Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the Galactic very-high-energy γ-ray source population

Context. The High Energy Stereoscopic System Galactic plane survey (HGPS) is to date the most comprehensive census of Galactic γ-ray sources at very high energies (VHE; 100 GeV ≤ E ≤ 100 TeV). As a consequence of the limited sensitivity of this survey, the 78 detected γ-ray sources comprise only a small and biased subsample of the overall population. The larger part consists of currently unresolved sources, which contribute to large-scale diffuse emission to a still uncertain amount. Aims. We study the VHE γ-ray source population in the Milky Way. For this purpose population-synthesis models are derived based on the distributions of source positions, extents, and luminosities. Methods. Several azimuth-symmetric and spiral-arm models are compared for spatial source distribution. The luminosity and radius function of the population are derived from the source properties of the HGPS data set and are corrected for the sensitivity bias of the HGPS. Based on these models, VHE source populations are simulated and the subsets of sources detectable according to the HGPS are compared with HGPS sources. Results. The power-law indices of luminosity and radius functions are determined to range between −1.6 and −1.9 for luminosity and −1.1 and −1.6 for radius. A two-arm spiral structure with central bar is discarded as spatial distribution of VHE sources, while azimuth-symmetric distributions and a distribution following a four-arm spiral structure without bar describe the HGPS data reasonably well. The total number of Galactic VHE sources is predicted to be in the range from 800 to 7000 with a total luminosity and flux of (1.6 − 6.3) × 1036 ph s−1 and (3 − 15) × 10−10 ph cm−2 s−1, respectively. Conclusions. Depending on the model, the HGPS sample accounts for (68 − 87)% of the emission of the population in the scanned region. This suggests that unresolved sources represent a critical component of the diffuse emission measurable in the HGPS. With the foreseen jump in sensitivity of the Cherenkov Telescope Array, the number of detectable sources is predicted to increase by a factor between 5 and 9.
ACM SIGSOFT Empirical Standards

Empirical Standards are brief public document that communicate expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for common research methods in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, can be used to make peer review more transparent, structured, harmonious and fair.
The ICARUS Mote: Employing Off-Chip RTC to Attain 22 nA Sleep Current in Duty-Cycled IoT Devices

In this work we demonstrate the ICARUS mote, the first wireless sensor node that features the extremely-low sleep current of 22 nA. To achieve this, we employ an off-chip on-board Real-Time-Clock (RTC) circuit with an ultra-low power consumption profile. The RTC is configured to control the power supply of the under consideration mote, by enabling or disabling its power in a power-gating fashion, while it is the only module that remains powered during sleep, hence the overall mote’s consumption is substantially diminished. The proposed principle can be adopted by any IoT mote, in order to extend the life expectancy of battery-powered applications, by pushing sleep currents an order of magnitude lower.
Successful Undergraduate-level Experiential Learning Projects: A Stakeholder Perspective

Experiential learning posits that learning occurs on the basis of practice; in technology courses this is often practiced as real or near to real life projects. Successful student-led project implementation has multiple definitions in the undergraduate learning context. In the best of cases, all stakeholders including students, instructors, community partners, and clients assess the course as having concluded successfully. This experience report blends these perspectives about the methods of and barriers to success in real-life student project scenarios. Based on the results of semester-long, enterprise level software implementation projects, we deliver generalizable lessons and implementation principles for instructors who wish to implement experiential learning in technology courses as a function of classroom learning and assessment.
Interactive Visualizations to Introduce Data Science for High School Students

Artificial intelligence integrates with our daily life in more ways each day with the invention of new tools and applications. For example, businesses utilize machine learning, which is a subfield of artificial intelligence, to identify the ideal customer for targeted advertisement. Machine learning and data science scale previous manual problems to a wider customer base. With all the various applications for artificial intelligence, there is still a noticeable gap in the number of qualified job applicants who can solve some of the machine learning problems of the day. There is a movement to introduce computer science education earlier to K-12 students. One motivation behind an earlier introduction is to better prepare students to study computer science at universities and fulfill these coveted technology positions in the future. Given this motivation, there needs to be more computer science instructional activities and lesson plans to support K-12 teachers. Currently there are several new tools and curricula available for computer science. However, there is a need for beginner and K-12 accessible data science and machine learning instructional methods. These topics are advanced computer science electives but an earlier introduction can still benefit students. Interactive visual activities support high school students to learn challenging topics. In this paper, we consider how a visual analytic process can present difficult data science concepts. We present an interactive visual educational tool to teach data science to high school students. The first activity introduces the visual analytic pipeline. The next interactive activities present machine learning classification and regression topics. We select datasets and applications which are relatable to our main audience. This paper describes the design and implementation of the data science educational tool for high school students.
Genome-Wide Identification, Expression Profiling, and Evolution of Phosphate Transporter Gene Family in Green Algae

Phosphorus (P) is an essential nutrient for plant growth and development. Phosphate transporters (PHTs) are trans-membrane proteins that mediate the uptake and translocation of phosphate (Pi) in green plants. The PHT family including PHT1, PHT2, PHT3 and PHT4 subfamilies are well-studied in land plants; however, PHT genes in green algae are poorly documented and not comprehensively identified. Here, we analyzed the PHTs in a model green alga Chlamydomonas reinhardtii and found 25 putative PHT genes, which can be divided into four subfamilies. The subfamilies of CrPTA, CrPTB, CrPHT3, and CrPHT4 contain four, eleven, one, and nine genes, respectively. The structure, chromosomal distribution, subcellular localization, duplication, phylogenies, and motifs of these genes were systematically analyzed in silico. Expression profile analysis showed that CrPHT genes displayed differential expression patterns under P starvation condition. The expression levels of CrPTA1 and CrPTA3 were down-regulated, while the expression of most CrPTB genes was up-regulated under P starvation, which may be controlled by CrPSR1. The transcript abundance of most CrPHT3 and CrPHT4 genes was not significantly affected by P starvation except CrPHT4-3, CrPHT4-4, and CrPHT4-6. Our results provided basic information for understanding the evolution and features of the PHT family in green algae.
The fabric of agency: Navigating human potentialities through introspection

Building on new trends in neuroscience and philosophy of mind, this article provides a roadmap for incorporating consciousness into social research. Drawing upon Alexander Wendt’s Quantum Mind and Social Science, it is argued that one of the greatest obstacles encountered by social research when approaching social reality is that it provides neither an epistemological nor an ontological place for consciousness. The present article intends to address that problem by introducing a sophisticated method of guided introspection that has the potential to revolutionize our understanding of agency. This method, whose epistemological situation is isomorphic to that of quantum physics, enables researchers to gain insight into the structures of decisionmaking processes. Such an endeavor is extremely relevant for security studies: the consideration of consciousness could lead to a radical new understanding of the dynamics of conflict, cooperation, strategic defense, political polarization, radicalization, violent extremism, and terrorism, as well as of the processes of social transformation required to cope with climate change. Moreover, bringing consciousness into social research could foster ongoing biological, evolutionary, and neuroscientific approaches to international relations. Thus, through the introduction of the ‘phenomenal consciousness’ of actors, it is shown how we can dive into the fountainhead of human agency and map mental processes.
Online control of random access with splitting

For slotted random access systems, the slotted ALOHA protocol provides the maximum throughput of 0.368 (packets/slot) while in the category of splitting (or tree) algorithms, the maximum achievable throughput can reach up to 0.487 with the first-come first-serve (FCFS) algorithm. It has been so far demonstrated that the FCFS algorithm can achieve this maximum throughput only for Poisson traffic. This may limit its application in practical systems, where packet arrivals may not be Poissonian. In this paper, we propose a novel online transmission control framework that introduces random splitting upon collisions and controls the transmission probabilities optimally at each slot by estimating the number of active users in the system. The proposed algorithm is said to be online as it estimates the number of active users slot by slot recursively, and thus can adapt to network dynamics. We first show that the splitting algorithm of our interest can achieve the throughput of 0.532 if the number of users involved in a collision could be known, which serves as a guideline for the upper limit for the random access systems with splitting. Then, when the information on the number of collided users is not available, we show that the proposed algorithm can achieve the maximum throughput of 0.487 for Poisson arrivals while achieving shorter access delay than FCFS. When more bursty traffic than Poisson process is applied, the proposed algorithm shows much better throughput and delay performance than FCFS.
Fanning the flames: How U.S. newspapers framed 10 historically significant U.S. wildfires

Wildfire news coverage gains importance not only for information provided, but for images created. Using 10 historically significant U.S. wildfires for analysis, with framing and social cohesion theory as framework, this study examines news cycles for news hook, theme and source usage. Results show agency personnel and citizens are nearly equal, with scientists later in coverage. Framing mirrors patterns in environmental reporting: presenting this hazard in capital terms rather than a social or societal issue.
Human CD8+ T Cells Release Extracellular Traps Co-Localized With Cytotoxic Vesicles That Are Associated With Lesion Progression and Severity in Human Leishmaniasis

Cell death plays a fundamental role in mounting protective and pathogenic immunity. Etosis is a cell death mechanism defined by the release of extracellular traps (ETs), which can foster inflammation and exert microbicidal activity. While etosis is often associated with innate cells, recent studies showed that B cells and CD4+ T cells can release ETs. Here we investigate whether CD8+ T cells can also release ETs, which might be related to cytotoxicity and tissue pathology. To these ends, we first employed an in vitro system stimulating human CD8+ T cells isolated from healthy volunteers with anti-CD3/anti-CD28. Using time-frame video, confocal and electron microscopy, we demonstrate that human CD8+ T cells release ETs upon stimulation (herein LETs – lymphocyte extracellular traps), which display unique morphology and functional characteristics. CD8+ T cell-derived LETs form long strands that co-localize with CD107a, a marker of vesicles containing cytotoxic granules. In addition, these structures connect the LET-releasing cell to other neighboring cells, often resulting in cell death. After demonstrating the release of LETs by human CD8+ T cells in vitro, we went on to study the occurrence of CD8-derived LETs in a human disease setting. Thus, we evaluated the occurrence of CD8-derived LETs in lesions from patients with human tegumentary leishmaniasis, where CD8+ T cells play a key role in mediating pathology. In addition, we evaluated the association of these structures with the intensity of the inflammatory infiltrate in early and late cutaneous, as well as in mucosal leishmaniasis lesions. We demonstrated that progression and severity of debilitating and mutilating forms of human tegumentary leishmaniasis are associated with the frequency of CD8+ T cells in etosis, as well as the occurrence of CD8-derived LETs carrying CD107a+ vesicles in the lesions. We propose that CD8+ T cell derived LETs may serve as a tool for delivering cytotoxic vesicles to distant target cells, providing insights into mechanisms of CD8+ T cell mediated pathology.
The NITOS Wireless Sensor Network Testbed for Experimenting with Long-Range Technologies

In this work we demonstrate the NITOS Wireless Sensor Network testbed that allows remote and reproducible experimentation at city-scale, employing long-range wireless protocols. Specifically, we deploy 10 custom-made LoRa-enabled nodes in the city of Volos, Greece, coupled with an embedded device utilized as a means to provide remote access and configuration as well as for monitoring the experiments upon execution. Moreover, the testbed offers power consumption monitoring capabilities being able to capture the power draw of the entire sensor node, while also of individual modules such as the attached wireless interface, to characterize the energy-efficiency of new protocols.
A hierarchical adaptive control framework of path tracking and roll stability for intelligent heavy vehicle with MPC

Roll stability is a major concern in the path tracking process of intelligent heavy vehicles in emergency steering maneuvers. Due to the coupling of lateral and roll motions of heavy vehicle, steering actions for path tracking may come into conflict with that for roll stability. In this paper, a hierarchical adaptive control framework composed of a supervisor, an upper controller and a lower controller is developed to mediate conflicting objectives of path tracking and roll stability via steering control. In the supervisor, path tracking control mode or cooperative control mode of path tracking and roll stability is determined by the predicted rollover index, and a weight function is introduced to balance the control objectives of path tracking and roll stability in cooperative control mode. Then, in order to achieve multi-objective real-time optimization, model predictive control with varying optimization weights is used in the upper controller to calculate the desired front steer angle. The lower controller which integrates the real electrically assisted hydraulic steering system based on Proportional-Integral-Derivative control is designed to control steering wheel angle. Simulation and hardware-in-loop implementation results in double lane change scenario show that the proposed hierarchical adaptive control framework can enhance roll stability in emergency steering maneuvers while keeping the accuracy of path tracking for intelligent heavy vehicle within an acceptable range.
Effects of genotype and cotyledon section on organogenesis in sunflower

The present study was conducted to identify the organogenetic potential of cotyledon sections from different genotypes of sunflower. Seeds were surface sterilized and germinated on hormone free half strength MS basal medium. Cotyledons from 2-dayold seedling were split in half and cultivated on MS medium supplemented with 4.4 µM BAP and 5.4 µM NAA. The experiment was designed in a randomized complete block with 3 replications. There were differences among the genotypes and cotyledon sections for all studied organogenesis parameters. Inbred line CMS60/52 and proximal explants presented the highest values for the percentage of explants producing shoots, 57.1% and 58.5%, respectively. CMS60/52 × proximal explants’ interactions showed the highest values for average number of shoots per explants plated as well (13.3) as regenerant explant (21.3 shoots) respectively. Shoot organogenesis was optimized on proximal explants.
Efficacy of Combined Topical Timolol and Oral Propranolol for Treating Infantile Hemangioma: A Meta-Analysis of Randomized Controlled Trials

Background Oral propranolol has become the first-line treatment for infantile hemangioma (IH). However, combined therapy with topical timolol and oral propranolol has been proposed as a more effective IH treatment strategy. We aimed to compare the safety and efficacy of topical timolol, oral propranolol, and their combination for treating IH in a meta-analysis. Methods Relevant randomized controlled trials (RCTs) were obtained after searching the PubMed, Embase, Cochrane’s Library, China National Knowledge Infrastructure, and WanFang databases. A random-effect model was used to pool the results. Results Eight RCTs with 759 patients with IH were included in this meta-analysis. Treatment with topical timolol alone showed a similar response rate compared to oral propranolol (risk ratio [RR] = 0.97, p = 0.63), but resulted in fewer adverse events (RR = 0.36, p = 0.002). Combined treatment with topical timolol and oral propranolol showed a favorable response rate compared to treatment with oral propranolol (RR = 1.14, p = 0.03) or topical timolol (RR = 1.36, p = 0.01) alone. Moreover, combined treatment showed similar risks of adverse events compared to oral propranolol (RR = 0.80, p = 0.24) or topical timolol (RR = 1.31, p = 0.25) alone. Conclusions Combined treatment with topical timolol and oral propranolol may be more effective than either single treatment strategy in patients with IH. Topical timolol alone conferred similar efficacy for IH compared to oral propranolol, but with less incidence of adverse events.
Recent Discoveries in the Androgen Receptor Pathway in Castration-Resistant Prostate Cancer

The androgen receptor (AR) is the main therapeutic target in advanced prostate cancer, because it regulates the growth and progression of prostate cancer cells. Patients may undergo multiple lines of AR-directed treatments, including androgen-deprivation therapy, AR signaling inhibitors (abiraterone acetate, enzalutamide, apalutamide, or darolutamide), or combinations of these therapies. Yet, tumors inevitably develop resistance to the successive lines of treatment. The diverse mechanisms of resistance include reactivation of the AR and dysregulation of AR cofactors and collaborative transcription factors (TFs). Further elucidating the nexus between the AR and collaborative TFs may reveal new strategies targeting the AR directly or indirectly, such as targeting BET proteins or OCT1. However, appropriate preclinical models will be required to test the efficacy of these approaches. Fortunately, an increasing variety of patient-derived models, such as xenografts and organoids, are being developed for discovery-based research and preclinical drug screening. Here we review the mechanisms of drug resistance in the AR signaling pathway, the intersection with collaborative TFs, and the use of patient-derived models for novel drug discovery.
Genome-wide analysis of PRR gene family uncovers their roles in circadian rhythmic changes and response to drought stress in Gossypium hirsutum L.

Background The circadian clock not only participates in regulating various stages of plant growth, development and metabolism, but confers plant environmental adaptability to stress such as drought. Pseudo-Response Regulators (PRRs) are important component of the central oscillator (the core of circadian clock) and play a significant role in plant photoperiod pathway. However, no systematical study about this gene family has been performed in cotton. Methods PRR genes were identified in diploid and tetraploid cotton using bioinformatics methods to investigate their homology, duplication and evolution relationship. Differential gene expression, KEGG enrichment analysis and qRT-PCR were conducted to analyze PRR gene expression patterns under diurnal changes and their response to drought stress. Results A total of 44 PRR family members were identified in four Gossypium species, with 16 in G. hirsutum, 10 in G. raimondii, and nine in G. barbadense as well as in G. arboreum. Phylogenetic analysis indicated that PRR proteins were divided into five subfamilies and whole genome duplication or segmental duplication contributed to the expansion of Gossypium PRR gene family. Gene structure analysis revealed that members in the same clade are similar, and multiple cis-elements related to light and drought stress response were enriched in the promoters of GhPRR genes. qRT-PCR results showed that GhPRR genes transcripts presented four expression peaks (6 h, 9 h, 12 h, 15 h) during 24 h and form obvious rhythmic expression trend. Transcriptome data with PEG treatment, along with qRT-PCR verification suggested that members of clade III (GhPRR5a, b, d) and clade V (GhPRR3a and GhPRR3c) may be involved in drought response. This study provides an insight into understanding the function of PRR genes in circadian rhythm and in response to drought stress in cotton.
Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm

Dynamic spectrum access (DSA) is a promising technology to increase the spectrum efficiency of Internet of Things (IoT) networks, where the traffic demand grows up dramatically recently. In this paper, an intelligent DSA-assisted IoT network is introduced, where we investigate the spectrum sensing through neuromorphic computing (NC) and spectrum access through genetic algorithm (GA)-based power allocation. To be specific, we apply the NC's unconventional computing architectures that exploit and harness the intrinsic dynamics for computation, and thus provide increased functionality with better spectrum sensing performance requiring significantly lower size, weight, and power budgets. Furthermore, we design a GA algorithm to intelligently search the desirable transmission power for multiple IoT devices sharing the same channel to enhance the capacity of the highly dynamic DSA-assisted IoT network. Extensive simulation results have demonstrated the benefits of NC and GA compared to other baseline algorithms and methodologies.