4,376 research outputs found

    Developing A Road Freight Transport Performance Measurement System To Drive Sustainability:An Empirical Study Of Egyptian Road Freight Transport Companies

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    While several road freight performance measurement systems have been developed, only a limited number of quantified performance measurement frameworks encompassing diverse sets of performance metrics from multiple sustainable perspectives are available on a technological platform. These sets of metrics could be integrated as crucial performance indicators for assessing the operational performance of various road freight transport companies. These indicators include fuel efficiency, trip duration, vehicle loading, and cargo capacity. The objective of this research is to construct a conceptual road freight performance measurement framework that comprehensively incorporates performance elements from sustainable viewpoints (economic, environmental, and social), leveraging technology to measure the performance of road freight transport companies. This proposed framework aims to aid these companies in gauging their performance using technology, thus enhancing their operations towards sustainability.Within the road freight transport sector, several challenges exist, with congestion, road infrastructure maintenance, and driver training and qualifications being particularly pressing issues. The developed performance measurement framework offers the means for companies to evaluate the effects of technology integration on vehicles and overall performance. This allows companies to measure their performance from an operational standpoint rather than solely a strategic one, thereby identifying areas requiring improvement. Egypt was chosen as the empirical study location due to its relatively low level of technological integration within its road freight sector.This thesis employs an explanatory mixed methods approach, encompassing four distinct phases. The first phase entails a review to formulate the proposed theoretical performance measurement framework. Subsequently, the second phase involves conducting semi-structured interviews using a Delphi method to both develop a conceptual performance measurement framework and explore the present state of Egypt's road freight transport sector. Following this, the third phase encompasses surveys based on the results derived from Delphi analysis, involving diverse participants from the road freight transport industry. The aim is to validate the developed performance measurement framework through an empirical study conducted in Egypt. Lastly, the fourth phase centres around organizing focus groups involving stakeholders within road freight transport companies. The goal here is to propose a roadmap for implementing the developed road freight transport performance measurement framework within the Egyptian context.The primary theoretical contribution of this research is the development of a road freight transport performance measurement framework that integrates the three sustainability dimensions with technology. Additionally, this study offers practical guidance for the application of the developed framework in various countries and contexts. From a practical standpoint, this research aids road freight transport managers in evaluating their operational performance, thereby identifying challenges, devising action plans, and making informed decisions to mitigate these issues and enhance sustainability-oriented performance. Ultimately, the developed road freight transport performance measurement framework is poised to promote performance measurement aligned with technology, fostering progress towards achieving the sustainable development goals by 2030

    Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022

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    In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet

    An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

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    This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Digital Innovations for a Circular Plastic Economy in Africa

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    Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE). This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy. Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa. The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license

    Why Climate Change Adaptation is Elusive: The Lived Reality of Farming Households in the Central Dry Zone of Myanmar

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    Farming households in the Global South are vulnerable to climate change because of their livelihoods’ direct link to the natural environment. Farm households adapt to climate through altering their farming practices and by diversifying their livelihoods through the non-farm sector and migration. However, previous research has suggested that most of these adaptations are incremental, meaning they may not address the root cause of climate change vulnerability in the long term. The aim of this thesis is to assess these claims using the experiences of farm households in Myanmar’s Central Dry Zone, a highly climate-stressed region. According to fieldwork conducted in the Central Dry Zone, farmers’ responses to climate change vary considerably. In many cases, although farmers may be aware of the effects of climate change, their livelihood adaptations are motivated by a wider array of concerns, which mitigate or even subvert their capacities to respond to climate challenges. These mixed responses, and the notable reluctance of many farmers in the Central Dry Zone to take adaptive measures to the clear and present risks of climate change, forms the central problem this research seeks to resolve. The thesis argues that these outcomes can be explained through the adoption of a broad-based livelihoods approach which acknowledges that although climate change is an important factor influencing famers’ decision making, other factors are also involved, and these are often prioritized over climate risks. This highlights the position of climate change on farmers' daily lives by emphasising the significance of geographical context and local traditions in relation to making decisions about rural livelihoods, farming, non-farm activities and migration. These findings underscore the need to recognise and comprehend how multiple stresses interact with climate effects to exacerbate the vulnerability of rural households and spotlight the importance of understanding the underlying causes of vulnerability. This perspective is crucial for understanding how farmers and agriculture-dependent communities respond to climate risks. Using the Central Dry Zone of Myanmar as a case study, the research generates an analytical framework that explains why farming households respond to climate change incrementally while being aware of it

    Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer

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    Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues. Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice. Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings. This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer. Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis. Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene. Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Innovation-driven human resource management practices : a systematic review, integrative framework, and future research directions

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    CEL: Coraz częściej podkreśla się, że duże znaczenie dla tworzenia innowacji mają praktyki zarządzania zasobami ludzkimi (HRMP), które odnoszą się do rekrutacji i selekcji, szkolenia i podnoszenia kompetencji, wynagrodzenia oraz oceny. Jednak pandemia COVID-19 pokazała, że tradycyjne HRMP już są niewystarczające, co pociąga za sobą potrzebę ich ponownego przemyślenia i przeformułowania w kierunku bardziej skutecznych dla tworzenia innowacji, ale także pozwalającym organizacjom przetrwać kryzysy na skalę COVID-19. Chociaż istnieje obszerna literatura w zakresie zarządzania zasobami ludzkimi i innowacji, nadal nie ma zgody co do praktyk zarządzania zasobami ludzkimi napędzających innowacje. Niniejsze badanie ma na celu identyfikację oraz syntezę najbardziej znaczące i godne zaufania wkłady w badania praktyk zarządzania zasobami ludzkimi napędzających innowacje. Dodatkowo, aby ułatwić budowanie teorii w zakresie HRMP niniejszy artykuł konsoliduje istniejący stan wiedzy w ramy integracyjne. Ramy te mogą być wykorzystywane przez przyszłych badaczy do identyfikacji luk i niejasności w rozumieniu praktyk zarządzania zasobami ludzkimi napędzających innowacje. METODYKA: Artykuł prezentuje wyniki systematycznego przeglądu literatury 71 empirycznych artykułowych. Literatura przedmiotu została wyłoniona w oparciu o przeszukiwania zagranicznych baz danych, takich jak: Scopus i Web of Science. WYNIKI: Przeprowadzony przez nas systematyczny przegląd literatury pozwolił na identyfikację praktyk zarządzania zasobami ludzkimi napędzających innowacje z uwzględnieniem trzech poziomów analizy: indywidualnym, grupowym oraz organizacyjnym, przy czym ten ostatni poziom analizy jest dominujący w dotychczasowych publikacjach. Rozpoznane praktyki zarządzania zasobami ludzkimi napędzające innowacje z uwzględnieniem poziomów ujęliśmy w integracyjne ramy, które stanowią podstawę teoretyczną do kierowania przyszłymi badaniami. Nasze wyniki potwierdziły rosnący trend liczby publikacji w prezentowanej tematyce począwszy od 2010 r. Większość badaczy wykorzystywała podejście ilościowe. Na podstawie afiliacji pierwszego autora, najwięcej publikacji dostarczyli autorzy z Wielkiej Brytanii. Artykuły są publikowane w różnych czasopismach, przeważnie jednak o tematyce zarządzania zasobami ludzkimi. Badania uwzględniały różnorodne konteksty organizacyjne, przeważnie w dynamicznych i złożonych branżach. Nasze ustalenia potwierdzają, że obecny stan badań nad praktykami zarządzania zasobami ludzkimi napędzającymi innowacje wskazują na konieczność prowadzenia dalszych badań w tym zakresie. W oparciu o to dostarczyliśmy luki poznawcze oraz potencjalne przyszłe pytania badacze z podziałem na trzy poziomy praktyk zarządzania zasobami ludzkimi napędzające innowacje. IMPLIKACJE: Przeprowadzony przez nas systematyczny przegląd literatury pozwolił na zaproponowanie implikacji dla przyszłych badaczy planujących prowadzenie badań w zakresie praktyk zarządzania zasobami ludzkimi napędzających innowacje. ORYGINALNOŚĆ I WARTOŚĆ: Nasz systematyczny przegląd literatury koncentruje się na identyfikacji praktyk zarządzania zasobami ludzkimi napędzających innowacje, ustaleniu obecnego stanu wiedzy oraz przyszłych kierunków badań w tym zakresie. Dodatkowo opracowaliśmy ramy integracyjne, których celem jest uporządkowanie istniejącej literatury, ale także zidentyfikowanie obiecujących przyszłych kierunków badań nad praktykami zarządzania zasobami ludzkimi napędzającymi innowacje.PURPOSE: It is increasingly emphasized that human resource management practices (HRMP), which refer to recruiting and selection, training and development, compensation and performance appraisal, are of great importance for creating innovation. However, the COVID-19 pandemic has shown that traditional HRMPs are already insufficient, which entails the need to rethink and reformulate them in the direction of more effective innovation while also allowing organizations to survive COVID-19-like crises. While there is an extensive literature on human resources management and innovation, there is still no consensus on innovation-driven HRMP. This study aims to identify and synthesize most significant and trustworthy research contributions of innovation-driven HRMP. In addition, to facilitate theory building in the field of HRMP, this article consolidates the existing knowledge into an integrative framework. This framework can be used by future researchers to identify gaps and ambiguities in the meaning of innovation-driven HRMP. METHODOLOGY: The article presents the results of a systematic literature review of 71 empirical research articles referring to innovation-driven HRMP from the Web of Science and Scopus databases. FINDINGS: The systematic literature review allowed us to identify innovation-driven HRMP, taking into account three levels of analysis: individual, group and organizational, with the latter level of analysis being dominant in previous publications. Recognition of innovation-driven HRMP, taking into account the levels in question, is included in an integrative framework, which is the theoretical basis for guiding future research. Our results confirmed the growing trend in the number of publications on the subject since 2010. Most researchers used aquantitative approach. Based on the first author’s affiliation, authors from Great Britain contributed the largest number of publications. Articles are published in various journals, but mainly in those on human resources management. The research took into account a variety of organizational contexts, predominantly in dynamic and complex industries. Our findings show that the current state of research on innovation-driven HRMP confirms the need for further research in this area. Based on this, we provided thematic gaps and potential questions for future research divided into three levels of innovation driven HRMP. IMPLICATIONS: Our systematic literature review allowed us to propose implications for future researchers planning to conduct research in the field of innovation-driven HRMP. ORIGINALITY AND VALUE: Our systematic literature review focuses on identifying innovation-driven HRMP along with determining the current state of knowledge and future research directions in this area. In addition, we developed an integrative framework that aims at organizing existing literature but also at identifying promising future research directions into innovation-driven HRMP
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