4,464 research outputs found
Radar Placement Optimization Based on Adaptive Multi-Objective Meta-Heuristics
Airspace surveillance is a significant issue for many countries to control and manage their airspace. The
number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this
paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with
the least possible number of radars in a certain region. The radar placement problem is considered as a
multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To
perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated
annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with
differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are
tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on
the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested
algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and
make it more suitable for solving this optimization problem. The experimental results show that a coverage
rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it
exceeds 99%
A Novel Intelligent Traffic Recovery Model for Emergency Vehicles Based on Context-aware Reinforcement Learning
Management of traffic emergencies has become very popular in recent years. However,
timely response to emergencies and recovering from an emergency is an important prob-
lem in itself. The strategies in the current studies merely suggest that after an emergency
vehicle passes, the state should iterate to the next phase. Therefore, this paper proposes a
novel approach for recovering from an emergency situation at an intersection based on real
scenarios. The proposed method is a combination of context-aware and Reinforcement
Learning (RL) models that predicts better alternatives for different states rather than just
iterating to the next phase. In this regard, a new algorithm, named Interrupt Algorithm,
is proposed to predict proper actions for recovering the emergency situation. This algo-
rithm uses a Q-learning-based model that learns from traffic context for an emergency sit-
uation and chooses viable action from an action set. The recovery actions are categorized as
max, min, and avg, respectively. Test results show that our proposed model outperforms
traffic flow over than standard single choice recovering action-based approach by approx-
imately 80%. Based on this, it may be more beneficial to choose different actions and there-
fore, proposed algorithm with the help of RL presents a more dynamic emergency recovery
model
Release Kinetics Modelling and in Vivo-Vitro, Shelf-Life Study of Resveratrol Added Composite Transdermal Scaffolds
In this article, the suitability of composite transdermal biomaterial for wound dressing applications is discussed.
Bioactive, antioxidant Fucoidan and Chitosan biomaterials were doped into polyvinyl alcohol/β-tricalcium
phosphate based polymeric hydrogels loaded with Resveratrol, which has theranostic properties, and biomembrane design with suitable cell regeneration properties was aimed. In accordance with this purpose, tissue
profile analysis (TPA) was performed for the bioadhesion properties of composite polymeric biomembranes.
Fourier Transform Infrared Spectrometry (FT-IR), Thermogravimetric Analysis (TGA) and Scanning Electron
Microscopy (SEM-EDS) analyses were performed for morphological and structural analyses of biomembrane
structures. In vitro Franz diffusion mathematical modelling of composite membrane structures, biocompatibility
(MTT test) and in vivo rat tests were performed. TPA analysis of resveratrol loaded biomembrane scaffold design;
compressibility; 13.4 ± 1.9(g.s), hardness; 16.8 ± 1(g), adhesiveness; − 11 ± 2.0(g.s), elasticity; 0.61 ± 0.07,
cohesiveness; 0.84 ± 0.04 were found. Proliferation of the membrane scaffold was 189.83 % at 24 h and 209.12
% at 72 h. In the in vivo rat test; at the end of 28th day, it was found that biomembrane_3 provided 98.75 ± 0.12
% wound shrinkage. The shelf-life of RES in the transdermal membrane scaffold, which was determined as Zero
order according to Fick's law in in vitro Franz diffusion mathematical modelling, was found to be approximately
35 days by Minitab statistical analysis. The importance of this study is that the innovative and novel transdermal
biomaterial supports tissue cell regeneration and cell proliferation in theranostic applications as a wound
dressing
Forced Vibrations of an Elastic Rectangular Plate Supported by Unilateral Edge Lateral Springs
The present study deals with static and dynamic behavior, including forced vibrations, of an elastic rectangular plate supported
along its edges by unilateral elastic springs. The plate is assumed to be subjected to a distributed and concentrated load applied
eccentrically and the external moments as well. Equations of motion are derived by considering the dynamic response of the
plate, assuming a series of the Chebyshev polynomials for the displacement function and applying Galerkin’s method. Effects
of the boundary conditions of the plate, i.e., the shear forces, the bending moments and the corner forces, are included in the
equation of motion to compensate for the non-satisfied boundary conditions and increase the accuracy of Galerkin’s method.
The numerical solution is accomplished by using an iterative process due to the nonlinearity of the unilateral character of
the support. Static behavior of the plate under static concentrated load and uniformly distributed load is investigated in detail
by taking into consideration a wide range of support stiffnesses. Numerical treatment of the problem in the time domain
is carried out by assuming a stepwise change in the external loads, and the linear acceleration procedure is adopted for the
solution of the governing differential equation derived from the equation of motion. Various numerical results are presented
in the figures focusing on the nonlinearity of the problem due to the plate lift-off from the unilateral springs at the corners of
the edge supports
İhtiyar Dünyamızı Korumak ve Kaynakları Doğru Kullanmak
İnsanoğlu, dünyamız kaynaklarını doğa kanunları doğrultusunda bilimin ve ahlakın ışığında kullanmıyor, maalesef cansız ve canlı yaşama çok müdahale ediliyor. Yeraltı ve yerüstü kaynaklarının yanlış ve aşırı tüketimi ekosistemi tahrip etmekte ve biyoçeşitliliği bozmaktadır. Unutulmamalıdır ki; küresel emisyonun %23’ü ekosistemin bozulmasıyla ilişkilidir (The Economist, The World In 2020, p:84). 2010 Yılındaki Nagoya/Japonya biyoçeşitlilik zirvesinde hükümetler 2020 için 20 hedef koymuşlardı. 196 ülkeyi bağlayan 20 hedefe balık tutmada sürdürülebilirlikten, hava kirliliğini düşürmeye kadar birçok konu girmiştir. 2022 Yılında Montreal’daki/Kanada COP15 BM Biyoçeşitlilik Konferansı’nda Nagoya’da karar altına alınan hiçbir hedefin gerçekleşmediği, canlı yaşamı savunma yerine yeşil badana denen makyajlamanın öne çıktığı itiraf edilmiştir. Montreal Zirvesi’nde 2030 itibariyle karaların ve denizlerin %30’unun korunması kararı çıkmıştır. Burada önemli olan hükümetlerin korumaya almadan ne anladıkları ve yaptıklarının ekolojistlerin söyledikleriyle ne ölçüde örtüştüğüdür. Canlı yaşam alanlarını korumak ve biyoçeşitlilik insan sağlığı için de çok önemlidir (ilaç yapımı gibi)
Machine Learning Approaches for Cell Viability
Hücre canlılığı, kök hücre tedavileri, kanser tedavileri, estetik ve kozmetik gibi klinik araştırmalarda önemli yer tutmaktadır. Doğru tedavi ve yaklaşımın uygulanabilmesi için alınan örnekteki toplam hücre canlılık oranı bilinmelidir. Bu noktada alınan örnekteki hücrelerin canlı veya ölü olarak doğru sınıflandırılması kritik öneme sahiptir. Bu çalışma, hücrelerin ölü veya canlı olarak sınıflandırılmasını yapay ögrenme algoritmalarını kullanarak yapmayı amaçlamaktadır. Çalışma kapsamında, yapay ögrenme sınıflandırıcılarının topluluk ögrenmeye dayalı yöntemlerinden olan rastgele orman, XGBoost ve LightGBM algoritmaları kullanılarak başarımları karşılaştırılmıştır. Deneysel çalışmada, fibroblast hücreleri ve mezenkimal kök hücrelerini içieren iki farklı veri kümesi kullanılmıştır. İki veri kümesi için de her algoritma için hiper-parametre optimizasyonu yapıldıktan sonra en iyi parametre değerleri ile algoritmalar çalıştırılmıştır. Fibroblast hücreleri için en iyi doğruluk değeri ˘ %97, 69 değeri ˘ ile XGBoost algoritmasından elde edilirken mezenkimal kök hücreleri için en iyi doğruluk değeri % ˘ 92, 42 degeri ile LightGBM algoritmasından elde edilmiştir.Cell viability is important for clinical studies such as
stem cell treatments, cancer treatments, aesthetics, and cosmetics.
In order to apply the right treatment and approach, the total
cell viability rate in the sample should be known. At this point,
it is critical to correctly classify the cells in the sample as live
or dead. This study aims to classify cells as dead or live by
using machine learning algorithms. Within the scope of the study,
the performances of artificial learning classifiers were compared
using random forest, XGBoost, and LightGBM algorithms, which
are ensemble learning methods. The experimental study used
two different datasets including fibroblast cells and mesenchymal
stem cells. For both datasets, algorithms were run with the best
parameter values after hyper-parameter optimization for each
algorithm. While the best accuracy value for fibroblast cells was
obtained from the XGBoost algorithm with a value of 97.69%,
the best accuracy value for mesenchymal stem cells was obtained
from the LightGBM algorithm with a value of 92.42
The Effect of Muqarnas on Acoustic Quality of Traditional Turkish Bath Interior Space
Decorative “muqarnas” add charm to the design element and may influence the characteristics of an interior space. This
study hypothesises that muqarnas in traditional Turkish baths (hammams) affect acoustic performances of such interior
spaces. To test this hypothesis, a bath with muqarnas as the dome transition element was selected and a comparative study,
in terms of acoustics quality with and without the decorative muqarnas, was conducted. Furthermore, the effect of different
muqarnas configurations on acoustic quality of the interior space has been tested via optimisation simulations. With the
use of optimisation model, a form of parametric muqarnas in the selected interior space was optimised according to the
acoustic objectives. The optimisation tests were conducted with the objectives of increasing C50 (Clarity), C80, Reverberation
Time (RT), Speech Transmission Index (STI), and decreasing RT. It was found that muqarnas do have an impact on acoustic
performance of the interior space and the optimisation experiments suggest that different muqarnas configurations have
different impact on the acoustic quality
Non-invasive Microwave Glucose Sensor by Using a Hybrid Sensor Composed of a Frequency Selective Surface and Microstrip Patch Antenna
In this paper, a hybrid sensor composed of a frequency selective surface (FSS)
and microstrip patch antenna is investigated numerically for non-invasive glucose sensing in the
microwave region. The sensing method relies on detecting changes in the dielectric constant of
the sample under test (SUT) in response to variations in the concentration of glucose-deionized
water solutions. The SUT consists of the area between the microstrip patch antenna and FSS.
Four glucose-deionized water solutions (i.e., 72 mg/dL, 216 mg/dL, 330 mg/dL, and 600 mg/dL)
are tested in the SUT. The dielectric properties of the solutions are determined using the Debye
model. For the sensitivity analyses, the return losses (|S11| (dB)) of the proposed sensor and
the dependence of the resonance frequencies on the volume percentage of glucose in glucosedeionized water solutions have been noted. When the glucose-deionized water solutions changed
from 72 mg/dL to 600 mg/dL, the resonance frequency of the sensor blue shifted from 11.281 GHz
to 11.296 GHz. The sensitivity of the glucose sensor is calculated by absolute resonance frequency
shift in response to glucose-deionized water solution concentration change. When the FSS structure is removed from the hybrid sensor (i.e., the sensor structure has consisted of just an antenna),
the sensitivity value drops from 28.409 kHz/mg dL−1
to 18.939 kHz/mg dL−1
(i.e., 33.3%). Furthermore, the sensitivity of the sensor obtained by removing the antenna part from the hybrid
sensor (i.e., the sensor consisting only of FSS) is calculated as 15.152 kHz/mg dL−1
in simulations. The results show that the proposed hybrid sensor structure exhibits heightened sensitivity
compared to sensors solely reliant on antennas or FSS structures. This proposed novel hybrid
sensor structure that is lightweight, low-cost, easy-to-fabricate, portable, and easy to integrate
with microwave-integrated circuits will contribute to the non-invasive glucose sensor literature
A New Prediction-Based Algorithm for Dynamic Multi-objective Optimization Problems
The mechanism for reacting to the changes in an environ-
ment when detected is the key issue that distinguishes various algorithms
proposed for dynamic multi-objective optimization problems (DMOPs).
The severity of change is a significant approach to identify the dynamic
characteristics of DMOPs. In this paper, a prediction-based strategy
based on utilizing the degree of the changes is presented to address envi-
ronmental changes. In case of a change detection in the given DMOP, the
severity of change is evaluated and an appropriate reaction mechanism
is followed based on the degree of the observed change. To accelerate
the convergence process, the algorithm may respond multiple times for
the same change. The performance of our algorithm is evaluated by com-
paring it with dynamic multi-objective evolutionary algorithms using six
benchmarks. The effectiveness of our algorithm is demonstrated in the
experimental study where it outperforms other compared algorithms in
most of the tested instances considered
Recycling of Laser Powder Bed Fusion Scraps in Conventional Plastic Injection Systems
It is known that plastic materials, separated from the circular economy, are divided into small pieces over many years and
create risks by mixing with nature, seas, freshwater resources, and terrestrial ecosystems in micro dimensions. It is thought
that micro-size powder material and production scraps not used in Additive Manufacturing (AM) production processes will
turn into a waste problem in the future in parallel with the increasing usage intensity. In this direction, this study presents
a new and sustainable usage model within the scope of recycling Laser Powder Bed Fusion (LPBF) wastes. In the study,
granule materials obtained from AM waste material mixtures with different parameters are recommended to be recycled by
using them to produce functional plastic parts in the automotive industry plastic injection systems. In this context, materials
recycled with different methods and function tests in automotive company acceptance standards are shared
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