Aggregate Level Research Articles

The Productivity Spectrum: An Individual-Level Examination of Servers’ Reactions to Workload and Overwork

Purpose: This study explores the diverse reactions of human servers to variations in workload and overwork, focusing on how these stressors influence service time in a call center setting.Study design/methodology/approach: OLS regressions with fixed effects and clustered standard errors were used to analyze the impact of workload and overwork on service time at both aggregate and individual levels.Sample and data: This research utilizes archival data from a call center at a large Kuwaiti private hospital, covering 58,460 calls handled by 23 agents during October 2016.Results: At the aggregate level, workload has a negative effect on service time, indicating a general speedup response, while overwork shows no significant effect. At the individual level, responses vary significantly among agents, with some agents speeding up, others slowing down, and some demonstrating non-linear responses to workload. Overwork responses also vary, with some agents slowing down, others speeding up, and some showing flat responses.Originality/value: This study contributes to the behavioral queuing literature by emphasizing the importance of individual-level analysis in understanding server responses to workload and overwork. The findings reveal that aggregate analyses can mask significant variations in individual behaviors, crucial for optimizing call center performance. By exposing the spectrum of individual reactions, the study provides deeper theoretical and practical insights into the complex dynamics of service time under different work stressors.Research limitations/implications: The findings provide insights for optimizing call center performance, but further research is needed to validate and generalize these findings across different settings and call center environments.

The use of the Bennet indicators and their transitive versions for scanner data analysis

Although modern price index theory is based on comparisons of ratios of prices, quantities and expenditures, we may be more interested in the magnitude of differences in these characteristics in many business applications. The benefit of using these differences is that there is no problem associated with the occurrence of zero prices and quantities, a problem that arises when we work with ratios. In practice, we most often care about decomposing value difference into indicators of contributions from price and quantity differences. The best-known price and quantity indicators are the Bennet indicators, which are not transitive. Although there have been papers in the literature that propose a transitive version of the Bennet indicators, they deal with comparisons across firms in cross-section or panel contexts. This paper revises the price and quantity Bennet indicators and their multilateral versions for the analysis of scanner data. Specifically, i nstead o f c onsidering c omparisons across firms, c ountries or r egions, the t ransitive versions of t he Bennet i ndicators a re a dapted to work on scanner data sets observed over a fixed time w indow. Since the scanner data sets have a high turnover of products, which can make it difficult to interpret the difference in sales values over the compared time periods, the paper also considers a matched sample approach. One of the objectives of the study is to compare bilateral and multilateral Bennet indicator results across all available products or strictly matched products over time. It also examines the impact of data filters used and the level of data aggregation on the price and quantity Bennet indicators. According to the best author’s knowledge, this study is a pioneer in the field of implementing the multilateral Bennet indicators in scanner data analysis.

Temporal control of acute protein aggregate turnover by UBE3C and NRF1-dependent proteasomal pathways.

A hallmark of neurodegenerative diseases is the progressive loss of proteostasis, leading to the accumulation of misfolded proteins or protein aggregates, with subsequent cytotoxicity. To combat this toxicity, cells have evolved degradation pathways (ubiquitin-proteasome system and autophagy) that detect and degrade misfolded proteins. However, studying the underlying cellular pathways and mechanisms has remained a challenge, as formation of many types of protein aggregates is asynchronous, with individual cells displaying distinct kinetics, thereby hindering rigorous time-course studies. Here, we merge a kinetically tractable and synchronous agDD-GFP system for aggregate formation with targeted gene knockdowns, to uncover degradation mechanisms used in response to acute aggregate formation. We find that agDD-GFP forms amorphous aggregates by cryo-electron tomography at both early and late stages of aggregate formation. Aggregate turnover occurs in a proteasome-dependent mechanism in a manner that is dictated by cellular aggregate burden, with no evidence of the involvement of autophagy. Lower levels of misfolded agDD-GFP, enriched in oligomers, utilizes UBE3C-dependent proteasomal degradation in a pathway that is independent of RPN13 ubiquitylation by UBE3C. Higher aggregate burden activates the NRF1 transcription factor to increase proteasome subunit transcription, and subsequent degradation capacity of cells. Loss or gain of NRF1 function alters the turnover of agDD-GFP under conditions of high aggregate burden. Together, these results define the role of UBE3C in degradation of this class of misfolded aggregation-prone proteins and reveals a role for NRF1 in proteostasis control in response to widespread protein aggregation.

Characteristics of Gambling Helpline Callers in Macao: A Regional Comparison.

Despite the continued focus on profiling gambling helpline callers, little is known about the characteristics of callers in Macao. To address this gap, this study aims to investigate the characteristics of problem gamblers calling the helpline in Macao and explore the regional differences between callers from Macao, mainland China, and Hong Kong. Specifically, the study examined their demographics, helpline usage, and gambling-related characteristics. The study used data collected by SKHSSCO between 2015 and 2021. The results showed that, at the aggregate level, the helpline callers in Macao were male, young, and full-time employees. The majority used the telephone and reported problems with casino table games. In addition, the referral rate was relatively low, while the rate of suicidal ideation was comparatively high. The results also revealed that callers from Macao, mainland China, and Hong Kong were notably distinct in these characteristics. The findings shed light on how to increase helpline accessibility and create tailored interventions and prevention efforts to better support individuals affected by problem gambling.

The influence of data aggregation levels on accident severity researches

Abstract This paper aims at assessing the influence of data aggregation on the estimation of the accidents’ severity. For this purpose, 27,400 accidents are investigated on three aggregation levels: individually (idl) which is highly disaggregated hourly (hrl), and daily (dil). A Severity Score (SS) is defined based on the injuries’ severity and relative cost weights. Multiple Linear Regressions are developed by assessing the SS’s variation as a collective explanatory effect of 83 predictors related to road environment, weather and lighting conditions, adherence conditions, and number of the involved vehicles. F and t tests are employed in order to assess the developed models and to investigate which variable is of statistical significance. This research shows that the higher the level of aggregation is, the higher the coefficient of determination. SS_dil model has the higher R2 value and direct relationships between SS and the number of accidents occurring in “U” curves, on roads with no shoulders, where the roadway was covered with slime or black ice and where the roadway was wet was found. One of the interesting findings of this paper shows that in the models with low R2 values (based on individual and hourly aggregated data), snow and fog significantly explain the variation of SS. As these are seasonally and short time phenomena, a further research should be focused on how the estimated models based on individual or hourly aggregated data could be improved by adding other significant predictors related to driver’s and vehicle’s characteristics and road environment’ features.

Guiding supervisors in artificial intelligence-enabled forecasting: Understanding the impacts of salience and detail on decision-making

In many real-world situations, multiple humans are involved in decision-making when interacting with machine recommendations. We investigated a setting where an artificial intelligence system creates demand forecasts that a human planner can either accept or revise, and a supervisor then makes the final decision about which forecast to select. We designed and conducted two experimental studies to understand decision-making by a supervisor. First, we provided the improvement probabilities of adjustments at an aggregated level and found evidence for overoptimism bias and mean anchoring. Second, we provided decomposed guidance based on two adjustment attributes, direction and magnitude, to investigate the role of salience based on the distance between the improvement probabilities and level of detail in guidance effectiveness. We found no significant difference in using less and more salient guidance provided that the detail level was fixed. However, revealing more details when the guidance was more salient increased the use of guidance.

Does customer concentration matter in business model value: Threshold effects of carbon emissions and dynamic capabilities?

Prior studies have examined the relationship between customer concentration and business model value (hereinafter BMV). This research further examines their relationship by introducing the roles of external carbon emissions and internal dynamic capabilities, and identifies the potential threshold effects induced by the roles. Drawing on a sample of Chinese-listed manufacturing firms from 2012 to 2017, we use a set of regression models, threshold models and robustness checks. The results show that customer concentration is positively related to BMV and carbon emissions modulate a triple-threshold effect on this positive relationship, while dynamic capabilities exhibit threshold effects at both the aggregate and individual levels. This paper provides valuable insights into how firms promote their BMV and achieve carbon neutrality by recognizing the effect of customer concentration on BMV, as well as how firms can leverage their dynamic capabilities to support BMV enhancement and achieve sustainability improvements.

Weak vs Strong Knowledge Spillover Effects. Evidence from Geographic Distribution of Innovative Startup in Italy

This paper introduces the concepts of weak and strong knowledge spillover effects within the context of the Knowledge Spillover Theory of Entrepreneurship. In this context, the fundamental idea of our proposition is to provide a comprehensive analysis of the factors and conditions promoting and supporting entrepreneurship development at the regional and provincial level using a unique unbalanced panel database, consisting of 9242 Italian Innovative startups for the period 2008-2018. In general terms, we argue that the evidence from our unbalanced panel dataset indicates that these dynamics are as follows. First, spillover effects deriving from competition are initially strong at the province level, and later decay to weak spillover effects at the regional level. Second, in terms of specialization (intra-industry) spillover effects, they are initially strong at the provincial level, and later become weak at the regional level. Third, spillover effects deriving from diversity (inter-industry) are predominantly strong at the regional level, yet weak at the provincial level of aggregation. At the provincial level, higher levels of competition result in strong knowledge spillover effects leading to higher new firm formation, with weak knowledge spillover effects deriving from specialization. Furthermore, in line with the Knowledge Spillover Theory of Entrepreneurship, the existence of non-homogeneous distribution of opportunities and related knowledge spillover effects create heterogeneity of patterns of new firm formation across different units of geographic aggregation. These results provide evidence of constraint potential for growth that remains a puzzle and challenge for academics and policymakers.

Stability and heavy metals accumulation of soil aggregates under different land uses in the southwest coastal Bangladesh

Agricultural soil contamination is increasing day-by-day and becoming a major problem over the globe. Trace elements accumulation in the bulk soil is frequently documented, however, there is no precise mechanism of their distribution in the different soil aggregates level. We collected twelve composite soil samples from banana fields, fallow land, rice cultivated with pond water (rice field-I), and rice cultivated with rain water (rice field-II). We separated soil samples into four different size of aggregates (4-2, 2–0.25, 0.25–0.053, <0.053-mm) and then, aggregate stability (MWD), soil organic carbon (SOC), and heavy metals content (Pb, Cd, Cr, As, Fe, Mn, Zn, Ni, Co, Cu) in the soil samples were measured with different techniques. Results showed that MWD was higher in the rice-based land use, which was significantly contributed by SOC (p < 0.001). The concentration of Pb, As, Cd, Fe, and Mn were increased, while Cu and Zn concentration were reduced with increasing aggregate size (p < 0.05). In contrast, aggregate size did not influence on Ni and Co accumulation (p > 0.05). Moreover, macroaggregate acted as an accumulator for Fe, Mn, and As, while all the aggregate fractions acted as accumulators for Cu and Zn. Our study indicated that MWD, SOC, aggregate size and composition, and metal species were the controlling factors of trace elements accumulation and distribution in the various sizes of soil aggregates.

Smooth vetch covering alters soil aggregate microbial metabolic limitations in citrus orchards

Green manure covering alters the supply-demand relationship between soil resources and microorganisms by improving soil structure and increasing carbon inputs. However, it remain unclear how soil microorganism respond to the imbalance between resources and demands at the aggregate scale. The present study analyzed the stoichiometric ratios of organic carbon, nitrogen, and phosphorus nutrients, microbial extracellular enzyme activities, and biomass in large macroaggregates (LMA, 2–8 mm), small macroaggregates (SMA, 0.25–2 mm), and microaggregates (MIA, < 0.25 mm) under smooth vetch covering. The relationship between microbial carbon use efficiency and nutrient restriction in soil aggregates was elucidated and the alteration was revealed in microbial metabolic pathways and carbon sequestration functions within soil aggregates under green manure covering. The results showed that: (1) Smooth vetch covering significantly increased the C: N resource imbalance (ln(MBC: MBN)/ln(SOC: TN)) in soil aggregates from 0.50 to 0.69, and thus increased the nitrogen requirement of soil aggregates. However, the N:P resource imbalance (ln(MBN: MBP)/ln(TN: TP)) decreased from 2.25 to 1.78, especially in microaggregates. (2) To mitigate the limitation imposed by nitrogen availability at the aggregate level, microorganisms have ramped up the activity of C- (46.72 %–98.64 %), N- (2.32 %–121.00 %), and P- acquiring enzymes (119.11 %–187.78 %). Nevertheless, the spatial heterogeneity inherent in soil aggregates, characterized by varying particle sizes, has led to a pivotal shift in microbial nutrient limitation. As soil aggregate size diminishes, the primary constraint on microbial activity transitions from nitrogen limitation to phosphorus limitation, reflecting the dynamic interplay between soil structure and nutrient availability. (3) In addition, smooth vetch covering increased the microbial carbon use efficiency of soil aggregates, and it increased with the increase of the particle size of soil aggregates: LMA (178.80 %) > SMA (147.23 %) > MIA (9.99 %). Microorganisms allocated more energy to generate biomass instead of obtaining limiting nutrient elements. These results indicate that smooth vetch covering could increase the organic carbon content of soil aggregates. In addition, in order to adapt to nutrient imbalance, soil aggregates microorganisms choose a “egoistic” metabolic pathway, which alleviates nutrient limitation and assimilated more energy and nutrients into their own biomass and reduces the release of organic carbon. It is evident that integrating Vicia villosa Roth var. glabresens Koch as a ‘green manure’ in orchards serves a dual purpose: it mitigates the environmental impact of chemical fertilizers while simultaneously promoting soil carbon sequestration. This approach offers a robust theoretical framework for achieving the harmonious integration of ecological sustainability and economic viability in economic forestry, such as citrus orchards.

The relationships between Urban Tree Canopy Cover and Crime in São Paulo City, Brazil

Prior research has indicated lower crime rates in areas with greater tree canopy cover predominantly in the Global North. There are few studies from the Global South, and more specifically in Latin America. Given the high prevalence of crime in Latin American cities, the need to address social and environmental inequalities using nature-based solutions is urgent. This study examines crime and tree canopy cover through a series of spatial analyses for São Paulo city, Brazil, while controlling for potential confounders. We tested six levels of data aggregation that were combined with three different crime types and two denominators of crime, to account for spurious findings from Modifiable Areal Unit Problems (MAUP). Of the 36 models, a majority of 27 models (75 %) indicate a negative relationship between crime and tree cover canopy, while 4 models (11 %) show a positive correlation, and 5 models (14 %) suggest no statistically significant relationship. Our best models allow us to infer that 10 % greater tree canopy cover is associated with a 1.20 % fewer property, personal and total reported crimes, with statistical significance at the p < 0.001 level. These findings support our hypothesis that there is a negative relationship between tree canopy cover and crime, with a stronger association observed for property crime than personal or total crime. The negative relationship persisted independently of the level of data aggregation, crime types, crime denominator and spatial models specifications (lag and SARMA), avoiding statistical bias of MAUP. The results follow the literature that have observed tree cover associated with lower crime rates, for different types of crime, while adding a new climatic and cultural context to the evidence base.

Ideological extremism, left-right orientations, and invalid voting in two-round presidential elections in Latin America

ABSTRACT Ideology is one of the most studied factors affecting voting behavior in modern mass democracies. Despite that, literature on invalid voting has focused on other explanatory factors at the aggregate and individual levels. This article argues that ideology is a relevant explanatory factor affecting invalid voting at the individual level and advances two hypotheses. Specifically, voters with known preferences on the left-right dimension and those further from the center will be less prone to cast an invalid ballot in presidential elections. I test these hypotheses in the first and second rounds of nine presidential elections in four Latin American countries between 2002 and 2019. Additionally, the hypotheses were tested for voters caught in the runoff dilemma, i.e. those who did not vote for one of the two candidates who advanced to the second round. Using the Comparative Study of Electoral Systems data, the analysis confirms that left-right orientations and ideological extremism are negatively associated with the probability of casting an invalid vote. The article’s findings help expand our knowledge about how some specific features of ideology affect voting behavior, thus contributing to the literature on ideological and invalid voting.

Building a Defence Manpower Planning Tool Using Survival Analysis Models

ABSTRACT A vibrant economy results in strong competition in the marketplace for highly skilled and versatile workforce. Therefore, the retention of such workers is an important issue for most organisations, including the Royal Navy, which is sensitive to economic pressures from the civilian economy. This paper considers early exits from the Royal Navy as the termination of an individual’s career in the naval force at any time before the termination of their contractual agreement with the organisation. The paper uses a survival analysis model to forecast/predict early exits from the Royal Navy and help build a defence manpower planning tool to manage retentions. At the micro-level, it provides two sets of predictions, the first on who is more likely to leave early, and the second on when that event is most likely to occur. At the aggregate level, the predictive model provides a management tool for performing stress testing in relation to potential changes in economic and labour market conditions and how the heterogeneous skilled workforce within the organisation is likely to respond to different changes.

Does hosting FIFA World Cup attract inward FDI? An empirical investigation using sectoral panel data of 12 countries

PurposeThe primary objective is to analyze the direct and short-run impact of hosting the FIFA World Cup on inward FDI, considering both aggregate and sectoral levels. Additionally, the study aims to investigate the moderating role of governance quality on this impact, emphasizing the importance of robust institutional frameworks in attracting FDI.Design/methodology/approachThis paper uses panel data spanning 1970–2022, encompassing 12 countries that have hosted FIFA World Cup events. The study employs a linear regression model with a robust weighted least squares (RWLS) estimation method. It incorporates various control variables and the institutional quality as moderating variables, to evaluate the impact of hosting the FIFA World Cup on inward FDI at both aggregate and sectoral levels.FindingsHosting the FIFA World Cup is associated with a significant average increase of $4.33 bn in inward FDI at the aggregate level. Notably, governance quality serves as a critical moderating factor, with well-governed countries experiencing a more substantial increase in FDI, totaling $10.5 bn. At the sectoral level, the results reveal that poorly governed countries attract FDI in primary sectors, while well-governed countries attract FDI in secondary and tertiary sectors. This highlights the nuanced dynamics of FDI attraction depending on the institutional quality of the host countries.Research limitations/implicationsA primary limitation lies in the scarcity of sectoral-level data, constraining the comprehensive study of the relationship between hosting mega-sport events and FDI. Future research could explore alternative data sources and methodologies to overcome this limitation. Additionally, extending the analysis to include other economic indicators beyond FDI could provide a more holistic understanding of the economic implications of hosting major international sporting events.Originality/valueThis study contributes to the literature by focusing exclusively on the FIFA World Cup and undertaking a comprehensive sectoral analysis. By incorporating governance quality as a moderating variable, it adds a nuanced layer to the understanding of the impact of hosting international events on FDI at the sectoral level. The findings underscore the importance of targeted strategies and robust institutional quality in enhancing FDI attractiveness.

Household spending diversity, aggregation, and the value of product variety

This paper studies the diversity of household consumption spending, i.e. how widely households distribute their spending across different types of goods. Using detailed UK expenditure data (1990–2015), we show that the diversity of household spending rises in income up to a certain level and then declines as richer households increasingly concentrate their spending on specific expenditure categories. As these categories differ across households, spending diversity on the aggregate level can keep rising in income while spending diversity on the household level falls. We build a model with heterogeneous nonhomothetic preferences that can explain the observed patterns, highlighting the role of aggregation. Our model shows that ignoring preference heterogeneity and assuming representative households leads to a (potentially very large) underestimation of the value of product variety. Estimating the welfare effects of trade or innovation policies therefore requires to appropriately account for the empirically observed heterogeneity and nonhomotheticity of preferences.

Evaluating stability and bioactivity of Rehmannia-derived nanovesicles during storage

Plant-derived nanovesicles (PDNVs) have garnered growing attention in the biomedical field owing to their abundance in plant-derived ribonucleic acids (RNA), proteins, lipids and metabolites. The question about the preservation of PDNVs is a crucial and unavoidable concern in both experiments’ settings and their potential clinical application. The objective of this research was to examine the impact of varying storage temperatures on the stability and bioactivity of Rehmannia-derived nanovesicles (RDNVs). The results showed that RDNVs aggregated after 2 weeks of storage period at 4 °C, and the particle size of some RDNVs gradually increased with time, along with the increase of solution potential. After 2 months of storage, all RDNVs exhibited varying levels of aggregation irrespective of storage temperature. The bioactivities of nanovesicles under different temperature storage conditions revealed a gradual decline in cell proliferation inhibition bioactivity over time, significantly lower than that of freshly prepared RDNVs. In contrast, the preservation of anti-migratory activity in RDNVs was found to be more effective when subjected to rapid freezing in liquid nitrogen followed by storage at − 80 °C, as opposed to direct storage at − 80 °C. These findings suggest that temperature alone may not be sufficient in safeguarding the activity and stability of RDNVs, highlighting the necessity for the development of novel protective agents for PDNVs.

Lacticaseibacillus rhamnosus HA-114 and Bacillus subtilis R0179 Prolong Lifespan and Mitigate Amyloid-β Toxicity in C.elegans via Distinct Mechanisms.

Recent advances linking gut dysbiosis with neurocognitive disorders such as Alzheimer's disease (AD) suggest that the microbiota-gut-brain axis could be targeted for AD prevention, management, or treatment. We sought to identify probiotics that can delay Aβ-induced paralysis. Using C. elegans expressing human amyloid-β (Aβ)1-42 in body wall muscles (GMC101), we assessed the effects of several probiotic strains on paralysis. We found that Lacticaseibacillus rhamnosus HA-114 and Bacillus subtilis R0179, but not their supernatants or heat-treated forms, delayed paralysis and prolonged lifespan without affecting the levels of amyloid-β aggregates. To uncover the mechanism involved, we explored the role of two known pathways involved in neurogenerative diseases, namely mitophagy, via deletion of the mitophagy factor PINK-1, and fatty acid desaturation, via deletion of the Δ9 desaturase FAT-5. Pink-1 deletion in GMC101 worms did not modify the life-prolonging and anti-paralysis effects of HA-114 but reduced the protective effect of R0179 against paralysis without affecting its life-prolonging effect. Upon fat5 deletion in GMC101 worms, the monounsaturated C14:1 and C16:1 FAs conserved their beneficial effect while the saturated C14:0 and C16:0 FAs did not. The beneficial effects of R0179 on both lifespan and paralysis remained unaffected by fat-5 deletion, while the beneficial effect of HA-114 on paralysis and lifespan was significantly reduced. Collectively with clinical and preclinical evidence in other models, our results suggest that HA-114 or R0179 could be studied as potential therapeutical adjuncts in neurodegenerative diseases such as AD.

Mulching drive changes in soil microbial community assembly processes and networks across aggregate fractions

Soil microbial community assembly processes and networks in croplands have been widely explored; however, their dynamics and how they regulate winter wheat yield across distinct soil aggregate fractions under the combined effects of mulching and soil horizons have not been comprehensively understood. Therefore, based on a 9-y field experiment, the responses of soil bacterial and fungal community assembly processes and interkingdom association networks to mulching were specifically investigated at the soil aggregation level. Soil properties and microbial biomass were separated into distinct mulching in the topsoil (0–10 cm), and soil water content was considered the most critical factor. The soil bacterial community was affected mainly by mulching and soil horizon compared with the fungal community in microaggregates (<0.25 mm). Notably, the bacterial community displayed more robust stochastic processes than the fungal one, and microbial interkingdom association networks were more complex and stable in micro-than macroaggregates. Soil potential carbon mineralization, pH, and total nitrogen were the dominant properties regulating winter wheat grain yield in combination with microbial community composition, assembly processes, and networks in each soil aggregate class. Wheat yield decreased under straw mulching and was mainly regulated by bacterial community composition and assembly processes. Thus, this study enhanced our understanding of the regulations for wheat yield, which could facilitate soil microbial community management at the aggregation level for sustainable crop production in mulching conservation agroecosystems.

Technology and the Global Economy

Interpreting individual heterogeneity in terms of probability theory has proved powerful in connecting behavior at the individual and aggregate levels. Returning to Ricardo's focus on comparative efficiency as a basis for international trade, much recent quantitative equilibrium modeling of the global economy builds on particular probabilistic assumptions about technology. We review these assumptions and discuss how they deliver a unified framework underlying a wide range of static and dynamic equilibrium models.

Enhancing electric vehicle charging efficiency at the aggregator level: A deep-weighted ensemble model for wholesale electricity price forecasting

The proliferation of electric vehicle (EV) adoption strains low-voltage distribution networks, particularly in aggregated charging scenarios, prompting utility companies to incentivize charging aggregators for optimizing load balancing within thermal limits. These aggregators utilize machine learning algorithms to understand electricity price signals and orchestrate the optimization of the EV charging process. However, conventional machine learning approaches fall short when dealing with the dynamic and volatile nature of electricity prices, emphasizing the necessity for advanced ensemble models. This paper introduces a novel Deep-Weighted Ensemble Model (DWEM) rooted in standard and stacked Long Short-Term Memory (LSTM) networks designed for wholesale electricity price forecasting, to manage the EV charging at the aggregator level. The ensemble development process involves developing an architecture that highlights the significance of the DWEM model in supporting aggregators for the charging optimization of EVs. The charging optimization problem of aggregated EVs is formulated, and the heuristic mechanism is systematically presented, evaluating various weight configurations, and selecting those characterized by the highest levels of accuracy to comprise the ensemble model. Moreover, we incorporated a standard deviation mechanism to evaluate the impact of the proposed DWEM on forecasting accuracy, mean squared error, and mean absolute error across various standard deviation levels. We leveraged a publicly available Houston electricity dataset and performed a detailed data engineering mechanism, accounting for data both with and without outliers. Subsequently, we applied the proposed DWEM to this dataset and conducting three types of comparative analysis: (a) evaluating model performance in terms of accuracy, mean square error, and mean absolute error; (b) assessing aggregator charging analysis focusing on charging load and cost; and (c) analyzing computational complexity and execution time. The simulation results demonstrated a improvement in accuracy and reduction in charging load and cost compared to state-of-the-art methods, while maintaining competitive computational complexity.