Data-Driven Analysis of Optimal Repositioning in Dockless Bike-Sharing Systems

Bike-sharing systems provide sustainable and convenient mobility services for short-distance transportation in urban areas. The dockless or free-floating bike-sharing systems allow users to leave vehicles at any location in the service zones which leads to an imbalance of inventory between different areas across a city. Hence, vehicles in such dockless bike-sharing systems need to be repositioned throughout the day to be able to capture and serve more demand. In this study, we analyze the impact of optimal repositioning on the efficiency of dockless bike-sharing systems under several performance measures. We first develop a multi-period network flow model to find the optimal repositioning decisions which consist of the origin, destination, and the time of the repositioning that maximize the total profit of the bike-sharing system. The proposed model is then implemented on the real-world bike-sharing data of New York, Toronto, and Vancouver. After finding the optimal repositioning actions, we analyze the effect of repositioning on the fulfilled demand, the number of required vehicles, and the utilization rates of the vehicles. Through computational experiments, we show that repositioning significantly increases the efficiency of bike-sharing systems under these performance measures. In particular, our analyses show that up to 41\% more demand can be satisfied with repositioning. Moreover, it is possible to reduce the required fleet size up to 61\% and increase the average utilization rate of the vehicles up to 21\% by employing repositioning. We also demonstrate that the effect of optimal repositioning is robust against the uncertainty of demand

Multiscale reinforcing interlayers of self-same P(St-co-GMA) nanofibers loaded with MCF for polymer composites and nanocomposites

Electrospinning has become a proven technique to introduce polymeric sub-phases into composites. The sub-phases such as nanofibers can also be used as a carrier platform for reinforcing particles at different scales, enabling a multiscale reinforcement approach. However, the polymeric nanofibers may lose their intended fibrous morphology during the composite curing at elevated temperature. As such, polymeric sub-phase can not contribute effectively as fibers to the mechanical properties of the composite. This paper exemplifies introduction of milled carbon fibers (MCF) carried by electrospun polymeric nanofibers and the use of the resultant multi-scale reinforcement as interlayer within conventional structural composites. The issue of polymeric nanofibers exposed to elevated temperature curing is circumvented by implementing a novel self-same nanofibrous strategy. While a base polymer for the nanofibers is chosen as epoxy compatible P(St-co-GMA), its derivative by a cross-linker Phthalic Anhydrate, P(St-co-GMA)/PA is also incorporated by dual-electrospining, i.e. simultaneous electrospinning of the two polymers. It was shown that the nanofibers of the base polymer melt and fuse over the cross-linkable nanofibers forming the self-same nanofibrous morphology during the heat treatment in accordance with the cure cycle of the epoxy resin in this study. MCFs were mixed into the cross-linkable polymer solution and electrospun with the P(St-co-GMA)/PA nanofibers. The dual polymer and MCF loaded nanofibrous structures were analyzed morphologically before and after heat treatment. Homogenous distribution of particles in the fibrous structures, melting of the neat copolymer, crosslinking of the polymer mix, and selfsame fibrous structure were characterized. The nanofiber mats were used as the reinforcement to epoxy resin films and as interlayers for carbon fiber-reinforced composites. In the case of nanocomposites, MCF enhanced the elastic modulus by about 9%. In the use of multiscale nanofibrous mats as interlayers of continuous carbon fiber composites, they improved the ultimate tensile strength of a cross-ply laminate by 9%

Network-Aware AutoML Framework for Software-Defined Sensor Networks

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays

A Microwave Ring Resonator Based Glucose Sensor

A microwave ring resonator based glucose detecting biosensor incorporating glucose oxidase enzyme is presented. Sensor uses a split ring resonator as a transducer, where the sensing operation is done by the observation of shifts in its resonant frequency. Resonator was fabricated with basic fabrication techniques and the enzyme was immobilized via conductive polymer agent PEDOT:PSS. Experimentally observed redshift of resonant frequency of the sensor in response to different loading conditions are in agreement with simulation results and theoretical expectations. Sensor selectivity is confirmed with control experiments conducted with NaCl solutions. Experiments done with different glucose solution concentrations yielded a sensor sensitivity of 0.174MHz/mgml-1

Understanding the structural diversity of chitins as a versatile biomaterial

From The Royal Society via Jisc Publications RouterHistory: accepted 2021-06-08, pub-electronic 2021-08-02, pub-print 2021-09-20Article version: VoRPublication status: PublishedChitin is one of the most abundant biopolymers, and it has adopted many different structural conformations using a combination of different natural processes like biopolymerization, crystallization and non-equilibrium self-assembly. This leads to a number of striking physical effects like complex light scattering and polarization as well as unique mechanical properties. In doing so, chitin uses a fine balance between the highly ordered chain conformations in the nanofibrils and random disordered structures. In this opinion piece, we discuss the structural hierarchy of chitin, its crystalline states and the natural biosynthesis processes to create such specific structures and diversity. Among the examples we explored, the unified question arises from the generation of completely different bioarchitectures like the Christmas tree-like nanostructures, gyroids or helicoidal geometries using similar dynamic non-equilibrium growth processes. Understanding the in vivo development of such structures from gene expressions, enzymatic activities as well as the chemical matrix employed in different stages of the biosynthesis will allow us to shift the material design paradigms. Certainly, the complexity of the biology requires a collaborative and multi-disciplinary research effort. For the future's advanced technologies, using chitin will ultimately drive many innovations and alternatives using biomimicry in materials science. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)'

Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset

Cancer is an extremely difficult and dangerous health problem because it manifests in so many different ways and affects so many different organs and tissues. The primary goal of this research was to evaluate deep learning models' ability to correctly identify breast cancer cases using the BreakHis dataset. The BreakHis dataset covers a wide range of breast cancer subtypes through its huge collection of histopathological pictures. In this study, we use and compare the performance of five well-known deep learning models for cancer classification: VGG, ResNet, Xception, Inception, and InceptionResNet. The results placed the Xception model at the top, with an F1 score of 0.9 and an accuracy of 89%. At the same time, the Inception and InceptionResNet models both hit accuracy of 87% . However, the F1 score for the Inception model was 87, while that for the InceptionResNet model was 86. These results demonstrate the importance of deep learning methods in making correct breast cancer diagnoses. This highlights the potential to provide improved diagnostic services to patients. The findings of this study not only improve current methods of cancer diagnosis, but also make significant contributions to the creation of new and improved cancer treatment strategies. In a nutshell, the results of this study represent a major advancement in the direction of achieving these vital healthcare goals.Comment: 7 pages, 1 figure, 2 table

Türkiye’de diş hekimlerinin bifosfonatlar konusundaki farkındalık ve bilgilerinin değerlendirilmesi

Aim: Bisphosphonates (BPs) are inorganic pyrophosphate analogs used for the treatment of various diseases. This study aimed to evaluate the knowledge and attitudes of general dental practitioners (GDP), specialist trainees (ST), and specialists (S) in Turkey toward medication-related osteonecrosis of the jaw (MRONJ) and to optimize future training programs in this field. Materials and Methods: A self-report questionnaire consisting of 7 questions about demographic data, knowledge about BPs, MRONJ and treatment modalities was prepared and send to the members of Turkish Dental Association via email. Results: A total of 209 participants were included in this survey. The mean age of the ST group was significantly lower than the mean ages of the GDP and S groups (P= 0.003, P= 0.038). GDP are less likely to think of BPs administration and radiotherapy treatment than S or ST upon observation of an exposed bone in the head-and-neck region (P=0.048, P=0.008). In comparison to the S and ST groups, the GDP group displayed less knowledge regarding the radiological and intraoral examination of patients undergoing BP therapy (P= 0.034). Conclusion: The increasing awareness of dentists about usage and side effects of BPs is important for preventing MRONJ. The GDP group displayed significantly less knowledge regarding the radiological and intraoral examinations of patients undergoing BP therapy. Greater efforts are required to increase education and knowledge of MRONJ and BPs among dental practitioners.Amaç: Bisfosfonatlar çeşitli hastalıkların tedavisinde kullanılan inorganik pirofosfat analoglarıdır. Bu çalışma uzman olmayan diş hekimlerinin, uzmanlık öğrencilerinin ve uzmanların ilaca bağlı gelişen çene osteonekrozuna yönelik bilgi ve tutumlarını değerlendirmenin yanında, gelecekteki eğitim programlarını optimize etmeyi amaçlamıştır. Materyal ve Metot: Çalışmaya 209 katılımcı dahil edilmiştir. Türk Dişhekimleri Birliği’nin desteğiyle bir anket hazırlanmış ve üyeler arasında e-posta ile paylaşılmıştır. Katılımcıların demografik bilgiler, ilaca bağlı çene osteonekrozları, bisfosfonatlar ve tedavi yaklaşımları ile ilgili 7 sorudan oluşan anketi yanıtlamaları istenmiştir. Bu anket sorularına hekimlerin verdiği cevaplar sayı ve yüzde ile tanımlanmıştır. Bulgular: Ankete katılan uzmanlık öğrencilerinin ortalama yaşı, uzman olmayan diş hekimleri ve uzmanlardan anlamlı olarak düşük bulunmuştur (P = 0.003, P = 0.038). Uzman olmayan diş hekimleri, baş-boyun bölgesinde gözlemlenen ekspoze kemiğin bisfosfonat tedavisi ya da radyoterapiye bağlı olabilme ihtimalini diğer gruplara göre daha az değerlendirmiştir (P=0.048, P=0.008). Uzman ve uzmanlık öğrencilerine kıyasla uzman olmayan diş hekimleri, bisfosfonat kullanan hastaların radyolojik ve ağız içi bulguları hakkında daha az bilgi sahibi olduğunu belirtmiştir (P = 0.034). Sonuç: Diş hekimlerinin bisfosfonatların kullanımı ve yan etkileri konusunda artan farkındalığı MRONJ’un önlenmesi için önemlidir. Diş hekimleri arasında MRONJ ve bisfosfonatlar ile ilgili bilincin artırılması için daha fazla çaba gösterilmesi ve hedef kitleye yönelik eğitim planları oluşturulması gerekmektedi

Risk Tabanlı Deniz Ambulansı Tasarımı

Denizde, emniyeti en üst düzeye çıkarmak için risk faktörlerini modellemek ve risk tabanlı tasarım araçlarını kullanmak önemlidir. Emniyeti arttırmak ve müşteri taleplerini karşılamak için etkin risk modelleme teknikleri ve karar verme araçlarının geliştirilmesi ve uygulanması gerekmektedir. Bu çalışmada, mevcut bir deniz ambulans teknesi operasyon riskleri incelenmiş ve mevcut tekne risk tabanlı bir yaklaşımla yeniden tasarlanmıştır. Risk değerlendirmesi ve model tasarımında, Hata Türü ve Etki Analizi (FMEA) kullanılmış ve risk öncelik sayıları (RPNs) hesaplanmıştır. Bu çalışmadan elde edilen sonuçlar, deniz ambulans teknelerinin emniyetini arttırmaya ve potansiyel risklerin önlenmesine veya azaltılmasına katkıda bulunacaktır

The dynamic lot-sizing problem with convex economic production costs and setups

In this work the uncapacitated dynamic lot-sizing problem is considered. Demands are deterministic and production costs consist of convex costs that arise from economic production functions plus set-up costs. We formulate the problem as a mixed integer, non-linear programming problem and obtain structural results which are used to construct a forward dynamic-programming algorithm that obtains the optimal solution in polynomial time. For positive setup costs, the generic approaches are found to be prohibitively time-consuming; therefore we focus on approximate solution methods. The forward DP algorithm is modified via the conjunctive use of three rules for solution generation. Additionally, we propose six heuristics. Two of these are single-stepSilver–Meal and EOQ heuristics for the classical lot-sizing problem. The third is a variant of the Wagner–Whitin algorithm. The remaining three heuristics are two-step hybrids that improve on the initial solutions of the first three by exploiting the structural properties of optimal production subplans. The proposed algorithms are evaluated by an extensive numerical study. The two-step Wagner–Whitin algorithm turns out to be the best heuristic