Assist Decision Makers Research Articles

A Kriging Method for the Estimation of ALS Point-Cloud Accuracy without Ground Truth

Airborne LiDAR scanning is a promising approach to providing high-resolution products that are appropriate for different applications, such as flood management. However, the vertical accuracy of airborne LiDAR point clouds is not constant and varies in space. Having a better knowledge of their accuracy will assist decision makers in more accurately estimating the damage caused by flood. Data producers often report the total estimation of errors by means of comparison with a ground truth. However, the reliability of such an approach depends on various factors including the sample size, accessibility to ground truth, distribution, and a large enough diversity of ground truth, which comes at a cost and is somewhat unfeasible in the larger scale. Therefore, the main objective of this article is to propose a method that could provide a local estimation of error without any third-party datasets. In this regard, we take advantage of geostatistical ordinary kriging as an alternative accuracy estimator. The challenge of considering constant variation across the space leads us to propose a non-stationary ordinary kriging model that results in the local estimation of elevation accuracy. The proposed method is compared with global ordinary kriging and a ground truth, and the results indicate that our method provides more reliable error values. These errors are lower in urban and semi-urban areas, especially in farmland and residential areas, but larger in forests, due to the lower density of points and the larger terrain variations.

Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study

BackgroundThromboprophylaxis has been determined to be safe, effective and cost-effective for hospitalised patients at venous thromboembolism (VTE) risk. However, Chinese medical institutions have not yet fully used or improperly used...

RETRACTED ARTICLE: COVID-19 Forecast and Bank Credit Decision Model Based on BiLSTM-Attention Network

The COVID-19 pandemic has caused drastic fluctuations in the economies of various countries. Meanwhile, the governments’ ability to save the economy depends on how banks provide credit to troubled companies. Therefore, the impact of the epidemic on bank credit and inclusive finance are worth exploring. However, most of the existing studies focus on the reform of the financial and economic system, only paying attention to the theoretical mechanism analysis and effect adjustment, scant data support, and insufficient scheme landing. At the same time, with the rise and rapid development of artificial intelligence technology in recent years, all walks of life have introduced it into real scenes for multi-source heterogeneous big data analysis and decision-making assistance. Therefore, we first take the Chinese mainland as an example in this paper. By studying the impact of the epidemic on bank credit preference and the mechanism of inclusive finance, we can provide objective decision-making basis for the financial system in the post-epidemic era to better flow credit funds into various entities and form a new perspective for related research. Then, we put forward a model based on Bi-directional Long Short-term Memory Network (BiLSTM) and Attention Mechanism to predict the number of newly diagnosed cases during the COVID-19 pandemic every day. It is not only suitable for COVID-19 pandemic data characterized by time series and nonlinearity, but also can adaptively select the most relevant input data by introducing an Attention Mechanism, which can solve the problems of huge calculation and inaccurate prediction results. Finally, through experiments and empirical research, we draw the following conclusions: (1) The impact of the COVID-19 pandemic will promote enterprises to increase credit. (2) Banks provide more credit to large enterprises. (3) The epidemic has different impacts on credit in different regions, with the most significant one on central China. (4) Banks tend to provide more credit to manufacturing industries under the epidemic. (5) Digital inclusive finance plays a (positive) regulating effect on bank credit in COVID-19 pandemic. Inspired by the research results, policymakers can consider further solving the information asymmetry and strengthening the construction of a credit system, and more direct financial support policies for enterprises should be adopted. (6) By adopting the COVID-19 prediction model based on the BiLSTM-Attention network to accurately predict the epidemic situation in the COVID-19 pandemic, it can provide an important basis for the formulation of epidemic prevention and control policies.

Deep learning models for assisted decision-making in performance optimization of thin film nanocomposite membranes

The performance of thin film nanocomposite nanofiltration membranes (TFNNMs) is frequently constrained by the trade-off between the permeability and rejection/selectivity of the membrane. In this study, to provide assisted decision-making in performance optimization of TFNNMs, we utilized a dataset comprising four categories of impact parameters, including membrane manufacturing conditions, membrane performance, nanoparticle material performance, and organic solvent nanofiltration conditions, and trained a dual output neural network (D-ANN) to simulate the relative permeability (RP) and relative rejection/selectivity (RR) of TFNNMs. The D-ANN model outperformed conventional machine learning models and single-output neural networks in predicting both RP and RR, indicating its potential in uncovering correlations between output variables. The Shapley additive explanation method was employed to elucidate the contribution and marginal effects of each influencing parameter on RP and RR, among them, NP size, NP loading, water contact angle, and chloride concentration were influential parameters that are important for both RP and RR. Based on the D-ANN model, we extrapolated two sets of unknown data and achieved good predictive performance (RP-error <15%, RS-error <7% for most sample points). Furthermore, we observed that the predicted performance of the data points rarely exceeded the actual performance, indicating a low probability of overestimation. Based on these premises, the influential parameters can be optimized using the D-ANN model, which ultimately led to exceptional performance for a total of 22 datasets in four pairs of organic solvent and solute purifications guided by the D-ANN model. This study demonstrates that the D-ANN model can provide decisions for the performance optimization of TFNNMs while highlighting the enhancement of nanofiltration performance by thin film nanocomposites, significantly facilitating the design of high-performance TFNNMs.

Incidence, risk factors, and outcomes of recurrent focal segmental glomerulosclerosis in pediatric kidney transplant recipients: A systematic review and meta-analysis.

Focal segmental glomerulosclerosis is the most prevalent acquired kidney disease leading to end-stage renal disease in children and has a propensity for recurring in the transplanted kidney. The recurrence of FSGS after kidney transplantation in children varies greatly. In addition, the risk factors and outcomes of recurrence of FSGS remain controversial. This study evaluated the recurrence rate, risk factors, and prognosis of FSGS after kidney transplantation in order to provide advice and assistance in clinical decision-making for pediatric kidney transplantation. PubMed, Embase, Web of Science, CNKI, and other databases were searched from the establishment of the repository to March 2022. We extracted data on incidence, risk factors, and outcomes. The results showed that the recurrence rate of primary FSGS in children after renal transplantation was 48% (95% CI 36%-59%) and the recurrence rate of FSGS (all forms) was 35% (95% CI 17%-52%). The graft loss rate of primary FSGS in children after kidney transplantation was 29% (95% CI 17%-42%) and the graft loss rate of FSGS (all forms) was 29% (95% CI 4%-62%). 57% (95% CI 42%-73%) of pediatric patients with recurrent primary FSGS showed complete remission. Risk factor analyses showed that age of onset (SMD .69, 95% CI .20-1.19, p=.006) was related to the recurrence of primary FSGS, whereas the living related donor was not a risk factor for recurrent primary FSGS in pediatrics after kidney transplantation (OR 1.22, 95% CI .48-3.10, p=.674). The recurrence rate and graft loss rate of FSGS in children after kidney transplantation were relatively high. Age at onset was associated with a risk for recurrent primary FSGS, whereas the living related donor was not a risk factor for recurrent FSGS in pediatric kidney recipients.

317 Analysing the social history aspects of an integrated care comprehensive geriatric assessment incorporating patients views and wishes

Abstract Background An evaluation of social well-being is an important component of a Comprehensive Geriatric Assessment (CGA). There is no clear guidance about how best to structure and examine these factors as part of a CGA. We sought to examine current practice with respect to the collection of information from a social care perspective across different integrated care CGA proformas. Methods We completed a cross sectional study of the social care domain of integrated care based CGA proformas. All operational leads for each integrated care hub in the country were contacted and invited to share their local CGA. We completed a thematic analysis of the social care section including social, financial and wellbeing assessments. Results We included 16 different CGAs that are in use across different integrated care hubs. All CGAs had a dedicated section for assessing social history with variability in what was included in different CGAs. Patient’s wishes and expectations were commonly included but the manner in which they were collected and emphasis varied. 7 (43%) specifically captured whether the patient has an enduring power of attorney in place, 5 (31%) collected information about formal advanced care directives, and 2 (12%) enquired about nominated assisted decision makers. Ability to manage finances was captured through the Lawton Brody IADL scale in two CGAs. Half of CGAs included a section to capture carer strain and the term ‘next of kin’ was used in 6 of 16 CGAs. Conclusion There is significant heterogeneity in the structure of the social care section of a CGAs across different integrated care hubs, particularly with respect to capturing information surrounding advanced care planning. Signposting of patients’ wishes and expectations was useful for focusing on patient-centred care.

152 Case study: the use of Talking Mats to facilitate assisted decision making in hospital settings with older adults

Abstract Background Talking Mats is a picture based standardised tool to support person-centred decision making for those with impaired communication (or cognition), which is a useful communication tool for Speech &amp; Language Therapists (SLTs). The tool ensures our client’s voice is heard and that they are central to the decision making process. With the newly implemented Assisted Decision Making Capacity Act 2015 (ADMCA) in the forefront of our minds, we must consider our client’s will and preference thoroughly with facilitated discussions, to be fully compliant with this legislative framework, and always ensuring our client’s wishes/requests are central in the decision-making process. In this case example, Talking Mats were used with an 85 year old lady after her second stroke. After a primary stroke (2019), she presented with a mild receptive and moderate expressive dysphasia, impeded by Apraxia Of Speech. She previously communicated with a communication book, with written words. A second stroke (2023), resulted in a moderate–severe receptive and expressive dysphasia. Methods Discussions involved a ‘Talking Mat’ with pictures placed on it and the ‘Topic’ cue card. Along the top of the Talking Mat, a standard scale of ‘Yes’, ‘Maybe/unsure’ and ‘No’ icons. Picture cards have written words to assist comprehension. Talking Mats was used in therapy for communicating wishes and problem-solving around discharge planning. The scales used were ‘Managing’ to ‘Not Managing’ or ‘Coping with’/‘Not Coping with’. Results The qualitative data collated from the client and her family was very powerful, in helping the client make her wishes known for home, spontaneously she articulated “But not on my own!. Conclusion Talking Mats help people with communication difficulties have a greater understanding about the topic and ensures they are ‘directly involved in the process, is more in control, and can consider the options in his/her own time’.

Software Requirement Risk Prediction Using Enhanced Fuzzy Induction Models

The development of most modern software systems is accompanied by a significant level of uncertainty, which can be attributed to the unanticipated activities that may occur throughout the software development process. As these modern software systems become more complex and drawn out, escalating software project failure rates have become a critical concern. These unforeseeable uncertainties are known as software risks, and they emerge from many risk factors inherent to the numerous activities comprising the software development lifecycle (SDLC). Consequently, these software risks have resulted in massive revenue losses for software organizations. Hence, it is imperative to address these software risks, to curb future software system failures. The subjective risk assessment (SRM) method is regarded as a viable solution to software risk problems. However, it is inherently reliant on humans and, therefore, in certain situations, imprecise, due to its dependence on an expert’s knowledge and experience. In addition, the SRM does not allow repeatability, as expertise is not easily exchanged across the different units working on a software project. Developing intelligent modelling methods that may offer more unbiased, reproducible, and explainable decision-making assistance in risk management is crucial. Hence, this research proposes enhanced fuzzy induction models for software requirement risk prediction. Specifically, the fuzzy unordered rule induction algorithm (FURIA), and its enhanced variants based on nested subset selection dichotomies, are developed for software requirement risk prediction. The suggested fuzzy induction models are based on the use of effective rule-stretching methods for the prediction process. Additionally, the proposed FURIA method is enhanced through the introduction of nested subset selection dichotomy concepts into its prediction process. The prediction performances of the proposed models are evaluated using a benchmark dataset, and are then compared with existing machine learning (ML)-based and rule-based software risk prediction models. From the experimental results, it was observed that the FURIA performed comparably, in most cases, to the rule-based and ML-based models. However, the FURIA nested dichotomy variants were superior in performance to the conventional FURIA method, and rule-based and ML-based methods, with the least accuracy, area under the curve (AUC), and Mathew’s correlation coefficient (MCC), with values of approximately 98%.

International Transportation Mode Selection through Total Logistics Cost-Based Intelligent Approach

Background: International transportation has grown substantially, causing total logistics costs (TLCs) to rise. Companies are increasingly striving for their reduction. The most crucial factor affecting TLCs is the transportation mode, and its appropriate selection has become vital for firms. Maritime transport is the most preferred mode for international shipments, while air transport is also increasingly preferred due to the rise in underweight and high-frequency shipments, the expectation of reduced delivery times, and inventory costs. However, a thorough comparative analysis is necessary for the selection. Methods: This paper proposes an intelligent approach based on TLCs. Non-linear optimization is adopted for regular replenishment, while maching-learning classifiers are employed to establish a decision boundary for the chargeable weight of shipments. Conclusions: The study assists in decision making and also establishes a country-wide threshold, highlighting the importance of a country-based logistics strategy. The paper successfully establishes the trends and relations between logistics parameters, which assists the logistics decision making. Research identifies the gaps in the existing literature and bridges them by addressing the required concerns.

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.

Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM. The study proposes a deep learning framework combining early fusion feature autoencoder (AE) and late fusion feature AE for risk subtype prediction of SKCM. The deep learning framework integrates mRNA, miRNA, and DNA methylation data of SKCM patients from The Cancer Genome Atlas (TCGA), and clusters the screened multi-omics features associated with survival prognosis to identify risk subtypes. Differential expression analysis and functional enrichment analysis were performed between risk subtypes, while SVM classifiers were constructed between differentially expressed genes (DEGs) obtained by Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression screening and risk subtype labels inferred from multi-omics data, and the predictive robustness of risk subtypes inferred from the risk subtype classification prediction model was validated using two independent datasets. The deep learning framework that combined early fusion feature AE with late fusion feature AE distinguished the two best risk subtypes compared to the multi-omics integration approach with single strategy AE or PCA. A promising C-index (C-index = 0.748) and a significant difference in survival (log-rank P value = 4.61 × 10-9) were found between the identified risk subtypes. The DEGs with the top significance values together with differentially expressed miRNAs provided the biological interpretation of risk subtypes on SKCM. Finally, the framework was applied to predict risk subtypes in two independent test datasets of SKCM patients, all of which showed good predictive power (C-index > 0.680) and significant survival differences (log-rank P value < 0.01). The SKCM risk subtypes identified by integrating multi-omics data based on deep learning can not only improve the understanding of the molecular mechanisms of SKCM, but also provide clinicians with assistance in treatment decisions.

Current Practice Trends in Knee Arthroplasty in India Among ISHKS Members: ISHKS Annual Meeting 2019.

To assess the current practice trends in knee arthroplasty in Indiaamong ISHKS members. Literature review on 12 areas of key interest in total knee arthroplasty was conducted. Twenty-eight questions pertaining to these key areas in total knee arthroplasty surgery were presented to the delegates for a physical voting with audience response system (ARS) at ISHKS meeting, Bangalore, 2019. The members who could not attend the meeting voted by an online monkey survey. All questions were given three responses: a. Agree, b. Disagree, and c. Abstain. 310 delegates participated and 295 voted in the physical meeting and 110 voted online. Consensus was reached for 28 statements representing five domains on the best practices. Overall, 92% of statements reached unanimous or strong consensus. The statements that achieved strong majority (> 90%) were the factors on pre-operative skin preparations, administration of antibiotics 30min prior skin incision, and current practices of bilateral TKR. Furthermore, there was strong consensus approval on the role of aspirin for prophylaxis of DVT, lack of benefit on use of drain, considerations for pre-emptive analgesia, and neuraxial anesthesia. Finally, there were disparate views and weak consensus on use of laminar airflow theaters and use of surgical space suits, length of antibiotic administration postoperatively, and linking of DVT with PE and death. This study gives the surgeon practice preferences in knee arthroplasty in India and assists in clinical decision-making for all healthcare professionals.

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau.

Solar Panel Cooling System Evaluation: Visual PROMETHEE Multi-Criteria Decision-Making Approach

The energy sector is interested in sustainable solar power plants. It is obvious that the working temperature of solar panels, which is significantly higher than the specified working cell temperature in hot climes, has a significant impact on efficiency and longevity. The selection of solar panel cooling systems, on the other hand, is worrisome since the choice process incorporates ergonomic, technical, economic, and environmental issues. The goal of this research is to (1) present a multi-criteria decision-making approach that is both quantitative and qualitative in nature for selecting solar panel cooling systems; (2) outrank nine alternative solar panel cooling systems with eleven performance measures for each alternative to assist decision makers in determining the best viable choice; and (3) visualize the relationship between the different solar panel cooling systems and performance measures under consideration. The proposed approach is to compare and rank solar panel cooling systems, as well as their validation and evaluation through sensitivity analysis. When operating efficiency is prioritized, finned air cooling is shown to be the best solar panel cooling technique, whereas thermosiphon cooling is the best alternative when emission reduction criteria are prioritized. A comparison of the findings shows that phase change material cooling and forced convection cooling performed worst in almost all cases.

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer

To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC). This multicenter study incorporated 437 patients from five centers, divided into training (n=271), internal validation (n=68), and external validation (n=98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P<0.05 was considered significantly different. The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P=0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA. A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.

What are the most common controversial clinical issues in fertility preservation? A content analysis of a collaborative professional online consultation group

Research questionClinicians involved in fertility preservation (FP) are often required to make prompt and consequential decisions despite the absence of evidence-based data. We established a collaborative professional online consultation group for fertility preservation issues. We sought to determine the main controversial clinical issues in FP as raised by participants of this group.DesignContent analysis of a dedicated community of practice interacting via a messaging application (WhatsApp) and a survey of group participants.ResultsBetween January 2019 and July 2022, group members posed 39 clinical questions which were discussed and debated by the group. Common themes included management of oncofertility cases (33%), potential gonadotoxicity of various therapies (23%), fertility preservation in women and girls with premature ovarian insufficiency (POI) (18%), and technical aspects of ovarian tissue cryopreservation (10%). All but one query received prompt response (mean time for first response for 95% of queries 7.1 ± 9.0 min) from a mean of 5.4 ± 3.2 members.An anonymous online survey of group members was conducted during August 2022 (n = 31, response rate 94%). The majority of respondents stated they gained knowledge and assistance in clinical decision making from participation in the discussion group (90% and 58% of respondents, respectively).ConclusionsManagement of clinical oncofertility cases, potential gonadotoxic effect of therapeutics and fertility preservation in women and girls with POI were the most common controversial issues in our fertility preservation community of practice. Intra-professional collaborative communication via a messaging application can aid in clinical management of fertility preservation and augment clinician’s knowledge.

Estimating plume reach and trapping radii for male and female Cydia pomonella (Lepidoptera: Tortricidae) captured in pheromone-kairomone baited traps in Washington State apple orchards under mating disruption.

Male Cydia pomonella (L.) (Lepidoptera: Tortricidae) dispersion has largely been studied in nonmating disrupted orchards due to synthetic pheromone interference with capture in monitoring traps. Little is known about female dispersion. This study aimed to characterize male and female dispersion in mating disrupted commercial apple orchards. Sterile C. pomonella recapture data from single-trap multiple-release experiments using PHEROCON CM-DA COMBO + AA Lure-baited orange Pherocon VI delta traps was interpreted to determine pheromone-kairomone lure-baited trap effective area, trap deployment density for effective monitoring, and absolute male and female C. pomonella density in mating disrupted Washington commercial apple orchards. The maximum plume reach of the pheromone-kairomone lure in mating disrupted orchards was <5 m from the baited trap for both sexes. Maximum dispersive distances for 95% of the released C. pomonella in mating disrupted orchards were 106 and 135 m for males and females, yielding trapping areas of 3.87 and 6.16 ha, respectively. Estimates were consistent across 3 growing seasons and represent the first records of male and female dispersal distance and monitoring trap efficacy from commercial C. pomonella mating disrupted apple orchards. With relevance to commercial monitoring programs and economic thresholds in mating disrupted orchards, traps should be deployed at a density of 1 per 3-6 ha. Capture of a single male or female C. pomonella corresponds to at least 82-104 C. pomonella within the 3-6 ha trapping area. This refined C. pomonella capture interpretation in pheromone-kairomone baited traps in mating disrupted commercial apple orchards yields more precise damage estimates and assists in insecticide-use decision making.

A Novel Hybrid Approach for Prioritizing Investment Initiatives to Achieve Financial Sustainability in Higher Education Institutions Using MEREC-G and RATMI

Financial sustainability is a crucial challenge for higher education institutions due to various challenges and constraints. This necessitates determining their investment priorities accurately based on multiple criteria to ensure a sustainable income. This study puts forward a novel, hybrid approach for prioritizing the financial sustainability plan investment initiatives in higher education institutions using an updated method for the removal effects of criteria with a geometric mean (MEREC-G) and ranking the alternatives based on the trace-to-median index (RATMI) techniques. The developed approach is applied to the strategic financial sustainability plan investment initiatives at King Abdulaziz University (KAU). The study’s results prioritized the investment initiatives based on quantitative and qualitative criteria with different weights. Results also revealed the potential initiatives that can be considered quick-winning initiatives. Furthermore, results determined one high-potential initiative for each of KAU’s financial sustainability plan pillars. Based on the results, the study recommended four steps that assist KAU in ranking the initiatives effectively. Implications of the novel approach include assisting decision makers in higher education institutions to evaluate investment initiative priorities based on objective and subjective criteria to ensure the financial sustainability of their institutions.

In-Depth Appraisal of Bus Transport Services for Sustainability Performance: A Cost–Benefit Analysis Approach

Public transport is arguably considered the backbone of today’s urban mobility ecosystem and is generally regarded as an important element of the sustainable mobility paradigm. As systems, they comprise vast societal investment owing to the infrastructures and operations required to provide the designated service. As projects, public transport systems have financial, societal, and environmental impacts that ought to be assessed through the scope of sustainability. Through sustainability, future generations’ financial, societal, and environmental needs are not compromised to meet the present generation’s needs. Thus, sustainability assessments can assist decision makers in perceiving the impacts and implications of an existing or under-consideration system on society and deciding on corrective actions. For the case of public transport systems, such assessments can assist in evaluating the expenditure distribution of the system and suggest actions that could maximize welfare gains under financial, social, and environmental criteria. Within this scope, the current paper proposes a methodological framework for unraveling transport systems’ sustainability to reveal their spatiotemporal dependencies. The study proposes a cost–benefit analysis framework where a public transport system is segmented and assessed into three levels: stops, lines, and administrative areas. Stop and line levels incorporate essential characteristics allowing their independent evaluation. The assessment of administrative areas aggregates characteristics from previous levels, spatially distributing the public transport system’s evaluation. By incorporating additional sociodemographic data, the administrative area level’s assessment links the evaluated transport system to societal characteristics, enhancing the decision maker’s perspective. The framework is showcased with Nicosia’s public bus transport system.

Perancangan Engine Decision Support System untuk Aplikasi Kefarmasian dengan Penggunaan Metode Analytical Hierarchy Process

This study aims to increase effectiveness in the selection of drug suppliers in the Tangerang public hospital pharmacy. Tangerang General Hospital is an agency engaged in the health and pharmaceutical services sector. This pharmaceutical division has an internal pharmaceutical application that has been implemented with various features used for recording transaction data both drug sales and drug purchases. Currently there is more than one supplier that supplies the same drug. Therefore the main focus of this research is to design an engine to feature a decision support system by utilizing the analytical hierarchy process approach for selecting the best specific medicine supplier. A decision support system (DSS) is an information system designed to assist decision makers in processing complex information and providing relevant data to support these decisions. The approach that used in this research is analytical hierarchy process with the criteria as needed. The conclusion of this research is to design a prototype engine that is able to provide information and recommendations about the best drug suppliers.

The Impact of Competitiveness, Information Technology, Risk Perception, and Financial Literacy on The Intention to Invest in Cryptocurrency

This study aims to analyze the impact of competence, information technology, risk perception, and financial literacy on interest in investing in cryptocurrency. The method used is quantitative, with data collected from 98 respondents through simple random sampling. The focus of this research is to reveal the relationship between factors that influence the interest of individuals who invest in cryptocurrencies. The results show that competency, information technology, risk perception, and financial literacy simultaneously or partially have a positive and significant effect on interest in cryptocurrency investment. These results suggest that the higher the competence of individuals in the field of financial knowledge and understanding of information technology, the greater their interest in investing in cryptocurrencies. In addition, the perception of low risk and high levels of financial literacy also contribute to increased interest in investing in cryptocurrencies. This research makes an important contribution in understanding the factors that influence the interest of individuals interested in cryptocurrencies. In the era of the rapidly developing digital economy, a deeper understanding of the impact of competency, information technology, risk perception and financial literacy has the potential to assist decision makers in developing more effective investment strategies and reducing risks. These findings also provide deeper insight into the dynamics of the cryptocurrency market and the consequences of growing investment in this sector. This research has actual relevance in the context of cryptocurrency investment, where the influence of factors such as competence, information technology, risk perception, and financial literacy on interest in investing is still not fully revealed. Through the help of the significant relationship between these factors and investment intentions, this research provides valuable insights for practitioners, academics, and decision makers to better understand and manage cryptocurrency investments more effectively. Thus, this research also makes an important contribution in developing knowledge and managing cryptocurrency investments in this increasingly digital era.