The menisci are crescent-shaped, fibrocartilaginous structures that play a crucial role in the load transition and distribution of the contact forces along the tibiofemoral articulation. Meniscal extrusion (ME) is a radiological finding, especially in magnetic resonance imaging (MRI) scans, for which there has been growing interest in recent years. ME, in the coronary plane, is defined as the maximum distance of the most distal end of the meniscus from the border of the tibial plateau, where the tibial eminences are the most prominent, without taking into account the osteophytes. Although there is still controversy in the literature in respect of the optimal cutoff value, a threshold of 3 mm is considered significant. ME has no specific clinical finding or sign and it is encountered in many knee pathologies. It is associated with either rapidly progressive knee osteoarthritis or early onset of knee osteoarthritis and increased morbidity. In this review, we delineate the clinical significance of ME in various knee pathologies, as well as when, why and how it should be managed. To the best of our knowledge, this is the first study to elaborate on these topics.
The current research paper proposes a novel information processing Deep Learning framework for unsupervised fault detection applications, where during the training process only samples from the normal class are available. Recently, unsupervised fault detection methods based on the Auto-Encoders have been successful and are used extensively. They are supported by the assumption that abnormal unknown samples that don’t belong to the learned manifold of the training dataset of normal points produce higher reconstruction cost than the normal samples. The presented scheme is based on an ensemble of different types of Auto-Encoders; each of them is trained independently for the one-class fault detection task. The final agreement of the normality of the testing sample is made from a soft voting process, where the confidence of each Auto-Encoder about its decision is considered. The significance of each individual Auto-Encoder in the final decision is extracted from a statistical analysis of the independent training process of each one. Simulation results with three widely used fault detection datasets show the effectiveness of the proposed model.
The advent of distributed renewable energy sources (DRESs) has led to a series of technical issues affecting the secure and reliable operation of active distribution networks. Among them, under-/overvoltages, current overload, and voltage unbalance can be considered as the most important problems limiting the increase of DRES penetration. In this paper, a new control architecture is proposed to overcome these issues using the reactive power of DRESs and the active/reactive power of distributed battery energy storage systems (DBESSs). Its distinct feature is the implementation in the symmetrical components domain, allowing the efficient decoupling between under-/overvoltage and voltage unbalance mitigation techniques. Furthermore, a central controller is introduced to improve the system performance in terms of reduced network losses and effective DBESS utilization by coordinating the response of DRESs and DBESSs. The validity of the proposed control strategy is evaluated by performing time-domain and time-series simulations on the IEEE European LV test feeder.
In this paper, a multi-signal identification technique is developed to estimate oscillatory modes contained in power system responses. The proposed technique utilizes least-squares optimization to analyze simultaneously several system measurements and determine close-to-real-time the modal parameters of the examined power system. The Monte Carlo method is applied to synthetic signals to thoroughly quantify the impact of several parameters on the accuracy of the proposed technique and comparisons with conventional identification techniques are performed. The accuracy of the proposed method is also validated using simulated responses obtained from a combined transmission–distribution network. Finally, the scalability of the proposed technique is demonstrated using power hardware-in-the-loop experiments.
A series of 3-hydroxy, 3-ethoxy and 3-benzyloxy-2-methylquinazolin-4[3H]-one derivatives were synthesized in one-pot four-component or in a two-step protocol under microwave irradiation. More specifically, anthranilic acids in combination with triethyl-orthoacetate, the corresponding hydroxylamine hydrochloride derivative (NH2OR·HCl, R = H, Et, Bn) and pyridine in AcOH were irradiated in a microwave reactor at 130–160 °C for 0.5–2 h to give the desired 2-methyl quinazolin-4[3H]-one derivatives in moderate to excellent yields. Isolation of the benzoxazinone intermediates via the reaction of the corresponding anthranilic acids with Ac2O and sequential reaction with NH2OR·HCl in AcOH in the presence of Na2CO3 provided an alternative second formation pathway. The scope and the limitations of the procedures are discussed for anthranilic acids in relation to each hydroxylamine derivative, evaluating data obtained from all attempted reactions and HRMS/MS analyses. The two protocols investigate the formation of molecules that consist of a quinazolinone “privileged” structure fragment accompanied by hydroxamic acid as an additional pharmacophore incorporated within the same molecule, in a rigid, compact form. Both pharmacophores may be highly functionalized and serve as useful intermediates in organic, medicinal and material chemistry, giving the profile of a “synthon” to such molecules. The green character of the reaction is qualified due to: a) the use of acetic acid as the green solvent to replace toxic pyridine; b) the quantity of pyridine, which at the one-pot protocol is in molar equivalency to the amine hydrochloride; c) the short reaction time; d) the relatively high yields.
Every modern and organized country is making progress when its citizens pay their taxes regardless of whether they will be audited or not by the tax authorities. This research attempts to understand the impact of various tax morale related factors on the taxpaying behavior of the exhausted, due to the 10 years long financial crisis, Greek citizens. A new conceptual model is proposed and empirically tested using data from 1.014 Greek citizens from 50 regions of Greece. The results suggest that tax conscience exerts the highest positive and direct influence on tax morale. Furthermore, the crucial role of trust in reciprocity, trust in democracy and trust in public services, which directly affect tax morale, is also confirmed. In addition, it is found that women have higher tax morale than men, while the importance of perceived (level of) tax justice is highlighted, since it indirectly influences tax morale through its strong direct effect on a large number of trust in the quality of governance related factors.
Local open markets, trading fruits and vegetables, are widespread in Mediterranean countries, such as Tunisia and Jordan, producing large amounts of organic waste. Applying an anaerobic digestion process on this substrate makes it crucial to evaluate the waste mixture composition and seasonal variability properly. In this study, after defining an average composition of the fruit and vegetable waste (FVW) mixture produced in Sfax (Tunisia) and Amman (Jordan) in three seasonal intervals (autumn–winter, spring, and summer), the biochemical methane potential (BMP) of an artificially created FVW mixture was individually determined by three European institutions located in Spain, Italy, and Greece. The average BMP from all three seasons and laboratories was 286 ± 52 NmL CH4 g CODadded⁻¹, close to the theoretical maximum yield of 350 NmL CH4 g CODadded⁻¹, indicating a high biodegradability of the waste. Τhe biochemical methane yields of the spring mixtures were not statistically different across the three labs. The most significant differences among the BMP results were obtained for the autumn/winter and the summer mixtures used in Spain, likely due to the variety or ripeness of fruits and vegetables collected in the local markets. In the other two labs in Italy and Greece, no statistical difference was observed for the BMPs of the three season mixtures within the same lab. Therefore, not a critical difference in the biodegradability of such FVW is expected along the different seasons, indicating that the operation of a full-scale digester over a whole year would constantly benefit from the supplementation of a high biochemical methane potential feedstock. Graphical Abstract
A phase-fitting, first and second derivatives phase-fitting method is produced. The new algorithm is singularly P-Stable and belongs to the economic algorithms. The new method is symbolized as PF2DPFN2SPS. It can be used to any problem with periodical and/or oscillating solutions. We chosen to be applied to a well known problem of Quantum Chemistry. The new scheme is an economic one because 5 function evaluations per step are used in order an algebraic order (AOR) of 12 to be achieved.
An approach to phase-fitting is devised, which includes methods for fitting the phase-lag and for fitting its first, second, and third derivatives. Singularly P-Stable, the novel system belongs to the family of economic algorithms [(i.e. algorithms which use the minimum number of function evaluations (FEvs) per integration step in order to achieve the maximum algebraic order (AOR)]. The new approach is denoted by the notation PF3DPFN2SPS. The proposed method can be used to solve a wide range of problems involving periodic and/or oscillating solutions. We applied the novel approach to a well-known problem in quantum chemistry, the Schrödinger-type coupled differential equations. To reach an AOR of 12, the new technique makes use of 5 FEvs per step and for this belongs to the economic algorithms.
Purpose Living alone, low social support and severe psychopathology have been found to independently constitute risk factors for involuntary psychiatric hospitalization; their interaction has not been adequately investigated. This study examines the role of social and clinical parameters in rendering living alone a risk factor for involuntary admission Method Data from 1003 consecutive admissions of psychiatric patients in all public psychiatric clinics of Thessaloniki, Greece, collected over one-year period were analyzed via Latent Profile Analysis. Patient profiles were created on the basis of social and clinical factors; these were subsequently associated with living arrangement status as independent variable and admission status as distal outcome Results Two of the four ensued profiles were associated with involuntary admission. Both profiles were characterized by severe clinical indicators, but only one by poor social indicators; this was the only profile associated with living alone, suggesting that individuals living alone tend to present with severe clinical and social indicators and to be involuntarily admitted Conclusion Living alone seems to operate as sufficient but not necessary condition for involuntary admission. Severe deterioration of mental state appears to be a necessary condition; moreover, when combined with low social support and social networks, originating from living alone, the odds for involuntary admission increase. Living alone seems to constitute the most adverse living condition with regard to risk for involuntary hospitalization, due to its associated combination of adverse clinical and social parameters. Supporting individuals living alone through interventions in their living arrangement and social network might prevent involuntary admission.
Landfill leachate, due to its recalcitrant nature and toxicity, poses a serious environmental threat, which requires the implementation of effective treatment processes. In this work, a full-scale treatment system consisting of two Sequencing Batch Reactors (SBRs) was used for the processing of landfill leachate of intermediate to mature age (BOD/COD ratio of 0.16). Biosystem operation resulted in BOD5, COD and TKN removal efficiencies of 81%, 39% and 76%, respectively, whereas the low residual NO3--N concentration in the effluent (4.01 ± 0.10 mg/L) was indicative of the efficient denitrification process. Assessment of hydrolytic potential of activated sludge revealed high endocellular and extracellular lipase activities, which reached values up to 206 and 141 U/g protein respectively, possibly as the consequence of plastics degradation during maturation process. Implementation of Illumina sequencing indicated the predominance of Alphaproteobacteria, accompanied by members of Bacteroidetes, Betaproteobacteria and Chloroflexi. Paracoccus was the predominant genus identified, followed by representatives of the genera Bellilinea, Flavobacterium, Thauera and Truepera. Nitrosomonas was the major ammonia-oxidizing bacterium (AOB), while nitrite oxidation was mainly achieved by the uncultured nitrite-oxidizing bacterium (NOB) Candidatus Nitrotoga.
The aim of the study is to investigate mitochondrial diversity in Neolithic Greece and its relation to hunter-gatherers and farmers who populated the Danubian Neolithic expansion axis. We sequenced 42 mitochondrial palaeogenomes from Greece and analysed them together with European set of 328 mtDNA sequences dating from the Early to the Final Neolithic and 319 modern sequences. To test for population continuity through time in Greece, we use an original structured population continuity test that simulates DNA from different periods by explicitly considering the spatial and temporal dynamics of populations. We explore specific scenarios of the mode and tempo of the European Neolithic expansion along the Danubian axis applying spatially explicit simulations coupled with Approximate Bayesian Computation. We observe a striking genetic homogeneity for the maternal line throughout the Neolithic in Greece whereas population continuity is rejected between the Neolithic and present-day Greeks. Along the Danubian expansion axis, our best-fitting scenario supports a substantial decrease in mobility and an increasing local hunter-gatherer contribution to the gene-pool of farmers following the initial rapid Neolithic expansion. Οur original simulation approach models key demographic parameters rather than inferring them from fragmentary data leading to a better understanding of this important process in European prehistory.
ESR1 mutations have been recently associated with resistance to endocrine therapy in metastatic breast cancer and their detection has led to the development and current evaluation of novel, highly promising therapeutic strategies. In ovarian cancer there have been just a few reports on the presence of ESR1 mutations. The aim of our study was to evaluate the frequency and the clinical relevance of ESR1 mutations in high-grade serous ovarian cancer (HGSOC). Drop-off droplet digital PCR (ddPCR) was first used to screen for ESR1 mutations in 60 primary tumors (FFPEs) from HGSOC patients and in 80 plasma cell-free DNA (cfDNA) samples from advanced and metastatic ovarian cancer patients. We further used our recently developed ESR1-NAPA assay to identify individual ESR1 mutations in drop-off ddPCR-positive samples. We report for the first time the presence of ESR1 mutations in 15% of FFPEs and in 13.8% of plasma cfDNA samples from advanced and metastatic ovarian cancer patients. To define the clinical significance of this finding, our results should be further validated in a large and well-defined cohort of ovarian cancer patients.
Over the previous two decades, a tremendous impact has been created on each stage of the production value chain, through digitization of the traditional industrial processes and procedures. Since warehouses are at the heart of distributed supply chain networks, it is critical to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet the demanding standards. Towards this end, we describe the design of a health and safety (H&S) inspection robot capable of autonomously detecting hazard events without human intervention in warehouses. It makes use of computer vision (CV) techniques, edge computing (EC) and artificial intelligence (AI) to identify critical occurrences that have a detrimental impact on H&S. while counting available resources using inventory tracking methodologies. Furthermore, action-based modules are activated in response to the recognised event, informing warehouse workers about it and notifying other systems, operators and stakeholders, where appropriate, as foreseen by the protocol. Lastly, the conceptual architecture of the proposed autonomous robot is presented, which classifies the needed vision-based and action-based modules.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.