Drivers of trade openness, exchange rates, and production efficiency: firm and country level study.

This research focuses on the factors influencing trade openness and their flow-through effect on the production efficiency of both firms and countries. It further explores how enhanced production efficiency can lead to increased financial inflows into a country and examines the resultant impact on the nation's currency exchange rates. This thesis aims to bridge the existing knowledge gaps in the field of trade openness by conducting a comprehensive analysis across several pivotal areas. Firstly, it investigates the relationship between trade openness and associated factors. Secondly, it explores the impact of trade openness on improving production efficiency at the firm level. Thirdly, the study examines the flow through effects of trade openness on countries' productivity. Lastly, it assesses how enhanced production efficiency influences trade flows, subsequently leading to financial inflows that have a bearing on currency exchange rates. Data from the World Trade Organization (WTO) was collected for the period spanning 1995 to 2020, covering 101 nations categorized into three groups based on their levels of trade openness (high, moderate, and low). Additionally, a sample of 600 manufacturing companies from economies with high and low trade openness was included for the period between 2010 and 2019. The study employed several statistical methods, such as stepwise regression, ordinary least squares (OLS), fixed/random effect methods, and fully modified least squares (FMOLS) to estimate the results. The Granger Causality test was also utilized to determine the direction of causality. Regarding the first objective, the findings reveal a positive relationship between trade openness drivers and level of trade openness. The study specifically identifies gross national savings, trade reserves, per capita income, net flows of foreign direct investment and exchange rate as significant factors driving trade openness. In contrast, per capita income is recognized as having the most considerable influence on trade openness. The findings related to the second objective suggest that firms in economies with high trade openness witness a more significant positive effect on their total factor productivity, primarily due to improvements in efficiency and technology, in comparison to firms in economies with lower levels of trade openness. Furthermore, the analysis reveals a unidirectional relationship between technological changes and total factor productivity. In relation to the third objective, the research findings demonstrate that the growth in total factor productivity for 20 countries engaged in open trading is at 10 percent. When comparing the two distinct groups, the study reveals that the average total factor productivity in ten countries with a high level of trade openness stands at 16 percent, a figure that is threefold higher than in ten countries characterized by lower trade openness. The results related to the fourth objective reveal that trade reserves have a significantly negative effect on exchange rates across the full spectrum and in three distinct categories of trade openness. It is particularly noteworthy that economies with low trade openness display the most substantial influence of trade reserves on their exchange rates, whereas economies with high and moderate trade openness exhibit a lesser impact

Remediation of Leachate-Metal-Contaminated Soil Using Selected Bacterial Consortia

Approximately 95% of urban solid waste worldwide is disposed of in landfills. About 14 million metric tonnes of this municipal solid waste are disposed of in landfills every year in Malaysia, illustrating the importance of landfills. Landfill leachate is a liquid that is generated when precipitation percolates through waste disposed of in a landfill. High concentrations of heavy metal(loid)s, organic matter that has been dissolved and/or suspended, and inorganic substances, including phosphorus, ammonium, and sulphate, are present in landfill leachate. Globally, there is an urgent need for efficient remediation strategies for leachate-metal-contaminated soils. The present study expatiates on the physicochemical conditions and heavy metal(loid)s’ concentrations present in leachate samples obtained from four landfills in Malaysia, namely, Air Hitam Sanitary Landfill, Jeram Sanitary landfill, Bukit Beruntung landfill, and Taman Beringin Landfill, and explores bioaugmentation for the remediation of leachate-metal-contaminated soil. Leachate samples (replicates) were taken from all four landfills. Heavy metal(loids) in the collected leachate samples were quantified using inductively coupled plasma mass spectrometry. The microbial strains used for bioaugmentation were isolated from the soil sample collected from Taman Beringin Landfill. X-ray fluorescence spectrometry was used to analyze heavy metal(loid)s in the soil, prior to the isolation of microbes. The results of the present study show that the treatments inoculated with the isolated bacteria had greater potential for bioremediation than the control experiment. Of the nine isolated microbial strains, the treatment regimen involving only three strains (all Gram-positive bacteria) exhibited the highest removal efficiency for heavy metal(loid)s, as observed from most of the results. With regard to new findings, a significant outcome from the present study is that selectively blended microbial species are more effective in the remediation of leachate-metal-contaminated soil, in comparison to a treatment containing a higher number of microbial species and therefore increased diversity. Although the leachate and soil samples were collected from Malaysia, there is a global appeal for the bioremediation strategy applied in this study

Born this way or formed this way? Distal personality traits and proximal self-efficacy of Malaysian students and their academic performance

It has been acknowledged that academic performance has important consequences in one’s career, thus, a better understanding of both distal and proximal predictors deserves consideration. Based on social cognitive theory, this study contributes to the limited research investigating the academic performance of university students in Malaysia using the trait model which considers the mediation of self-efficacy (proximal characteristic) in the relationship between student personality (distal trait) and academic performance (outcomes). In a sample of 264 participants, self-efficacy positively relates to academic performance and positively mediated effects of all traits (except neuroticism) on academic performance. Contrary to past research, conscientiousness, extraversion, and agreeableness do not exert direct effects on academic achievement but instead through self-efficacy. Openness to experience turned out to be the strongest predictor pointing to a need for in-depth investigations into this dimension and for more complex model incorporating other proximal attributes in predicting academic performance in future research

Giving and Responding to Feedback: Guidelines for Authors and Reviewers

This article offers guidelines for enhancing scholarly discourse in academic publishing, focusing on effective feedback mechanisms. We present two structured frameworks: REVIEW for reviewers and REACT for authors. The REVIEW framework guides reviewers in providing constructive and insightful feedback, emphasizing thorough reading, theoretical and methodological evaluation, verification of claims and sources, identification of strengths and shortcomings, critical engagement, and clear, constructive writing. The REACT framework assists authors in systematically responding to feedback, covering review, evaluation, addressing feedback, clear communication of revisions, and thanking reviewers. These frameworks aim to improve the quality and impact of scholarly work by fostering productive interactions between authors and reviewers. This latest issue of Activities, Adaptation, and Aging also features eight studies that exemplify the successful application of these guidelines, highlighting their importance in advancing dignified and purposeful living for older adults. The frameworks and accompanying studies demonstrate the journal’s commitment to promoting rigorous peer review and responsive manuscript development in academic publishing

Out of the way, human! Understanding post-adoption of last-mile delivery robots

The pace of technological development is exceeding expectations and transforming the landscape of last-mile delivery. This study investigates how users' post-adoption behavior in using delivery robots is formed. Based on the task-technology fit (TTF) model, we present a research model that includes both direct and indirect factors that have been previously overlooked in the literature. We collected data from 550 users of delivery robots. Our structural equation modelling results show that two hedonic- (i.e., gratification and anthropomorphism) and three utilitarian- (i.e., service quality experience, delivery task requirements, and user-facing technology performance) driven factors predict perceived TTF in using delivery robots. Value-in-use and trust have sequential mediating effects that connect perceived TTF and service reuse likelihood and word-of-mouth recommendation. Our findings suggest ways to improve last-mile delivery robot strategies and provide practical implications for the industry

Object tracing from synthetic fluid spray through instance segmentation

Since the advent of rapid transportation systems, combustion engines have been a significant technological advancement. These engines require fuel to be atomized for efficient combustion. Atomization involves breaking down liquid sprays into smaller ligaments and droplets, which is crucial for optimal combustion in liquid-fueled propulsion devices. However, accurately measuring and analyzing the size and distribution of objects in a spray can be challenging, especially near the nozzle, due to the complexity of the spray and limited digital detectors. To address this, a method using two region-based R-CNN models named Mask R-CNN and BMask R-CNN and two transformer-based models named PatchDCT and FastInst has been developed to detect and segment individual droplets from synthetic liquid spray images. This approach involved training the model on augmented images and testing it on both augmented and original images. A total of 5791 epochs for Mask R-CNN, 3000 epochs for BMask R-CNN, 20k epochs for PatchDCT and FastInst have been used. Initially, the object detection rate was not good due to the dense objects all over the images. A divide and conquer technique using cropping window extraction was employed with nine window sizes to reduce object density, resulting in a significant increase in object count. The total number of objects on the first test dataset increased from 517 to 1231 using (height, width/3) and 2887 using (height/3, width/3) window with Mask R-CNN. A similar increment happened with BMask R-CNN, PatchDCT and FastInst model testing. After filtering all of the crop windows, it is shown that the models are able to identify significantly more droplets while maintaining a good mask prediction when the width is decreased by three while maintaining the height as the fluid flow is horizontal. The resulting objects were then analyzed using a customized nearest neighbor algorithm to calculate their correspondence between frames and a BFS algorithm was used to trace their movement path. On the training dataset, Mask R-CNN achieved 74.93 mean average precision with 75% IoU, where BMask R-CNN has only 44.68, PatchDCT shows 81 and FastInst achieved 41.49. Along with the movement path, objects’ contact tracing and breakdown rate has also been calculated. The information obtained, including object size, distribution, and tracing, can be valuable for engineers designing fuel injectors, leading to improved performance and efficiency

Antiviral activity of SP81 peptide against Enterovirus A71 (EV-A71)

The hand, food, and mouth disease (HFMD) is primarily caused by Enterovirus A71 (EV-A71). EV-A71 outbreaks in the Asia Pacific have been associated with severe neurological disease and high fatalities. Currently, there are no FDA-approved antivirals for the treatment of EV-A71 infections. In this study, the SP81 peptide, derived from the VP1 capsid protein of EV-A71 was shown to be a promising antiviral candidate for the treatment of EV-A71 infections. SP81 peptide was non-toxic to RD cells up to 45 μM, with a half-maximal cytotoxic concentration (CC50) of 90.32 μM. SP81 peptide exerted antiviral effects during the pre- and post-infection stages with 50% inhibitory concentrations (IC50) of 4.529 μM and 1.192 μM, respectively. Direct virus inactivation of EV-A71 by the SP81 peptide was also observed with an IC50 of 8.076 μM. Additionally, the SP81 peptide exhibited direct virus inactivation of EV-A71 at 95% upon the addition of the SP81 peptide within 5 min. This study showed that the SP81 peptide exhibited significant inhibition of EV-A71 and could serve as a promising antiviral agent for further clinical development against EV-A71 infections

Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios

Optimizing reservoir operation is a complex problem with non-linearities, numerous decision variables, and challenging constraints to simulate and solve. Researchers have tested various metaheuristics algorithms (MHAs) to reduce water deficit in reservoirs and presented them to decision-makers for adoption. Optimization methods vary depending on objectives, reservoir type, and algorithms used. The paper utilizes the CSS algorithm to study the impact of various scenarios on the optimal operation of the Mujib reservoir in Jordan to reduce water deficits using historical date between 2004 and 2019. The study explores different scenarios, including sediment impact, water demand management, and increasing the storage volume for the reservoir, to identify the optimal operation of the reservoir. The study compares the results of these scenarios with the current operation of the reservoir. Risk analysis (volumetric reliability, shortage index (SI), resilience, vulnerability) and error indexes (correlation coefficient R2, the root mean square error (RMSE), and the mean absolute error (MAE)) were used to compare results between scenarios, in addition to the annual water deficit values from the CSS algorithm for each scenario. The simulation of monthly sediment values in the Mujib reservoir showed that sediment accumulation accounts for 14.6% of the reservoir's volume at the end of 2019. Removing sediments retained by the dam can reduce water deficit by 19.42% when using the CSS algorithm. Additionally, reducing agricultural water demand by 11% and removing sediment reduced water deficit by 42.40%. The study also examined the impact of increasing the storage capacity of the reservoir by 10%, 20%, and 30%, revealing a decrease in water deficit by 35.44% when sediment removal was included in the analysis. The study examined the scenario of increasing the storage capacity of the Mujib reservoir by 30%, reducing water demand by 11%, and removing sediment. This scenario resulted in a 53.59% decrease in water deficit, providing decision-makers with viable solutions to address the water deficit problem in the reservoir

Influence of Dilution Upon the Ultraviolet-Visible Peak Absorbance and Optical Bandgap Estimation of Tin (IV) Oxide and Tin(IV) Oxde-Molybdenum(IV) Sulfide Solutions

The study investigated the constraints associated with the dilution technique in determining the optical bandgap of nanoparticle dispersion and modified nanocomposites, utilizing ultraviolet-visible absorbance spectra and Tauc plot analysis. A case study involving SnO2 dispersion and SnO2-MoS2 nanocomposite solutions, prepared through the direct solution mixing method, was conducted to assess the implications of dilution upon the absorbance spectra and bandgap estimation. The results emphasize the considerable impact of the dilution technique on the measured optical bandgap, demonstrating that higher dilution factors lead to shift in bandgap values. Furthermore, the study highlights that dilution can induce variations in the average nanoparticle sizes due to agglomeration, thereby influencing bandgap estimation. In the context of nanocomposites, the interaction between SnO2 nanoparticles and exfoliated MoS2 nanosheets diminishes with increasing dilution, leading to the estimated optical bandgap being primarily attributable to SnO2 nanoparticles alone. These observations underscore the necessity for caution when employing the dilution technique for bandgap estimation in nanoparticles dispersion and nanocomposites, offering valuable insights for researchers and practitioners in the field

Role of sirtuin 1 (SIRT1) in regulation of autophagy and nuclear factor-kappa Beta (NF-ĸβ) pathways in sorafenib-resistant hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) remains a major global health problem with high incidence and mortality. Diagnosis of HCC at late stages and tumour heterogeneity in patients with different genetic profiles are known factors that complicate the disease treatment. HCC therapy becomes even more challenging in patients with drug resistance such as resistance to sorafenib, which is a common drug used in HCC patients. Sorafenib resistance can further aggravate HCC by regulating various oncogenic pathways such as autophagy and nuclear factor-kappa Beta (NF-ĸβ) signalling. Sirtuin 1 (SIRT1), is a nicotinamide adenosine dinucleotide (NAD)-dependent histone deacetylases that regulates various metabolic and oncogenic events such as cell survival, apoptosis, autophagy, tumourigenesis, metastasis and drug resistance in various cancers, but its role in HCC, particularly in sorafenib resistance is underexplored. In this study, we generated sorafenib-resistant HepG2 and Huh-7 liver cancer cell models to investigate the role of SIRT1 and its effect on autophagy and nuclear factor-kappa Beta (NF-ĸβ) signalling pathways. Western blot analysis showed increased SIRT1, altered autophagy pathway and activated NF-ĸβ signalling in sorafenib-resistant cells. SIRT1-silenced HCC cells demonstrated down-regulated autophagy in both parental and chemoresistant cells. This may occur through the deacetylation of key autophagy molecules such as FOXO3, beclin 1, ATGs and LC3 by SIRT1, highlighting the role of SIRT1 in autophagy induction. Silencing of SIRT1 also resulted in activated NF-ĸβ signalling. This is because SIRT1 failed to deacetylate p65 subunit of NF-κB, translocate the NF-κB from nucleus to cytoplasm, and suppress NF-κB activity due to the silencing. Hence, the NF-κB transcriptional activity was restored. These findings summarize the role of SIRT1 in autophagy/NF-ĸβ regulatory axis, with a similar trend observed in both parental and sorafenib-resistant cells. The present work promotes a better understanding of the role of SIRT1 in autophagy and NF-ĸβ signalling in HCC and sorafenib-resistant HCC. As some key proteins in these pathways are potential therapeutic targets, a better understanding of SIRT1/autophagy/NF-ĸβ axis could further improve the therapeutic strategies against HCC