Miracle-dz/UniFAR-FTUs huggingface dataset:
Formal Modeling of Multi-Agent Systems using the Pi-Calculus and Epistemic Logic

Multi-agent systems have become important recently in computer science, especially in artificial intelligence (AI). We allow a broad sense of agent, but require at least that an agent has some measure of autonomy and interacts with other agents via some kind of agent communication language. We are concerned in this paper with formal modeling of multi-agent systems, with emphasis on communication. We propose for this purpose to use the pi-calculus, an extension of the process algebra CCS. Although the literature on the pi-calculus refers to agents, the term is used there in the sense of a process in general. It is our contention, however, that viewing agents in the AI sense as agents in the pi-calculus sense affords significant formal insight. One formalism that has been applied to agents in the AI sense is epistemic logic, the logic of knowledge. The success of epistemic logic in computer science in general has come in large part from its ability to handle concepts of knowledge that apply to groups. We maintain that the pi-calculus affords a natural yet rigorous means by which groups that are significant to epistemic logic may be identified, encapsulated, structured into hierarchies, and restructured in a principled way. This paper is organized as follows: Section 2 introduces the pi-calculus; Section 3 takes a scenario from the classical paper on agent-oriented programming [Sh93] and translates it into a very simple subset of the n-calculus; Section 4 then shows how more sophisticated features of the pi-calculus may bc brought into play; Section 5 discusses how the pi-calculus may be used to define groups for epistemic logic; and Section 6 is the conclusion.
Effects of internal exposure to neutron-activated 56MnO2 powder on locomotor activity in rats

Abstract At the detonation of the atomic bombing in Hiroshima and Nagasaki, a significant amount of radionuclides was produced by the neutron induced activation. The residual radiation from the explosion is crucial to the health risk of the people who entered these cities after the bombing and might have inhaled these radioactive materials. Because 56Mn is one of the major radionuclides produced in soil and have not been studied until now, we had conducted a series of experiments using rats to investigate the biological impacts of exposure of 56MnO2 particles. In these experiments, the rats’ spontaneous locomotor activity was also assessed to examine the possible effects of 56Mn on their behavior. However, the locomotor activity data obtained from an individual experiment failed to identify radiation effects due to the large variation among animals and the small sample size. In the present study, all available data from our previous studies on 56MnO2 exposure (0.02–0.15 Gy of whole-body doses) as well as 60Co-γ exposure (at 2–5 Gy of whole-body doses) were pooled. Our statistical method, which takes into account individual differences and daily fluctuations, successfully identified a decrease in locomotor activity caused by 56MnO2 exposure, where the changes were gradual and reached the maximum reduction around 2 weeks after exposure. In contrast, exposure to 60Co-γ rays produced the highest decline of activity within one day. These results suggest that internal exposure to 56Mn at whole-body doses of even less than 0.15 Gy may have a long-lasting impact on locomotor activity.
Addressing the migrant gap: maternal healthcare perspectives on utilising prevention of mother to child transmission (PMTCT) services during the COVID-19 pandemic, South Africa

ABSTRACT Background The COVID-19 pandemic has interrupted the prevention of mother-to-child transmission of HIV (PMTCT) programming in South Africa. In 2020, it was estimated that there were 4 million cross-border migrants in South Africa, some of whom are women living with HIV (WLWH), who are highly mobile and located within peripheral and urban areas of Johannesburg. Little is known about the mobility typologies of these women associated with different movement patterns, the impact of the COVID-19 pandemic on mobility typologies of women utilising PMTCT services and on how changes to services might have affected adherence. Objective To qualitatively explore experiences of different mobility typologies of migrant women utilising PMTCT services in a high mobility context of Johannesburg and how belonging to a specific typology might have affected the health care received and their overall experiences during the COVID-19 pandemic. Methods Qualitative semi-structured interviews with 40 pregnant migrant WLWH were conducted from June 2020-June 2021. Participants were recruited through purposive sampling at a public hospital in Johannesburg. A thematic approach was used to analyse interviews. Results Forty interviews were conducted with 22 cross-border and 18 internal migrants. Women in cross-border migration patterns compared to interprovincial and intraregional mobility experienced barriers of documentation, language availability, mistreatment, education and counselling. Due to border closures, they were unable to receive ART interrupting adherence and relied on SMS reminders to adhere to ART during the pandemic. All 40 women struggled to understand the importance of adherence because of the lack of infrastructure to support social distancing protocols and to provide PMTCT education. Conclusions COVID-19 amplified existing challenges for cross-border migrant women to utilise PMTCT services. Future pandemic preparedness should be addressed with differentiated service delivery including multi-month dispensing of ARVs, virtual educational care, and language-sensitive information, responsive to the needs of mobile women to alleviate the burden on the healthcare system.
Multiview Unsupervised Shapelet Learning for Multivariate Time Series Clustering

Multivariate time series clustering has become an important research topic in the time series learning task, which aims to discover the correlation among multiple sequences and partition multivariate time series data into several subsets. Although there are currently some methods that can handle this task, most of them fail to discover informative subsequences from multivariate time series instances. In this paper, we first propose a novel unsupervised shapelet learning with adaptive neighbors (USLA) model for learning salient multivariate subsequences (i.e., multivariate shapelets), where the importance of each variate can be auto-determined when given a candidate multivariate shapelet. USLA performs multivariate shapelet-transformed representation learning and local structure learning simultaneously, but the performance of USLA with multivariate shapelets of different lengths is comparable to that of isometric multivariate shapelets. In fact, the shapelet-transformed representations learned from multivariate shapelets of different lengths can all represent multivariate time series instances separately and often contain complementary information to each other. Therefore, we develop a novel multiview USLA (MUSLA) model which treats shapelet-transformed representations learned from shapelets of different lengths as different views. In this way, MUSLA learns the importance of each view and the neighbor graph matrix among multiview representations when candidate multivariate shapelets of different lengths are determined. Experimental results show that MUSLA outperforms other state-of-the-art multivariate time series algorithms on real-world multivariate time series datasets.
Continuous theta-burst stimulation to the sensorimotor cortex affects contralateral gamma-aminobutyric acid level and resting-state networks

Continuous theta-burst stimulation (cTBS) is a noninvasive repetitive brain stimulation protocol that suppresses the excitability of the primary motor cortex. It induces cerebral cortical inhibition by increasing inhibitory interneuronal excitability that is associated with increases in gamma-aminobutyric acid (GABA) concentration in the stimulated cortices. cTBS has been applied in the rehabilitation of stroke patients to modulate interhemispheric imbalance. However, the precise mechanisms of cTBS in remote brain areas remain uncertain. We evaluated cTBS-induced GABA level changes in bilateral sensorimotor cortices using GABA-edited magnetic resonance spectroscopy, alternations of motor evoked potentials (MEPs), and resting-state networks (RSNs) using resting-state functional magnetic resonance imaging in 24 healthy right-handed adults (mean age: 34.4 ± 5.0 years). GABA levels in the stimulated left hemisphere significantly increased from baseline (p = 0.013), which was comparable with those of previous reports. GABA levels in the unstimulated right hemisphere showed a trend decrease. cTBS induced a significant decrease in right hand-MEP amplitudes (22.06% ± 43.50%) from baseline (p = 0.026) in accordance with GABA concentrations. However, multiple RSNs, including the default mode and primary motor networks, did not show any obvious differences between pre- and post-stimulus comparisons in the sensorimotor network using the dual regression approach. These results suggest that cTBS simultaneously increases ipsilateral GABA in the stimulated left hemisphere and decreases contralateral GABA in the unstimulated right hemisphere. Neuromodulation following cTBS may be associated with the interhemispheric inhibition because of alterations in GABA levels between the stimulated and unstimulated cortices.
Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network

Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excellent feature extraction capability. Still, it is difficult for ordinary CNN to explore the dependencies of time-series data, so most researchers adopt the recurrent neural network or its variants (e.g., LSTM) for motion estimation tasks. This paper proposes a multi-feature temporal convolutional attention-based network (MFTCAN) to recognize joint angles continuously. First, we recruited ten subjects to accomplish the signal acquisition experiments in different motion patterns. Then, we developed a joint training mechanism that integrates MFTCAN with commonly used statistical algorithms, and the integrated architectures were named MFTCAN-KNR, MFTCAN-SVR and MFTCAN-LR. Last, we utilized two performance indicators (RMSE and $\text{R}^{2}$) to evaluate the effect of different methods. Moreover, we further validated the performance of the proposed method on the open dataset (Ninapro DB2). When evaluating on the original dataset, the average RMSE of the estimations obtained by MFTCAN-KNR is 0.14, which is significantly less than the results obtained by LSTM (0.20) and BP (0.21). The average $\text{R}^{2}$ of the estimations obtained by MFTCAN-KNR is 0.87, indicating the anti-disturbance ability of the architecture. Moreover, MFTCAN-KNR also achieves high performance when evaluating on the open dataset. The proposed methods can effectively accomplish the task of motion estimation, allowing further implementations in the human-exoskeleton interaction systems.
Posterior Instrumented Spinal Surgery Outcomes in the Elderly: A Comparison of the 5-Item and 11-Item Modified Frailty Indices

Study Design Retrospective Cohort Objectives To validate the most concise risk stratification system to date, the 5-item modified frailty index (mFI-5), and compare its effectiveness with the established 11-item modified frailty index (mFI-11) in the elderly population undergoing posterior instrumented spine surgery. Methods A single centre retrospective review of posterior instrumented spine surgeries in patients aged 65 years and older was conducted. The primary outcome was rate of post-operative major complications (Clavien-Dindo Classification ≥ 4). Secondary outcome measures included rate of all complications, 6-month mortality and surgical site infection. Multi-variate analysis was performed and adjusted receiver operating characteristic curves were generated and compared by DeLong’s test. The indices were correlated with Spearman’s rho. Results 272 cases were identified. The risk of major complications was independently associated with both the mFI-5 (OR 1.89, 95% CI 1.01-3.55, P = .047) and mFI-11 (OR 3.73, 95% CI 1.90-7.30, P = .000). Both the mFI-5 and mFI-11 were statistically significant predictors of risk of all complications (P = .007 and P = .003), surgical site infection (P = .011 and P = .003) and 6-month mortality (P = .031 and P = .000). Adjusted ROC curves determined statistically similar c-statistics for major complications (.68 vs .68, P = .64), all complications (.66 vs .64, P = .10), surgical site infection (.75 vs .75, P = .76) and 6-month mortality (.83 vs .81, P = .21). The 2 indices correlated very well with a Spearman’s rho of . 944. Conclusions The mFI-5 and mFI-11 are equally effective predictors of postoperative morbidity and mortality in this population. The brevity of the mFI-5 is advantageous in facilitating its daily clinical use.
Infectious disease specialists and teamwork strategies worldwide: the World Association against Infection in Orthopedics and Trauma (WAIOT) and SICOT continue to cooperate in fighting musculoskeletal infections

Bone and joint infections are associated with a devastating global burden. The successful treatment of these infections requires a multidisciplinary approach between orthopedic surgeons and experts of different disciplines. This multidisciplinary approach has gained ground over the past decades in modern infection units as a more effective treatment strategy, yielding better outcomes regarding infection eradication rates, length of hospital stay, and overall cost of treatments. Additionally, preventing and managing musculoskeletal infections requires strong connections between medical associations, biological laboratories, and the pharmaceutical industry worldwide. In this context, SICOT and World Association against Infection in Orthopaedics and Trauma (WAIOT) relationships have been increasing. The present editorial article discusses the multidisciplinary approach for managing bone and joint infections worldwide, explores the controversies in practices in terms of training, area of expertise, and extent of clinical involvement, and emphasizes the role of societies in research, prevention and management of musculoskeletal infections. The purpose is to acknowledge what orthopedics can obtain from specialists dealing with bone and joint infections and to consolidate their practice to provide the best care for orthopedic patients.
Biased belief priors versus biased belief updating: Differential correlates of depression and anxiety

Individuals prone to anxiety and depression often report beliefs and make judgements about themselves that are more negative than those reported by others. We use computational modeling of a richly naturalistic task to disentangle the role of negative priors versus negatively biased belief updating and to investigate their association with different dimensions of Internalizing psychopathology. Undergraduate participants first provided profiles for a hypothetical tech internship. They then viewed pairs of other profiles and selected the individual they would prefer to work alongside out of each pair. In a subsequent phase of the experiment, participants made judgments about their relative popularity as hypothetical internship partners both before any feedback and after each of 20 items of feedback revealing whether or not they had been selected as the preferred teammate from a given pairing. Scores on latent factors of general negative affect, anxiety-specific affect and depression-specific affect were estimated using participants’ self-report scores on standardized measures of anxiety and depression together with factor loadings from a bifactor analysis conducted previously. Higher scores on the depression-specific factor were linked to more negative prior beliefs but were not associated with differences in belief updating. In contrast, higher scores on the anxiety-specific factor were associated with a negative bias in belief updating but no difference in prior beliefs. These findings indicate that, to at least some extent, distinct processes may impact the formation of belief priors and in-the-moment belief updating and that these processes may be differentially disrupted in depression and anxiety. Future directions for enquiry include examination of the possibility that prior beliefs biases in depression might reflect generalization from prior experiences or global schema whereas belief updating biases in anxiety might be more situationally specific.
Racism and Quality of Neonatal Intensive Care: Voices of Black Mothers.

BACKGROUND AND OBJECTIVES Black preterm infants are more likely to die than White preterm infants within the same NICU. Racism may lead to disparate quality of NICU care contributing to disparities in preterm infant health outcomes. The objective of our study was to understand Black mothers' perspectives of the impact of racism on the quality of care for Black preterm infants in the NICU and what might be done to address it. METHODS Using qualitative research methods, we conducted in-depth, semistructured interviews with 20 Black mothers of preterm infants in a single Level IV NICU 6 to 18 months after hospital discharge regarding experiences with disparate quality of NICU care. We developed themes iteratively and conducted interviews until thematic saturation was reached. RESULTS The majority of mothers believed that racism impacted the quality of NICU care and described examples of racism in the NICU at structural, institutional, and interpersonal levels. Mothers also provided ideas for interventions that would decrease racism and improve quality of NICU care for Black families, including increased Black representation, increased peer support, and improved staff education and training. CONCLUSIONS Black mothers of preterm infants experienced racism during NICU hospitalization that impacted the quality of care they received. Interventional studies directed toward mitigating these racial disparities may focus on addressing racism during the NICU period and should include input from Black stakeholders at all stages of design, implementation, and analysis.
Leaf senescence attributes: the novel and emerging role of sugars as signaling molecules and the overlap of sugars and hormones signaling nodes

Abstract Sugars are the primary products of photosynthesis and play multiple roles in plants. Although sugars are usually considered to be the building blocks of energy storage and carbon transport molecules, they have also gradually come to be acknowledged as signaling molecules that can initiate senescence. Senescence is an active and essential process that occurs at the last developmental stage and corresponds to programmed degradation of: cells, tissues, organs, and entire organisms. It is a complex process involving: numerous biochemical changes, transporters, genes, and transcription factors. The process is controlled by multiple developmental signals, among which sugar signals are considered to play a vital role; however, the regulatory pathways involved are not fully understood. The dynamic mechanistic framework of sugar accumulation has an inconsistent effect on senescence through the sugar signaling pathway. Key metabolizing enzymes produce different sugar signals in response to the onset of senescence. Diverse sugar signal transduction pathways and a variety of sugar sensors are involved in controlling leaf senescence. This review highlights the processes underlying initiation of sugar signaling and crosstalk between sugars and hormones signal transduction pathways affecting leaf senescence. This summary of the state of current knowledge across different plants aids in filling knowledge gaps and raises key questions that remain to be answered with respect to regulation of leaf senescence by sugar signaling pathways.
Periacetabular osteotomy: A novel application of modified Stoppa approach

Background: The Bernese periacetabular osteotomy (PAO) is a well-established procedure for symptomatic hip dysplasia in adolescents and young adults. However, it remains a technically demanding procedure, and several major complications have been described, many of which are related to the approach and surgical exposure. The current study evaluates the efficacy and safety of PAO performed through a modified Stoppa approach. Methods: A prospective series of nine consecutive patients with hip dysplasia were treated PAO through the modified Stoppa approach. The mean age was 22.4 years (15–30 years) and the mean follow-up was 3.2 years (2–5 years). Harris hip score (HHS) was used as a functional score, and the radiographic indices included the lateral center-edge angle (LCEA) and Tönnis roof angle. Results: The approach allowed the osteotomy lines to be performed under direct visualization from the intra-pelvic surface of the acetabulum, aided by fluoroscopy. A lateral window was added to perform the final iliac cut and for subsequent mobilization and fixation of the acetabular fragment. The mean HHS improved significantly from 70.8 ± 4.9 points to 90.1 ± 3.3 points (p < 0.001). The mean LCEA improved from 8.2° ± 4.9 (range: 0–14) to 32.7° ± 5.3 (range: 26–40), with a mean improvement of 24.5°. The mean Tönnis angle improved from 28.4° ± 4.4 (range: 22–35) to 3.8° ± 3.3 (range: 0–10). Two patients had irritation from prominent screw heads that necessitated removal 1 year after the index procedure. One patient had radiographic progression of osteoarthritis. No cases of infection, non-union, heterotopic ossification, or nerve palsy were identified till the latest follow-up. Conclusion: Ganz PAO can be safely conducted through the modified Stoppa approach, providing direct exposure to the osteotomized surfaces, and protecting susceptible neuro-vascular structures. Level of evidence: IV.
Radio resource allocation algorithms for energy efficient cellular networks powered by renewable energy and smart grid. (Algorithmes d'allocation de ressources radio pour l'énergie Réseaux Cellulaires efficaces alimentés par l'énergie renouvelable et Smart Grid)

The Internet is one of the largest infrastructures created in recent years. With its growing popularity, its energy footprint becomes an important factor. One of the most frequent ways Internet access is via cellular wireless communication, which consumes more than 0.5% of the global energy supply. This percentage will increase more with the dramatic growth in user demand. Taking into account that the base stations are major consumers of energy, powering them with renewable energy sources has the potential to become an indispensable tool for mobile network operators. It is therefore important to study the subject in order to determine the potential gains, the applicability scenarios, deployment strategies and new system architectures. In the energy sector, significant efforts have been spent on the development of the power grid into smarter one, the Smart Grid. With the advancements in Smart Grid functionality, it is mandatory to be considered when studying the energy behavior of mobile networks that are connected to the grid. This thesis aims to contribute in the field of techniques used by mobile networks that are powered by renewable energy and the Smart Grid.