Search results for: proximal policy optimization

Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Yihao Kuang, Bowen Ding

Abstract:

With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Jingwei Du

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The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up.

Keywords: chiller plant, control methods, energy efficiency, proximal policy optimization, reinforcement learning

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Authors: Migena Mana, Ahmed Khalid Syed, Abdul Malik, Nikhil Cherian

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In recent years, advancements in deep learning enabled researchers to tackle the problem of self-driving cars. Car companies use huge datasets to train their deep learning models to make autonomous cars a reality. However, this approach has certain drawbacks in that the state space of possible actions for a car is so huge that there cannot be a dataset for every possible road scenario. To overcome this problem, the concept of reinforcement learning (RL) is being investigated in this research. Since the problem of autonomous driving can be modeled in a simulation, it lends itself naturally to the domain of reinforcement learning. The advantage of this approach is that we can model different and complex road scenarios in a simulation without having to deploy in the real world. The autonomous agent can learn to drive by finding the optimal policy. This learned model can then be easily deployed in a real-world setting. In this project, we focus on three RL algorithms: Q-learning, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). To model the environment, we have used TORCS (The Open Racing Car Simulator), which provides us with a strong foundation to test our model. The inputs to the algorithms are the sensor data provided by the simulator such as velocity, distance from side pavement, etc. The outcome of this research project is a comparative analysis of these algorithms. Based on the comparison, the PPO algorithm gives the best results. When using PPO algorithm, the reward is greater, and the acceleration, steering angle and braking are more stable compared to the other algorithms, which means that the agent learns to drive in a better and more efficient way in this case. Additionally, we have come up with a dataset taken from the training of the agent with DDPG and PPO algorithms. It contains all the steps of the agent during one full training in the form: (all input values, acceleration, steering angle, break, loss, reward). This study can serve as a base for further complex road scenarios. Furthermore, it can be enlarged in the field of computer vision, using the images to find the best policy.

Keywords: autonomous driving, DDPG (deep deterministic policy gradient), PPO (proximal policy optimization), reinforcement learning

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Authors: Qian Zaijian

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E-government is one of the hot issues in the current academic research of public policy and management. As the organic integration of information and communication technology (ICT) and public administration, e-government is one of the most important areas in contemporary information society. Policy information system is a basic subsystem of public policy system, its operation affects the overall effect of the policy process or even exerts a direct impact on the operation of a public policy and its success or failure. The basic principle of its operation is information collection, processing, analysis and release for a specific purpose. The function of E-government for public policy information system lies in the promotion of public access to the policy information resources, information transmission through e-participation, e-consultation in the process of policy analysis and processing of information and electronic services in policy information stored, to promote the optimization of policy information systems. However, due to many factors, the function of e-government to promote policy information system optimization has its practical limits. In the building of E-government in our country, we should take such path as adhering to the principle of freedom of information, eliminating the information divide (gap), expanding e-consultation, breaking down information silos and other major path, so as to promote the optimization of public policy information systems.

Keywords: China, e-consultation, e-democracy, e-government, e-participation, ICTs, public policy information systems

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Authors: Von Jerick Tenorio, Jonald Lucero, Marivic Vestal, Edwin Tiempo

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In patients with hostile aortic neck anatomy, the risks of proximal seal complications and stent migration remain with EVAR despite improved endograft technology. This case report discusses how the technical challenges of the hostile neck anatomy, proximal penetrating atherosclerotic ulcer (PAU) and tortuous femoral access were addressed. The CT aortogram of a 63-year-old hypertensive and diabetic man with recurring abdominal discomfort revealed a fusiform infra-renal aneurysm measuring 8.8 cm in length and 5.7 cm in diameter. The proximal landing zone only has a 3 mm healthy neck with a conicity of > 10% and a thrombus of 4 mm thick. Proximal to the aneurysm is a PAU with a circumferential mural thrombus. The right femoral artery is tortuous with > 90o angulation. A 20% oversized Endurant II endograft and Aptus Heli-FX EndoAnchors were deployed as prophylaxis for type I endoleaks and endograft migration consequent to the conical neck and proximal aneurysm extension consequent to the PAU. A stiff Backup Meier guide wire facilitated the deployment of the endograft. Coil embolization of the right internal iliac artery was performed as prophylaxis for type II endoleaks. EndoAnchors can be used as an adjunct to EVAR as prophylaxis for proximal seal complications and stent migration in patients with hostile aortic aneurysm neck anatomy and concomitant proximal PAU.

Keywords: endoAnchors, endoleaks, EVAR, hostile neck

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Leila Jafari, Viliam Makis

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In this paper, the joint optimization of the economic manufacturing quantity (EMQ), safety stock level, and condition-based maintenance (CBM) is presented for a partially observable, deteriorating system subject to random failure. The demand is stochastic and it is described by a Poisson process. The stochastic model is developed and the optimization problem is formulated in the semi-Markov decision process framework. A modification of the policy iteration algorithm is developed to find the optimal policy. A numerical example is presented to compare the optimal policy with the policy considering zero safety stock.

Keywords: condition-based maintenance, economic manufacturing quantity, safety stock, stochastic demand

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Authors: Zukhrof Romadhon, Nani Kurniati

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Availability of Spare parts is critical to support maintenance tasks and the production system. Managing spare part inventory deals with some parameters and objective functions, as well as the tradeoff between inventory costs and spare parts availability. Several mathematical models and methods have been developed to optimize the spare part policy. Many researchers who proposed optimization models need to be considered to identify other potential models. This work presents a review of several pertinent literature on spare part inventory optimization and analyzes the gaps for future research. Initial investigation on scholars and many journal database systems under specific keywords related to spare parts found about 17K papers. Filtering was conducted based on five main aspects, i.e., replenishment policy, objective function, echelon network, lead time, model solving, and additional aspects of part classification. Future topics could be identified based on the number of papers that haven’t addressed specific aspects, including joint optimization of spare part inventory and maintenance.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenance

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Authors: Ihab Saad Ahmed

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Background For Siewert type I and II gastroesophageal junction tumor (GEJ) laparoscopic proximal gastrectomy can be performed. It is associated with several perioperative benefits compared with open proximal gastrectomy. The use of laparoscopic proximal gastrectomy (LPG) has become an increasingly popular approach for select tumors Methods We describe our technique for LPG, including the preoperative work-up, illustrated images of the main principle steps of the surgery, and our postoperative course. Results Thirteen pts (nine males, four female) with type I, II (GEJ) adenocarcinoma had laparoscopic radical proximal gastrectomy and D2 lymphadenectomy. All of our patient received neoadjuvant chemotherapy, eleven patients had intrathoracic anastomosis through mini thoracotomy (two hand sewn end to end anastomoses and the other 9 patient end to side using circular stapler), two patients with intrathoracic anastomosis had flap and wrap technique, two patients had thoracoscopic esophageal and mediastinal lymph node dissection with cervical anastomosis The mean blood loss 80ml, no cases were converted to open. The mean operative time 250 minute Average LN retrieved 19-25, No sever complication such as leakage, stenosis, pancreatic fistula ,or intra-abdominal abscess were reported. Only One patient presented with empyema 1.5 month after discharge that was managed conservatively. Conclusion For carefully selected patients, LPG in GEJ tumour type I and II is a safe and reasonable alternative for open technique , which is associated with similar oncologic outcomes and low morbidity. It showed less blood loss, respiratory infections, with similar 1- and 3-year survival rates.

Keywords: LPG(laparoscopic proximal gastrectomy, GEJ( gastroesophageal junction tumour), d2 lymphadenectomy, neoadjuvant cth

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Authors: Jai Hyung Park, Eugene Kim, Jin Hun Park, Min Joon Oh

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Introduction: Subchondral insufficiency fracture of the femoral head (SIF) is a rare complication; however, it has been recognized to cause femoral head collapse. Subchondral insufficiency fracture (SIF) is caused by normal or physiological stress without any trauma. It has been reported in osteoporotic patients after the fixation of the proximal femur with an Intramedullary nail. Case presentation: We reported 5 cases with SIF of the femoral head after proximal femur fracture fixation with Intra-medullary nail. All patients had osteoporosis as an underlying disease. Good reduction was achieved in all 5 patients. SIF was found from about 3 months to 4 years after the initial operation, and all the fractures were solidly united at the final diagnosis. We investigated retrospectively the feature of those cases and several factors that affected the occurrence of SIF. Discussion: There are a few discussions regarding the SIF of the femoral head. These discussions may include the predisposing risk factors, how to diagnose the SIF in osteoporotic patients, and the peri-operative factors to prevent SIF. Conclusion: Subchondral insufficiency fracture of the femoral head is a considerable complication after the internal fixation of the proximal femur. There are several factors that can be modified. If they could be controlled in the peri-operative period, SIF could be prevented or handled in advance. Other options related to arthroplasty can be considered in old osteoporotic patients.

Keywords: insufficiency fracture of femoral head, intra-medullary nail, osteoporosis, proximal femur fracture

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: Seppo Kittilä, Sonja Dahlgren

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In this paper, a (morpho)phonological typology of proximal and distal demonstratives is proposed. Only the most basic proximal (‘this’) and distal (‘that’) forms have been considered, potential more fine-grained distinctions based on proximity are not relevant to our discussion, nor are the other functions the discussed demonstratives may have. The sample comprises 82 languages that represent the linguistic diversity of the world’s languages, although the study is not based on a systematic sample. Four different major types are distinguished; (1) Vowel type: front vs. back; high vs. low vowel (2) Consonant type: front-back consonants (3) Additional element –type (4) Varia. The proposed types can further be subdivided according to whether the attested difference concern only, e.g., vowels, or whether there are also other changes. For example, the first type comprises both languages such as Betta Kurumba, where only the vowel changes (i ‘this’, a ‘that’) and languages like Alyawarra (nhinha vs. nhaka), where there are also other changes. In the second type, demonstratives are distinguished based on whether the consonants are front or back; typically front consonants (e.g., labial and dental) appear on proximal demonstratives and back consonants on distal demonstratives (such as velar or uvular consonants). An example is provided by Bunaq, where bari marks ‘this’ and baqi ‘that’. In the third type, distal demonstratives typically have an additional element, making it longer in form than the proximal one (e.g., Òko òne ‘this’, ònébé ‘that’), but the type also comprises languages where the distal demonstrative is simply phonologically longer (e.g., Ngalakan nu-gaʔye vs. nu-gunʔbiri). Finally, the last type comprises cases that do not fit into the three other types, but a number of strategies are used by the languages of this group. The two first types can be explained by iconicity; front or high phonemes appear on the proximal demonstratives, while back/low phonemes are related to distal demonstratives. This means that proximal demonstratives are pronounced at the front and/or high part of the oral cavity, while distal demonstratives are pronounced lower and more back, which reflects the proximal/distal nature of their referents in the physical world. The first type is clearly the most common in our data (40/82 languages), which suggests a clear association with iconicity. Our findings support earlier findings that proximal and distal demonstratives have an iconic phonemic manifestation. For example, it has been argued that /i/ is related to smallness (small distance). Consonants, however, have not been considered before, or no systematic correspondences have been discovered. The third type, in turn, can be explained by markedness; the distal element is more marked than the proximal demonstrative. Moreover, iconicity is relevant also here: some languages clearly use less linguistic substance for referring to entities close to the speaker, which is manifested in the longer (morpho)phonological form of the distal demonstratives. The fourth type contains different kinds of cases, and systematic generalizations are hard to make.

Keywords: demonstratives, iconicity, language typology, phonology

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Authors: Maryam Moosavi, Hadi Khatibzadeh

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The main purpose of this paper is to study the proximal point algorithm for quasi-nonexpansive mappings in Hadamard spaces. △-convergence and strong convergence of cyclic resolvents for a finite family of quasi-nonexpansive mappings one to a fixed point of the mappings are established

Keywords: Fixed point, Hadamard space, Proximal point algorithm, Quasi-nonexpansive sequence of mappings, Resolvent

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Authors: Ahmed Elrewainy

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Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets

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Authors: Devasheesh Mathur

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The presentation reviews the literature on the phenomenon of policy innovation. Policy innovation refers to a shift in the way policy is made or executed. The paper covers comprehensively on the definition and also the various types of policy innovations. The emphasis is on the antecedents or the determinants of innovation in policies. The author has then made an effort to discover the knowledge gap in the field of policy innovation so as to identify the future scope of research. The objective is to lend more clarity in the area of policy innovation and help in creating a framework for policy-makers as well as academics.

Keywords: literature review, policy innovation, determinants, antecedents

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Authors: Kannapha Amaruchkul

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We formulate a dynamic overbooking model for a tour operator, in which most reservations contain at least two people. The cancellation rate and the timing of the cancellation may depend on the group size. We propose two overbooking policies, namely economic- and service-based. In an economic-based policy, we want to minimize the expected oversold and underused cost, whereas, in a service-based policy, we ensure that the probability of an oversold situation does not exceed the pre-specified threshold. To illustrate the applicability of our approach, we use tour package data in 2016-2018 from a tour operator in Thailand to build a data-driven robust optimization model, and we tested the proposed overbooking policy in 2019. We also compare the data-driven approach to the conventional approach of fitting data into a probability distribution.

Keywords: applied stochastic model, data-driven robust optimization, overbooking, revenue management, tour operator

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Authors: Jian Shao

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The effect of entrepreneurship on economic, innovation, and employment has been widely acknowledged by scholars and governments. As an essential factor of influencing entrepreneurship activities, entrepreneurship policy creates a conducive environment to support and develop entrepreneurship. However, the challenge in developing entrepreneurship policy is that policy is normally a combination of many different goals and instruments. Instead of examining the effect of individual policy instruments, we argue that attention to a policy mix is necessary. In recent years, much attention has been focused on comparing a single policy instrument to a policy mix, evaluating the interactions between different instruments within a mix or assessment of particular policy mixes. However, another required step in understanding policy mixes is to understand how and why mixes evolve and change over time and to determine whether any changes are an improvement. In this paper, we try to trace the development of the policy mix for entrepreneurship in China by mapping the policy goals and instruments and reveal the process of policy mix changing over time. We find two main process mechanisms of the entrepreneurship policy mix in China: patching and stretching. Compared with policy repackaging, patching and stretching are more realistic processes in the real world of the policy mix, and they are possible to achieve effectiveness by avoiding conflicts and promoting synergies among policy goals and instruments.

Keywords: entrepreneurship, China, policy design, policy mix, policy patching

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Authors: Mitat Uysal

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A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy.

Keywords: algorithms, Bezier curves, heuristic optimization, migrating birds optimization

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Shweta Singh, Sudaman Katti

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The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

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Authors: B. Tirpude, V. Surwade, P. Murkey, P. Wankhade, S. Meena

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There is no statistical data to establish variation in epiphyseal fusion in girls in central India population. This significant oversight can lead to exclusion of persons of interest in a forensic investigation. Epiphyseal fusion of proximal end of humerus in eighty females were analyzed on radiological basis to assess the range of variation of epiphyseal fusion at each age. In the study, the X ray films of the subjects were divided into three groups on the basis of degree of fusion. Firstly, those which were showing No Epiphyseal Fusion (N), secondly those showing Partial Union (PC), and thirdly those showing Complete Fusion (C). Observations made were compared with the previous studies.

Keywords: epiphyseal union, shoulder joint, proximal end of humerus

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Authors: Tarek Aly

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Introduction: Maintenance of the length, providing rotational stability, and preserving functional range of forearm motion is the mainstay of both bone forearm fractures treatment. Conservative treatment in older children may lead to malunion with poor remodeling capacity. Recent studies emphasized that the rate of complications with IM nailing was obviously increased in old children. Open reduction and internal fixation have been criticized for the amount of soft tissue dissection and periosteal stripping needed for fixation and excessive scar formation. The aim of this study was to evaluate the anatomical and functional outcomes of hybrid fixation in the treatment of closed proximal radius and ulna fractures in adolescents between 12 and 17 years of age. Patients and Methods: 30 cases of diaphyseal both bone forearm fractures treated with hybrid fixation (Nail radius – Plate ulna) and were available for a follow-up period of fewer than 24 months. Results: Clinically, 72% of cases had an excellent function, 22% had a good function, 4% had a fair function, and 2% had a poor function. Radiologically, signs of the union had appeared in the radius 2weeks earlier than in the ulna in 55% of cases. Conclusion: A hybrid fixation technique in adolescent proximal both-bones forearm fractures could be a viable option in managing these injuries.

Keywords: hyprid fixation, both bones, forearm, fractures

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Authors: Sarit Gideoni Cohen

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The aim of the study was to examine possible risk pathway which initiated by the distal risk factors of insecure attachment to the mother, the father and peers and then developed by means of proximal risk factors: stressful life events and emotional distress. 275 participants (aged 13-26) from high-schools, youth groups and university were requited. Twenty-two percent participants reported at least one episode of self-injury. The relationship between paternal and peer attachment were partly mediated by stressful life events and depressive symptoms. Paternal and peer attachment influences during adolescence as contributing to risk pathway for self-injury were acknowledged.

Keywords: self-injury, attachment, depression, stressful life-events, adolescence

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Authors: Kostas Metaxiotis

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Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model.

Keywords: evolutionary algorithms, portfolio optimization, skewness, stock selection

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Authors: Rameesha Javaid

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Agriculture is the backbone of the economy of the province of Balochistan which is known as the ‘fruit basket’ of Pakistan. Its climate zones comprising highlands and plateaus, dependent on rain water, are more suited for the production of deciduous fruit. The vagaries of weather and more so the persistent droughts prompted the government to announce flat rates of electricity bills per month irrespective of the size of the farm, quantum or water used and the category of crop group. That has, no doubt, resulted in increased cropping intensity, more production and employment but has enormously burdened the official exchequer which picks up the residual bills in certain percentages amongst the federal and provincial governments and the local electricity company. This study tests the desirability of continuing the subsidy in the present mode. Optimization of social welfare of farmers has been the focus of the study with emphasis on the contribution of positive externalities and distortions caused in terms of negative externalities. By using the optimization technique with due allowance for distortions, it has been established that the subsidy calls for limiting policy distortions as they cause sub-optimal utilization of the tube well subsidy and improved policy programming. The sensitivity analysis with changed rankings of contributing variables towards social welfare does not significantly change the result. Therefore it leads to the net findings and policy recommendations of significantly reducing the subsidy size, correcting and curtailing policy distortions and targeting the subsidy grant more towards small farmers to generate more welfare by saving a sizeable amount from the subsidy for investment in the wellbeing of the farmers in rural Balochistan.

Keywords: distortion, policy distortion, socio-cultural distortion, social welfare, subsidy

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Authors: Hakan Akin

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The aim of this study was to examine the policy making process in Turkish Health System. This policy making process will be examined through public policy change theories. Since political actors played in the formulation of public policies also explains the type of policy change, this actors will be inspected in the supranational and national basis. Also the transformation of public policy in the Turkish health care system will be analysed under the concepts of New right ideology, neo-liberalism, neo-conservatism and governance. And after this analyse, the outputs and outcomes of this transformation will be discussed in the context of developing countries.

Keywords: policy transfer, policy diffusion, policy convergence, new right, governance

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

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Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design.

Keywords: information entropy, structural optimization, truss structure, whale algorithm

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Authors: Ramin Javadzadeh

Abstract:

The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

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