Distribution Method Research Articles

Share Repurchase and Cash Dividend - Substitute or Complement? Analysis from the Vietnam Securities Market

Purpose: The purpose of this report was to assess the relationship among the cash dividend payout and share repurchase, through essential cash distribution methods adopted by Vietnamese publicly listed companies. The complementary along with substitute relationship from these financial practices might contribute to determine the financial managerial behavior in Vietnam as a leading development emerging market within Asia Pacific area. Design/methodology/approach: The research applied regression analysis technique to determine the nonlinear relationship among the share repurchase and cash dividend. Findings: The research found the nonlinear relationship among the change in dividend along with the repurchase yield. Moreover, the research found a significant positive correlation which reveals the prominent of complementary impact in the complete sample. The results and estimations of the research are consistent in robustness tests. There is a nonlinear relationship between the change in dividend and the repurchase yield. In addition, a significantly positive correlation reveals the dominant of complementary effect in the whole sample. The results are consistent in robustness tests. Research limitations/implications: It is crucial to ensure that there is an essential scope for the enhancement which remains and it is a component of the future study such as the influence of the stock dividend through various practice while companies are partly dominated or effected by the government, through the characteristics as the emerging markets such as Vietnam, as they are interesting topics which need further study. Originality/value: Through a comprehensive analysis, the study outcomes propose that Vietnamese listed companies should meticulously assess their future growth concerning share repurchase and other capital distribution activities. This strategy should take into account the involvement of various stakeholders and their benefit.

Linguistics incivility in student: Lecturer communication on WhatsApp

This research aims to identify (1) strategies of linguistic incivility used in student-lecturer communication, and (2) the aspects of speech acts used that represent linguistic incivility. The research analyzed data from six WhatsApp groups that include students and lecturers. The distributional method was used to analyze linguistic incivility representations and the identity method to analyze linguistic incivility strategies. The results demonstrated that the incivility strategies used include (1) respecting other people's time (34%), (2) asserting oneself (19%), (3) refraining from idle complaints (16%), (4) speaking kindly (9%), (5) listening (6%), (6) respecting even a subtle "no" (6%), (7) respecting others' opinions (6%), and (8) not shifting responsibility and blame (3%). Furthermore, students violated the rules of linguistic civility by these actions: initiating conversations without greetings, using excessive words, expressing personal complaints, conveying opinions impolitely, and shifting blame. Recognizing the importance of linguistic civility in student-lecturer communication is crucial for fostering respectful and productive interactions.

Role of climate change on Oriental spruce (Picea orientalis L.): Modeling and mapping

Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

Aerodynamic performance and flow field losses analysis: A study on nozzle governing turbine with varied regulated nozzle stator installation angle

During the energy release process in compressed air energy storage (CAES) system, the air storage pressure gradually diminishes. To optimize turbine performance in this scenario, a judicious air distribution method becomes imperative. This study adopts single nozzle governing method and elucidates the variation in aerodynamic characteristics under rated output work conditions by manipulating regulated nozzle stator installation angle (RNSIA) and total inlet pressure under varying base pressure. When the RNSIA ranges from −10° to 4° under the base pressure of 10.0 MPa, the maximum specific work increment is 13.2 % and efficiency increment is 14.3 % compared with throttle governing. Meanwhile, the maximum specific work increment is 4.0 % and efficiency increment is 2.9 % compared with designed RNSIA. The proportion of throttle entropy increase to the system progressively rises from 5 % to 40 %, mirroring the trend in system entropy increase. To maintain output work requirements, it's advisable to appropriately decrease the RNSIA to boost inlet pressure of the regulated nozzle and mitigate throttling losses. Additionally, the reduction in the RNSIA within a certain range prolongs the duration of the energy release process in the CAES system, which effectively enhances the round-trip efficiency of the CAES system.

Literature Review of Practice Evaluation of Good Drug Distribution Methods at Public and Private Sector Pharmaceutical Distribution Facilities in Indonesia

To ensure that the quality of pharmaceutical products is maintained throughout the distribution chain, it is necessary to implement Good Distribution Practices at distribution facilities that distribute pharmaceutical products. This literature review explores the evaluation of Good Drug Distribution (GDP) or CDOB guideline practices in pharmaceutical product distribution facilities engaged in the public and private sectors in Indonesia. The literature review method used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Articles were searched using the Google Scholar database and restricted to those related to GDP published from 2018 to 2022. The literature was reviewed using questions to assess studies organized by Population, Exposure, and Outcome (PEO) criteria. Articles obtained were then identified, screened, eligibility, then selected based on inclusion and exclusion criteria resulting in 19 pieces that will be extracted data. Of the 19 articles, the study was conducted in several pharmaceutical distribution facilities such as the Health Office, Puskesmas, Hospitals, Pharmacies, and Pharmaceutical Wholesalers spread across Indonesia. All articles used descriptive methods to show the conformity of implementing Good Distribution Practices guidelines. Some studies still do not use the latest regulation, namely the Food and Drug Administration Regulation Number 6 of 2020 concerning Amendments to BPOM Regulation Number 9 of 2019 concerning Technical Guidelines for Good Distribution Methods. The suggestions that can be made include the need for studies related to the evaluation of Good Distribution Practices of pharmaceutical distribution facilities in the public or government sector and studies on distribution facilities in Eastern Indonesia

The effect of a green supply chain on a company's success

The potential of a conceptual model which integrates sustainabile logistics and management strategies to financial performance and organizational performance to lead to long-term profitability is studied in this research. Green logistics and distribution methods, as per the research, currently have a significant impact on the financial market and organizational performance. The organizational performance of green supply chain management and logistics and management practices is also examined in light of the moderating effects of ethical, ecological, and financial performance. Google form is used to develop a questionnaire due to the factor of COVID-19 element for instant data Collection. A 5-point Likert scale is used for the validity of the questionnaire statements. A statistical tool SPSS is used to analyze and examine collected data. Further linear regression is used to find the correlation, variance of the model, and significance of the variables.

Multi-objective Scheduling of Tasks in Cloud Computing Using Fire Fly and Bee Colony Algorithms

The problem of allocating hardware and processing resources in cloud computing is considered in this research. Here, a virtual bee is assigned to any task that dynamically seeks the most appropriate computing resources. Each available computing source also has a virtual fire fly that its brightness is proportional to the amount of processing unit and the amount of computing residue that examines the computing source. The problem of distribution of computing resources among existing tasks will be investigated using the greedy technique of calculating the location of each task in general. The current method has many advantages compared to the random distribution method, but to complement the other topics, researchers can offer other tactics of distribution of computing resources simultaneously with the method proposed in this study and compare their performance with the implementation of both simulations. The simulation results show that the performance criteria of the cloud computing system such as time and computing resources have been greatly improved.

From Physics to Environmental Policy: Exploring Boltzmann Distribution for Carbon Trading Permit Allocation

Combating climate change and reducing atmospheric CO2 emissions are imperative. While carbon trade, including taxes and trading systems, has been proposed, the allocation of initial permits is challenging. This study addresses this issue, exploring the application of the Boltzmann distribution for carbon emission permit allocation in carbon trading. The Boltzmann distribution method uniquely considers each country's environmental and economic contexts, assigning more responsibility to nations with larger populations and higher emissions, and providing incentives to lower-emission countries. This promotes fairness in global climate change efforts and influences national environmental policies. High-emission countries like China receive a high number of permits, encouraging stronger environmental policies, whereas countries with lower emissions, such as Italy, benefit from additional permits as compared to conventional distribution models, bolstering their existing environmental conservation. This paper applies the Boltzmann distribution to eight countries, considering population, economic size, and CO2 emissions. It effectively balances permits between egalitarianism (population-based) and sovereignty (emission-based) principles, suitable for international carbon trading. This flexible approach provides a practical framework for international emissions rights allocation along with potential applications in broader contexts. Implementation of the Boltzmann distribution in real-world policy faces challenges due to the dynamic nature of international politics and economics. This research offers insights into the process of integrating this method into existing environmental policy frameworks, demonstrating its potential as a tool for enhancing global environmental sustainability. Future research should explore its application in the complex international political and economic environment, furthering its role in global climate policy.

Emerging Contaminants in the Effluent of Wastewater Should Be Regulated: Which and to What Extent?

Effluent discharged from urban wastewater treatment plants (WWTPs) is a major source of emerging contaminants (ECs) requiring effective regulation. To this end, we collected discharge datasets of pharmaceuticals (PHACs) and endocrine-disrupting chemicals (EDCs), representing two primary categories of ECs, from Chinese WWTP effluent from 2012 to 2022 to establish an exposure database. Moreover, high-risk ECs' long-term water quality criteria (LWQC) were derived using the species sensitivity distribution (SSD) method. A total of 140 ECs (124 PHACs and 16 EDCs) were identified, with concentrations ranging from N.D. (not detected) to 706 μg/L. Most data were concentrated in coastal regions and Gansu, with high ecological risk observed in Gansu, Hebei, Shandong, Guangdong, and Hong Kong. Using the assessment factor (AF) method, 18 high-risk ECs requiring regulation were identified. However, only three of them, namely carbamazepine, ibuprofen, and bisphenol-A, met the derivation requirements of the SSD method. The LWQC for these three ECs were determined as 96.4, 1010, and 288 ng/L, respectively. Exposure data for carbamazepine and bisphenol-A surpassed their derived LWQC, indicating a need for heightened attention to these contaminants. This study elucidates the occurrence and risks of ECs in Chinese WWTPs and provides theoretical and data foundations for EC management in urban sewage facilities.

Motion planning and contact force distribution for heavy‐duty hexapod robots walking on unknown rugged terrains

AbstractHeavy‐duty hexapod robots have impressive stability, high load‐bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming‐based Contact Force Distribution (DQP‐based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.

An optimization of the fully fuzzy transportation problem with triangular type-2 fuzzy parameters using novel ranking technique

This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach.

ANALYSIS OF CLIMATE CHANGE EFFECT ON EXTREME PRECIPITATION EVENT IN CONSTANTINE ALGERIA

Extreme precipitation events are critical climatic indicators that offer substantial insights into climate change and its variability. This study aims to investigate whether climate change has impacted precipitation frequencies in the Constantine region of Algeria, known for its semi-arid climate with hot-dry summers and cold-wet winters. Temporal variations of monthly air temperature and precipitation were analyzed from 1981 to 2014. Statistical analysis identified a threshold for estimating monthly extreme precipitation during the period from 1981 to 2005 using the Gumbel distribution method. Additionally, a climatic projection of precipitation for return periods of 50 (t = 50) and 100 (t = 100) years was conducted to forecast extreme precipitation values in the future. The results indicate a gradual increase in precipitation levels during the study period, with values generally remaining below extreme thresholds. This suggests no significant escalation in the occurrence of extreme precipitation events. Furthermore, the findings highlight that climate change has had a more pronounced impact on altering air temperature than on influencing extreme precipitation trends.

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach

The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.

CDFRS: A scalable sampling approach for efficient big data analysis

The sampling-based approximation method has demonstrated its potential in various domains such as machine learning, query processing, and data analysis. Most preceding sampling algorithms generate samples at the record level, making it impractical to apply them to very large datasets using a single machine. Even distributed solutions encounter efficiency issues when dealing with terabyte-scale datasets. In this paper, we introduce a scalable sampling approach named CDFRS, which can generate samples with a distribution-preserving guarantee from extensive datasets. CDFRS exhibits significantly improved speed compared to existing sampling algorithms when dealing with terabyte-scale datasets. We provide theoretical guarantees and empirical justifications, demonstrating that samples generated by the CDFRS approach maintain the distribution characteristics of the original dataset. Additionally, we propose a sample size determination algorithm, denoted as A2. Experiment results indicate that the running time of CDFRS shows at least an order of magnitude improvement over other distributed sampling methods. Notably, sampling a 10TB dataset using CDFRS only takes hundreds of seconds, while the compared method requires more than ten thousand seconds. In the context of big data analysis, including tasks such as classification and clustering, models trained with samples generated by CDFRS closely match those trained with the entire training set. Furthermore, the proposed A2 algorithm efficiently determines an appropriate sample size compared with traditional methods.

On foot, by boat: Distribution methods of raw materials suitable for lithics in Central Europe in c. 4900-3400 BCE

On foot, by boat: Distribution methods of raw materials suitable for lithics in Central Europe in c. 4900-3400 BCE

Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification

The Influence of E-Service Quality, Perceived Risk and Customer Trust in Repurchase Intentions (Study on Indonesian Shopee E-Commerce Users)

Shopee is an e-commerce that is often used by consumers when shopping online. As e-commerce, Shopee still has problems with service and risk. E-service quality and perceived risk in transactions made by Shopee users can generate customer trust so that it can be taken into consideration in repurchase intention. This research aims to determine the influence of e-service quality and perceived risk on repurchase intention mediated by customer trust in Shopee e-commerce users. Data collection was carried out using the online questionnaire distribution method with purposive sampling technique. The research sample consisted of 264 respondents with characteristics of at least 18 years of age who knew and had shopped at Shopee. Data analysis using SEM with the help of LISREL (Linear Structural Relation) software. The research results show that of the seven hypotheses proposed, five hypotheses were accepted and two hypotheses were rejected. E-service quality is proven to have a positive influence on customer trust and repurchase intention and e-service quality also has an indirect positive influence on repurchase intention which is mediated by customer trust. Perceived risk has no direct or indirect influence on customer trust. Apart from that, perceived risk has a direct negative influence on repurchase intention. Customer trust is proven to have a positive influence on repurchase intention, which means that the greater customer trust in Shopee, the greater the repurchase intention.

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Numerical simulation of the co-firing of pulverized coal and eucalyptus wood in a 1000MWth opposed wall-fired boiler

Currently, coal-fired coupled biomass power generation is widely regarded as a primary method for reducing carbon emissions in coal-fired power plants. This research investigates the co-firing of biomass in an opposed wall-fired boiler and the implementation of air-staged combustion technology using numerical simulation approaches. The study examines the impact of various air methods of distribution and blending ratios on combustion properties and NOx emissions. The findings demonstrate that the abrasive wear of fuel particles on the platen superheater is decreased by the addition of biomass to the fuel mix. Furthermore, a drop in the furnace chamber's overall temperature is the outcome of raising the blending ratio. Different blending ratios have their own adapted air distribution methods. At blending ratios of 10% and 30%, the W-type air distribution exhibits the lowest NOx outlet concentrations of 231.84 mg/Nm3 and 220.6 mg/Nm3, respectively. Moreover, the W-type air distribution shows the highest burnup rates of 98.74% and 98.43% at these blending ratios. This suggests that it is feasible to change the burner monolayer air distribution and combine it with co-firing of biomass technology to achieve low NOx and efficient operation of a hedge-fired boiler.

Rural American farmers’ perceptions of crime, safety and policing

PurposeThe purpose is to understand how farmers in rural American communities perceive crime, safety and policing.Design/methodology/approachAn online survey, adapted from a version used in Victoria, Australia (Harkness, 2017), was modified and administered through social media and farming organizations throughout three US states. The survey covers topics relating to crime and victimization, feelings of safety or fear in rural areas, policing practices and trust in police in their areas and any crime prevention practices that respondents use.FindingsWith nearly 1,200 respondents and four scales investigated, results indicate that those respondents with more favorable views of law enforcement and the criminal justice system had the highest fear of crime, those who had been prior victims of crime had a higher fear of crime than those who did not, those with higher community involvement had higher fear of crime, and those from Nebraska compared to Colorado had higher fear of crime.Research limitations/implicationsA better understanding of the agricultural community’s perceptions of crime, safety and policing will aid law enforcement in community policing efforts and in farm crime investigation and prevention. Limitations of the study, including the distribution method will be discussed.Originality/valueFarm- and agriculture-related crimes have serious financial and emotional consequences for producers and local economies. Stereotypes about rural areas being “safe with no crime” are still prevalent. Rural American farmers’ perceptions of crime, safety and police are largely absent from the literature and are important for improving farm crime prevention.