Specific Priorities Research Articles

Becoming Leaders - A Qualitative Research Study on the Priorities and Concerns of Early Career Women Faculty in Academic Medicine.

Gender inequity persists in high-level leadership within academic medicine. Understanding the perspectives of early career women faculty could clarify how to recruit and support women who pursue high-level leadership. This study explored the specific priorities and concerns that may influence the recruitment of women leaders in the future. Twenty-five assistant professors (mean: 1.8 years at rank) completed the Early Career Women's Leadership Program and participated in semi-structured, in-depth interviews in 2023. Data were analyzed using an iterative, thematic constant comparison process informed by constructivist grounded theory. The authors identified four themes. In theme one, participants described the importance of being seen and valued as a whole person, with consideration of their personal and professional lives. They sought mentorship and leadership positions that supported their work-life integration. In theme two, women balanced an interplay between the individual and the collective when considering themselves as future leaders. From the individual perspective, participants prioritized personal values like humility and compassion when discussing preferred leadership styles. From the collective perspective, they prioritized inclusivity and teamwork. Theme three showed discomfort with the unknown with a desire to fully understand a leadership position and its impact on personal and professional life. Participants wondered about having the authority and resources to realistically make meaningful change and whether they could adequately prepare to lead. The fourth theme was representation mattered. Participants valued having women leaders who have experienced similar challenges as themselves. These leaders inspired participants to believe that they could also achieve and succeed in high-level leadership. Early career women balanced numerous factors when considering whether to pursue high-level leadership in academic medicine. Understanding women's priorities and concerns can enable institutions to prepare women faculty to lead and ultimately recruit and retain them in high-level leadership.

Health care utilization and costs among coordinated care patients in Southeastern Ontario: A difference-in-differences study of a double propensity score-matched cohort.

Coordinated care plans (CCPs) for high-cost health care system users aim to improve system-level performance. We evaluated health care resource use and costs among CCP patients (enrollees) versus a control group that did not receive coordinated care (comparators) in Southeastern Ontario. A difference-in-differences analysis of a quasi-experimental, double propensity score-matched and adjusted cohort was conducted. Linked population-based administrative data were used to measure health care utilization and costs and to identify comparators for two enrollee groups who began CCPs between April 1, 2013, and March 31, 2019. Enrollees were recruited from hospitals in Quinte or community care centres in Rural Hastings/Thousand Islands, and were 1:1 propensity score matched to comparators. Difference-in-differences estimates were calculated using generalized estimating equations for hospitalization rates, homecare visits, primary care visits, other health care resources and total costs. A total of 558 enrollees in Quinte and 538 in Rural Hastings/Thousand Islands were identified and matched to comparators. Difference-in-differences estimates were significant in both enrollee groups for number of homecare visits ([IRR 1.72; 95% CI (1.44, 2.06)] and [IRR 1.73; 95% CI (1.45, 2.06)], respectively). Number of primary care visits were 1.76 times greater for Rural Hastings/Thousand Islands enrollees versus comparators [IRR 1.76; 95% CI (1.32, 2.35)]; total costs increased by 23% ([IRR 1.23; 95% CI (1.09,1.39)]. Homecare use significantly increased for enrollees versus comparators, indicating specific priority areas of Ontario CCPs were met. However, no reductions were shown for other health system performance indicators. We also showed increased 7-day primary care follow-up visits for community care centre-recruited patients, but not for hospital-recruited patients. Decision-makers may wish to target patients who are less advanced in their chronic disease trajectory.

Optimizing spatial distribution of wastewater-based epidemiology to advance health equity

In 2022, the US Centers for Disease Control and Prevention commissioned the National Academies of Sciences, Engineering, and Medicine to assess the role of community-level wastewater-based epidemiology (WBE) beyond COVID-19. WBE is recognized as a promising mechanism for promptly identifying infectious diseases, including COVID-19 and other novel pathogens. An important conclusion from this initiative is the critical importance of maintaining equity and expanding access to fully realize the benefits of wastewater surveillance for marginalized communities. To address this need, we propose an optimization framework that strategically allocates wastewater monitoring resources at the wastewater treatment plant (WWTP) level, ensuring more effective and equitable distribution of surveillance efforts to serve underserved populations.The purpose of the framework is to obtain a balanced spatial distribution, inclusive population coverage, and efficient representation of disadvantaged groups in the allocation of resources for WBE. Furthermore, the framework concentrates on areas with high population density and gives priority to vulnerable regions, as well as identifying signals that display significant variations from other monitored sources. The optimization objective is to maximize a weighted combination of these critical factors. This problem is formulated as an integer optimization problem and solved using simulated annealing. We evaluate various scenarios, considering different weighting factors, to optimize the allocation of WWTPs with monitoring systems. This optimization framework provides an opportunity to enhance WBE by providing customized monitoring strategies created to address specific priorities and situations, thus enhancing the decision-making processes in public health responses.

Design and implementation of blue-green infrastructure for socio-cultural benefits at community scales: The case of Wigram Basin in Ōtautahi Christchurch

Stormwater-related blue-green infrastructure (BGI) has been shown to play an important role in enhancing the socio-ecological-cultural resilience of communities under stress from climate change and in the wake of the COVID-19 pandemic. BGI has been defined as natural, semi-natural, and artificial spaces that deliver valued bio-physical and socio-cultural benefits to communities through the facilitation of natural processes, with a particular focus on stormwater management. While studies have identified a range of benefits associated with BGI, research has only partially examined their valuation, especially concerning socio-cultural outcomes. Research has also failed to consider the ways in which BGI operates at community scales, and how this may be influenced by the design process of individual projects. This study examines the range of socio-cultural benefits associated with BGI projects, alongside the design characteristics which may influence these outcomes using a mixed-methods case study of Wigram Basin. Results show that while a range of benefits were associated with the implementation of local BGI, broader socio-cultural outcomes such as social cohesion, the facilitation of a sense of place, and the ability to practice cultural values were infrequently identified by respondents. Results also demonstrated that the transdisciplinary design team, specific project priorities, and the approach taken to community and Indigenous engagement may have influenced the range of benefits achieved by Wigram Basin. As such, this research recommends that the design of BGI considers the range of disciplines required to be involved in project design, the pathways required to provide socio-cultural benefits, early, ongoing, and place-based community and Indigenous engagement and education, and the ongoing assessment and valuation of all project outcomes to maximize BGI implementation.

Educational needs and its associated factors among patients with ankylosing spondylitis in China: a multicenter cross-sectional study.

Patient education is an important part of ankylosing spondylitis (AS) management. Effective patient education should be targeted at specific priorities and needs of the patients. However, the educational needs of patients with AS in China have not been systematically explored. The purpose of this study was to assess the level of educational needs and analyze associated factors in patients with AS in China. This multicenter cross-sectional study was conducted at five hospitals in China. The Chinese version of the Educational Needs Assessment Tool (ENAT) was used to measure educational needs. Disease activity and physical function were assessed using the Bath Ankylosing Spondylitis Functional Index (BASFI) and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). Multiple linear regression analysis was used to identify the predictors of educational needs. This study included 163 patients with AS. The mean ENAT score was 86.25 (31.64). Patients wanted to know more about the arthritis process, self-help measures, and treatments. Multiple linear regression analysis indicated that younger age, being female, higher disease activity, and no family history of AS (p < 0.05) were positive predictors of educational needs. Patients with AS in China have considerable educational needs, particularly in the domains of arthritis, self-help, and treatment. Female patients and patients with younger age, higher disease activity, and no family history may have higher educational needs. These factors should be considered when conducting need-based patient education programs. Healthcare professionals should integrate need-based patient education into rheumatology care in the future.

Analyzing road traffic accidents through identification and prioritization of accident-prone areas on the dembecha to injibara highway segment in amhara region, ethiopia

Every year, millions die in road accidents globally, imposing significant economic and humanitarian costs. While road traffic accidents are a major health concern, many developing countries, including Ethiopia, struggle to address this issue effectively. Ethiopia ranks second in East Africa for severe road traffic accidents, highlighting the need for improved injury reduction strategies. This study introduces a novel approach by chronologically identifying and prioritizing accident black spots in the studied area, Ethiopia. This method provides a valuable tool for transportation authorities and traffic police to target high-risk areas for immediate intervention. Focusing on the Dembecha-Injibara highway segment, the study employs both descriptive and inferential analyses, using the Zegeer method to calculate accident rates. It also uses factors of weight contributing to road traffic accidents and their severity to rank accident-prone areas. The findings reveal that areas near Finote Selam, Banja, and Burie are highly prone to severe accidents, with specific accident frequencies and priority values identified. Recommendations are offered to address these high-risk areas and mitigate severe traffic accidents in the study region.

Project approach to control system creating for modernizing traffic control at territorial communities’ reconstruction

The reform of urban planning activities has opened up opportunities to apply a modern approach to the strategic planning of territorial community development by developing and implementing the Concept of Integrated Territorial Development (CITD) of the territorial community, as well as sectoral plans for the implementation of specific CITD priorities. One of these priorities is the creation of a sustainable transportation system for the community that provides residents with high-quality transportation services, minimizes environmental harm, and ensures long-term socio-economic benefits. To achieve this, a Sustainable Urban Mobility Plan (SUMP) is developed and implemented, which includes, among other things, the modernization of traffic control. The full-scale military aggression against Ukraine has necessitated a reevaluation of the concept of sustainable mobility and the implementation of measures to adapt it to the requirements of military and post-war community recovery. The focus is now on the resilience of territorial communities to critical situations (shock events) related to military actions and ongoing threats from the aggressor. Particular attention must be given to large cities, which represent one of the strategic targets for the aggressor. The proposed article examines the peculiarities of implementing a traffic control management modernization project for the military and the post-war recovery of a territorial community in accordance with the principles of sustainable mobility. It justifies the need to develop a version of the SUMP that includes scenarios for the development of sustainable mobility tailored to the requirements of military and post-war recovery. Three main directions are proposed for the implementation of the road traffic management system's responsibilities in developing the transportation system of the territorial community: soft measures, hard measures, and measures to enhance the transportation resilience of the territorial community. The study concludes that there is a need to develop new indicators that characterize the transportation resilience of territorial communities. conducting a SWOT analysis of the strengths and weaknesses of communities and their mobility, considering the impacts of military aggression; adjusting existing SUMPs and developing new ones, particularly for large cities and border communities; adjusting the composition, structure, and functions of the traffic management system to ensure effective current mobility management in response to existing challenges and threats; implementing and providing technical support for the execution of traffic control schemes and traffic signal timing plans according to scenarios that address critical moments (shock events); applying methodologies for managing territorial infrastructure projects with a focus on digitizing traffic control processes.

The Analysis of Trigger Factors of the Environmental Entrepreneurship Process in Saudi Arabia: An Innovative Approach

Environmental entrepreneurship is considered a critical avenue for sustainable development. Despite increasing studies on its benefits and impacts, we lack insights about its corresponding process. There is an increasing need for policymakers to know how to develop this process and what is required for its definition. This research aims to build an innovative theoretical model to explore the trigger factors for the environmental entrepreneurship process in Saudi Arabia, which is the ultimate focus point of this research. To achieve this objective, a deep literature review and a survey of expert entrepreneurs’ opinions were performed. Experts and professionals in entrepreneurship validated the opportunity of readiness factors identified. Data were gathered using a combined approach based on closed-ended questionnaires and arranged interviews according to a schedule. Employing the mixed influence matrix of cross-impact multiplications applied to classification (MICMAC) and the Interpretive Structural Modeling (ISM) approach, the research identifies a hierarchical model of these factors, addressing why and how they interact according to a specific order and priorities. The investigation reveals 15 factors that are structured into three main levels of influence: (1) factors driving the adoption of environmental entrepreneurship orientation, (2) factors related to environmental entrepreneurship intention, and (3) factors for materializing environmental entrepreneurship behavior. The findings emphasize the complementarity between environmental entrepreneurship intention and orientation as a key factor in generating entrepreneurial behavior. This research pioneers the empirical exploration of environmental entrepreneurship as a process. It contributes significantly to theoretical and practical domains by offering a pragmatic framework and better understanding for policymakers and stakeholders to focus on key factors that facilitate this process. This paper is innovative because it uses the integrative ISM–MICMAC approach, supported by a primary and brief bibliometric analysis of entrepreneurship.

An Exploratory Analysis of ChatGPT Compared to Human Performance With the Anesthesiology Oral Board Examination: Initial Insights and Implications.

Chat Generative Pre-Trained Transformer (ChatGPT) has been tested and has passed various high-level examinations. However, it has not been tested on an examination such as the American Board of Anesthesiology (ABA) Standardized Oral Examination (SOE). The SOE is designed to assess higher-level competencies, such as judgment, organization, adaptability to unexpected clinical changes, and presentation of information. Four anesthesiology fellows were examined on 2 sample ABA SOEs. Their answers were compared to those produced by the same questions asked to ChatGPT. All human and ChatGPT responses were transcribed, randomized by module, and then reproduced as complete examinations, using a commercially available software-based human voice replicator. Eight ABA applied examiners listened to and scored the topic and modules from 1 of the 4 versions of each of the 2 sample examinations. The ABA did not provide any support or collaboration with any authors. The anesthesiology fellow's answers were found to have a better median score than ChatGPT, for the module topics scores (P = .03). However, there was no significant difference in the median overall global module scores between the human and ChatGPT responses (P = .17). The examiners were able to identify the ChatGPT-generated answers for 23 of 24 modules (95.83%), with only 1 ChatGPT response perceived as from a human. In contrast, the examiners thought the human (fellow) responses were artificial intelligence (AI)-generated in 10 of 24 modules (41.67%). Examiner comments explained that ChatGPT generated relevant content, but were lengthy answers, which at times did not focus on the specific scenario priorities. There were no comments from the examiners regarding Chat GPT fact "hallucinations." ChatGPT generated SOE answers with comparable module ratings to anesthesiology fellows, as graded by 8 ABA oral board examiners. However, the ChatGPT answers were deemed subjectively inferior due to the length of responses and lack of focus. Future curation and training of an AI database, like ChatGPT, could produce answers more in line with ideal ABA SOE answers. This could lead to higher performance and an anesthesiology-specific trained AI useful for training and examination preparation.

Efficacy of continuous employee learning and development in the South African public service between 2014 to 2023 era

Modern technologies have intensified innovations and necessitated changes in public service processes and operations. Continuous employee learning development (CELD) is one means of the molecule-atom that keep employees motivated and sustain competitiveness. The study explored the efficacy of CELD in relation to modern technology in the South African (SA) public service departments between 2014 to 2023 era. Departments are faced with challenge of equipping their employees with adequate professional and technical skills for both the present and the future in order to deliver specific government priorities. Data for the study were gathered utilizing a qualitative semi-structured e-questionnaire. The study sample consisted of 677 human capital development practitioners from national and provincial government departments in SA. The inefficacy CELD and the inadequacy of technological infrastructure and service delivery can be attributed to the failure by executive management and senior managers to invest in CELD to prepare employees for digital world. It is recommended that departments should use Ruggles’s knowledge management, Kirkpatrick’s training, and Becker and Schultz’s human capital models as sound measurement tools in order to gain a true return on investment. The study adds pragmatic insight into the value of CELD in the new technological environment in public service departments.

Global disparities in cancer supportive care: An international survey.

The global cancer burden is rising, particularly in low- and middle-income countries (LMIC), highlighting a critical research gap in understanding disparities in supportive care access. To address this, the Multinational Association of Supportive Care in Cancer (MASCC) Health Disparities Committee initiated a global survey to investigate and delineate these disparities. This study aims to explore and compare supportive care access disparities between LMIC and High-Income Countries (HIC). An online cross-sectional survey was conducted among active members of MASCC. Members, representing diverse healthcare professions received email invitations. The survey, available for 3 weeks, comprised sections covering (1) sociodemographic information; (2) clinical service/practice-related disparities in their region/nation; (3) population groups facing disparities within their region or country. Chi-squared or Fisher's exact test for cross-sectional analyses, and a multivariable logistic regression model was employed for statistical analysis. A total of 218 active members participated, with one-quarter (26.6%) from LMIC and 18.4% ethnic minorities, timely cancer care (43.7%) and timely supportive care (45.0%) emerged as the most pressing disparities globally. Notably, participants from LMIC underscored cancer drug affordability (56.4%) and supportive care guideline implementation (56.4%) as critical issues. Economically disadvantaged populations were noted as more likely to face disparities by both LMIC and HIC (non-US-based) respondents, while US-based respondents identified racial/ethnic minorities as facing more disparities. This global survey reveals significant disparities in cancer supportive care between LMIC and HIC, with a particular emphasis on medication affordability and guideline implementation in LMIC. Addressing these disparities requires targeted intervention, considering specific regional priorities.

Marketing priorities and digital tactic effectiveness: A business strategy perspective

Digital marketing has emerged as a key component of marketing strategy and by extension has become an important source of competitive advantage for the firm. Firms adopt digital marketing strategies and tactics to address specific marketing priorities (eg sales growth, brand building etc). Digital marketing strategies represent the adoption of specific sets of digital marketing tactics. While these tactics are widely recognised, little attention has been paid to determining the conditions under which some digital marketing tactics are more effective than others. In this exploratory study, marketing managers were surveyed to understand the comparative importance of seven alternative strategic marketing priorities and the perceived effectiveness of seven alternative digital marketing tactics in addressing those priorities. Responses from 236 senior marketing managers/executives were analysed through a series of paired t-tests and one-way ANOVAs in SPSS Version 26. Significant differences were observed between firms pursuing prospector, analyser, low-cost defender and differentiated defender business strategies. These findings provide marketing and general managers alike with practical insights into the management of their digital marketing strategies.

How do social enterprises respond operationally to conflicting demands? A resource-based view

PurposeSocial enterprises live in the limbo between social and market objectives, posing many operations management challenges. This study extends the discussion of operational priorities, which has focused on purely for-profit organisations, to the context of social enterprises by exploring, from a resource-based perspective, which resources and operational priorities are most important to them and how they are used to respond to conflicting demands.Design/methodology/approachMultiple case study research was carried out involving five Portuguese social enterprises, representative of the main sectors in which social enterprises operate in Europe. Ten semi-structured interviews with directors and other high-ranking respondents were conducted, and content was analysed to gather evidence on the key resources and operational priorities pursued by social enterprises. Cross-case conclusions were drawn, resulting in theoretical propositions and a conceptual framework.FindingsFindings suggest that social enterprises rely on intangible resources and combine different operational priorities, which may vary throughout their lifecycle. Community engagement has emerged as a specific operational priority, in addition to those already reported in the manufacturing and services literature. To balance conflicting demands, most social enterprises studied combine innovation with community engagement or customer focus.Originality/valueThe study contributes to the development of knowledge about the operations strategy in the specific context of social enterprises, an organisational model that has not been systematically addressed in the operations management literature, and brings the discussion of operational priorities into the social enterprise field, thus strengthening the link between these two fields.

Relationship between milk intrinsic quality scores and the environmental impact scores of dairy farms

This study aimed to study the relationship between milk quality for cheese manufacturing and farm’s environmental impacts in grass-based mountain systems. Beforehand, the multi-criteria evaluation methodology proposed by Botreau et al. and Rey-Cadilhac et al. was adapted to a routine use. Milk quality scores were constructed by weighing traits possibly predicted by spectroscopic analyses currently available in milk labs into 4 dimensions: health, nutritional, technological and sensory. Environmental impact scores were constructed by combining 6 indicators of the CAP'2ER® software in 5 dimensions: greenhouse gas emissions, eutrophication, air acidification, consumption of space and non-renewable energies and ecosystem biodiversity. The study sample included 15 dairy farms located in the French Massif Central mountains. An analysis of variance was carried out to study the effect of system’s types on milk quality and environmental impact scores. A principal component analysis was used to study the relationship between milk quality and environmental impact dimensions scores. No correlation has been established between overall milk quality and overall environmental impact scores. High scores for the nutritional and biodiversity dimensions and low scores for the resource consumption dimension, were associated with farming practices such as a high proportion of grasslands in the usable agricultural area, low stocking rate and low productivity per cow. This demonstrates the importance of defining specific priority objectives, on a farm-by-farm basis, in order to drive changes in agricultural practices. The multi-criteria evaluation model tested here appeared sensitive, but it needs to be tested on a larger scale and in different contexts.

Cancer incidence and survival for 11 cancers in the Commonwealth: a simulation-based modelling study

The number of new cancer cases in Commonwealth countries rose by 35% between 2008 and 2018, but progress in cancer control has been slow in many low-income and lower-middle-income member states. We aimed to examine cancer outcomes and priority areas in the Commonwealth to provide insight and guidance on prioritisation of efforts to improve cancer survival and make the best use of scarce resources. We adapted a previously developed microsimulation model of global cancer survival for 11 cancer sites (oesophagus, stomach, colon, rectum, anus, liver, pancreas, lung, breast, cervix uteri, and prostate). All 56 Commonwealth countries were included and classified based on the 2020 World Bank Income groups (low-income, lower-middle-income, upper-middle-income, and high-income countries) and Commonwealth geographical areas. We modelled the number of incident cancer cases in each Commonwealth country in 2020, based on age group-specific estimates of incidence rates from GLOBOCAN 2020. We simulated 5-year net survival for each patient, accounting for the stage at diagnosis (I-IV), availability of specific treatment and imaging modalities, and quality of care (based on residual differences in expected versus observed survival after accounting for the availability and effectiveness of treatment and imaging modalities). We also simulated counterfactual policy scenarios, in which we scaled up various aspects of cancer care to the mean level of high-income countries to estimate the comparative effectiveness of different policies. Incident cancers in the Commonwealth accounted for an estimated 14·3% of global diagnosed cancer cases in 2020 among the 11 cancers modelled (1 610 000 Commonwealth cases [95% UI 1 556 000-1 674 000] of 11 227 000 global cases [11 069 000-11 406 000]) and are estimated to increase to 17·3% in 2050 due to population growth (3 330 000 [3 154 000-3 539 000] of 19 308 000 [18 706 000-19 911 000]). The 5-year net survival across 11 cancers combined in 2020 was 30·7% (95% UI 22·4-38·6) in Commonwealth countries, ranging from 4·1% (0·04-15·2) in low-income countries, 17·8% (3·7-30·9) in lower-middle-income countries, 33·1% (23·7-46·0) in upper-middle-income countries, to 59·0% (57·8-60·2) in high-income countries. Among single treatment policies, scaling up access to radiotherapy had the largest survival impact in low-income countries, surgery had the largest impact in lower-middle-income and upper-middle-income countries, and targeted therapy had the largest impact in high-income countries. By geographical area, improving radiotherapy availability was estimated to have the largest impact in Africa, surgery in Asia, targeted therapy in the Caribbean and the Americas and Europe, and quality of care in the Pacific Commonwealth countries. Comparing packages of scaling up the availability of all treatment modalities versus imaging modalities, expanding availability of imaging yielded the largest benefits in high-income countries, and in the Caribbean and the Americas, Europe, and the Pacific, whereas expanding treatment yielded larger benefits in all other income groups and geographical areas. We found large variation in 5-year net survival, with a nearly 15-times difference in cancer survival by country income group within the Commonwealth. Efforts to improve the availability of treatment and imaging modalities and quality of care will be crucial to reduce these disparities, with specific priorities of scale-up policies varying by setting. The Commonwealth could leverage a broad range of knowledge and resources and have an important role in supporting member countries with setting-specific priorities to improve cancer outcomes. Harvard T H Chan School of Public Health.

An optimization-based framework for personal scheduling during pandemic events

In recent years, companies have faced the challenge of adapting to new guidelines and strategies aimed at preventing and reducing the transmission of COVID-19 within the workplace. An essential aspect of this adaptation is effectively managing the workday schedule to minimize social contact.This paper introduces a comprehensive optimization framework designed to automate the planning of employee schedules during pandemic events. Our framework utilizes integer linear programming to establish a set of general constraints that can accommodate various types of distancing restrictions and cater to different objective functions.To employ the framework, a company simply needs to instantiate a subset of these constraints along with an objective function based on its specific priorities. We conducted tests on our scheduling framework within three distinct real-life companies, yielding promising results. Our approach successfully increased the number of in-person workers by 15%, all while adhering to the social distancing restrictions mandated by these companies. Furthermore, the solutions generated by our method were implemented and validated within these organizations.

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

Business Models Used in Smart Cities—Theoretical Approach with Examples of Smart Cities

This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including public–private partnerships, build–operate–transfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide.

Enhancing Energy Efficiency in Telehealth IoT through MultiObjective Optimization on a Hybrid Fog/Cloud Computing Platform

In the rapidly evolving field of Telehealth Internet of Things (IoT), the pursuit of energy-efficient solutions that coexist with optimal system performance is a critical concern. This paper introduces a novel approach to address this challenge by integrating multi-objective optimization techniques within a hybrid fog/ cloud computing platform. Building upon established research on a fog-based telehealth model, this study extends its investigation to encompass a broader spectrum of performance metrics, including energy efficiency, response time, throughput, and resource utilization. The study employs well-established multiobjective optimization algorithms, specifically NSGA-II (Non-dominated Sorting Genetic Algorithm II) and SPEA2 (Strength Pareto Evolutionary Algorithm 2), to construct a comprehensive optimization framework. An intricate objective function is meticulously formulated to quantify the trade-offs between energy efficiency and other key performance metrics, facilitating the identification of Pareto-optimal solutions. The resulting Pareto front offers illuminating insights into the nuanced interplay between energy efficiency and performance attributes, providing decision-makers with tailored options that cater to their specific priorities. Rigorous evaluation of these solutions through simulated experiments reveals a harmonious landscape where energy-saving imperatives coalesce harmoniously with response time, throughput, and resource utilization goals. The implications of this multi-objective optimization approach are analyzed in depth, underscoring its potential to reshape optimization paradigms for Telehealth IoT deployments within a fog/cloud hybrid platform. This research represents a pioneering stride towards reconciling energy efficiency and performance in Telehealth IoT systems, offering a nuanced perspective for informed decision-making and a sustainable future for energy-saving initiatives in Telehealth IoT applications

The approach development of data extraction from lambda terms

The study's object is the process of extracting the characteristics of lambda terms, which indicate the optimality of the reduction strategy and increase the productivity of compilers and interpreters. The solution to the problem of extracting specific strategy priority data from lambda terms using Machine Learning methods was considered. Such data was extracted using the large language model Microsoft CodeBERT, which was trained to solve the problem of summarizing the software code. The resulting matrices of embeddings were used to obtain vectors of average embeddings of size 768 and a latent space of size 8 thousand. Further, vectors of average embeddings were used for cluster analysis using the DBSCAN and Hierarchical Agglomerative clustering methods. The most informative variables affecting clustering were determined. Next, the clustering results were compared with the priorities of reduction strategies, which showed the impossibility of separating terms with RI priority. A feature of the obtained results is using machine learning methods to obtain knowledge. The clustering results showed many of the same informative variables, which is explained by the similar shape of the obtained clusters. The results of comparing the clustering values with the real priority are explained by the impossibility of clearly determining the priority and the use of the Microsoft CodeBERT model, which was not trained for the analysis of lambda terms. The proposed approach can find application in the development of compilers and interpreters of functional programming languages, allowing to analyze the code and extract important data to optimize the execution of programs. The obtained data can be used to develop rules aimed at improving the efficiency of compilation and interpretation