level.

This paper proposes a new model of the multinational firm, the Global Multi-business Firm (GMBF), with strategic foci on resource assembly and organizational animation. The demands of technology, globalization of customers, and competitive evolution do not permit haphazard local pursuit of either markets or capabilities, but require the GMBF to offer a broad global, multi-business perspective. The headquarters has a newly delicate, complex yet limited role of providing a global approach to strategic management. At the same time, the headquarters cannot hold tightly to its units, lest they become trapped in “managing for the last struggle”, rather than the future. We summarize this challenge as “command without control”.

This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.

Hereditary hemochromatosis is an iron overload disorder most commonly caused by a defect in the HFE gene. While the genetic defect is highly prevalent, the majority of individuals do not develop clinically significant iron overload, suggesting the importance of genetic modifiers. Murine hfe knockout models have demonstrated that strain background has a strong effect on the severity of iron loading. We noted that hepatic iron loading in hfe−/− mice occurs primarily over the first postnatal weeks (loading phase) followed by a timeframe of relatively static iron concentrations (plateau phase). We thus evaluated the effects of background strain and of age on hepatic gene expression in Hfe knockout mice (hfe−/−). Hepatic gene expression profiles were examined using cDNA microarrays in 4- and 8-week-old hfe−/− and wild-type mice on two different genetic backgrounds, C57BL/6J (C57) and AKR/J (AKR). Genes differentially regulated in all hfe−/− mice groups, compared with wild-type mice, including those involved in cell survival, stress and damage responses and lipid metabolism. AKR strain-specific changes in lipid metabolism genes and C57 strain-specific changes in cell adhesion and extracellular matrix protein genes were detected in hfe−/− mice. Mouse strain and age are each significantly associated with hepatic gene expression profiles in hfe−/− mice. These affects may underlie or reflect differences in iron loading in these mice.

In mathematics education, translation of resources from one language to another occurs in a wide range of situations. This paper explores how conceptual and technical apparatus from contemporary translation studies may be of use in guiding and analysing such translation. Key concepts—including those of source, target and intermediate text, of paradigms of equivalence, purpose, uncertainty and localisation, and of semantic, syntactic and epistemological equivalence—are introduced and illustrated, primarily with reference to translation of a series of school mathematics workbooks. Significant types of tool to assist translation—translation protocols and machine translation—are examined. A more detailed case study illustrates techniques (and associated tools) for analysing translation shifts and terminological translatability, applying them to examine translation of documentation of a framework for researching teachers’ interaction with resources—the Documentational Approach to Didactics. Specific insights that emerge are how translation is shaped by attention not just to fidelity to the source text but to the audience for, and function of, the target text; and how challenging it can be to formulate terms translatable across languages in such a way as to consistently anchor meaning in existing wordforms.

In the present paper, we consider a fractional discrete Schrödinger equation with Kirchhoff term. Through the fountain theorem and the dual fountain theorem, we obtain two different conclusions about infinitely many homoclinic solutions to this equation.

This paper studies the convergence of three temporal semi-discretizations for a backward semilinear stochastic evolution equation. For general terminal value and general coefficient with Lipschitz continuity, the convergence of the first two temporal semi-discretizations is established, and an explicit convergence rate is derived for the third temporal semi-discretization. The third temporal semi-discretization is applied to a general stochastic linear quadratic control problem, and the convergence of a temporally semi-discrete approximation to the optimal control is established.

In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimization method by using tools from stochastic calculus and the analysis of partial differential equations. Based on a time-continuous formulation of the particle dynamics as a system of stochastic differential equations, we establish convergence to a global minimizer of a possibly nonconvex and nonsmooth objective function in two steps. First, we prove consensus formation of an associated mean-field dynamics by analyzing the time-evolution of the variance of the particle distribution. We then show that this consensus is close to a global minimizer by employing the asymptotic Laplace principle and a tractability condition on the energy landscape of the objective function. These results allow for the usage of memory mechanisms, and hold for a rich class of objectives provided certain conditions of well-preparation of the hyperparameters and the initial datum. In a second step, at least for the case without memory effects, we provide a quantitative result about the mean-field approximation of particle swarm optimization, which specifies the convergence of the interacting particle system to the associated mean-field limit. Combining these two results allows for global convergence guarantees of the numerical particle swarm optimization method with provable polynomial complexity. To demonstrate the applicability of the method we propose an efficient and parallelizable implementation, which is tested in particular on a competitive and well-understood high-dimensional benchmark problem in machine learning., Comment: 28 pages, 4 figures

Recently, there has been a growing interest in understanding how decentralized clinical trial (DCT) solutions can mitigate existing challenges in clinical development, particularly participant burden and access, and the collection, management, and quality of clinical data. This paper examines DCT deployments, emphasizing how they are integrated and how they may impact clinical trial oversight, management, and execution. We propose a conceptual framework that employs systems thinking to evaluate the impact on key stakeholders through a reiterative assessment of pain points. We conclude that decentralized solutions should be customized to meet patient needs and preferences and the unique requirements of each clinical trial. We discuss how DCT elements introduce new demands and pressures within the existing system and reflect on enablers that can overcome DCT implementation challenges. As stakeholders look for ways to make clinical research more relevant and accessible to a larger and more diverse patient population, further robust and granular research is needed to quantify the impact of DCTs empirically.

Background Our knowledge about the ecological role of bacterial antimicrobial peptides (bacteriocins) in the human gut is limited, particularly in relation to their role in the diversification of the gut microbiota during early life. The aim of this paper was therefore to address associations between bacteriocins and bacterial diversity in the human gut microbiota. To investigate this, we did an extensive screening of 2564 healthy human gut metagenomes for the presence of predicted bacteriocin-encoding genes, comparing bacteriocin gene presence to strain diversity and age. Results We found that the abundance of bacteriocin genes was significantly higher in infant-like metagenomes ( Conclusions Our findings indicate that bacteriocins may play a role in the strain diversification during the infant gut microbiota establishment.

Background Many health systems embrace the normative principle that the supply of health services ought to be based on the need for healthcare. However, a theoretically grounded framework to operationalize needs-based supply of healthcare remains elusive. The aim of this paper is to critically assess current methodologies that quantify needs-based supply of physicians and identify potential gaps in approaches for physician planning. To this end, we propose a set of criteria for consideration when estimating needs-based supply. Methods We conducted searches in three electronic bibliographic databases until March 2020 supplemented by targeted manual searches on national and international websites to identify studies in high-resource settings that quantify needs-based supply of physicians. Studies that exclusively focused on forecasting methods of physician supply, on inpatient care or on healthcare professionals other than physicians were excluded. Additionally, records that were not available in English or German were excluded to avoid translation errors. The results were synthesized using a framework of study characteristics in addition to the proposed criteria for estimating needs-based physician supply. Results 18 quantitative studies estimating population need for physicians were assessed against our criteria. No study met all criteria. Only six studies sought to examine the conceptual dependency between need, utilization and supply. Apart from extrapolations, simulation models were applied most frequently to estimate needs-based supply. 12 studies referred to the translation of need for services with respect to a physician’s productivity, while the rest adapted existing population-provider-ratios. Prospective models for estimating future care needs were largely based on demographic predictions rather than estimated trends in morbidity and new forms of care delivery. Conclusions The methodological review shows distinct heterogeneity in the conceptual frameworks, validity of data basis and modeling approaches of current studies in high-resource settings on needs-based supply of physicians. To support future estimates of needs-based supply, this review provides a workable framework for policymakers in charge of health workforce capacity planning.

This paper studies a Balakrishnan–Taylor viscoelastic wave equation with strong time-dependent delay. Under suitable assumptions on the coefficients of the delay term, we establish a generalized stability result, which improve some earlier results in the literature.

In this paper we develop a Nash-Moser iteration type reducibility approach to prove the (inverse) localization for some $d$-dimensional discrete almost-periodic operators with power-law long-range hopping. We also provide a quantitative lower bound on the regularity of the hopping. As an application, some results of \cite{Sar82, Pos83, Cra83, BLS83} are generalized to the power-law hopping case., Comment: to appear in CMP

The error of single step-ahead output prediction is the information traditionally used to correct the state estimate while exploiting the new measurement of the system output. However, its dynamics and statistical properties can be further studied and exploited in other ways. It is known that in the case of suboptimal state estimation, this output prediction error forms a correlated sequence, hence it can be effectively predicted in real time. Such a suboptimal scenario is typical in applications where the process noise model is not known or it is uncertain. Therefore, the paper deals with the problems of analytical and empirical modeling, identification, and prediction of the output error of the suboptimal state estimator for the sake of improving the output prediction accuracy and ultimately the performance of the model predictive control. The improvements are validated on an empirical model of type 1 diabetes within an in-silico experiment focused on glycemia prediction and implementation of the MPC-based artificial pancreas.

A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient.

The city of Matera, as several other ancient historical town, is recovering a large part of its ancient parts developing a policy of urban regeneration. In the early 90s during the restoration works of the main square, Piazza Vittorio Veneto, it was re-discovered a huge underground space used as water reservoir named Palombaro Lungo. It was built linking ancient pre-existing smaller caves and completed at the end of the 19th century making it impermeable with opus signinum. The hypogeum is dug in a Plio-Pleistocene rock called Gravina Calcarenite. It is a weak rock with good mechanical properties, low permeability and easy to be dug. The internal geometry of the hypogeum and the rock thicknesses above the caves was reconstructed by the integration of topographic and GPR surveys. In this paper is presented a geo-mechanical model of Palombaro Lungo underground reservoir and an evaluation of its stability that is part of the studies for the evaluation for the recovery of the ancient historical town. The stability analysis were developed using the numerical code UDEC for several transect of Palombaro Lungo assuming the calcarenitic blocks as deformable, trying to address the evolution of the stress-strain conditions. The case study can be considered as an example of general interest for the study of rupestrian underground reservoir of a longed form, where one planimetric dimension strictly prevails over the other. Numerical simulations showed a stress-strain state compatible with the fractures detected in situ and confirmed the absence of instability problems in the groundwater reservoir.

The paper studies the convergence and growth behavior of 11 Central and Eastern European members states of the European Union between 2000–2019, using a modified development accounting approach based on the neoclassical growth model. The main goal of the exercise is to decompose the still existing development gap relative to Western Europe (Austria) into three components: convergence, productivity, and long-run factors. The latter may include general institutional features such as population growth, or capital market imperfections measured by the capital wedge. The capital wedge is identified by leveraging the neoclassical growth model’s ability to explain the observed behavior of the capital-output ratio. The main conclusions are that for most countries, lower productivity and capital distortions are both important to understand underdevelopment. Economic policy, therefore, should primarily target productivity growth and a free and efficient capital market.

Target detection and sonar contact classification with active sonar systems are not trivial especially when operating in coastal and shallow water environments with multipath propagation, high reverberation and clutter. It is even more difficult when the sonar receiver is hosted on unmanned platforms with limited maneuvering capabilities unable to perform long-lasting tracking procedures. In such environments with high clutter density, real-time classification algorithms to discriminate target contacts from clutter contacts become crucial. This paper describes a method for active sonar clutter classification that exploits the large number of undesired contacts to learn the “fingerprint” of the environmental clutter and thus to identify the target contacts as anomalies. The paper introduces the method to obtain the features of detected sonar contacts from the beamformed signal of a triplet array of hydrophones that can be towed by an autonomous underwater vehicle. The paper also shows the performance of the proposed unsupervised classification algorithm with real data collected at sea and compares it to what has been achieved by using a convolutional neural network. The results show the capability of the proposed anomaly detection algorithm to properly deal with a variety of clutter contacts without requiring labeling of training data.

The theft of medicines is a significant component of the illicit trade in pharmaceutical products. Besides small-scale thefts committed for personal usage, organised criminal networks are increasingly targeting high-priced medical products, either to reintroduce them into the legal supply chain or sell them on the black market. This crime has considerable implications that extend beyond the value of the stolen goods, including harmful impacts on citizens’ health, legitimate companies, and national health systems. However, knowledge on organised theft of medicines remains limited. This paper employs a crime script analysis approach, based on interviews with relevant stakeholders and case studies retrieved across European countries, to examine the most common modi operandi in the organised theft of medicines and medical devices. Potential policy implications are also discussed.

Objectives We present a 3-D approach to joint space width (JSW) measurement across the ankle from weight-bearing CT (WBCT) to demonstrate inter-operator reproducibility, test-retest repeatability, and how differences in angulation affect ankle JSW distribution. Methods One side from repeat WBCT imaging of both feet and ankles was analysed from 23 individuals as part of their routine clinical care pathway. Joint space mapping was performed at four facets across the talus: talonavicular, talar dome and medial gutter (dome-medial), lateral gutter, and posterior subtalar. Inter-operator reproducibility was calculated for two users, while test-retest repeatability was calculated by comparing the two visits, both presented as Bland-Altman statistics. Statistical parametric mapping determined any significant relationships between talocrural joint space angulation and 3-D JSW distribution. Results The average ± standard deviation interval between imaging was 74.0 ± 29.6 days. Surface averaged bias ± limits of agreement were similar for reproducibility and repeatability, the latter being: talonavicular 0.01 ± 0.26 mm, dome-medial 0.00 ± 0.28 mm, lateral gutter − 0.02 ± 0.40 mm, and posterior subtalar 0.02 ± 0.34 mm. Results are presented as 3-D distribution maps, with optimum test–retest repeatability reaching a smallest detectable difference of ± 0.15 mm. Conclusions Joint space mapping is a robust approach to 3-D quantification of JSW measurement, inter-operator reproducibility, and test–retest repeatability at the ankle, with sensitivity reaching a best value of ± 0.15 mm. Standardised imaging protocols and optimised metal artefact reduction will be needed to further understand the clinical value of these 3-D measures derived from WBCT. Clinical relevance statement Weight-bearing computed tomography is an increasingly important tool in the clinical assessment of orthopaedic ankle disorders. This paper establishes the performance of measuring 3-D joint space width using this technology, which is an important surrogate marker for severity of osteoarthritis. Key Points • Joint space width values and error metrics from across the ankle measured from weight-bearing CT can be presented as 3-D maps that show topographic variation. • The best sensitivity for detecting meaningful change in 3-D joint space width at the ankle was ± 0.15 mm, a value less than the isotropic imaging voxel dimensions. • Standardised imaging protocols and optimised metal artefact reduction will be needed to understand the clinical value of 3-D measures from weight-bearing CT.

Background The modelling of the sheet metal forming operations requires accurate and precise data of the material plastic behaviour along non-proportional strain paths. However, the buckling phenomenon severely limits the compressive strain range that could be used to deform thin metal sheets. Objective The main aim of this paper was to propose an effective device, that enables to determine of accurate stress-strain characteristics of thin metal sheet specimens subjected to axial deformation without buckling and with a special emphasis on friction correction. Methods In this paper, an anti-buckling fixture was proposed to assess the deformation characteristics of X10CrMoVNb9-1 (P91) power engineering steel, and DP500 and DP980 dual-phase steels, under compression loading. The fixture enables monitoring of the friction between the specimen and supporting blocks during the test, and thus the precise stress response of the material could be determined. Results The effectiveness of the fixture was evaluated under tension–compression cyclic loading and during the compression tests in which high-strength thin metal sheets were successfully deformed up to 10% without specimen buckling. Furthermore, the successful determination of a friction force variation between supporting blocks and the specimen during tests enabled to determine an actual force acting on the specimen. Conclusions The proposed testing fixture was successfully assessed during the compression and cyclic tension–compression of high-strength thin metal sheets as no buckling was observed. Its advantage lies in adapting to change its length with specimen elongation or shrinkage during a test. The friction force generated from a movement of both parts of the device could be effectively monitored by the special strain gauge system during testing and thus its impact on the stress-strain characteristics could be successfully eliminated.

This paper aims to develop a humanistic model of corporate social responsibility in e-commerce, relying on high technology in an artificial intelligence economy. The research is based on the experience of the top 30 publicly traded e-commerce companies, the 16 most responsible companies in the retail industry in the USA, and the leading global and Russian e-commerce business structures in 2020–2021. Based on econometric modeling, it is substantiated that the humanization (qualitative criterion) of jobs provides an increase in revenues of e-commerce businesses to a greater extent than an increase in the number (quantitative criterion) of jobs. The high technology of the artificial intelligence economy (AI economy) makes it possible to maximize the contribution of responsible HRM of the e-commerce business in increasing its revenues. For this purpose, a humanistic model of corporate social responsibility in e-commerce based on high technology in the AI economy has been developed. The theoretical significance lies in proving the need to humanize jobs in e-commerce and revealing the essence of this process. The practical significance lies in the fact that the developed humanistic model will increase the profitability and, consequently, the resilience of businesses to future economic crises that arise against the backdrop of the COVID-19 pandemic.

Developing ecotourism services is a suitable solution to help developing countries improve the status of sustainable development indicators and protect their environment. The primary purpose of this paper is to find out the effects of green governance variables and carbon dioxide emissions on ecotourism for 40 developing economies from 2010 to 2021. The results confirmed a uni-directional causal relationship between the green governance indicator and the inflation rate of the ecotourism indicator. In addition, with a 1% improvement in the green governance index of developing countries, the ecotourism of these countries will increase by 0.43%. In comparison, with a 1% increase in the globalization index of these countries, ecotourism will increase by 0.32%. Moreover, ecotourism in developing countries is more sensitive to macroeconomic variables changes than in developed economies. Geopolitical risk is an influential factor in the developing process of ecotourism. The practical policies recommended by this research are developing the green financing market, establishing virtual tourism, granting green loans to small and medium enterprises, and government incentives to motivate active businesses.

This paper studies the impact of internal and external factors on the profitability and financial strength of insurance groups across the European Union, focusing on the effect of the European sovereign debt crisis and the Solvency II Directive, which has not been addressed by the literature. The findings reveal that the efficiency of accounts receivable management and the state of the economy have a significant positive impact, whereas the underwriting risk and size have a significant negative impact on profitability and financial strength. Furthermore, long-term interest rates exert a significant positive influence, whereas the purchase of reinsurance, size of the domestic market, market structure, and inflation exert a significant negative influence on profitability. The European sovereign debt crisis has a significant negative impact on profitability and a positive but insignificant impact on financial strength. Solvency II has no statistically significant impact either on profitability or on financial strength. This paper contributes to the literature through the identification of the impact of the European sovereign debt crisis and Solvency II, which has not been depicted so far, as well as the impact of a series of internal and external factors on profitability and, for the first time, on financial strength.

Dental resin composites (DRCs) are popular materials for repairing caries or dental defect, requiring excellent properties to cope with the complex oral environment. Filler/resin interface interaction has a significant impact on the physicochemical/biological properties and service life of DRCs. Various chemical and physical modification methods on filler/resin interface have been introduced and studied, and the physical micromechanical interlocking caused by the modification of fillers morphology and structure is a promising method. This paper firstly introduces the composition and development of DRCs, then reviews the chemical and physical modification methods of the filler/resin interface, mainly discusses the interface micromechanical interlocking structures and their enhancement mechanism for DRCs, finally give a summary on the existing problems and development potential.

Experiential marketing (XM)—a form of marketing that uses branded experiences as a tool to influence consumers—has become one of the dominant tools within the marketing world. Despite this, it is poorly understood by both sides of the academic and practitioner dyad. What appears to be missing is a holistic conceptualisation of XM that explains not only its antecedent conditions and resulting outcomes but also the factors that may mediate and moderate those outcomes. This paper presents a Delphi study that seeks to provide such a conceptualisation. Across three phases, a panel of XM experts from academia and industry practice was recruited to establish a robust and holistic conceptualisation of XM as well as its success factors, consequences, psychological mechanisms, and boundary conditions. The current findings identify XM as a marketing strategy that uses the subjective, elaborative nature of experience to positively influence the salience of a brand in the mind of participants. The findings suggest that the ‘depth of immersion’ in the experience on three levels (experiential, brand, and conceptual) is an important underlying mechanism while the impact is moderated by the ability and motivation to become immersed.

Intimate Partner Violence Against Women (IPVAW) has been defined as a violation of human rights and a serious public health problem rooted in social inequality between women and men. Nevertheless, a significant amount of scientific literature on the topic of IPVAW continues to exclude the gender perspective in both research design and the interpretation of results, despite its conceptual and explanatory relevance. This paper considers certain gender-blind practices in the research on IPVAW. Based on this analysis, we propose explicitly stating the theoretical assumptions inherent in the definition of the construct and clarifying the intended interpretation for measures, so that they can be incorporated into the analysis of validity, and we argue in favor of the need for interdisciplinary studies based on multicausal explanatory models, which incorporate the category gender as a transversal explanatory factor of this type of violence, as well as multi-method approaches, with the aim of overcoming issues of construct underrepresentation and construct-irrelevant variance.

Exoskeletons are becoming the reference technology for assistance and augmentation of human motor functions in a wide range of application domains. Unfortunately, the exponential growth of this sector has not been accompanied by a rigorous risk assessment (RA) process, which is necessary to identify the major aspects concerning the safety and impact of this new technology on humans. This situation may seriously hamper the market uptake of new products. This paper presents the results of a survey that was circulated to understand how hazards are considered by exoskeleton users, from research and industry perspectives. Our analysis aimed to identify the perceived occurrence and the impact of a sample of generic hazards, as well as to collect suggestions and general opinions from the respondents that can serve as a reference for more targeted RA. Our results identified a list of relevant hazards for exoskeletons. Among them, misalignments and unintended device motion were perceived as key aspects for exoskeletons’ safety. This survey aims to represent a first attempt in recording overall feedback from the community and contribute to future RAs and the identification of better mitigation strategies in the field.

The coronavirus pandemic identified a clinical need for pediatric tele-neuropsychology (TeleNP) assessment. However, due to limited research, clinicians have had little information to develop, adapt, or select reliable pediatric assessments for TeleNP. This preliminary systematic review aimed to examine the feasibility of pediatric TeleNP assessment alongside (1) patient/family acceptability, (2) reliability, and (3) the quality of the literature. Between May 2021 and November 2022, manual searches of PubMed, PsycINFO, and Google Scholar were conducted using terms related to “pediatric” and “tele-neuropsychology.” After extracting relevant papers with samples aged 0–22 years, predefined exclusion criteria were applied. Quality assessment was completed using the AXIS appraisal tool (91% rater-agreement). Twenty-one studies were included in the review, with reported qualitative and quantitative data on the feasibility, reliability, and acceptability extracted. Across included studies, TeleNP was completed via telephone/video conference with participants either at home, in a local setting accompanied by an assistant, or in a different room but in the same building as the assessor. Pediatric TeleNP was generally reported to be feasible (e.g., minimal behavioral differences) and acceptable (e.g., positive feedback). Nineteen studies conducted some statistical analyses to assess reliability. Most observed no significant difference between in-person and TeleNP for most cognitive domains (i.e., IQ), with a minority finding variable reliability for some tests (e.g., attention, speech, visuo-spatial). Limited reporting of sex-assigned birth, racialized identity, and ethnicity reduced the quality and generalizability of the literature. To aid clinical interpretations, studies should assess underexamined cognitive domains (e.g., processing speed) with larger, more inclusive samples.

This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: Interdisciplinary Mathematics and Science Learning (https://imslearning.org/). The project developed learning sequences for 12 inquiry-based investigations involving 35 teachers and cohorts of between 25 and 70 students across years 1 through 6. The research used a design-based methodology to develop, implement, and refine a 4-stage pedagogical cycle based on students’ problem posing, data generation, organisation, interpretation, and reasoning about data. Across the stages of the IMS cycle, students generated increasingly sophisticated representations of data and made decisions about whether these supported their explanations, claims about, and solutions to scientific problems. The teacher’s role in supporting students’ statistical reasoning was analysed across two learning sequences: Ecology in year 1 and Paper Helicopters in year 2 involving the same cohort of students. An explicit focus on data modelling and meta-representational practices enabled the year 1 students to form statistical ideas, such as distribution, sampling, and aggregation, and to construct a range of data representations. In year 2, students engaged in tasks that focused on ordering and aggregating data, measures of central tendency, inferential reasoning, and, in some cases, informal ideas of variability. The study explores how a representation-focused interdisciplinary pedagogy can support the development of data modelling and statistical thinking from an early age.

The object of the presented paper is to demonstrate the possibility of equalizing the transmission properties of networks described by graphs with an equal number of nodes of the same degree but differing in diameter and average path length. A node of a graph is understood to be a specialised device performing commutation and transmission functions, while an edge is understood to be a transmission link. It was assumed that this alignment can be achieved by controlling the transmission resources assigned to specific edges of a graph through which the network nodes communicate with each other. The concept of transmission resource, counted in contractual units, is understood as, for example, the number of time slots or the bandwidth of the links used to transmit information. The authors show that with the proposed method, the equalization of the mentioned network properties is achieved with a minimal increase in the global resources used for transmission.

This paper addresses an integrated lot-sizing and scheduling problem in the industry of consumer goods for personal care, a very competitive market in which the good customer service level and the cost management show up in the competition for the clients. In this research, a complex operational environment composed of unrelated parallel machines with limited production capacity and sequence-dependent setup times and costs is studied. There is also a limited finished-goods storage capacity, a characteristic not found in the literature. Backordering is allowed but it is extremely undesirable. The problem is described through a mixed integer linear programming formulation. Since the problem is NP-hard, relax-and-fix heuristics with hybrid partitioning strategies are investigated. Computational experiments with randomly generated and also with real-world instances are presented. The results show the efficacy and efficiency of the proposed approaches. Compared to current solutions used by the company, the best proposed strategies yield results with substantially lower costs, primarily from the reduction in inventory levels and better allocation of production batches on the machines.

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated offshore wind and photovoltaic (PV) power system for improving power generation efficiency while simultaneously damping oscillations. A variable-speed offshore wind turbine (OWT) with electrical torque control is used in the integrated offshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and effectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the inflow wind conditions to maximize total power generation. Significant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of offshore wind and PV power can be achieved.

To address current challenges of difficult standardisation of dynamic determination of roadbed bearing capacity and excessive deviations in deflection control, this paper proposes a improved measurement method for roadbed load-bearing capacity based on the dynamic modulus control using a portable falling weight deflectometer (PFWD). The detection range of PFWD is determined by theoretical analysis. Based on the range, a finite element numerical model was developed, the spatial distribution and attenuation of the dynamic stress of the roadbed soil in the depth and horizontal directions are determined. The variation of the vertical dynamic stress with depth is not characterized by a gradual decrease, as is commonly believed, but rather by an increase followed by a decrease. The underlying reasons for the difference between static and dynamic indicators are explored. Using the improved method, a prediction model with dynamic bending as the detection index and static rebound modulus as the control index was developed. The proposed model can improve precision accuracy as well as detection efficiency, verify the effectiveness of the improved method, which is important for accurate dynamic measurement and evaluation of highway roadbed bearing capacity.

Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help identify suitable molecules for disease treatment. In this paper, we propose Taiga, a transformer-based architecture for the generation of molecules with desired properties. Using a two-stage approach, we first treat the problem as a language modeling task of predicting the next token, using SMILES strings. Then, we use reinforcement learning to optimize molecular properties such as QED. This approach allows our model to learn the underlying rules of chemistry and more easily optimize for molecules with desired properties. Our evaluation of Taiga, which was performed with multiple datasets and tasks, shows that Taiga is comparable to, or even outperforms, state-of-the-art baselines for molecule optimization, with improvements in the QED ranging from 2 to over 20 percent. The improvement was demonstrated both on datasets containing lead molecules and random molecules. We also show that with its two stages, Taiga is capable of generating molecules with higher biological property scores than the same model without reinforcement learning.

This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104.

Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 seconds. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source model.

There are several methods that have been discovered to improve the performance of Deep Learning (DL). Many of these methods reached the best performance of their models by tuning several parameters such as Transfer Learning, Data augmentation, Dropout, and Batch Normalization, while other selects the best optimizer and the best architecture for their model. This paper is mainly concerned with the optimization algorithms in DL. It proposes a modified version of Root Mean Squared Propagation (RMSProp) algorithm, called NRMSProp, to improve the speed of convergence, and to find the minimum of the loss function quicker than the original RMSProp optimizer. Moreover, NRMSProp takes the original algorithm, RMSProp, a step further by using the advantages of Nesterov Accelerated Gradient (NAG). It also takes in consideration the direction of the gradient at the next step, with respect to the history of the previous gradients, and adapts the value of the learning rate. As a result, this modification helps NRMSProp to convergence quicker than the original RMSProp, without any increase in the complexity of the RMSProp. In this work, many experiments had been conducted to evaluate the performance of NRMSProp with performing several tests with deep Convolution Neural Networks (CNNs) using different datasets on RMSProp, Adam, and NRMSProp optimizers. The experimental results showed that NRMSProp has achieved effective performance, and accuracy up to 0.97 in most cases, in comparison to RMSProp and Adam optimizers, without any increase in the complexity of the algorithm and with fine amount of memory and time.

The concept of collaborative R&D has been increasing interest among scholars and policy-makers, making collaboration a pivotal determinant to innovate nowadays. The availability of reliable data is a necessary condition to obtain valuable results. Specifically, in a collaborative environment, we must avoid mistaken identities among organizations. In many datasets, indeed, the same organization can appear in a non-univocal way. Thus its information is shared among multiple entities. In this work, we propose a novel methodology to disambiguate organization names. In particular, we combine supervised and unsupervised techniques to design a “hybrid” methodology that is neither fully automated nor completely manual, and easy to adapt to many different datasets. Thus, the flexibility and potential scalability of the methodology make this paper a worthwhile contribution to different research fields. We provide an empirical application of the methodology to the dataset of participants in projects funded by the first three European Framework Programmes. This choice is because we can test the quality of our procedure by comparing the refined dataset it returns to a well-recognized benchmark (i.e., the EUPRO database) in terms of the connection structure of the collaborative networks. Our results show the advantages of our approach based on the quality of the obtained dataset, and the efficiency of the designed methodology, leaving space for the integration of affiliation hierarchies in the future.

A topological index is a numerical value that describes the entire structure of a chemical compound’s molecular graph and aids in the understanding of its physical properties, chemical reactivities, and boiling activities. In chemical graph theory, these indices are instrumental in quantifying various chemical properties of chemical compounds such as SiO4. The selection of SiO4 in this work is an important aspect because of its structural flexibility, easy availability, and extraordinary nature to find its numerical values. In this paper, we compute systematically the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index, and the F temperature index of a molecular graph SiO4 embedded in a chain. All of the discussed interlinked networks in this manuscript are well motivated by the molecular structure of SiO4. 2020 AMS Mathematics Subject Classification: 05C07, 05C09, 05C31, 05C76, 05C99