Search results

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Castellated and cellular beams achieved the same strength as solid I-beams with the same depth, resulting in significantly lighter and more economical structures. The purpose of this study is to analyse the bending behaviour of I-beam steel sections with certain web openings by finite element analysis.

The accuracy of finite element results allows extensive numerical analysis of sections with web openings, concentrating on the web opening sizes and web opening positions. These assumptions can increase the induced section load with various shapes of web opening depth and web opening shapes of c-hexagon, hexagon, octagon, circular and square. This also includes spacing distances, with a 50-mm edge and 150-mm centre-to-centre distance and a section with a 100-mm edge and 200-mm centre-to-centre distance. Generally, the adjustment of the opening geometry (by reducing the angle of web pitch or reducing the opening depth depending on analysed parameters) may influence the bending behaviour.

Additionally, Model 2 was found to be the optimum model compared to Model 1, mainly in terms of bending. Moreover, the I-beam with a c-hexagon shape opening exhibited the lowest displacement compared to other sections with other web opening shapes. Section with a different arrangement of web opening, Type E shows the lower displacement while higher displacement is observed for Type A and also higher displacement considered for Type G. The optimum model is associated with Type E, followed by Type D, compared to other types of certain web opening and I-beam.

The use of sections with different arrangements of web opening improved the performance of the perforated section in terms of structural behaviour, compared to typical I-beam, thus leading to economic design.

Owing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption.

A nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality.

Finally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs.

The material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.

The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

This study aims to propose and evaluate the progress in the basic-pixel (a strategy to generate continuous trajectories that fill out the entire surface) algorithm towards performance gain. The objective is also to investigate the operational efficiency and effectiveness of an enhanced version compared with conventional strategies.

For the first objective, the proposed methodology is to apply the improvements proposed in the basic-pixel strategy, test it on three demonstrative parts and statistically evaluate the performance using the distance trajectory criterion. For the second objective, the enhanced-pixel strategy is compared with conventional strategies in terms of trajectory distance, build time and the number of arcs starts and stops (operational efficiency) and targeting the nominal geometry of a part (operational effectiveness).

The results showed that the improvements proposed to the basic-pixel strategy could generate continuous trajectories with shorter distances and comparable building times (operational efficiency). Regarding operational effectiveness, the parts built by the enhanced-pixel strategy presented lower dimensional deviation than the other strategies studied. Therefore, the enhanced-pixel strategy appears to be a good candidate for building more complex printable parts and delivering operational efficiency and effectiveness.

This paper presents an evolution of the basic-pixel strategy (a space-filling strategy) with the introduction of new elements in the algorithm and proves the improvement of the strategy’s performance with this. An interesting comparison is also presented in terms of operational efficiency and effectiveness between the enhanced-pixel strategy and conventional strategies.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

Basically, connections are used to transfer the force supported by structural members to other parts of the structure. The flush end-plate bolted beam to column connection is one type that has been widely used because of its simplicity in fabrication and rapid site erection. The purpose of this study is to determine the moment-rotation curve, moment of resistance (MR) and mode of failure, and the results were compared with existing results for normal flat web connections.

In this study, the connection modeled was the flush end-plate welded with triangular web profile (TriWP) steel beam section and then bolted to a UKC column flange. The bolted flush end-plate semi-rigid beam to column connection was modeled using finite element software. The specimen was modeled using LUSAS 14.3 finite element software, with dimensions and parameters of the finite element model sizes being 200 × 200 × 49.9 UKC, 200 × 100 × 17.8 UKB and 200 × 100 with a thickness of 20 mm for the endplate.

It can be concluded that the MR obtained from the TriWP steel beam section is different from that of the normal flat web steel beam by 28%. The value of MR for the TriWP beam section is lower than that of the normal flat web beam section, but the moment ultimate is higher by 21% than the normal flat web. Therefore, it can be concluded that the TriWP section can resist more acting force than the normal flat web section and is suitable to be used as a new proposed shape to replace the normal flat web section for a certain steel structure based on the end-plate connection behavior.

As a result, the TriWP section has better performance than the flat web section in resisting MR behavior.

When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

This study aims to understand how distributed agile teams (DATs), encouraged by globalisation, and recently accelerated by the COVID-19 outbreak, adopt agile practices to achieve project goals by working virtually.

This study developed a multiple-case study involving four companies undergoing several changes, especially during the COVID-19 pandemic. The authors collected data mainly through in-depth, face-to-face interviews with seven key informants. Moreover, this study operates data triangulation by referring to secondary data sources and developing a grounded theory data analysis.

The findings highlight three main categories associated with the DAT functioning, namely, “DATs’ implementation issues”, “elements supporting DATs’ implementation” and “outcomes of DATs’ implementation”, that show DATs’ primary triggers, critical aspects and supportive actions for team functioning.

This paper produced valuable theoretical knowledge of DATs’ dynamics within a socio-technical approach that distinguishes soft and hard variables supporting DAT implementation. Moreover, the evidence provides useful suggestions for managers about creating an objective-oriented virtual work environment based on DATs’ self-organisation, digitally shared leadership and occasional on-site socialisation.

This paper provides new and interesting insights that bring to evidence the main variables related to DATs’ adoption and dynamics, showing supporting activities that enhanced their operativity. It provides a valuable descriptive framework for academics and practitioners to understand DATs’ functioning better and take action to improve their implementation.