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This study focuses on the movement of money in a financial infrastructure built to conceal its origin and destination. The literature has addressed this theme in the field of money laundering, converting illegal money into a legitimate source, yet less is known about the reverse process, hiding and transferring legally generated resources to pay kickbacks to officials. We aim to analyze how these money dirtying schemes operate and explore their hidden mechanisms. The paper studies what happens to money in an unseen financial cluster and identifies the main actors and infrastructures essential for managing such process. Using desk research and qualitative analysis of various documents, the article examines the corruption scheme designed by the Odebrecht group to generate resources to bribe politicians and bureaucrats in Latin American and Sub-Saharan countries. It zooms in on one key component of this system, a cluster around two Peruvian entrepreneurs, where money becomes hidden from the eyes of law enforcement agencies and investigators. The main contribution lies in the conceptual distinction between money laundering and money dirtying schemes and the opportunity to expose and interpret the otherwise invisible critical processes of such illicit resource movements. [ABSTRACT FROM AUTHOR]

Group decision‐making (GDM) processes shape the evolution of open source software (OSS) products, thus playing an important role in the governance of open source software communities. While these GDM processes have attracted the attention of researchers, the rationale behind decisions, that is, how decisions are made that enhance the OSS, have not received much attention. This work bridges this gap by extracting these rationales from a large open source repository comprising 1.55 million emails available in Python development archives. This work makes a methodological contribution by presenting a heuristics‐based rationale extraction system called Rationale Miner that employs information retrieval, natural language processing, and heuristics‐based techniques. Using these techniques, it extracts the rationale behind specific decisions (for example, whether a new module was added based on core developer consensus or a benevolent dictator's pronouncement). This work unearths 11 such rationales behind decisions in the Python community and thus makes a knowledge contribution. It also analyzes the prevalence of these rationales across all PEPs and three sub‐types of PEPs: Process, Informational, and Standard Track PEPs. The effectiveness of our contributions has been positively evaluated using quantitative and qualitative approaches (e.g., comparison against baselines for rationale identification showed up to 47% improvement in the most conservative case, and feedback from the Python steering committee showed the accurate identification of rationales respectively). The approach proposed in this work can be used and extended to discover the rationale behind decisions that remain hidden in communication repositories of other OSS projects, which will make the decision‐making (DM) process transparent to stakeholders and encourage decision‐makers to be more accountable. [ABSTRACT FROM AUTHOR]

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver's position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle. [ABSTRACT FROM AUTHOR]

Good governance practices are pivotal to the success of Open Source Software (OSS) projects. However, the decision‐making processes that are made available to stakeholders are at times incomplete and may remain buried and hidden in large amounts of software repository data. This work bridges this gap by unearthing enacted decision‐making processes available for Python Enhancement Proposals (PEPs) from 1.54 million email messages that embody decisions made during the evolution of the Python language. This work employs a design science approach in operationalizing a framework called DeMaP miner that is used to discover hidden processes using information retrieval and information extraction techniques. It also uses process mining techniques to visualize the processes, and comparative structural analysis techniques to compare different decision processes. The work identifies a richer set of decision‐making activities than those reported on the Python website and in prior research work (48 new decision activities, 199 new pathways and 6 new stages). The extracted decision process has been positively evaluated by a prominent member of the Python steering council. The extracted process can be used for process compliance checking and process improvement in OSS communities. Additionally, the DeMaP Miner framework can be extended and customized to suit other OSS projects, such as the OpenJDK project. [ABSTRACT FROM AUTHOR]

In the automotive field, the introduction of keyless access systems is revolutionizing car entry techniques currently dominated by a physical key. In this context, this paper investigates the possible use of smartphones to create a PEPS (Passive Entry Passive Start) system using the BLE (Bluetooth Low-Energy) Fingerprinting technique that allows, along with a connection to a low-cost BLE micro-controllers network, determining the driver’s position, either inside or outside the vehicle. Several issues have been taken into account to assure the reliability of the proposal; in particular, (i) spatial orientation of each microcontroller-based BLE node which ensures the best performance at 180° and 90° referred to as the BLE scanner and the advertiser, respectively; (ii) data filtering techniques based on Kalman Filter; and (iii) definition of new network topology, resulting from the merger of two standard network topologies. Particular attention has been paid to the selection of the appropriate measurement method capable of assuring the most reliable positioning results by means of the adoption of only six embedded BLE devices. This way, the global accuracy of the system reaches 98.5%, while minimum and maximum accuracy values relative to the individual zones equal, respectively, to 97.3% and 99.4% have been observed, thus confirming the capability of the proposed method of recognizing whether the driver is inside or outside the vehicle.

Transcranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear. To characterize and minimize the contribution of sensory inputs to TEPs. Twenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition. Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs. The responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after ∼60 ms, regardless of the intensity and stimuli waveform. Among the three filters, SSP-SIR showed the best trade-off between removing sensoryrelated signals while preserving data not related to the control condition. The findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses. • EEG responses to TMS over M1 (TEPs) and shoulder (PEPs) were compared. • Long latency components (>∼60 ms) were correlated between TEPs and PEPs. • Changing TMS intensities and waveforms did not alter TEP-PEP relationships. • Offline filters showed promise for isolating TMS-evoked neural activity. • More realistic control conditions may lead to more accurate comparisons. [ABSTRACT FROM AUTHOR]

We prove boundedness results for the injectivity regions for projected entangled pair states. Our result is a higher-dimensional generalization of the quantum Wielandt inequality. [ABSTRACT FROM AUTHOR]

Highlights • Potential emerging pollutants (PEPs) have severe environmental threats. • Of all methods of PEP treatment, membrane-based approaches are useful. • MF, UF, NF RO, and forward osmosis techniques are effective. • Advantages/applications of MBSPs over other treatment methods are covered. Abstract The potential emerging pollutants (PEPs) such as hazardous chemicals, toxic metals, bio-wastes, etc., pose a severe threat to human health, hygiene and ecology by way of polluting the environment and water sources. The PEPs are originated from various industrial effluent discharges including pharmaceutical, food and metal processing industries. These PEPs in contact with water may pollute the water and disturb the aquatic life. Innumerable methods have been used for the treatment of effluents and separating the toxic chemicals/metals. Of these methods, membrane-based separation processes (MBSPs) are effective over the conventional techniques for providing clean water from wastewater streams at an affordable cost with minimum energy requirement. Microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), and forward osmosis (FO) methods as well as hybrid technologies are discussed citing the published results of the past decade. [ABSTRACT FROM AUTHOR]

The well-being of the microbial community that densely populates the rhizosphere is aided by a plant's root exudates. Maintaining a plant's health is a key factor in its continued existence. As minute as rhizospheric microbes are, their importance in plant growth cannot be overemphasized. They depend on plants for nutrients and other necessary requirements. The relationship between the rhizosphere-microbiome (rhizobiome) and plant hosts can be beneficial, non-effectual, or pathogenic depending on the microbes and the plant involved. This relationship, to a large extent, determines the fate of the host plant's survival. Modern molecular techniques have been used to unravel rhizobiome species' composition, but the interplay between the rhizobiome root exudates and other factors in the maintenance of a healthy plant have not as yet been thoroughly investigated. Many functional proteins are activated in plants upon contact with external factors. These proteins may elicit growth promoting or growth suppressing responses from the plants. To optimize the growth and productivity of host plants, rhizobiome microbial diversity and modulatory techniques need to be clearly understood for improved plant health. [ABSTRACT FROM AUTHOR]

Introduction. The aim of the study was to evaluate the effects of plyometric and isometric training protocol on power in 46 team sports (basketball: n = 23; volleyball: n = 23) players, also in relation to genetic background (i.e., ACE and PPARA genes polymorphisms). Methods. The following tests were administered: squat jump (SJ), countermovement jump (CMJ), drop jump (dJ), sprint, handgrip, and agility test. Genetic analysis was based on saliva samples. Results. The training protocol proved to be effective in improving jump performance in basketball players and volleyball players, respectively: 25-meter sprint test (p = 0.006; p = 0.008); agility test (p = 0.000; p = 0.000); SJ test (p = 0.001; p = 0.000); CMJ test (p = 0.005; p = 0.000); and dJ test (p = 0.03 only for basketball players; dJ test improvement in volleyball players was not significant), regardless of sports practice. Furthermore, data confirm that the 'd' allele of ACE and the 'C' allele of PPARA are positive alleles associated to power and strength performances. Conclusions. The importance to train strength skills should be emphasized, above all through isometric and plyometric exercises, not neglecting the key role played by the genetic background. [ABSTRACT FROM AUTHOR]

A positive family history is a strong and consistently reported risk factor for gastric cancer (GC). So far, it has been demonstrated that serum pepsinogens (PGs), and gastrin 17 (G17) are useful for screening individuals at elevated risk to develop atrophic gastritis but they are suboptimal biomarkers to screen individuals for GC. The main purpose of this study was to investigate serum metabolomic profiles to find additional biomarkers that could be integrated with serum PGs and G17 to improve the diagnosis of GC and the selection of first-degree relatives (FDR) at higher risk of GC development. Serum metabolomic profiles included 188 serum metabolites, covering amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexoses. Serum metabolomic profiles were performed with tandem mass spectrometry using the Biocrates AbsoluteIDQ p180 kit. The initial cohort (training set) consisted of n = 49 GC patients and n = 37 FDR. Differential metabolomic signatures among the two groups were investigated by univariate and multivariate partial least square differential analysis. The most significant metabolites were further selected and validated in an independent group of n = 22 GC patients and n = 17 FDR (validation set). Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic power and the optimal cut-off for each of the discriminant markers. Multivariate analysis was applied to associate the selected serum metabolites, PGs, G17 and risk factors such as age, gender and Helicobacter pylori (H. pylori) infection with the GC and FDR has been performed and an integrative risk prediction algorithm was developed. In the training set, 40 metabolites mainly belonging to phospholipids and acylcarnitines classes were differentially expressed between GC and FDR. Out of these 40 metabolites, 9 were further confirmed in the validation set. Compared with FDR, GC patients were characterized by lower levels of hydroxylated sphingomyelins (SM(OH)22:1, SM(OH)22:2, SM(OH)24:1) and phosphatidylcholines (PC ae 40:1, PC ae 42:2, PC ae 42:3) and by higher levels of acylcarnitines derivatives (C2, C16, C18:1). The specificity and sensitivity of the integrative risk prediction analysis of metabolites for GC was 73.47% and 83.78% respectively with an area under the curve of the ROC curve of 0.811 that improves to 0.90 when metabolites were integrated with the serum PGs. The predictive risk algorithm composed of the C16, SM(OH)22:1 and PG-II serum levels according to the age of individuals, could be used to stratify FDR at high risk of GC development, and then this can be addressed with diagnostic gastroscopy. [ABSTRACT FROM AUTHOR]

Tensor networks (TNs) are generated by a set of small rank tensors and define many-body quantum states in a succinct form. The corresponding map is not one-to-one: different sets of tensors may generate the very same state. A fundamental question in the study of TNs naturally arises: what is then the relation between those sets? The answer to this question in one-dimensional setups has found several applications, like the characterization of local and global symmetries, the classification of phases of matter and unitary evolutions, or the determination of the fixed points of renormalization procedures. Here we answer this question for projected entangled pair states in any dimension and lattice geometry (including, for example, the Kagome lattice, hyperbolic lattices, or tree tensor networks), as long as the tensors generating the states are normal, which constitute an important and generic class.

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This work presents the new version of PEPS software tool, designed for solving models expressed using Stochastic Automata Networks (SAN). The SAN formalism is historically the first Markovian structured formalism employing Tensor Algebra. PEPS tool was first launched in the late 80's and it's still a live project, therefore a short timeline of its previous versions, as well as the new features included in version 2015, are presented, such as an efficient and powerful just-in-time function evaluation. [ABSTRACT FROM AUTHOR]

Summary Background In most adult education, teachers use methods that assume all students learn in the same way. But knowledge of students' learning style preferences highlights the importance of adequate teaching and learning adaptation. Objectives The aim of the study was to describe and compare final year nursing students' learning style preferences in two campuses during three semesters. A further aim was to identify differences between learning style preferences and personal characteristics. Design A descriptive cross-sectional study using the Productivity Environmental Preference Survey (PEPS) questionnaire was conducted at a Swedish rural university. Three semester groups with 263 nursing students participated in 2012–2013. Results The majority of the students were ‘flexible’ in their learning style preferences and had none or few strong preferences. Students with strong preferences preferred high structure (75%) and an authority figure present (40%). About a third were highly auditory, tactile and/or kinesthetic while 8% were highly visual. Few significant differences were revealed between the groups of campuses and the groups of semesters or between learning style preferences and upper secondary school and care experience. There were no significant differences between learning style preferences and age and assistant nurse graduation. More women than men were highly motivated, auditory, tactile and kinesthetic and preferred structure and mobility. Conclusions The PEPS questionnaire provides nursing students with self-awareness regarding their strengths and shortcomings in learning and teachers with a valuable and practical basis for their selection of adapted individual and group teaching methods. The findings suggest the need for wide variation and interactive teaching approaches, conscious didactic actions between cooperating teachers and conscious learning strategies for nursing students. [ABSTRACT FROM AUTHOR]