Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergency Craniotomy and Burr-Hole Trephination in a Low-Resource Setting: Capacity Building at a Regional Hospital in Cambodia

To evaluate the teaching effect of a trauma training program in emergency cranial neurosurgery in Cambodia on surgical outcomes for patients with traumatic brain injury (TBI). We analyzed the data of TBI patients who received emergency burr-hole trephination or craniotomy from a prospective, descriptive cohort study at the Military Region 5 Hospital between January 2015 and December 2016. TBI patients who underwent emergency cranial neurosurgery were primarily young men, with acute epidural hematoma (EDH) and acute subdural hematoma (SDH) as the most common diagnoses and with long transfer delay. The incidence of favorable outcomes three months after chronic intracranial hematoma, acute SDH, acute EDH, and acute intracerebral hematoma were 96.28%, 89.2%, 93%, and 97.1%, respectively. Severe traumatic brain injury was associated with long-term unfavorable outcomes (Glasgow Outcome Scale of 1–3) (OR = 23.9, 95% CI: 3.1–184.4). Surgical outcomes at 3 months appeared acceptable. This program in emergency cranial neurosurgery was successful in the study hospital, as evidenced by the fact that the relevant surgical capacity of the regional hospital increased from zero to an acceptable level.
Phenolic Acid Composition and Antioxidant Activity of Whole and Defatted Seeds of Italian Hemp Cultivars: A Two-Year Case Study

The study investigated the nutritional profile and nutraceutical composition of the seeds (whole and defatted) of two monoecious hemp cultivars (Carmaleonte, Codimono) and one dioecious cultivar (CS) grown during the 2018 and 2019 crop seasons. The phenolic acid profiles, both free and bound, antioxidant activity (AA), protein content (PC), total phenolic content (TPC), β-carotene, lutein content, and condensed tannins (CT) were studied, and the effects of genotype (G), year (Y), and GxY interaction were also measured. The results indicated the stronger involvement of the year in the nutritional and antioxidant properties of the whole seeds than in those of the defatted seeds, as indicated by the analysis of the variance. The PC, TPC, AA, sum of phenolics free (SPF), and sum of phenolics bound (SPB) were significantly affected by year, while the lutein and some phenolic acids, free and bound (ferulic and p-coumaric acids and N-trans-caffeoyltyramine), showed significant effects of the genotype. In this respect, the Carmaleonte revealed the highest content of ferulic and p-coumaric acids, as well as CS of N-trans-caffeoyltyramine. A prevalence of Y effect over G was measured in the free and bound fraction of the phenolics of the whole seeds, in contrast to the defatted seeds, in which significant effects of GxY were also measured. Moreover, the Pearson’s correlation coefficients indicated a strict involvement of precipitations in the variation of the phenolics accumulation, above all with bound p-hydroxybenzoic acid (r = 0.71 **), bound syringic acid (r = 0.69 *), bound N-trans-caffeoyltyramine (r = 0.64 *), and SPB (r = 0.60 *). As phenolics bound fractions have strong biological activities, (including antioxidant and anti-inflammatory activities) the high concentrations of N-trans-Caffeoyltyramine B in the CS defatted seeds suggest that it is valuable ingredient for functional foods.
On the Controllability of Artificial Intelligence: An Analysis of Limitations

The invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid the pitfalls of such a powerful technology it is important to be able to control it. However, the possibility of controlling artificial general intelligence and its more advanced version, superintelligence, has not been formally established. In this paper, we present arguments as well as supporting evidence from multiple domains indicating that advanced AI cannot be fully controlled. The consequences of uncontrollability of AI are discussed with respect to the future of humanity and research on AI, and AI safety and security.
1-Perfect Codes Over the Quad-Cube

A vertex subset <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> of a graph <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> constitutes a 1-perfect code if the one-balls centered at the nodes in <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> effect a vertex partition of <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>. This paper considers the quad-cube <inline-formula> <tex-math notation="LaTeX">$CQ_{m}$ </tex-math></inline-formula> that is a connected <inline-formula> <tex-math notation="LaTeX">$(m+2)$ </tex-math></inline-formula>-regular spanning subgraph of the hypercube <inline-formula> <tex-math notation="LaTeX">$Q_{4m+2}$ </tex-math></inline-formula>, and shows that <inline-formula> <tex-math notation="LaTeX">$CQ_{m}$ </tex-math></inline-formula> admits a vertex partition into 1-perfect codes iff <inline-formula> <tex-math notation="LaTeX">$m=2^{k}-3$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$k\ge 2$ </tex-math></inline-formula>. The scheme for that purpose makes use of a procedure by Jha and Slutzki that constructs Hamming codes using a Latin square. The result closely parallels the existence of a 1-perfect code over the dual-cube, which is another derivative of the hypercube.
Climate Change Vulnerability Assessment: An Evaluation of Social Dimension

Climate change vulnerability is the function of exposure, sensitivity, and adaptive capacity of its natural and socioeconomic systems. Following spatial “scale” of the assessment, administrative-territorial units were selected for this study. A field survey questionnaire was used to collect data for the study based on the sample size of 500 questionnaires that was administered to household heads in the study area. In the questionnaire, questions were designed to give respondents the opportunity to choose from several alternatives given in the instrument while descriptive statistics was for the analysis of data. Descriptive statistics analysis based on mean ranking was carried out to identify the level of peoples’ vulnerability to climate change in the study area. The exposure assessment was based on the response analysis of baseline information. The sensitivity assessment for the study was analyzed by using physiographical and socioeconomic characteristics, described by a set of specific indicators and responses of the residents. The adaptive capacity was captured by general economic and agricultural indicators, taking into consideration the major occupation the predominant lifestyle of the residents. Through a ranking approach, the relative vulnerability of each ATU was calculated by summing its sensitivity and adaptive capacity ranks; the latter were obtained as combinations of their primary indicator ranks, arranged in an increasing and decreasing order, respectively. The major climate change exposure pathway in the study area were reduction in green environment, decrease in total annual rainfall, warmer weather, early cessation of rainfall, late on-set of rainfall and shrinkage of water bodies. The major sensitivity to climate change was decrease in crop yield, whereas increase in cost of food crops, drought incidents, famine, poverty, indiscriminate falling down of trees and disaster. It was also established that the major adaptive strategies to climate change in the study area were irrigation farming, use of organic manure, planting of drought tolerant varieties and early planting.
Emerging Blood-Based Biomarkers for Predicting Immunotherapy Response in NSCLC

Simple Summary Treatment with immunotherapy has been established as a standard treatment for lung cancer in recent years. Unfortunately, still, only a small proportion of patients benefit from the treatment, being the first leading cause of cancer death worldwide. Therefore, there is an urgent need for predictive biomarkers to help clinicians to discern whose patients are more likely to respond to immunotherapy. Since liquid biopsy opens the door to select patients and monitor the response during the treatment in a non-invasive way, in this review, we focus on the most relevant and recent results based on blood soluble biomarkers. Abstract Immunotherapy with Immune Checkpoint Inhibitors (ICIs) has demonstrated a profitable performance for Non-Small Cell Lung Cancer (NSCLC) cancer treatment in some patients; however, there is still a percentage of patients in whom immunotherapy does not provide the desired results regarding beneficial outcomes. Therefore, obtaining predictive biomarkers for ICI response will improve the treatment management in clinical practice. In this sense, liquid biopsy appears as a promising method to obtain samples in a minimally invasive and non-biased way. In spite of its evident potential, the use of these circulating biomarkers is still very limited in the real clinical practice, mainly due to the huge heterogeneity among the techniques, the lack of consensus, and the limited number of patients included in these previous studies. In this work, we review the pros and cons of the different proposed biomarkers, such as soluble PD-L1, circulating non-coding RNA, circulating immune cells, peripheral blood cytokines, and ctDNA, obtained from liquid biopsy to predict response to ICI treatment at baseline and to monitor changes in tumor and tumor microenvironment during the course of the treatment in NSCLC patients.
Proposals and Evaluations of Robotic Attendance at On-Site Network Maintenance Works

SUMMARY While the introduction of softwarelization technologies such as software-deﬁned networking and network function virtualization transfersthemainfocusofnetworkmanagementfromhardwaretosoftware,networkoperatorsstillhavetodealwithvariousandnumerousnetworkand computingequipmentlocatedinnetworkcenters.Towardfullyautomatednetworkmanagement,webelievethataroboticapproachwillbeessen-tial,meaningthatphysicalrobotswillhandlenetwork-facilitymanagementworksonbehalfofhumans.Thispaperfocusesonroboticassistancefor on-sitenetworkmaintenanceworks. Currently, formanynetworkoperators, some network maintenance works (e.g., hardware check, hardware installation/replacement, high-impact update of software, etc.) are outsourced to computing and network vendors. Attendance (witness work) at the on-site vendor’s works is one of the major tasks of network operators. Network operators conﬁrm the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three es- sential works of robots, namely delegated attendance at on-site meetings , progress check by periodical patrol , and remote monitoring , which support the various forms of attendance. The paper presents our implementation of enabling these forms of support, and reports the results of experiments conducted in a commercial network center.
The Pan‐Arctic Continental Slope as an Intensifying Conveyer Belt for Nutrients in the Central Arctic Ocean (1985–2015)

Primary production in the Central Arctic Ocean (CAO) is limited by light and bioavailable nutrients. With the decline of the sea‐ice cover in recent decades, and the resulting increase in light availability, nitrate limitation has been speculated to become more prominent. We used an eddy‐permitting biogeochemical model simulation to estimate nitrate advective fluxes at different spatio‐temporal scales (synoptic, mesoscale and sub‐mesoscale) over the 1985–2015 period. We found that the pan‐Arctic continental slope contributes disproportionately to the Dissolved Inorganic Nitrogen supply and that this supply is intensifying through two main processes: lateral eddy transport and upwelling. Despite this increasing supply in nitrate and an intensification of ocean dynamics, the nutrient supply is decreasing everywhere else in the central basins and the simulation indicates that the CAO is still shifting from light to nutrient limitation.
Covid-19 and analysis of needs of the primary school teachers in online-schooling in Iran

This study is a basic qualitative research design in which 48 primary school teachers voluntarily attended a guided focus-group interview in Google Meet platform in April 2021. To design an effective focus group 4 interviews were made with 12 teachers in each. The whole interviews were audio recorded, transcribed, and coded based on the needs and issues mentioned by the teachers. Five problems were highlighted: 1) unfit teaching materials for online schooling, 2) communication issues with their students from the distance, 3) the need for teacher training courses on various aspects of online assessment methods, 4) the need for teacher training courses on various teaching methods in the e-learning platforms, and 5) long hours of preparation for their online classes. The results point the need of preparation of prospective and practice for teachers on online schooling system, abording various aspects of e-learning instructional strategies, classroom management, and assessment methods.
Segmentation of vegetation and microplots in aerial agriculture images: A survey

Because of the increasing global population, changing climate, and consumer demands for safe, environmentally friendly, and high‐quality food, plant breeders strive for higher yield cultivars by monitoring specific plant phenotypes. Developing new crop cultivars and monitoring through current methods is time‐consuming, sometimes subjective, and based on subsampling of microplots. High‐throughput phenotyping using unmanned aerial vehicle‐acquired aerial orthomosaic images of breeding trials improves and simplifies this labor‐intensive process. To perform per‐microplot phenotype analysis from such imagery, it is necessary to identify and localize individual microplots in the orthomosaics. This paper reviews the key concepts of recent studies and possible future developments regarding vegetation segmentation and microplot segmentation. The studies are presented in two main categories: (a) general vegetation segmentation using vegetation‐index‐based thresholding, learning‐based, and deep‐learning‐based methods; and (b) microplot segmentation based on machine learning and image processing methods. In this study, we performed a literature review to extract the algorithms that have been developed in vegetation and microplots segmentation studies. Based on our search criteria, we retrieved 92 relevant studies from five electronic databases. We investigated these selected studies carefully, summarized the methods, and provided some suggestions for future research.
Impact of program-based sustainable urban agricultural intervention on women empowerment in Addis Ababa: Evidence from women empowerment in agriculture index analysis

Urban agriculture is increasingly recognized in academic research, policy practice, and advocacy as rural land is increasingly shrinking for agricultural production; and is argued as pro-poor urban intervention. This study argued that previous researches focused on: (1) rural agriculture; (2) women’s economic empowerment; and (3) positive impacts of urban agricultural interventions on the poor. By observing 10 indicators of women’s empowerment in agriculture, the study administered household survey, interviews, and cases of beneficiary women. The study revealed that while 80% of beneficiary women were empowered (to the extent of 0.93 scores), the remaining 20% were not. When the evidence is disaggregated to intersectional differences among the women who claimed to be empowered, unintended impacts were visibly observable. Though urban agriculture contributes to women's empowerment, lack of tailored support and the use of program approach exclude segments of women’s beneficiaries. Thus, a right-based approach that considers holistic aspects of women’s empowerment is advised to inform interventions, and a feminist gender analysis is required to document the invisible and intersectional barriers to beneficiary targeting.
Carbonaceous FexP Synthesized via Carbothermic Reduction of Dephosphorization Slag as Hydrogen Evolution Catalyst for Water Splitting

Developing the high-efficiency and cheap non-noble catalysts towards hydrogen evolution reaction (HER) is of significance for water splitting. Herein, for the first time, we report a simple method of acid leaching combined with carbothermic reduction with dephosphorization slag to construct a carbonaceous FexP/C catalyst. In alkaline medium, the corresponding overpotential when the output current density was 10 mA cm−2 (η10) was only 145 mV. Additionally, there was no obvious attenuation after 3000 cycles, which showed significantly better activity and stability than that of non-carbonaceous FexP catalysts prepared by gas–solid phosphating. The structure and composition of FexP/C were characterized by X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy, and inductively coupled plasma atomic emission spectrometer. The electrochemical properties of the electrode were evaluated by cyclic voltammetry, linear scanning voltammetry, electrochemical impedance spectroscopy, and cyclic stability. The results showed that the prepared FexP/C was composed of FeP-Fe2P mixed nanocrystals supported on amorphous carbon. Compared with FexP, the synergistic catalysis of the FeP and Fe2P phases as well as the interactive support effect between the FeP-Fe2P mixed nanocrystals and the amorphous carbon support will attribute the rich active sites for electrocatalytic reaction and reduce the charge transfer resistance. Thus, FexP/C has good hydrogen evolution activity and stability. Overall, the preparation of catalysts with high additional value based on dephosphorization slag was preliminarily explored.
Economic Feeding of Pigs in Mizoram: A Field Study for Assessment of Nutritional Constraints and Identifying the Useful Local Feed Resources for Economic Feeding

The study was conducted in the Department of Animal Nutrition, College of Veterinary Sciences and Animal Husbandry, Central Agricultural University (Imphal), Aizawl, Mizoram during 2018-19. The objective of the research was to assess the nutritional constraints of feeding pigs in Mizoram and to put forward some solutions by identifying and utilizing local feed resources for economic feeding. Numbers of unconventional feeds were used for feeding pigs in Mizoram and fourteen among them were identified to be commonly used in day-to-day preparation of home-made cooked feed, which was the composite feed given to all categories of pigs. Constraints of the existing feeding practice were identified as high cost of conventional feeds, low dry matter intake causing deficient energy consumption, poor digestibility of nutrients when home-made cooked feed was fed, high tannin content of unconventional feeds. Except crude protein, apparent digestibility of dry matter, ether extract, crude fibre, nitrogen free extract and organic matter were low under the existing feeding practice. Based on nutrient contents, unconventional local feeds were categorized as protein and energy rich for substitution of conventional ingredients in ration formulation. Few rations with unconventional feeds substituting conventional ones suggested for grower and finisher pigs. Complete feed block of conventional and unconventional feeds was found acceptable to pigs. The study indicated the possibility of economic feeding of pigs by utilizing the local feeds keeping in view their nutritive and anti-nutritive contents and taking into consideration the constraints associated with nutrient intakes under the existing feeding management.
Ported Shroud Flow Processes and Their Effect on Turbocharger Compressor Operation

The ported shroud (PS) self-recirculating casing treatment is widely used to delay the onset of the surge by enhancing the aerodynamic stability of the turbocharger compressor. The increase in the stable operation region of the turbocharger compressor is achieved by recirculating the low momentum fluid that blocks the blade passage to the compressor inlet through a ported shroud cavity. While the ported shroud design delays surge, it comes with a small penalty in efficiency. This work presents an investigation of the flow processes associated with a ported shroud compressor and quantifies the effect of these flow mechanisms on the compressor operation. The full compressor stage is numerically modelled using a Reynolds Averaged Navier-Stokes (RANS) approach employing the shear stress transport (SST) turbulence model for steady state simulations at the design and near surge conditions. The wheel rotation is modelled using a multiple reference frame (MRF) approach. The results show that the flow exits the PS cavity at the near surge condition in the form of three jet-like structures of varying velocity amplitudes. Net entropy generation in the compressor model is used to assess the influence of the ported shroud design on the compressor losses, and the results indicate a small Inlet-PS mixing region is the primary source of entropy generation in the near surge conditions. The analysis also explores the trends of entropy generation at the design and the near surge condition across the different speed lines. The results show that the primary source of entropy generation is the impeller region for the design condition and the inlet-PS cavity region for the near surge condition.
The Reﬂective Explorer: Online Meta-Exploration from Ofﬂine Data in Realistic Robotic Tasks

Reinforcement learning is difﬁcult to apply to real world problems due to high sample complexity, the need to adapt to frequent distribution shifts and the com-plexities of learning from high-dimensional inputs, such as images. Over the last several years, meta-learning has emerged as a promising approach to tackle these problems by explicitly training an agent to quickly adapt to new tasks. However, such methods still require huge amounts of data during training and are difﬁcult to optimize in high-dimensional domains. One potential solution is to consider ofﬂine or batch meta-reinforcement learning (RL) - learning from existing datasets without additional environment interactions during training. In this work we develop the ﬁrst ofﬂine model-based meta-RL algorithm that operates from images in tasks with sparse rewards. Our approach has three main components: a novel strategy to construct meta-exploration trajectories from ofﬂine data, which allows agents to learn meaningful meta-test time task inference strategy; representation learning via variational ﬁltering and latent conservative model-free policy optimization. We show that our method completely solves a realistic meta-learning task involving robot manipulation, while naive combinations of previous approaches fail.