Windows account for a significant proportion of the total energy lost in buildings. The interaction of window type, Window-to-Wall Ratio (WWR) scheduled and window placement height would influence the natural lighting and heat transfer through windows. This is a pressing issue for non-tropical regions considering their high emissions and distinct climatic characteristics. A limitation exists in the adoption of common simulation-based optimisation approaches in the literature, which are hardly accessible to practitioners. This article develops a numerical-based window design optimisation model using a common Building Information Modelling (BIM) platform adopted throughout the industry, focusing on non-tropical regions of Australia. Three objective functions are proposed; the first objective is to maximize the available daylight, and the other two emphasize on the undesirable heat transfer through windows in summer and winter respectively. The developed model is tested on a case study located in Sydney, Australia, and a set of Pareto-optimum solutions is obtained. Through the use of the proposed model, energy savings of up to 16.43% are achieved. Key findings on the case example indicate that leveraging winter heat gain to reduce annual energy consumption should not be the top priority when designing windows for Sydney.

Together with display and input, storage and processing are requisite to computing. While storage technologies have improved tremendously in capacity and speed over the years, the basic principles enabling information storage into digital 1 and 0 remains the same: induction of phase change in the storage substrate. But recently, there has been much research into structural phase change material (SPCM) and exploration of its possible use in various types of memory storage applications. Despite unconventional use of structural change between amorphous and crystalline state as well as that between crystal structures for encoding information, key barriers for its widespread use remains access speed, capacity to cost ratio, and fidelity of storage. Hence, given the performance requirement of SPCM for memory applications, what are the material and design considerations that feed into translating a promising application into a practical reality? Given the important role of kinetic and thermal energy in structural organization of a phase change material, precise characterization of structural change in the material with external physical factors such as heat, voltage and current, is critical for storage material design. Next comes the precision at which individual memory cells for storing single bits of information could be defined reproducibly and at high fidelity using SPCM. In congruent with memory cell definition lies the equally important aspect of constraining the field characteristics used in modulating the phase state of the memory medium. Specifically, while heat is useful for mediating the “melting” of a crystalline material into its amorphous state, heat conduction is less useful for transferring the “switch command” from the effector to the memory material. More importantly, choice of structural phase change material for memory applications likely revolves around those where individual memory cells could be defined in a cross array format, which is amenable to high density information storage. Durability and fidelity of information storage are additional factors of design that favours selection of SPCM with phase change occurring at narrow operating windows without hysteresis over extended cycling. But, the most important requirement is speed of access. To this end, energetic cost of phase transitions might affect operation of the phase change memory at the system level: for example, usage of large current for high energy transition step may impact on device durability. Ultimately, there is a fundamental limit on the number of reproducible phase transitions in a SPCM before fidelity of information storage is no longer guaranteed. Hence, what are the drivers for uptake of phase change memories in consumer devices? Performance gains must be realized in access speed, storage capacity, form factor, and fidelity of information storage for practical application of structural phase change memories.

Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis – Satellite Application Facility to calibrate its LST algorithms applied both for current and forthcoming sensors.

Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.

The concept of this prototype is to create a cold air film layer, in indoor side of the window. The function of this extra cold air layer is to increase the amount of window resistance and decrease its U-Value. It provide air film layer by using the same concept of desert cooler or wind catcher. They both bring the hot air from outside then let it go throw straw miost with cold water. After hot air move through straw, it would become cold, thus increase the window resistance and decrease the haet gian, therefore, decrease the cooling load. So, this window model works by heating the air in air room by sun radiation, then after a while, air pressure will rise and its volume will increase then it will impulse to wet hay, and become cold. Then there is fins to guide air flow to its correct direction, thus inner side of window will become cold, not like other type of window which when temperature rise, the inner side of window become hot. So, this window prevents heat transition and creare regenereted trubouland air flim in the inner side of the window. Using calculation to find air film characteristics and WINDOW7.7 software to calculate heat gain through window, shows that it is expected from this prototype to reduce heat gain by 6.85 % for double glazed clear window.

Gold nanoparticles have been indicated for use in a diagnostic and/or therapeutic role in several cancer types. The use of gold nanorods (AuNRs) with a surface plasmon resonance (SPR) in the second Near-Infrared II (NIR-II) optical window promises deeper anatomical penetration through increased maximum permissible exposure and lower optical attenuation. In this study, the targeting efficiency of anti-epidermal growth factor receptor (EGFR) antibody functionalised AuNRs with an SPR at 1064 nm was evaluated in vitro. Four cell lines, KYSE-30, CAL-27, Hep-G2 and MCF-7 that either over or under expressed EGFR were used. This expression was confirmed by flow cytometry and immunofluorescence. Cytotoxicity assays showed no AuNRs toxicity to both EGFR positive and EGFR negative cell lines up to a concentrations of 19 µg/ml. Optical microscopy demonstrated a significant difference (p<0.0001) between targeted AuNRs (tAuNRs) and untargeted AuNRs (uAuNRs) in all four cancer cell lines. This study demonstrates that anti-EGFR functionalisation significantly increased the number of tAuNRs associated with each EGFR positive cancer cell. This successful targeting highlights the use of tAuNRs for molecular photoacoustic imaging or tumour treatment through plasmonic photothermal therapy.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

When all countries are battling Covid-19 by adopting different policies, it could help address the issue of government response policy efficiency. This study examines the efficiency of government intervention policies in 19 countries, the efficiency of preventing the spread, and also the mortality caused by the virus. The policies are categorized based on the Oxford COVID-19 Government Response Tracker, which combines various indicators, including Containment and closure, Economic response, and Health systems. Given that the governments apply a top-down approach to design these policies, the current research evaluates the efficiency of policies based on the same approach. Here, two different models have been used to calculate the efficiency of policies. We designed a combined index in both models and applied Window DEA method to determine the output quantities in the efficiency ratio. The results of our study show that in both models, Japan, Finland and Thailand had the highest level of government response efficiency. The countries with the lowest government response efficiency were the United States, Spain, and Russia in the first model, and China, Italy, and Spain in the second.

Owing to the reduction of rupture instability and the avoidance of wrinkle defect, hydrodynamic deep drawing (HDD) process is gradually becoming attractive for fabricating lightweight and complicated products. Meanwhile, since metallic material presents anisotropic deformation behavior, it is necessary to select an appropriate constitutive model for the prediction of plastic deformation behavior of applied material with high precision. In the present research, several anisotropic yield criteria namely, Hill’48, Yld2000-2d and BBC2005 are implemented to investigate the effect of yield functions on the prediction accuracy of the critical process window and deformation behavior for HDD process of 2024 and 5754 aluminum alloys. Material constants in the yield criteria are determined by applying uniaxial and equi-biaxial tension tests and optimizing an error-function by using the Levenberg-Marquardt algorithm. Furthermore, the process window diagram is computed utilizing the stress analytical model combined material properties with workpiece geometrical features. Numerical simulation results of predicted material anisotropic parameters, process window and HDD deformation for aluminum alloys are compared with the experimental data. Through the comparison of diverse yield criteria based on materials anisotropic coefficients, critical process window prediction, earing profile, and thickness distribution, it is revealed that the Yld2000-2d and the BBC2005 yield criteria can offer more precise models of material behavior in planar anisotropy properties for HDD process of 2024 and 5754 aluminum alloys.

Multiple biological pathways manifest and latent, meant for human survival, become a liability in cancer cure. With an increasing understanding of innumerable complex paths, cancer progression and development of resistance is no surprise. For the three “vasculature-immune-phenotypic” fundamental changes, hypoxia is the maestro orchestrating the whole gamut of changes (through the master manipulator - HIF-1 α), simultaneously transactivating hundreds of pro-angiogenic genes. Such a complex molecular bio-network begs the question, “Is our cancer research caught in such a tangled web that we have lost sight of the Spider?”. Hypoxia is this Spider weaving compensatory webs with every intervention/ obstruction. Anti-angiogenic (AAG) research has been conducted mainly in silos – exploring independent paths. This review conceptualizes a convergence of a multitude of research worldwide to a single theme of normalizing vasculature as a primary baseline for overcoming resistance to AAGs or their combinations.

This paper presents a cooperative parallel tabu search meta-heuristic for the vehicle routing problem with time windows. It is based on the scheme in which several search threads cooperate by asynchronously exchanging information on the best solutions identified. The exchanges are performed through a mechanism called adaptive memory which holds and manages a pool of solutions. This enforces the asynchronous strategy of information exchanges and ensures the independence of the individual search threads. Each of these independent threads implements a tabu search meta-heuristic. Comprehensive computational experiments and comparisons to best known solutions show that the proposed cooperative parallel tabu search algorithm is able to achieve 48 new best solutions on VRPTW benchmark instances.

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.

The use of machine learning techniques in predictive health care is on the rise with minimal data used for training machine-learning models to derive high accuracy predictions. In this paper, we propose such a system, which utilizes Heart Rate Variability (HRV) as features for training machine learning models. This paper further benchmarks the usefulness of HRV as features calculated from basic heart-rate data using a window shifting method. The benchmarking has been conducted using different machine-learning classifiers such as artificial neural network, decision tree, k-nearest neighbour and naive bays classifier. Empirical results using MIT-BIH Arrhythmia database shows that the proposed system can be used for highly efficient predictability of abnormality in heartbeat data series.

Quadrotor drones have rapidly gained interest recently. Numerous studies are underway for the commercial use of autonomous drones, and especially the distribution businesses are taking serious reviews on drone delivery services. However, there are still many concerns about urban drone operations. The risk of failures and accidents makes it difficult to provide drone-based services in the real world with ease. There have been many studies that introduced supplementary methods to handle drone failures and emergencies. However, we discovered the limitation of the existing methods. The majority of approaches were improving PID-based control algorithms which is the dominant drone control method. This type of low-level approach lacks situation awareness and the ability to handle unexpected situations. This study introduces an event-based control methodology that takes a high-level diagnosing approach that can implement situation awareness via time-window. While leaving the low-level controller to involve in operating the drone for most of the time in normal situations, our controller operates at a higher level and detects unexpected behaviors and abnormal situations of the drone. We tested our method with real-time 3D computer simulation environments with Unreal Engine[15] and AirSim[31]. We were able to verify that our approach can provide enhanced double safety and better ensure safe drone operations. We hope our discovery to possibly contribute to the advance of real-world drone services in the near future.

Comparison of the influence of conditioning temperature of microspheres made of medical grade poly(L-lactide) (PLLA) and polylactide with 4wt.% of D-lactide content (PLA), on its thermal and structural properties is presented. The microspheres were fabricated by solid-in-oil-in-water method for application in additive manufacturing. The microspheres were annealed below glass transition temperature (Tg), above Tg but below onset of cold crystallization, and at two temperatures selected from the range of cold crystallization corresponding to crystallization of α’ and α form of poly(L-lactide) respectively, i.e., at 40°C, 70°C, 90°C and 120°C, in order to verify the influence of conditioning temperatures on sinterability of microspheres set as sintering window (SW). Based on differential scanning calorimetry measurements SW of microspheres were evaluated with consideration of existence of cold crystallization and reorganization of crystal polymorph. The results indicate that the conditioning temperature influence on availability and range of SW that depending on the D-lactide presence. We postulate the need for an individual approach for polylactide powders in determining the SW as a temperature range free of any thermal events. Moreover, other core powder characteristic, such as residual solvent content, morphology, particle size distribution, powder flowability and thermal conductivity, as a key property for successful laser sintering, are characterized. The microspheres are close to sphere and the size of microspheres are below 100 µm. Residual solvents content decreases with the increase of annealing temperature. The thermal conductivity is 0.073 W/mK and 0.064 W/mK for PLA and PLLA microspheres, respectively, and it depends on the spherical shape of the microspheres. Furthermore, the WAXD studies prove that an increase in the conditioning temperature causes a slight increase in crystallinity degree for PLLA microspheres and clear increase in crystallization for PLA microspheres.

The Doughnut Economy is a new approach for the inclusion of planetary boundaries and social foundation in the development of societies. The Sustainable Development Goals of the UN determine another view for development targets. The developed Sustainability Window approach provides a means for operationalisation and quantification of the Doughnut Economy. The developed method calculates minimum economic development to guarantee sustainable social development and maximum economic development not to exceed environmental sustainability. The developed method, ASA Doughnut, is illustrated with case data from Thailand. The sustainability Doughnut for Thailand has been calculated for both weak and strong sustainability criteria. It seems that strong sustainability is a too strict requirement regarding several environmental dimensions of development while the weak sustainability criteria are fulfilled. The developed method and tool is flexible and can be used for comparative analysis of different countries or regions, for dynamic analysis of sustainability development, for gap analysis of the required improvement of environmental or social efficiency, and analysis of degrowth possibilities. The selection of indicators for the analyses and their reliability is crucial for the validity of the results and usefulness in policy planning.

Building envelope can play a crucial role in building improvement efficiency and the adoption of Phase Change Materials (PCMs) coupled with transparent elements may (i) allow a better control of the heat flows from/to the outdoor environment, (ii) increase the exploitation of solar energy at building scale and (iii) modulate the light transmission to avoid glare effects. Starting from a literature review of experimental works, this research identifies the main possible integration of PCM in transparent/translucent building envelope components (in glazing, in shutters and in multilayer façade system) drawing a global picture potentials and limitations of these technologies. Transparent envelopes with PCMs have been classified from the simplest “zero” technology, which integrates the PCM into the double glass unit (DGU), to more complex solutions – with different numbers of cavities of the glass (TGU), different position of PCM layer (internal/external shutter), and in combination with other materials (TIM, aerogel, prismatic solar reflector, PCM curtain controlled by an electric pump). The results of the analysis are resumed in a SWOT (strengths, weakness, opportunities and threats) analysis table to underline strengths and weaknesses of transparent building envelope components with PCMs, and to indicate opportunities and threats for future research and building application.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.