Performance Indicators Research Articles

Stretchable Ultrasound Metalens for Biomedical Zoom Imaging and Bone Quality Assessment with Subwavelength Resolution.

Ultrasound imaging is extensively used in biomedical science and clinical practice. Imaging resolution and tunability of imaging plane are key performance indicators, but both remain challenging to be improved due to the longer wavelength compared with light and the lack of zoom lens for ultrasound. Here, the ultrasound zoom imaging based on a stretchable planar metalens that simultaneously achieves the subwavelength imaging resolution and dynamic control of the imaging plane is reported. The proposed zoom imaging ultrasonography enables precise bone fracture diagnosis and comprehensive osteoporosis assessment. Millimeter-scale microarchitectures of the cortical bones at different depths can be selectively imaged with a 0.6-wavelength resolution. The morphological features of bone fractures, including the shape, size and position, are accurately detected. Based on the extracted ultrasound information of cancellous bones with healthy matrix, osteopenia and osteoporosis, a multi-index osteoporosis evaluation method is developed. Furthermore, it provides additional biological information in aspects of bone elasticity and attenuation to access the comprehensive osteoporosis assessment. The soft metalens also features flexibility and biocompatibility for preferable applications on wearable devices. This work provides a strategy for the development of high-resolution ultrasound biomedical zoom imaging and comprehensive bone quality diagnosis system.

Commercialization Efforts of Capacitive Deionization Technology in Water Treatment Processes

AbstractAlthough capacitive deionization (CDI) technology has been studied intensively for more than 20 years, its commercialization remains in the initial stage, which is partly caused by the insufficient knowledge exchange between academia and industry. This concept reviews multiple scaling‐up efforts in the CDI technology for treating real water streams, following a case‐by‐case fashion. While the cell architecture in pilot scales is limited to the membrane CDI, highlighting the necessary role of ion‐exchange components during scaling‐ups, different ways of electrode stacking, i. e., monopolar and bipolar, are available. The performance indicators of these CDI systems when treating real water streams are summarized to gain insights into industrial practices. Importantly, key discrepancies in cell components and performances between pilot‐scale and lab‐scale studies are emphasized. The main challenges in large‐scale CDI systems for industrial purposes are discussed, providing hints for a better integration of research and applications.

Advances in Paper‐Based Photodetectors: Fabrications, Performances, and Applications

AbstractWith the development of wearable electronic technology, the flexible photodetectors have attracted widespread attention, and it is of great significance to develop flexible, eco‐friendly and low‐cost photodetectors. Cellulose paper, as a flexible, eco‐friendly, low‐cost, lightweight, customizable, biodegradable and renewable material, has received enthusiastic attention and rapid development in the field of photodetectors in recent years. In this review, it is focused on the research progress of paper‐based (PB) photodetectors. First, the fabrication methods of PB photodetectors are discussed, including electrode materials and optoelectronic functional materials. Then, this review systematically summarizes and analyzes the achievements of PB photodetectors on photoelectric performances (spectral response range, responsivity, detectivity, response/recovery times and on/off ratio) and flexibility characteristics (bending angle and bending cycle). In terms of key performance indicators, the PB photodetectors reported so far can detect multiple wavelengths of light from UV to near‐infrared with the maximum detectivity of 1013 Jones. In addition, the various applications of PB photodetectors is reviewed and discussed. Finally, it is look forward to the future development of PB photodetectors in terms of fabrication methods, photoelectric and flexible performances, and applications. With this review, it is hope that it will promoted the future development of PB photodetectors.

Analysis of Energy Performance Indicators Based on ISO 50001-2018 in Shopping Centers Using RETScreen Expert

The ISO 50001 energy management system has been implemented by many organizations around the world, including in Indonesia. The purpose of this application is to sustainably improve energy performance to reduce energy consumption and energy costs. In determining the measurement of energy performance improvement, energy performance indicators and energy baseline are used. This study will analyze the energy performance indicators obtained from the energy audit results of one shopping center in Indonesia using RETScreen Expert software. Multiple linear regression is used to develop the energy baseline equation and examine the relationship between energy consumption and significantly influencing variables, such as Service Usage (person) and Cooling Degree Days (CDD). This baseline equation will be used to monitor energy performance after energy performance improvement measures are taken. From the results of this equation, the predicted value of energy consumption in 2023 after improving energy performance in 2022 is 7,014,081 kWh then from the actual measurement results obtained energy consumption value of 6,113,632 kWh the results of the calculation of energy savings obtained from the energy baseline equation using RETScreen Expert software is 900,449 kWh / year.

CBIF-M: A content-based image filtering and retrieval scheme using multi-classifier filtering framework

Recently, the image retrieval process appears to be a challenging task to filter out a huge volume of objectionable images from retrieval. It is very easy for people of all ages to obtain such obscene images with just a few clicks on the internet, which could harm the adolescent. To prevent such serious social problems, this paper introduces a content-based image filtering (CBIF) framework in an image retrieval system. The proposed CBIF approach is split into two components. In the first component, the skin region of exposed human body parts is extracted based on a color segmentation method, and an efficient feature vector generation process is introduced based on frequent skin color pixels and scale-invariant structural features. A novel texture feature representation is presented in the retrieval and filtering process obtained by block-paired local binary pattern (BP-LBP). The final component introduces a novel image filtering framework for the retrieved images found from the first component based on a majority of voting method on three different machine learning classifiers backed by SVM, MLP, and CNN. The retrieval efficiency is analyzed with standard performance indicators such as precision, recall, and F-score on two large datasets: NPDI and NSFW.

Variation in the Photosynthetic Leaf Pigments of Different Basil (Ocimum spp.) Genotypes under Varying Conditions at the Flowering Stage

Basil is a culinary herb in high demand all year round, either fresh, dried, or frozen. Field basil crops are commercially predominant, while greenhouse crops can cover the demand for fresh basil during the off-season. The leaf aspect of basil has great importance for the food industry, and a great diversity of genotypes has been created. The aim of this research was to conduct a comparative characterization of 12 basil genotypes (green and purple leaf) that present interest for breeding programs from a national germplasm collection. The study examines the accumulation of photosynthetic pigments in basil cultivated in field versus greenhouse conditions as indicators of plant performance and herb quality from the perspective of ensuring fresh plant material all year round. The experimental median indicated that photosynthetic leaf pigments accumulated in higher concentrations in the field relative to the greenhouse conditions, in descending order: β-carotene, lutein, chlorophyll a, and chlorophyll b. The trend was not as consistent for chlorophyll b, since four out of twelve genotypes presented higher levels in the greenhouse than in the field, while the overall values were not much lower in the greenhouse than the field (16.82%). All genotypes accumulated much higher carotenoid contents in field conditions relative to greenhouse conditions (>200%) and could also provide better nutritional advantages given their demonstrated health benefits. The differences in photosynthetic leaf pigments have both nutritional (the carotenoids), shelf-life, and processing relevance (chlorophylls) and serve as quality markers.

Numerical and Experimental Determination of Selected Performance Indicators of the Liquid Flat-Plate Solar Collector under Outdoor Conditions

The paper proposes applying an in-house mathematical model of a liquid flat-plate solar collector to calculate the collector time constant. The described model, proposed for the first time in an earlier study, is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all collector components. The formulated differential equations are solved iteratively using an implicit difference scheme. To obtain a stable numerical solution, it is necessary to use appropriate steps of time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model’s practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. By comparing, on an ongoing basis, the measured and calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The simplicity of the model and the low computational demands enable such comparisons in an online mode.

Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design.

Electricity subsidies targeting performance indicators in selected Latin American countries

ABSTRACT This document calculates targeting performance indicators of electricity subsidies in Mexico, Panama, the Dominican Republic, and Uruguay. To do this, beneficiary and benefit incidence indicators are calculated using official household survey data and administrative records on electricity tariffs. In addition, this paper calculates inclusion and exclusion errors in the targeting of electricity subsidies. The results show that the share of poor households that receive the subsidy is very large in Mexico, Panama, and the Dominican Republic. However, the distribution of the subsidy is regressive in absolute terms, which implies that most of every dollar spent on keeping electricity prices low will benefit the wealthiest households. This document also finds large inclusion errors suggesting that an important share of the non-poor is also benefiting from the subsidy. These results shed light on the evaluation of electricity subsidies in the region to improve targeting and optimisation of resources.

Natural ventilation cooling effectiveness classification for building design addressing climate characteristics

The evaluation of natural ventilation potential for effective sustainable options and innovative green building design strategies is of great interest to architects, researchers and governments. From a retrospective review, we found that the potential evaluation of natural ventilation (NV) cooling effectiveness in the same category based on similar meteorological uncertainty, research objectives and objects showed significant differences. Uncertainties added and uncertainty propagation (both model form uncertainties and parameter uncertainties) could result in large discrepancies between simulation outcomes and real scenarios, especially in the design performance modeling (DPM) phase. In this conceptual design stage, a few parameters are available and therefore decisive. It is necessary to review and identify the key performance indicators and explore the extent to which deviations are caused by inconsistencies or biases in model information. As a basis for more concrete research, we propose statistical tests based on quantitative evaluations to explore the rule of natural ventilation potential volatility and identify whether there is a significant potential improvement resulting from the critical parameter enhancement with the optimal relationship. The showcase is applied in China, where there has been a significant amount of criticism regarding the current building climate zoning due to the perceived coarseness of the system and where there has been an active exploration into the possibility of redefining building climate zoning with a view toward improving its accuracy and effectiveness.

Effects of β-Mannanase Supplementation and Soyhull Inclusion on Production Performance, Economics, Egg Quality, Blood Biochemicals, Nutrient Digestibility, and Intestinal Morphology in Golden Brown Hens (RIR × Fayoumi) during Late Peak Production.

This study investigated the effects of the β-mannanase enzyme and soyhulls on production performance, economics, egg quality, hematology and serum biochemistry, nutrient digestibility, gut morphology, digesta viscosity, and excreta consistency in laying hens during the late peak production phase (37 to 40 weeks of age). Golden brown hens (RIR × Fayoumi; n = 200) were fed a control diet (no soyhulls or enzymes) and diets containing four combinations, i.e., 3% soyhulls with 20 mg/kg β-mannanase (D1), 3% soyhulls with 30 mg/kg β-mannanase (D2), 9% soyhulls with 20 mg/kg β-mannanase (D3), and 9% soyhulls with 30 mg/kg β-mannanase (D4), for four weeks in four replicates of 10 birds each. Overall, a significantly higher (p < 0.05) feed intake, weight gain, feed conversion ratio, and water intake were calculated in the D2 group as compared to the control and remaining combinations of soyhulls and β-mannanase. No mortality was recorded during the entire experiment. Economically, the D1 and D2 groups showed the best results as compared to the D3 and D4 groups. Egg quality parameters like egg weight, shell weight and shell thickness, yolk weight, albumen weight and height, and the Haugh unit remained unchanged (p > 0.05). Similarly, the D2 group showed significantly lower total cholesterol, LDL, and VLDL levels and enhanced gut morphology with greater villus width, height, crypt depth, and surface area across intestinal segments. Crude protein (CP), crude fiber (CF), crude fat, and ash digestibility were higher (p < 0.05) in the D1 and D2 groups compared to the control. Digesta viscosity, excreta consistency, and other egg quality parameters remained unaffected. In conclusion, the dietary inclusion of a combination of 3% soyhulls and 30 mg/kg β-mannanase may have potential benefits for laying hens by improving some production performance and egg quality indicators and economics, lowering blood cholesterol, LDL, and VLDL levels, enhancing nutrient digestibility, and improving gut morphology without affecting egg quality.

Implementation and evaluation of a mentorship program in clinical master in family medicine during the COVID-19 pandemic at the Arabian Gulf University: a longitudinal study

BackgroundWe implemented a contextualized innovative mentorship program in the Clinical Master in Family Medicine (CMFM) program established in April 2020 at Arabian Gulf University. In this paper, we describe the process of this program and derive the major challenges faced by trainees and related corrective actions and their outcomes on high-risk trainees for optimal performance.MethodsWe conducted a mixed-method longitudinal study of 80 trainees, analyzing information extracted from the Moodle learning platform about five key performance indicators as well as the contents (quantitative and qualitative) of mentoring meeting reports submitted through a validated online form between 2020 and 2022. We analyzed frequencies and themes of challenges and compared trainees' performance according to time and level of risk.ResultsThe follow-up of all 80 trainees in two cohorts (40 for each cohort) shows that most are female (93.75%) and the mean age is 30.00 ± 2.19 years with a ratio of mentors to mentees of 1 to 5. Meetings are conducted through phone calls, virtually, and face-to-face in 62%, 29%, and 8.3% respectively. The mean number and duration of meetings are 30.88 ± 2.31 and 20.08 ± 9.50 min respectively. Time management is the most reported challenge (41.3%), followed by health, social, and psychological-related issues in 7.6%, 4.6%, and 3% respectively. We extracted four main themes related to trainees, settings of training, e-Portfolio, and the COVID-19 pandemic. The mentorship program captured 12 trainees at high risk for low academic progress (12%) of whom six graduated on time and the remaining had to repeat a few courses the following terms. The performance of the program is stable over time (mean GPA of 3.30 (SE = 0.03), versus 3.34 (SE = 0.05) for cohorts 1 and 2 in the two years respectively, (P = 0.33). However, it is slightly lower among high-risk trainees compared to the remaining (GPA = 3.35 (SE = 0.03) versus 3.14 (SE = 0.08), P = 0.043) though above the minimum of the threshold of 3 out of 4, required for the master's degree.ConclusionThe mentorship program captured the struggling trainees and permitted to implement pertinent corrective actions timely, particularly in the context of a two-year intensive CMFM program during the COVID-19 pandemic.

A Novel Diastolic Doppler Index Less Affected by Aortic Arch Anomalies Co-existing with Coarctation.

Severity assessment for coarctation of the aorta (CoA) is challenging due to concomitant morphological anomalies (complex CoA) and inaccurate Doppler-based indices. Promising diagnostic performance has been reported for the continuous flow pressure gradient (CFPG), but it has not been studied in complex CoA. Our objective was to characterize the effect of complex CoA and associated hemodynamics on CFPG in a clinical cohort. Retrospective analysis identified discrete juxtaductal (n = 25) and complex CoA (n = 43; transverse arch and/or isthmus hypoplasia) patients with arm-leg systolic blood pressure gradients (BPG) within 24 h of echocardiography for comparison to BPG by conventional Doppler indices (simplified Bernoulli equation and modified forms correcting for proximal kinetic energy and/or recovered pressure). Results were interpreted using the current CoA guideline (BPG ≥ 20 mmHg) to compare diagnostic performance indicators including receiver operating characteristic curves, sensitivity, specificity, and diagnostic accuracy, among others. Echocardiography Z-scored aortic diameters were applied with computational simulations from a preclinical CoA model to understand aspects of the CFPG driving performance differences. Diagnostic performance was substantially reduced from discrete to complex CoA for conventional Doppler indices calculated from patient data, and by hypoplasia and/or long segment stenosis in simulations. In contrast, diagnostic indicators for the CFPG only modestly dropped for complex vs discrete CoA. Simulations revealed differences in performance due to inclusion of the Doppler velocity index and diastolic pressure half-time in the CFPG calculation. CFPG is less affected by aortic arch anomalies co-existing with CoA when compared to conventional Doppler indices.

A 16S RNA Analysis of Yangzhou Geese with Varying Body Weights: Gut Microbial Difference and Its Correlation with Body Weight Parameters.

China is a major goose-raising country, and the geese industry plays a significant role in animal husbandry. Therefore, goose growth performance (body weight) is a critical topic. Goose gut microbiota influences weight gain by regulating its energy metabolism and digestion. Additionally, the impact of cecal microbial community structure on goose growth and development, energy metabolism, and immunity has been examined. However, most studies have used different additives or feeds as variables. Improving the understanding of the dynamic changes in gut microbial communities in geese of different body weights during their growth and development and their correlation with the host's body weight is necessary. In this study, the cecal microbiota of healthy Yangzhou geese with large (L) and small (S) body weights, all at the same age (70 days old) and under the same feeding conditions, were sequenced using 16S rRNA. The sequencing results were annotated using QIIME2 (classify-sklearn algorithm) software, and the linkET package was used to explore the correlation between intestinal microorganisms and the body weight of the Yangzhou goose (Spearman). At the phylum level, the Firmicutes/Bacteroidetes ratio in the large body weight group was approximately 20% higher than that in the small body weight group, with Bacteroidetes and Firmicutes exhibiting a highly significant negative correlation. At the genus level, Bacteroides constituted the most abundant microbial group in both groups, although the Prevotellaceae_Ga6A1_group exhibited a higher abundance in the large than the small weight group. Spearman correlation analysis and the linkET package were used to analyze the correlation between cecal microflora and production performance indicators that showed significant differences between the two groups and showed that birth weight was significantly positively correlated with Deferribacterota at the phylum level. At the genus level, leg and chest muscle weights exhibited significant positive correlations with Prevotellace-ae_Ga6A1_group, suggesting its critical role in promoting the growth and development of goose leg and chest muscles. A significant negative correlation was observed between [Ruminococ-cus]_torque and Prevotellaceae_Ga6A1_group. These findings offer a crucial theoretical foundation for the study of gastrointestinal microorganisms and provide insights into the development and formulation of poultry probiotics.

Enhancing Employee Performance through Transformational Leadership: A Study

This research investigates the influence of transformational leadership on employee performance, with a specific focus on the mediating roles of job stress reduction and organizational work environment. Transformational leadership, characterized by its ability to enhance employee engagement and clarity, is examined as an independent variable impacting employee performance, the dependent variable. The study aims to achieve the following objective: (1) explore the impact of transformational leadership on organizational work environments, (2) understand the effect of transformational leadership on reducing job stress, and (3) examine the role of transformational leadership in improving employee performance. The research delves into how transformational leadership fosters a positive work environment—by reciprocating performance, establishing role clarity, and increasing employee participation—and mitigates job stress—by alleviating anger and depression. Conducted in the Meerut district of Delhi NCR, the study considered academicians from private higher educational institutes, selected through stratified random sampling (sample size N=151). Utilizing a descriptive research design, data was collected via a structured questionnaire with a high- reliability score (Cronbach's alpha = 0.88) and analyzed using SmartPLS 4.0 (student version) software. Findings reveal that transformational leadership significantly reduces job stress and enhances the organizational work environment, which collectively boosts employee performance. Key performance indicators observed include improved work quality, effective communication, timely completion of tasks, teamwork, reliability, and problem-solving skills. This study underscores the significant role of transformational leadership in cultivating a supportive work environment and reducing job stress to elevate overall employee performance.

ESPRIT adventure: Assessing hybrid fuzzy-crisp rule-based AI method effectiveness in teaching key performance indicators

Key performance indicators (KPIs) are a fundamental tool for understanding the outcomes of business policies. Teaching these indicators is conventionally accomplished through verbal lecturing using mathematical equations, accompanied by presentation graphics and tabular information representing the company's operational, financial, and strategic achievements. The conventional teaching method enables the student to understand what a KPI means and how it is calculated, however, it does not enable the student to understand easily how to interpret it. Here, a hybrid fuzzy-crisp rule-based AI system is presented and its effectiveness when applied with a group of management and organization students at the University of Belgrade, Faculty of Organizational Sciences, during the 2022/2023 academic year is evaluated. The potential of this tool is reflected in its ability to simulate adaptive and realistic business economic situations, its high interactivity and user-friendly interface, and intuitive graphical and tabular results that are easy to interpret enhanced with natural-language-inference-explanations. Univariate analysis along with some complementary statistical tests was used to examine students' satisfaction with the system and to distinguish between students who used the system, and students who attended conventional classes, both in terms of acquiring knowledge and retaining knowledge. In general, the results showed high students' satisfaction with the system. The scores obtained by the former group on the objective knowledge assessment test were on average significantly higher compared to those of the latter, proving the positive effect of the hybrid fuzzy-crisp rule-based AI teaching approach.

Key performance indicators in pre-hospital response to disasters and mass casualty incidents: a scoping review.

The objective of this study was to offer a comprehensive synthesis of the existing Key performance indicators (KPIs) used in the evaluation of the pre-Hospital response to disasters and mass casualty incidents (MCIs). At the end of December 2022 a scoping review has been performed on PubMed, Scopus, Embase, and Medline to identify articles describing the use of KPIs to assess the performance of first responders during the prehospital phase of an MCI (real or simulated). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, fourteen articles were included in the analysis. Eleven articles applied indicators in exercises and/or simulations. Two articles proposed new KPIs, and one used KPIs for developing a model for benchmarking pre-Hospital response. All articles analyzed quantitative indicators of time, whereas two studied indicators of structure, of process, and of outcome as well. The findings from this review emphasize the need for employing common terminology and using uniformed data collection tools, if obtaining standardized evaluation method is the goal to be achieved.

Performance evaluation of bank branches in the atmosphere of grey uncertainty

Purpose As some data to evaluate the efficiency of bank branches is qualitative or uncertain, only grey numbers should be used to calculate the efficiency interval. The combination of multi-stage models and grey data can lead to a more accurate and realistic evaluation to assess the performance of bank branches. This study aims to compute the efficiency of each branch of the bank as a grey number and to group all branches into four grey efficiency areas. Design/methodology/approach The key performance indicators are identified based on the balanced scorecard and previous research studies. They are included in the two-stage grey data envelopment analysis (DEA) model. The model is run using the GAMS program. The grey efficiencies are calculated and bank branches have been grouped based on efficiency kernel number and efficiency greyness degree. Findings As policies and management approaches for branches with less uncertainty in efficiency are different from branches with more uncertainty, considering the uncertainty of efficiency values of branches may be helpful for the policy-making of managers. The grey efficiency of branches of one bank is examined in this study using the two-stage grey DEA throughout one year. The branches are grouped based on kernel and greyness value of efficiency, and the findings show that considering the uncertainty of data makes the results more consistent with the real situation. Originality/value The performance of bank branches is modeled as a two-stage grey DEA, in which the efficiency value of each branch is obtained as a grey number. The main originality of this paper is to group the bank branches based on two grey indexes named “kernel number” and “greyness degree” of grey efficiency value.

Validation of Challenges for Implementing ESG in the Construction Industry Considering the Context of an Emerging Economy Country

In pursuit of profit maximization through practices that promote sustainable development, companies increasingly use environmental, social, and governance (ESG) criteria to guide investment. However, there are challenges in implementing these practices across diverse sectors, such as the construction industry, especially in emerging countries with limited related studies. This study validated the challenges of implementing ESG in the construction industry in an emerging country like Brazil, considering its relationship with the United Nations Sustainable Development Goals (SDGs). A literature review identified challenges associated with ESG implementation, followed by the development of a questionnaire based on these challenges. Subsequently, the questionnaire was administered to professionals in the construction industry and validated using the Lawshe method. Twenty-seven challenges were identified, of which twelve were validated, including the lack of standardized performance indicators, regulatory guidelines associated with ESG practices, organizational resistance, and insufficient transparency in non-financial indicators. This study provides guidance for overcoming challenges in the successful adoption of ESG in the construction industry. It also identifies the most-impacted SDGs and lays the foundation for future actions promoting sustainable development in the construction industry in emerging countries.

Green business process management in a Polish municipal waste treatment plant-regional case study

AbstractDespite COVID-19, the world economy still contributes to the growth of production and consumption worldwide. Waste disposal, recycling management and energy generation are challenges for many companies in developing economies, including Poland. This article aims to assess the operation of a municipal waste treatment plant (MWTP) from the perspective of green business process management (BMP) solutions. The processes implemented in the MWTP were discussed, with specific consideration of the mechanical waste processing (sorting) process, including the reuse and recycling of materials, composting, energy production (anaerobic process), landfill storage and efficiency parameters of the sorting line. A sustainable waste management system was identified; the cost as well as social and environmental perspectives were analyzed. Also, strategic goals and key performance indicators were considered. The performed analysis included costs, environmental criteria and key environmental indicators. This paper has shown the successful implementation of green BPM, with potential cost and material savings results. The findings of this case study are expected to inspire other waste management companies to adopt green BPM. The presented case study might help raise awareness and promote the implementation of green BPM in municipal plants in Eastern and Southern Europe.