Good Time Research Articles

Supply Chain Networks and the Prospects of Sustainable Industrialization in Asia

India and China are home to the world’s 2.8 billion people. Ancient nations but young republics, India and China have been through their good and bad times. There are numerous studies suggesting that the urban population, rate of growth, and population percentage residing in agglomerations all have a statistically notable and constructive effect on Human Development Index (HDI). Industrialization has historically encouraged urbanization by fostering economic expansion and job opportunities that entice people to relocate to cities. This article exclusively focuses on strategic planning regarding how to build and improve supply chain networks. It also looks at how factories built in a location increase demand for manufacturing and managerial workers personnel, resulting in a population inflow. Following the factories, other businesses such as construction makers, retailers, and service providers follow to meet the workers’ product demands. As a result, more jobs and housing demand are created, forming a city.

A New Insect-Scale Piezoelectric Robot With Asymmetric Structure

To realize the switching of the multiple locomotion modes, a new insect-scale piezoelectric robot with asymmetric structure is proposed, fabricated, and tested. The robot prototype has a weight of 32.8 g and size of 55 mm×50 mm×20 mm. Driven by two pieces of piezoelectric ceramics, working at six vibration modes, the piezoelectric robot can realize two kinds of linear motion and four kinds of circular motion by contact friction. Performance assessments show that the robot has a maximum velocity of 155 mm/s and a minimum motion radius of 45 mm. In addition, the robot can move at a speed up to 97 mm/s (100 V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p-p</sub> ) with integrated drive device (45.7 g). The insect-scale piezoelectric robot has simple structure, but has multiple motion modes, large speed range, good output characteristics and fast response time, which provides a new idea for the design and driving of micro robot.

Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results.

Video aided extinction measurement: A competitive method for dust density diagnostics

Among the most popular methods to measure dust density in a laboratory setup are 1D extinction, Abel inversion for circularly symmetric geometries, and computer tomography (CT) for arbitrary geometries. We present a new method based on a 1D extinction measurement in correlation with a video taken at an acute angle. It works well with limited optical access and has a good time resolution (at least several hertz). It measures the dust density within a slice of a nanodust cloud with precision comparable to other methods. Depending on the setup, this video aided extinction measurement can replace CT.

Damage Identification in Reinforced Concrete Beams Using Wavelet Transform of Modal Excitation Responses

This study focuses on identifying damage in reinforced concrete (RC) beams using time-domain modal testing and wavelet analysis. A numerical model of an RC beam was used to generate various damage scenarios with different severities and locations. Acceleration time histories were recorded for both damaged and undamaged structures. Two damage indices, DI_MW and DI_SW, derived from the wavelet analysis, were employed to determine the location and severity of the damage. The results showed that different wavelet families and specific mother wavelets had varying effectiveness in detecting damage. The Daubechies wavelet family (db2, db6, and db9) detected damage at the center and sides of the RC beams due to good time and frequency localization. The Biorthogonal wavelet family (bior2.8 and bior3.1) provided improved time–frequency resolution. The Symlets wavelet family (sym2 and sym7) offered a balanced trade-off between time and frequency localization. The Shannon wavelet family (shan1-0.5 and shan1-0.1) exhibited good time localization, while the Frequency B-Spline wavelet family (fbsp2-1-0.1) excelled in frequency localization. Certain combinations of mother wavelets, such as shan1-0.5 with the DI_SW index, were highly effective in detecting damage. The DI_SW index outperformed DI_MW across different numerical models. Selecting appropriate wavelet analysis techniques, particularly utilizing shan1-0.5 in the DI_SW, proved effective for detecting damage in RC beams.

Enhancing Polyantimonic-Based Materials’ Moisture Response with Binder Content Tuning

Humidity sensors are of huge importance in diverse domains. Several types of materials present a moisture-sensing capacity; however, frequently, their electrical response does not display time stability. Due to its high thermal stability, high ionic conductivity, and different conduction mechanism contributing to its overall conductivity, polyantimonic acid (PAA) is seen as a promising material for humidity-sensing devices. In the present work, crystalline PAA was obtained via a simple and safe hydrolysis technique using SbCl3 as a precursor. Bulky sensor samples were produced using different amounts of polyvinyl alcohol (10–20 wt.% PVA) as a binder. The obtained PAA solid sensors were tested at room temperature (RT) in order to evaluate their moisture detection/measuring ability in the relative humidity range 0–100%; the evaluation was carried out with electrical impedance spectroscopy. The sample’s structure and morphology were studied using diverse experimental techniques (porosimetry, scanning electron microscopy, X-ray diffraction analysis, and thermogravimetry analysis, etc.). The sensors’ electrical response was in line with the found structural and morphological features. The slope of the resistance variation with an RH percentage concentration between 1 kΩ and 1.5 kΩ was noted for all sensors (showing no changes with time) in the interval between 30 and 100% RH. A good repeatability and reproducibility of the evaluated sensors’ electrical response was observed: the ones that displayed a higher sensitivity were the ones with a high PVA binder content, higher than previously published results for PAA, as well as a very good time stability along the time and low hysteresis.

DESIGN OF FIRST ORDER DIFFERENTIATOR WITH PARALLEL ALL-PASS STRUCTURE

In this paper a new method for design of the first order differentiator is presented. The proposed differentiator consists of two parallel branches, i.e. direct path and IIR all-pass filter. The described design method allows one to obtain solution with minimum mean relative error at the desired region by controlling the ratio of phase response extremes. A small relative magnitude error, as well as a low phase error, at low frequencies is condition for good time domain behaviour. The obtained differentiator can be realized by means of only two multipliers, hence being a good choice for real time applications. The proposed solution provides a lower magnitude error than several known differentiators with similar phase error.

Computational modelling of language acquisition: an introduction

One approach to studying how children acquire language is to simulate language acquisition through computational modelling. Computational models implement theories of language acquisition and simulation outcomes can then be tested against existing real-world data or in new empirical research. It is more than ten years ago that Journal of Child Language published a special issue on the topic, edited and introduced by Brian MacWhinney (MacWhinney, 2010). Now is thus a good time to take stock of recent developments by bringing together a collection of articles that explore recent research and insights from computational modelling of child language acquisition.

Omni-fulfillment network design for supply chain service platform considering cost, service and resilience tradeoffs

Supply chain service platform (SCSP) plays an indispensable role in omni-fulfillment supply chain network services in the digital economy era. In this context, this paper studies the omni-fulfillment supply chain network design considering cost, service, and resilience tradeoffs. The problem is modeled as a multi-objective mixed integer nonlinear programming (MOMINLP) model based on omni-fulfillment services. To improve the solving efficiency, the model is linearized, and a tailored multi-objective optimization algorithm is developed. The significant advantage of this algorithm is that the multi-loop skipping mechanism is embedded in the Epsilon-constraint method to obtain the Pareto front faster. The effectiveness of the proposed decision-making framework is verified by real-case data. Numerical results show that the proposed algorithm outperforms AUGMECON2 algorithm in terms of problem size and computational time. In the case of order segmentation, the shorter the delivery time of goods, the higher the cost of the supply chain network. Furthermore, implementing capacity redundancy in the central warehouse can mitigate the impact of different disrupting scenarios.

Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification

Multi-label learning is a class of machine learning algorithms that study the classification problem of data associated with multiple labels simultaneously. Ensemble-based method is one of the representative methods in multi-label learning. In the existing ensemble-based multi-label classification methods, the essence of its base learner is still a binary or multi-class classifier. At present, there is no ensemble-based multi-label method that uses a multi-label classifier based on algorithm adaptation as a base learner. Multi-label algorithms suffer from expensive time costs, and ensemble-based methods further exacerbate the time cost. To address these issues, we propose an efficient multi-label classification method based on kernel extreme learning machine and ensemble learning. Firstly, to solve the problem of high time complexity of multi-label classifiers, we use a Gaussian kernel function to generate a kernel extreme learning machine based multi-label classifier (KELM_ML). Then, based on random sampling, we construct the base classifier, i.e., KELM_ML, in ensemble learning. Finally, to build an ensemble model of multi-label classifiers, an elimination optimization ensemble strategy is proposed by defining an encoding vector of the base classifiers and updating it. Although our method is an ensemble model, it inherits the advantages of good time complexity of ELM, i.e., fast learning time. The experimental and statistical results show that compared with baseline methods and other ensemble-based multi-label methods, our proposed method has better classification performance and stable results.

Book Review: Metadata for Digital Collections, Second Edition

The ability to create digital collections has become more accessible to libraries and cultural heritage institutions of all sizes over the last few years, making it a good time to release the second edition of Steven Jack Miller’s Metadata for Digital Collections. Miller is successful in his goal to write a textbook that is “accessible to beginners and library and information studies students as well as experienced professionals with little formal metadata training” (xxl). Throughout the book he introduces fundamental concepts that everyone developing a digital collection will need in order to be successful. Complex concepts—such as interoperability, linked data, and controlled vocabulary—are introduced early in the book in a manner that is appropriate for someone who is unfamiliar with the topics. Later chapters examine each of these topics in-depth providing a solid grounding and understanding of these concepts. In addition to writing plainly, Miller includes many examples that clearly illustrate the concepts being introduced. He also created a number of useful sidebars; I particularly found the “Typology of Metadata Standards” (15) and “Five Ways to Improve Metadata Quality and Interoperability: Summary Overview” (316) sidebars helpful in how they summarize the information in their respective sections which will make it easy for me to reference in the future.

Towards an Inclusive Pedagogy: Applying the Universal Design for Learning in an Introduction to History of Global Art Course in Ghana

This convergent parallel mixed methods study was aimed at addressing the lack of empirical studies in the implementation of Universal Design for Learning (UDL) as an inclusive pedagogy in the Ghanaian higher education context. The overarching objective was to find out whether UDL has the potential in improving the learning processes and learning outcomes of the diverse students reading a History of Global Art course. Quantitative and qualitative data sets were garnered from 122 conveniently sampled students using an adapted version of the Inclusive Teaching Strategies Inventory-Students (ITSI-S) survey instrument. The findings of the study revealed that the UDL principles of multiple means of representation, multiple means of engagement and multiple means of action and expression impacted positively on students’ learning processes and outcomes. UDL assisted greatly in the development of collaborative, problem-solving, good time management and critical thinking skills, while increasing learners’ level of motivation. The study contends that though the UDL as an inclusive pedagogical approach requires a lot of dedication on the part of the instructor as well as a great deal of time and material resources, the accrued benefits of its implementation on the students’ learning processes and learning outcomes are far-reaching.

In good times and bad: Low-cost mobile teaching during a pandemic

In view of school closures due to the COVID-19 pandemic, this paper examines how a Home-Based Learning program affects learning outcomes of children in under-resourced communities. To overcome limited internet connectivity, the program provides remote instructions via phone calls and simple text messages along with automated voice calls to engage children enrolled in grades one to five in activity-based learning content. This intervention was conducted in three districts in the state of Odisha in India. Using a difference-in-differences framework, we find that the intervention led to a statistically significant improvement in basic number recognition and arithmetic operations, and language learning scores of children by 4.69 percentage points and 5.52 percentage points, respectively. Our results are robust to alternative methods of estimation and application of Lee bounds, thus indicating that well-designed low cost interventions could be a useful supplement for continued learning in the face of sudden shocks in low income countries. With a rise in hybrid format of teaching and learning, such interventions have the capability to cushion the decline in learning levels and provide a safety net in the event of school closures.

Reserve Accumulation, Macroeconomic Stabilization, and Sovereign Risk

Abstract In the past three decades, governments in emerging markets have accumulated large amounts of international reserves, especially those with fixed exchange rates. This article proposes a theory of reserve accumulation that can account for these facts. Using a model of endogenous sovereign default with nominal rigidities, we argue that the interaction between sovereign risk and aggregate demand amplification generates a macroeconomic-stabilization hedging role for international reserves. We show that issuing debt to purchase reserves during good times allows the government to stabilize aggregate demand when sovereign spreads rise and rolling over the debt becomes more expensive. We provide empirical evidence consistent with the model’s predictions.

Backward-bending labor supply and urban location

This study attempts to combine a labor supply model with a housing location model. We focus on the trade-off between hours of work, commute times, and leisure time as well as the trade-off between the consumption of a good, housing space, and leisure time. We show that both labor supply and urban location choice have an inverted U-shaped relationship regarding the wage rate. These results are empirically shown by using Japanese data on the hours of work and commute times by household income class and on the number of households by income class.

Comparative study of performances of UHPLC-MS/MS and HPLC/UV methods for analysis of ziprasidone and its main impurities

AbstractZiprasidone is the second generation antipsychotic drug with unique multipotent G-protein-coupled (GPCR) receptor binding profile. Since ziprasidone is a highly lipophilic and unstable compound, development of efficient method for a concurrent assay of ziprasidone and its main impurities was a very challenging task.The UHPLC-MS/MS method that we developed for simultaneous determination of ziprasidone and its main impurities (BITP, Chloroethyl-chloroindolinone, Zip-oxide, Zip-dimer, and Zip-BIT) was compared with some other related HPLC-UV methods of our own and other authorship. An increase of the mobile phase pH value from 2.5 to 4.7 units in the examined analytical methods influenced elution order of the investigated compounds. It was found out that the UHPLC-MS/MS method is more selective and sensitive than the earlier developed HPLC-UV method. Similar to our earlier HPLC-UV method, the UHPLC-MS/MS method is linear with a correlation coefficient (r) above 0.99 for all the analysed compounds, but with a negligibly lower precision and accuracy. Finally, with shorter analysis time, smaller column size and reduction of solvent consumption, UHPLC-MS/MS is assumed as a greener method than HPLC-UV for the ziprasidone purity assay.After transfer of the UHPLC-MS/MS method to the UHPLC-DAD system, suitability of the UHPLC-DAD method for routine control of ziprasidone and its main impurities is examined and confirmed based on the retained good selectivity, resolution and short analysis time.

Financial Market Sustainability in a Dual-Track System: Venture Capital and Startups’ Speed of Passing

The government’s intervention under the approval system seriously affects the healthy and sustainable development of the financial market. An IPO is an important way for a venture capitalist (VC) to gain income, which impacts the efficiency of resource allocation in the capital market. From the perspective of resource allocation efficiency, this paper compares the influence of venture capital on the IPO process of startup enterprises under registration and approval systems. The findings are as follows: (1) after the trial registration system, the speed of passing and listing of VC-owned startup enterprises can be significantly accelerated. (2) Venture capitalists can accelerate the startup enterprises’ speed of passing by sending directors to startup enterprises and improving the level of risk disclosure, which is only significant under the registration and issuance system. (3) Further research shows that VC-supported startups perform better after listing. (4) VCs can help startup enterprises to choose hot season listing, which has a good timing effect. The conclusion of this text study is still robust after using propensity score matching (PSM) and Heckman to eliminate endogeneity. The conclusion of this study provides a theoretical basis and empirical support for emerging market countries to promote market-oriented reform.

Discussion of “policy packages and policy space: Lessons from COVID-19″ by Bergant & Forbes

This note provides a discussion of “Policy Packages and Policy Space: Lessons from COVID-19” by Bergant & Forbes. Their empirical work delivers new and thought-provoking findings together with a very useful new dataset. Their findings support the need for building policy space in good times but they suggest that high-debt countries faced no constraints in terms of fiscal support. This note argues that the special features of the COVID-19 recession and policy response provide some reasons for doubting the finding that suggests irrelevance of fiscal space or at least its general nature.

Advanced framework for enhancing ultrasound images through an optimized hybrid search algorithm and a novel motion compounding processing chain

Ultrasound imaging is a fast, widespread, and essential diagnostic technique for examining the body’s internal anatomy to find abnormal tissues or diseases. However, speckle noise in ultrasound imaging corrupts fine details and edges and degrades the image’s resolution and contrast, making diagnosing more difficult. In this work, a speckle reduction method using motion compounding is proposed. The objective of this work is ultrasound image enhancement keeping all the diagnostics details and edges. The proposed method uses the pre-locations frames that the sonographer generates before locating the required diagnostic frame. These frames are applied to the proposed optimized Modified Three-Step Search (O-MTSS) algorithm to enhance the final processed frame. The O-MTSS algorithm is a hybrid between the Three Step Search algorithm (TSS) and the New Diamond Search Algorithm (NDS). The method requires a scoring layer for storing additional frames to select optimal frames that will be used for enhancement. The proposed methodology is tested on synthetic and real ultrasound images. The outcomes produce considerable speckle reduction while maintaining the image edges with good computational time for real-time scanning. The result is evaluated by subjective physicians, radiologists, and image evaluation metrics. According to the percentages of the subjective analysis, there is a remarkable improvement in the processed image. The proposed algorithm result is compared with existing algorithms; It is observed that the improvement in terms of signal to noise ratio, peak signal to noise ratio, mean square error, root mean square error and structural similarity index values of the proposed method are 4.6%, 3.32%, 12.02%, 6.2% and 1.57% respectively over Non-Local Low-Rank (NLLR) method. According to qualitative and quantitative analysis, the suggested method outperforms existing speckle reduction techniques regarding edge and fine detail preservation.

Preface: 2023 International Conference on Energy Research and Environmental Technology (ERET 2023)

The 2023 International Conference on Energy Research and Environmental Technology (ERET 2023) was held during June 10-11, 2023 in Xi'an, China. This conference aims to bring together academic scientists, leading engineers, industry researchers and scholar students to exchange and share their experiences and research results about all aspects of energy research, energy materials, hydrogen and fuel cell, pollution control, environmental technology, and ecological system, and will discuss the practical challenges encountered and the solutions adopted. This conference becomes a platform for the delegates from different countries to meet and gain fruitful results.&#x0D; Over 70 experts and researchers in related fields attended ERET 2023 to share their innovative ideas, discuss the state-of-art methods, exchange their experiences, and establish beneficial collaborations. Delegates enjoyed the good time they had in the conference. The papers collected for publication are of high quality and related to different areas. The conference ended successfully and all the delegates looked forward to attending the next meeting.&#x0D; The success of ERET 2023 has relied on many people's efforts and contributions. Many thanks are extended to the colleagues and students for their assistance in organizing the conference. The reviewers have helped not only review the papers but also provide valuable comments to improve the quality of the papers. Their efforts and assistance are prominent in making the conference a success. The success of the conference is a natural outcome of their contributions and assistance.&#x0D; The ERET Conference Committee&#x0D; Xi'an, China