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This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs. [ABSTRACT FROM AUTHOR]

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source. [ABSTRACT FROM AUTHOR]

Lead-free (Na0.5Bi0.5)1-xSrxTiO3 ceramics (x = 0-0.04) were synthesized by a conventional mixed-oxide technique. The microstructure study showed a dense structure, in good agreement with that of above 96% relative density determined by Archimedes method. X-ray diffraction measurements showed that the obtained specimens possess a pure perovskite structure with rhombohedral symmetry. The dielectric and ferroelectric behavior of these ceramics were examined. The temperature dependence of the dielectric spectra revealed a frequency dependence near the depolarization temperature Td, which is characteristic of a relaxor mechanism. This suggests that the ceramics lacked long-range ferroelectric order about temperature Td, which was evidenced by observation of deformed and pinched hysteresis loops, and significant decrease of remnant polarization Pr and coercive field Ec near this temperature. [ABSTRACT FROM AUTHOR]

We studied changes of morphology and magnetic properties of Co/Cu multilayered nanowires, electrodeposited in polycarbonate membranes, as a function of Cu layer thickness. The morphology and structure of wire assemblies with an average diameter of 200 nm and length of 10 μm, investigated by X-ray diffraction and scanning electron microscopy techniques, revealed polycrystalline structure of Cu and Co layers with smooth lateral surface of nanowires. Overdeposited nanowires created caps which showed flower-like dendrites with shape changing as a function of Cu thickness and electrodeposition parameters. Chemical composition of Co and Cu nanowires analysed by energy dispersive spectroscopy and proton induced X-ray emission showed Cu nanowires free from Co atoms while in Co nanowires, Cu contamination with concentration below 10% was observed. The oxidation traces observed in single-component Cu nanowires did not appear in multilayered nanowires. Magnetic measurements indicated easy axis of magnetization in membrane plane for nanowires with Cu thickness smaller than 20 nm, whereas for larger Cu thicknesses isotropic orientation of magnetization was observed. The presence of Cu atoms in single-component Co nanowires resulted in the appearance of magnetic anisotropy with easy axis along nanowire axis and the increase of coercivity value. [ABSTRACT FROM AUTHOR]

Preliminary studies on the sediments collected from water meters of Krakow water supply system were performed in the cooperation with the Municipal Water Supply and Sewage. Creation and deposition of sediments on the measuring devices installed in the water supply system is a serious technological and economical problem for water companies, defectively operating for this reason water meters must be replaced. It is evident that knowledge of the chemical and phase composition of sediments is an important step towards resolving the problem of impurities in water supply systems. Four different samples of sediments, coming from water meters, were investigated using the proton-induced X-ray emission, the X-ray diffraction, the Fourier transform infrared and Raman spectroscopy. The X-ray methods revealed presence of amorphous and fine-crystalline phases as well as high content of iron-based compounds. As a crystalline phase, the most frequently appeared: goethite, lepidocrocite, iron oxides (hematite, maghemite, magnetite), calcium carbonate, and quartz. In one of the samples, the nanocrystalline phase was found and identified as hydrous iron oxyhydroxide ferrihydrite. Vibrational spectroscopy methods confirmed the composition of crystalline phases as well as enabled to estimate the abundance of amorphous phase in samples. [ABSTRACT FROM AUTHOR]

0.95Na0.5Bi0.5TiO3-0.05PbTiO3ceramic was prepared by a conventional solid phase sintering process. X-ray diffraction analysis shows that the obtained specimens possess perovskite structure with rhombohedral symmetry. The microstructure study showed a dense structure, in good agreement with the relative density of above 95% as determined by the Archimedes method. The crystal structure, phase transitions behavior and dielectric properties of these ceramics were investigated. Relaxor-like behavior was revealed. [ABSTRACT FROM PUBLISHER]

Determination of physical properties of porous geological materials is of great importance for oil industry. The knowledge of rocks properties is usually obtained from porosity studies such as pore size distribution, specific surface area determination, and hydrodynamic permeability calculations. This study describes determination of elemental composition and measurements of the particular physical properties of geological samples (porous sandstone rocks) by means of the nuclear and X-ray microprobes at the Institute of Nuclear Physics, Polish Academy of Sciences in Kraków, Poland. The special emphasis has been put on the computed microtomography method. Measurements have been carried out in close cooperation with Department of Geophysics, FGGEP AGH in Kraków, Poland. Chemical composition of the Rotliegend sandstone rock samples (few millimeters diameter), extracted from a borehole at 2679.6 m, 2741.4 m and 2742.4 m depth have been investigated using the 2.2 MeV proton beam (proton induced X-ray emission technique). Next, measurements of the porosity and the specific surface area of the pore space have been carried out using the X-ray microtomography technique. Basing on microtomographic data obtained with the high spatial resolution, simulations of the fluid dynamic in the void space of porous media have been carried out. Lattice Boltzmann method in the 3DQ19 geometrical model has been used in order to predict the hydraulic permeability of the media. In order to avoid viscosity-permeability dependence the multiple-relaxation-time model with half-way bounce back boundary conditions has been used. Computing power-consuming processing has been performed with the use of modern grid infrastructure. [ABSTRACT FROM AUTHOR]

The analysis of river and lake sediments indicates that the physical, chemical, biochemical and geochemical processes that influence the fate of toxic compounds and elements in sediments are numerous and complex (for example: sorption -- desorption, oxidation -- reduction, ion-exchange, biological activity). Due to the above-mentioned general statement, only a long term and complex research programme can lead to satisfactory answers to the questions relating to possible changes of water and environmental quality in the future. The aim of our study consisted in physical and chemical characterisation of sediments in in-depth profiles taken from the Dobczyce reservoir in southern Poland that is a main source of drinking water for the city of Kraków. Due to morphological reasons, 7 layers of sediment samples were distinguished from the ground level to about 90 cm below (total thickness of the sediments in the sampling site). Analysis of grain size distribution and application of x-ray diffraction method, enabled mineralogical description of sediments. The use of proton-induced x-ray emission (PIXE) and atomic absorption spectrometry (AAS) revealed elemental composition of the samples (Al, P, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn). Concentrations of natural 40K and artificial 137Cs radionuclides were determined by the use of gamma spectrometry. The following facts were established: 1) the oldest (deepest) and newest, recently deposited layers of sediments are similar in their physical and chemical properties. It means that the inflow of contaminants and biogenic compounds to the reservoir has changed little since it was constructed and filled with water; 2) the severe flood in 1997 changed significantly sediment composition and, in fact, led to purification of sediments in the Dobczyce reservoir. [ABSTRACT FROM AUTHOR]

(1) Background: Liver damage is an important component of acute COVID-19, and the advancement of preexisting liver disease is associated with a worse prognosis; (2) Methods: A nationwide retrospective study including 7444 patients aimed to evaluate levels of selected markers of liver damage and disease advancement and their association with mortality and mechanical ventilation (MV); (3) Results: Elevation of the following markers in multivariate models were associated with increased odds of mortality: Alanine transaminase (ALT), aspartate transaminase (AST), gamma-glutamyltransferase (GGT), lactate dehydrogenase (LDH), fibrosis-4 score (FIB-4), AST-to-platelet ratio index (APRI), and decreased levels of platelet count (PLT). Elevated levels of AST, LDH, APRI, FIB-4, and the AST/ALT ratio and decreased levels of PLT were associated with increased odds of MV in multivariate models. The best predictive accuracy against mortality was achieved with FIB-4 with AUC = 0.733 (95% CI, 0.718-0.749) at the optimal cut-off point of 2.764, while against MV was achieved with LDH with AUC = 0.753 (95% CI, 0.727-0.778) at the optimal cut-off point of 449.5 IU/L. (4) Conclusions: Our study confirms that the advancement of liver damage contributes to a worse prognosis in COVID-19 patients. Markers for liver damage and the advancement of liver disease can provide predictive value in clinical practice among COVID-19 patients.

PCOS is characterized by ovarian hyperandrogenism and insulin resistance (IR), which give rise to symptoms of hyperandrogenism and central obesity, which in turn may cause depression, lower self-esteem, and deteriorate coping strategies in stressful situations., The Purpose: to examine the mental condition, self-esteem, and ways of coping with stress in women with PCOS compared to age and BMI-matched healthy controls and to correlate them with clinical and laboratory hyperandrogenism, central obesity, and IR., Methods: 42 women with PCOS and 39 controls were assessed for the above-mentioned psychological measures and correlated with serum hormonal and metabolic parameters., Results: Compared to controls, women with PCOS had more symptoms of depression (p = 0.026), a higher level of tension induction (p = 0.032), were more prone to alcohol consumption (p = 0.015), and were less likely to use the strategy of active coping in stressful situations (p = 0.014) and to seek instrumental (p = 0.048) and emotional support (p = 0.043). The presence of hirsutism correlated negatively with the level of emotional induction (R = -0.32, p < 0.05), and androgenic alopecia positively with the hedonistic tone (R = 0.36, p < 0.05). Serum testosterone (TST) correlated positively with the likelihood of seeking instrumental support in stressful situations (R = 0.31, p < 0.05) and with emotional focus (R = 0.34, p < 0.05). Serum androstenedione (A4-dione) correlated negatively with the escape behavior (R = -0.32, p < 0.05). No correlations were found between waist circumference and IR with the studied psychological measures., Conclusions: Women with PCOS are characterized by depression, higher levels of tension induction, and impaired coping strategies in stressful situations, which correlate with clinical and laboratory indices of hyperandrogenism and not with central obesity and IR., (© 2024. The Author(s).)

Ancient adhesives used in multicomponent tools may be among our best material evidences of cultural evolution and cognitive processes in early humans. African Homo sapiens is known to have made compound adhesives from naturally sticky substances and ochre, a technical behavior proposed to mark the advent of elaborate cognitive processes in our species. Foragers of the European Middle Paleolithic also used glues, but evidence of ochre-based compound adhesives is unknown. Here, we present evidence of this kind. Bitumen was mixed with high loads of goethite ochre to make compound adhesives at the type-site of the Mousterian, Le Moustier (France). Ochre loads were so high that they lowered the adhesive's performance in classical hafting situations where stone implements are glued to handles. However, when used as handheld grips on cutting or scraping tools, a behavior known from Neanderthals, high-ochre adhesives present a real benefit, improving their solidity and rigidity. Our findings help understand the implications of Pleistocene adhesive making.

The earliest European carvings, made of mammoth ivory, depict animals, humans, and anthropomorphs. They are found at Early Aurignacian sites of the Swabian Jura in Germany. Despite the wide geographical spread of the Aurignacian across Europe, these carvings have no contemporaneous counterparts. Here, we document a small, intriguing object, that sheds light on this uniqueness. Found at the Grotte des Gorges (Jura, France), in a layer sandwiched between Aurignacian contexts and dated to c. 36.2 ka, the object bears traces of anthropogenic modifications indicating intentional carving. Microtomographic, microscopic, three-dimensional roughness and residues analyses reveal the carving is a fragment of a large ammonite, which was modified to represent a caniformia head decorated with notches and probably transported for long time in a container stained with ochre. While achieving Swabian Jura-like miniaturization, the Grotte des Gorges specimen displays original features, indicating the craftsman emulated ivory carvings while introducing significant technical, thematic, and stylistic innovations. This finding suggests a low degree of cultural connectivity between Early Aurignacian hunter-gatherer groups in the production of their symbolic material culture. The pattern conforms to the existence of cultural boundaries limiting the transmission of symbolic practices while leaving space for the emergence of original regional expressions., (© 2023. Springer Nature Limited.)

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Introduction: Up to now, COVID‑19 caused more than 6 million deaths worldwide. So far, 5 variants of concerns have been identified, with Delta and Omicron being the subject of our analysis., Objectives: We aimed to compare baseline characteristics and outcomes of patients hospitalized during the Delta and Omicron predominance in Poland., Patients and Methods: The study population consisted of 2225 patients divided into 2 groups depending on the variant with which they were infected during the corresponding period of the pandemic., Results: During the Delta wave, the median age of patients was significantly lower (65 vs 73 years; P <0.001), and the cohort was significantly less burdened with comorbidities than during the Omicron surge. The Omicron‑infected patients presented significantly less often in an unstable symptomatic state with SpO2 equal to or below 90% on admission (49.9% for Delta vs 29.9% for Omicron; P <0.001). Regardless of the pandemic period, the 2 most common early symptoms of COVID‑19 were fever and cough. In‑hospital treatment consisted of antiviral drugs, more frequently used in the Omicron wave, and immunomodulatory drugs, more frequently used during the Delta wave. The risk of mechanical ventilation was significantly lower in the patients infected with the Omicron variant (7.2% for Delta vs 3.1% for Omicron; P <0.001). For the age group above 80 years old, the risk of death was significantly higher during the Delta wave than during the Omicron wave. The risk of death was significantly lower in the patients treated with antiviral drugs regardless of the pandemic wave., Conclusions: The Delta variant is associated with a more severe clinical course of the disease and a higher risk of death than the Omicron variant.

The COVID-19 pandemic proceeds in waves, with variable characteristics of the clinical picture resulting from the evolution of the SARS-CoV-2 virus. This study aimed to compare the epidemiological characteristics, symptomatology, and outcomes of the disease in patients hospitalized for COVID-19 during periods of different variants dominance. Comparing the periods of dominance of variants preceding the Delta variant, the Delta period was characterized by a higher share of hospitalized females, less frequent comorbidities among patients, and a different age distribution. The lowest need for oxygen therapy and mechanical ventilation was observed under Omicron dominance. The triad of classic COVID-19 symptoms, cough, fever, dyspnoea, and fatigue, were most prevalent during the Delta period, and significantly less common under the Omicron dominance. During the Omicron period, nearly twice as many patients as in the previous periods could be discharged from the hospital within 7 days; the overall 28-day mortality was significantly lower compared to that of the Delta period. It also did not differ between periods that were dominated by the BA.1 and BA.2 subvariants. The study indicates that the Omicron SARS-CoV-2 variant that dominated between January and June 2022 caused a disease which resembled the common cold, and was caused by seasonal alpha and beta-coronaviruses with a low pathogenicity for humans. However, one should note that this effect may not only have been related to biological features of the Omicron lineage, but may additionally have been driven by the increased levels of immunization through natural infections and vaccinations, for which we could not account for due to a lack of sufficient data.

Intrusion detection (ID) on the cloud environment has received paramount interest over the last few years. Among the latest approaches, machine learning-based ID methods allow us to discover unknown attacks. However, due to the lack of malicious samples and the rapid evolution of diverse attacks, constructing a cloud ID system (IDS) that is robust to a wide range of unknown attacks remains challenging. In this article, we propose a novel solution to enable robust cloud IDSs using deep neural networks. Specifically, we develop two deep generative models to synthesize malicious samples on the cloud systems. The first model, conditional denoising adversarial autoencoder (CDAAE), is used to generate specific types of malicious samples. The second model (CDAEE-KNN) is a hybrid of CDAAE and the K -nearest neighbor algorithm to generate malicious borderline samples that further improve the accuracy of a cloud IDS. The synthesized samples are merged with the original samples to form the augmented datasets. Three machine learning algorithms are trained on the augmented datasets and their effectiveness is analyzed. The experiments conducted on four popular IDS datasets show that our proposed techniques significantly improve the accuracy of the cloud IDSs compared with the baseline technique and the state-of-the-art approaches. Moreover, our models also enhance the accuracy of machine learning algorithms in detecting some currently challenging distributed denial of service (DDoS) attacks, including low-rate DDoS attacks and application layer DDoS attacks.

Background: The real-world effectiveness of molnupiravir (MOL) during the dominance of Omicron SARS-CoV-2 lineage is urgently needed since the available data relate to the period of circulation of other viral variants. Therefore, this study assessed the efficacy of MOL in patients hospitalized for COVID-19 in a real-world clinical practice during the wave of Omicron infections., Methods: Among 11,822 patients hospitalized after 1 March 2020 and included in the SARSTer national database, 590 were treated between 1 January and 30 April 2022, a period of dominance of the Omicron SARS-CoV-2 variant. MOL was administered to 203 patients, whereas 387 did not receive any antiviral regimen. Both groups were similar in terms of sex, BMI and age allowing for direct comparisons., Results: Patients who did not receive antiviral therapy significantly more often required the use of Dexamethasone and Baricitinib. Treatment with MOL resulted in a statistically significant reduction in mortality during the 28-day follow-up (9.9 vs. 16.3%), which was particularly evident in the population of patients over 80 years of age treated in the first 5 days of the disease (14.6 vs. 35.2%). MOL therapy did not affect the frequency of the need for mechanical ventilation, but patients treated with MOL required oxygen supplementation less frequently than those without antivirals (31.7 vs. 49.2%). The time of hospitalization did not differ between groups., Conclusions: The use of molnupiravir in patients hospitalized for COVID-19 during the dominance of Omicron variant reduced mortality. This effect is particularly evident in patients over 80 years of age., (© 2022. The Author(s).)

Internet of Things (IoT) has emerged as a cutting-edge technology that is changing human life. The rapid and widespread applications of IoT, however, make cyberspace more vulnerable, especially to IoT-based attacks in which IoT devices are used to launch attack on cyber-physical systems. Given a massive number of IoT devices (in order of billions), detecting and preventing these IoT-based attacks are critical. However, this task is very challenging due to the limited energy and computing capabilities of IoT devices and the continuous and fast evolution of attackers. Among IoT-based attacks, unknown ones are far more devastating as these attacks could surpass most of the current security systems and it takes time to detect them and "cure" the systems. To effectively detect new/unknown attacks, in this article, we propose a novel representation learning method to better predictively "describe" unknown attacks, facilitating supervised learning-based anomaly detection methods. Specifically, we develop three regularized versions of autoencoders (AEs) to learn a latent representation from the input data. The bottleneck layers of these regularized AEs trained in a supervised manner using normal data and known IoT attacks will then be used as the new input features for classification algorithms. We carry out extensive experiments on nine recent IoT datasets to evaluate the performance of the proposed models. The experimental results demonstrate that the new latent representation can significantly enhance the performance of supervised learning methods in detecting unknown IoT attacks. We also conduct experiments to investigate the characteristics of the proposed models and the influence of hyperparameters on their performance. The running time of these models is about 1.3 ms that is pragmatic for most applications.

In the Paleolithic, geometric signs are abundant. They appear in rock art as well as on mobile objects like artworks, tools, or personal ornaments. These signs are often interpreted as a reflection of symbolic thought and associated with the origin of cognitively modern behavior. SignBase is a project collecting the wealth of geometric signs on mobile objects in the European Upper Paleolithic, African Middle Stone Age (MSA), as well as selected sites from the Near East and South East Asia. Currently, more than 500 objects of the Aurignacian techno-complex (ca. 43,000 to 30,000 years BP) are registered in SignBase. They are linked to information about geographic and archaeological provenience, the type of object and material, size and preservation, and respective literature references. We identify around 30 different sign types found on these objects across Europe in the Aurignacian and illustrate how SignBase can be used to analyze geographical clusters. Ultimately, we aim to enable quantitative analyses of abstract graphical expression before the emergence of writing.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs., (This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.)

Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents' locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

The crystal structures of four cocrystals of pyrazine with benzene polycarboxylic acids were determined, namely pyrazine-phthalic (benzene-1,2-dicarboxylic) acid (1/1), C 4 H 4 N 2 ·C 8 H 6 O 4 (1), pyrazine-hemimellitic (benzene-1,2,3-tricarboxylic) acid (1/1), C 4 H 4 N 2 ·C 9 H 6 O 6 (2), pyrazine-hemimellitic acid-water (1/2/2), C 4 H 4 N 2 ·2C 9 H 6 O 6 ·2H 2 O (2a), and pyrazine-pyromellitic (benzene-1,2,4,5-tetracarboxylic) acid (3/1), 3C 4 H 4 N 2 ·C 10 H 6 O 8 (3). In all cases, infinite chains of alternating acid and base molecules, bonded by O-H...N hydrogen bonds, are formed. However, the details of the supramolecular structures are different. The additional carboxylic acid groups in the tri- and tetracarboxylic acids participate in hydrogen bonding with neighbouring acid molecules (in 2), water molecules, which makes the structure more complicated (in 2a), or with additional pyrazine molecules (in 3). π-π interactions between aromatic rings help organize the crystal architectures in all cases except for hydrate 2a. In that case, the hydrogen-bond-enriched structure enforces a disposition of the rings in which no stacking is observed. The Hirshfeld surface analysis allows better visualization of the differences between the structures by fingerprint plots in particular.

Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A "lifestyle risk score" (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD.

Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.

Introduction: The diagnostic process of bone tumors, including malignant ones, is based on conventional radiological methods, such as radiography and computed tomography, and with precise assessment of local advancement in magnetic resonance imaging. Ultrasonography is not included in the diagnostic algorithms as a tool suitable to detect this type of pathology. More and more frequent usage of musculoskeletal ultrasound in children as the first imaging method or, in some cases, as the only diagnostic method, makes it necessary to be familiar with sonographic presentation of bone tumors to suggest this diagnosis early enough and, after its verification, start treatment without a significant delay. Aim: The aim of this study was to determine changes in the sonographic image that might indicate a bone malignancy and suggest the need to extend the diagnostic process in this direction., Material and Method: This article discusses 10 bone tumors in 9 children who had an ultrasound scan performed at the beginning of the diagnostic process before the histopathological diagnosis was established and treatment initiated. The assessment involved ultrasonographic features indicating the presence of a tumor. Results: In the group of 9 patients, 8 malignant bone tumors were diagnosed in ultrasonography and later verified histopathologically: 4 osteosarcomas and 4 Ewing's sarcomas. In one case, two bone tumors were detected in ultrasonography without specification of their nature (malignant/benign, primary/secondary)., Material and Method: In the analyzed cases, ultrasonography enabled the correct diagnosis of a focal bone lesion, and in most cases (8/9) it presented an image that suggested its malignant nature and the necessity of further diagnosis and treatment., (© Polish Ultrasound Society.)

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).

In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a real-time signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers' highest scores. Our experiments were conducted on the dataset of the BCI Competition III and resulted in a successful character rate of over 96% with just approximately 15 to 20 seconds for each character spelling session. As compared with the fixed 31.5 seconds of the best original approach of the competition, our proposed method significantly reduces the required spelling time by over 30% while maintaining the desired classification accuracy.

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another. However, channel sharing opens up new vulnerabilities as selfish MBANs may manipulate their online channel requests to gain unfair advantage over others. In this paper, we address this issue by proposing a truthful online channel sharing algorithm and a companion protocol that allocates channel efficiently and truthfully by punishing MBANs for misreporting their channel request parameters such as time, duration and bid for the channel. We first present an online channel sharing scheme for unit-length channel requests and prove that it is truthful. We then generalize our model to settings with variable-length channel requests, where we propose a critical value based channel pricing and preemption scheme. A bid adjustment procedure prevents unbeneficial preemption by artificially raising the ongoing winner's bid controlled by a penalty factor λ. Our scheme can efficiently detect selfish behaviors by monitoring a trust parameter α of each MBAN and punish MBANs from cheating by suspending their requests. Our extensive simulation results show our scheme can achieve a total profit that is more than 85% of the offline optimum method in the typical MBAN settings.

Recent developments in capsule endoscopy have highlighted the need for accurate techniques to estimate the location of a capsule endoscope. A highly accurate location estimation of a capsule endoscope in the gastrointestinal (GI) tract in the range of several millimeters is a challenging task. This is mainly because the radio-frequency signals encounter high loss and a highly dynamic channel propagation environment. Therefore, an accurate path-loss model is required for the development of accurate localization algorithms. This paper presents an in-body path-loss model for the human abdomen region at 2.4 GHz frequency. To develop the path-loss model, electromagnetic simulations using the Finite-Difference Time-Domain (FDTD) method were carried out on two different anatomical human models. A mathematical expression for the path-loss model was proposed based on analysis of the measured loss at different capsule locations inside the small intestine. The proposed path-loss model is a good approximation to model in-body RF propagation, since the real measurements are quite infeasible for the capsule endoscopy subject.