As multi-core processors scale to tens or hundreds of cores, efficiently mapping dynamic application workloads becomes critical to avoid thermal hotspots, energy waste, and deadline violations. Conventional static or heuristic mapping schemes fall short under run-time variability in task sizes, thermal coupling, and load imbalance. In this paper, a Reinforcement Learning–Driven Task-to-Core Mapping framework that jointly optimizes temperature, energy, performance, and load balance in a closed-loop simulation environment using ARM based multicore architecture is presented. The model is mapped as a state space model: each decision state encodes per-core load and temperature along with the next task’s profiled features; actions assign the task to one of N cores; and a multi-objective reward, dynamically weighted by task criticality and sensitivity parameters, guides learning via Q-learning. To ensure timeliness, the mapping is followed with a novel urgency-score scheduler that recalculates each task’s WCET and deadline based on run-time load and temperature and combines deadline urgency with starvation balancing. Evaluated on MiBench-derived workloads, our approach reduces makespan by up to 13%, saves ~6% energy, lowers peak temperature by ~5.95%, achieves sub-1% deadline miss-rate and delivers smallest jitter—outperforming task assignment and scheduling baselines.
An optimized gas sensor that operates at room temperature is highly suitable for industrial safety applications. Polyvinyl alcohol (PVA) was embedded with gold nanoparticles (Au NPs) to produce the ammonia (NH3) gas sensor. These films were synthesized successfully using two different stages: (1) pulsed laser ablation in liquid method to prepare Au nanoparticles, and (2) solvent casting method to embed nanoparticles into PVA substrates. The gas detection capability of the films was investigated based on their optical, morphological, structural, crystallographic, and dielectric features. Field emission scanning electron microscopy confirmed nanoscale and well-dispersed Au NPs within the PVA substrate. X-ray diffraction analysis showed crystallinity improvement from 44.91% (pure PVA) to 76.41%. UV-visible spectroscopy confirmed the slight decrease in the optical bandgap from 4.89 eV to 4.27 eV. The dielectric measurements exhibit enhanced electrical conductivity of PVA embedded with 3% of Au nanoparticles, which is directly correlated with the improvement of gas sensing capability. The sensor exhibited an effective response of around 64.2% and had a sensitivity of 1.62 with a response time of 6s at room temperature under a 600 ppm NH3 gas concentration. That showed a greater sensitivity compared to pure PVA film.
The present study was conducted during the spring of 2024 to investigate the distribution and speciation of iron in main soil orders from various locations in the Erbil Governorate. The analysis was carried out using a sequential extraction procedure to fractionate iron into operationally defined forms: Exchangeable Fe, Carbonate Fe, Organic bound Fe, Reducible Fe, Residual Fe, Soluble Fe, Available Fe and Total Fe. Soils representing four main soil orders which included Inceptisols, Mollisols, Vertisols and Aridisols were collected from various sites including Mergasur, Choman, Shaqlawa, Rawanduz, Koya, Daraban, Harir, Grdarasha, Omermamk, Dibaga, Makhmour, and Qaraj. The results revealed significant variability in iron fractions among the locations. The residual iron form was dominant across all samples, with the highest value observed in Qaraj (85.53 mg kg-1) and the lowest in Omermamk (62.85 (mg kg-1). Exchangeable and available iron were remarkably high in Omermamk (61.00 mg kg-1 and 63.08 mg kg-1 respectively), suggesting unique geochemical conditions at that site. In contrast, most other soils contained exchangeable iron below (5 mg kg-1). Soluble iron remained generally low across the study area, with the highest value detected in Koya (5.02 mg kg-1). Overall, the findings indicate that while residual forms of iron dominate, certain areas like Omermamk exhibit unusually high levels of exchangeable iron, potentially enhancing plant iron uptake. These results contribute to a better understanding of iron dynamics in Erbil soils and support the development of site-specific soil fertility management strategies.
The relationship between dietary mineral supplementation and sex preselection in livestock animals was not fully understood. Conflict results have been shown from difference methods of applying to supplement minerals by earlier studies. Hence, in this study, sheep fed diets supplemented with dietary Ca (Calcium) and Mg (Magnesium) via feeding route to examine sex ratios skewing toward female. Thirty Kurdish breed ewes were divided into two groups; control and CaMg (15 ewes per each group). Control group ewes offered a diet (1 kg concentrate with ad libitum wheat straw). While, ewes in CaMg group fed diet supplemented with Ca and Mg. Diets were offered twice a day (in the morning and evening) over a period of one-month. There was no effect (P>0.05) of dietary supplementation of CaMg on final live weigh of ewes. Although, ewes at CaMg had numerically (1.7 kg on average) lower live body weight than control. Similarly, serum (glucose, Ca, Mg, estrogen, and progesterone) were not difference (P>0.05) among control and CaMg group. In addition, the ratio of male and female new born lambs were not affected by CaMg supplementation (P>0.05). Although, in controls, female offsprings (47%) were which was 6% fewer than males (53%), whereas CaMg supplementation increased the proportion of females from 39% in control to 61% (+14 percentage). This represents a shift in sex bias of +28 percentage, from 6% fewer females to 22% more. Results of this study suggested a potential of skewing sex ratio towards female through dietary supplementation of Ca and Mg.
One of the most frequent types of asphalt pavement failure is rutting. Pavement failures can be greatly reduced or delayed by improving asphalt binder’s characteristics. High-performance pavements require high-quality asphalt that contains composite modifiers. In this study, a combination of polymer and nanoparticles was used to modify a local 40/50 penetration-grade asphalt binder (PG 70-16). Fixed dosages of carbon nanotubes (CNTs), nano silica (NS), styrene-butadiene-styrene (in both granular (SBSG) and powdered (SBSP) forms) were applied individually and in hybrid combinations. A series of physical, rheological tests, field emission scanning electron microscopy (FESEM) and energy-dispersive spectroscopy (EDS) were used to examine unmodified and modified asphalt binders to evaluate improvements in key performance characteristics. As well as, the comparison of the effectiveness of SBSG and SBSP in asphalt binder modification. According to the results, the additions improved viscosity, raised the softening point, penetration index, and flash point, while penetration decreased. High-temperature improvements were validated by DSR results. While BBR showed changes in low-temperature grades. The improvements were attained by increasing high temperature grade as follows: 4% SBS raised the grade to PG 76-16, while CNTs and hybrid of nanomaterials increased it to PG 82-16, NS alone gave PG 70-16. PG 88-10 was attained by the triple hybrid of polymer with nanomaterials. FESEM results revealed improved compatibility and dispersion, particularly in composite modified asphalt binders of (SBSP + NS + CNTs). SBSP outperformed SBSG in most of the tests, enhancing rutting resistance, penetration, softening point, and fatigue performance due to its stronger elastic polymer network.
