Search results for: assumptions

Authors: Laith F. Gulli, Nicole M. Mallory

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The undertaking to develop a psychometric instrument is monumental. Understanding the relationship between variables and events is important in structural and exploratory design of psychometric instruments. Considering this, we describe a method used to group, pair and combine multiple Philosophical Assumption statements that assisted in development of a 13 item psychometric screening instrument. We abbreviated our Philosophical Assumptions (PA)s and added parameters, which were then condensed and mathematically modeled in a specific process. This model produced clusters of combinatorics which was utilized in design and development for 1) information retrieval and categorization 2) item development and 3) estimation of interactions among variables and likelihood of events. The psychometric screening instrument measured Knowledge, Assessment (education) and Beliefs (KAB) of New Addictions Research (NAR), which we called KABNAR. We obtained an overall internal consistency for the seven Likert belief items as measured by Cronbach’s α of .81 in the final study of 40 Clinicians, calculated by SPSS 14.0.1 for Windows. We constructed the instrument to begin with demographic items (degree/addictions certifications) for identification of target populations that practiced within Outpatient Substance Abuse Counseling (OSAC) settings. We then devised education items, beliefs items (seven items) and a modifiable “barrier from learning” item that consisted of six “choose any” choices. We also conceptualized a close relationship between identifying various degrees and certifications held by Outpatient Substance Abuse Therapists (OSAT) (the demographics domain) and all aspects of their education related to EB-NAR (past and present education and desired future training). We placed a descriptive (PA)1tx in both demographic and education domains to trace relationships of therapist education within these two domains. The two perceptions domains B1/b1 and B2/b2 represented different but interrelated perceptions from the therapist perspective. The belief items measured therapist perceptions concerning EB-NAR and therapist perceptions using EB-NAR during the beginning of outpatient addictions counseling. The (PA)s were written in simple words and descriptively accurate and concise. We then devised a list of parameters and appropriately matched them to each PA and devised descriptive parametric (PA)s in a domain categorized information grid. Descriptive parametric (PA)s were reduced to simple mathematical symbols. This made it easy to utilize parametric (PA)s into algorithms, combinatorics and clusters to develop larger information grids. By using matching combinatorics we took paired demographic and education domains with a subscript of 1 and matched them to the column with each B domain with subscript 1. Our algorithmic matching formed larger information grids with organized clusters in columns and rows. We repeated the process using different demographic, education and belief domains and devised multiple information grids with different parametric clusters and geometric arrays. We found benefit combining clusters by different geometric arrays, which enabled us to trace parametric variables and concepts. We were able to understand potential differences between dependent and independent variables and trace relationships of maximum likelihoods.

Keywords: psychometric, parametric, domains, grids, therapists

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Authors: Tony Wilson

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This paper sets out a practices perspective or practices theory, which has become pervasive from business to sociological studies. In doing so, it locates the perspective historically (in the work of the philosopher Heidegger) and provides a contemporary illustration of its application to communication, consumer and cultural studies as central to this conference theme. The structured account of practices (as articulated in eight ‘axioms’) presented towards the conclusion of this paper is an initial statement - planned to encourage further detailed qualitative and systematic research in areas of interest to the conference. Practice theories of equipped and situated construction of participatory meaning (as in media and marketing consuming) are frequently characterized as lacking common ground, or core principles. This paper explores whether by retracing a journey to earlier philosophical underwriting, a shared territory promoting new research can be located as current philosophical hermeneutics. Moreover, through returning to hermeneutic first principles, the paper shows that a series of spatio-temporal metaphors become available - appropriate to analyzing communication as a process across disciplines in which it is considered. Thus one can argue, for instance, that media users engage (enter) digital text from their diverse ‘horizons of expectation’, in a productive enlarging ‘fusion’ of horizons of understanding, thereby ‘projecting’ a new narrative, integrated in a ‘hermeneutic circle’ of meaning. A politics of communication studies may contest a horizon of understanding - so engaging in critical ‘distancing’. Marketing’s consumers can occupy particular places on a horizon of understanding. Media users pass over borders of changing, revised perspectives. Practices research can now not only be discerned in multiple disciplines but equally crosses disciplines. The ubiquitous practice of media use by managers and visitors in a shopping mall - the mediatization of malls - responds to investigating not just with media study expertise, but from an interpretive marketing perspective. How have mediated identities of person or place been changed? Emphasizing understanding of entities in a material environment as ‘equipment’, practices theory enables the quantitative correlation of use and demographic variable as ‘Zeug Score’. Human behavior is fundamentally habitual - shaped by its tacit assumptions - occasionally interrupted by reflection. Practices theory acknowledges such action to be minimally monitored yet nonetheless considers it as constructing narrative. Thus presented in research, ‘storied’ behavior can then be seen to be (in)formed and shaped from a shifting hierarchy of ‘horizons’ or of perspectives - from habituated to reflective - rather than a single seamless narrative. Taking a communication practices perspective here avoids conflating tacit, transformative and theoretical understanding in research. In short, a historically grounded and unifying statement of contemporary practices theory will enhance its potential as a tool in communication, consumer and cultural research, landscaping interpretative horizons of human behaviour through exploring widely the culturally (in)formed narratives equipping and incorporated (reflectively, unreflectively) in people’s everyday lives.

Keywords: communication, consumer, cultural practices, hermeneutics

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Authors: Bill Wason

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143 Crude Palm Oil Coop mills on Sumatra Island are participating in a program to transfer land from defaulted estates to small farmers while improving the sustainability of palm production to allow for biofuel & food production. GCarbon will be working with farmers to transfer technology, fertilizer, and trees to double the yield from the current baseline of 3.5 tons to at least 7 tons of oil per ha (25 tons of fruit bunches). This will be measured via evaluation of yield comparisons between participant and non-participant farms. We will also capture methane from Palm Oil Mill Effluent (POME)throughbelt press filtering. Residues will be weighed and a formula used to estimate methane emission reductions based on methodologies developed by other researchers. GCarbon will also cover mill ponds with a non-permeable membrane and collect methane for energy or steam production. A system for accelerating methane production involving ozone and electro-flocculation will be tested to intensifymethane generation and reduce the time for wastewater treatment. A meta-analysis of research on sweet potatoes and sorghum as rotation crops will look at work in the Rio Grande do Sul, Brazil where5 ha. oftest plots of industrial sweet potato have achieved yields of 60 tons and 40 tons per ha. from 2 harvests in one year (100 MT/ha./year). Field trials will be duplicated in Bom Jesus Das Selvas, Maranhaothat will test varieties of sweet potatoes to measure yields and evaluate disease risks in a very different soil and climate of NE Brazil. Hog methane will also be captured. GCarbon Brazil, Coop Sisal, and an Australian research partner will plant several varieties of agave and use agronomic procedures to get yields of 880 MT per ha. over 5 years. They will also plant new varieties expected to get 3500 MT of biomass after 5 years (176-700 MT per ha. per year). The goal is to show that the agave can adapt to Brazil’s climate without disease problems. The study will include a field visit to growing sites in Australia where agave is being grown commercially for biofuels production. Researchers will measure the biomass per hectare at various stages in the growing cycle, sugar content at harvest, and other metrics to confirm the yield of sugar per ha. is up to 10 times greater than sugar cane. The study will look at sequestration rates from measuring soil carbon and root accumulation in various plots in Australia to confirm carbon sequestered from 5 years of production. The agave developer estimates that 60-80 MT of sequestration per ha. per year occurs from agave. The three study efforts in 3 different countries will define a feedstock pathway for jet fuel that involves very high yield crops that can produce 2 to 10 times more biomass than current assumptions. This cost-effective and less land intensive strategy will meet global jet fuel demand and produce huge quantities of food for net zero aviation and feeding 9-10 billion people by 2050

Keywords: zero emission SAF, methane capture, food-fuel integrated refining, new crops for SAF

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Authors: Zahirah Dhurmeea, Iker Zudaire, Heidi Pethybridge, Emmanuel Chassot, Maria Cedras, Natacha Nikolic, Jerome Bourjea, Wendy West, Chandani Appadoo, Nathalie Bodin

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Scientific advice on the status of fish stocks relies on indicators that are based on strong assumptions on biological parameters such as condition, maturity and fecundity. Currently, information on the biology of albacore tuna, Thunnus alalunga, in the Indian Ocean is scarce. Consequently, many parameters used in stock assessment models for Indian Ocean albacore originate largely from other studied stocks or species of tuna. Inclusion of incorrect biological data in stock assessment models would lead to inappropriate estimates of stock status used by fisheries manager’s to establish future catch allowances. The reproductive biology of albacore tuna in the western Indian Ocean was examined through analysis of the sex ratio, spawning season, length-at-maturity (L50), spawning frequency, fecundity and fish condition. In addition, the total lipid content (TL) and lipid class composition in the gonads, liver and muscle tissues of female albacore during the reproductive cycle was investigated. A total of 923 female and 867 male albacore were sampled from 2013 to 2015. A bias in sex-ratio was found in favour of females with fork length (LF) <100 cm. Using histological analyses and gonadosomatic index, spawning was found to occur between 10°S and 30°S, mainly to the east of Madagascar from October to January. Large females contributed more to reproduction through their longer spawning period compared to small individuals. The L50 (mean ± standard error) of female albacore was estimated at 85.3 ± 0.7 cm LF at the vitellogenic 3 oocyte stage maturity threshold. Albacore spawn on average every 2.2 days within the spawning region and spawning months from November to January. Batch fecundity varied between 0.26 and 2.09 million eggs and the relative batch fecundity (mean  standard deviation) was estimated at 53.4 ± 23.2 oocytes g-1 of somatic-gutted weight. Depending on the maturity stage, TL in ovaries ranged from 7.5 to 577.8 mg g-1 of wet weight (ww) with different proportions of phospholipids (PL), wax esters (WE), triacylglycerol (TAG) and sterol (ST). The highest TL were observed in immature (mostly TAG and PL) and spawning capable ovaries (mostly PL, WE and TAG). Liver TL varied from 21.1 to 294.8 mg g-1 (ww) and acted as an energy (mainly TAG and PL) storage prior to reproduction when the lowest TL was observed. Muscle TL varied from 2.0 to 71.7 g-1 (ww) in mature females without a clear pattern between maturity stages, although higher values of up to 117.3 g-1 (ww) was found in immature females. TL results suggest that albacore could be viewed predominantly as a capital breeder relying mostly on lipids stored before the onset of reproduction and with little additional energy derived from feeding. This study is the first one to provide new information on the reproductive development and classification of albacore in the western Indian Ocean. The reproductive parameters will reduce uncertainty in current stock assessment models which will eventually promote sustainability of the fishery.

Keywords: condition, size-at-maturity, spawning behaviour, temperate tuna, total lipid content

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Authors: Selin Guney, Andres Riquelme

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Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

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Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

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In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

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Authors: Eugenia Vomvoridi-Ivanovic

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It is broadly assumed that participation in field experiences will help pre-service teachers (PSTs) bridge theory to practice. However, this is often not the case since PSTs who are placed in classrooms with large numbers of students from diverse linguistic, cultural, racial, and ethnic backgrounds (culturally diverse students (CDS)) usually observe ineffective mathematics teaching practices that are in contrast to those discussed in their teacher preparation program. Over the past decades, the educational research community has paid increasing attention to investigating out-of-school learning contexts and how participation in such contexts can contribute to the achievement of underrepresented groups in Science, Technology, Engineering, and mathematics (STEM) education and their expanded participation in STEM fields. In addition, several research studies have shown that students display different kinds of mathematical behaviors and discourse practices in out-of-school contexts than they do in the typical mathematics classroom since they draw from a variety of linguistic and cultural resources to negotiate meanings and participate in joint problem solving. However, almost no attention has been given to exploring these contexts as field experiences for pre-service mathematics teachers. The purpose of this study was to explore how participation in a community based after-school field experience promotes understanding of the content pedagogy concepts introduced in elementary mathematics methods courses, particularly as they apply to teaching mathematics to CDS. This study draws upon a situated, socio-cultural theory of teacher learning that centers on the concept of learning as situated social practice, which includes discourse, social interaction, and participation structures. Consistent with exploratory case study methodology, qualitative methods were employed to investigate how a cohort of twelve participating pre-service teacher's approach to pedagogy and their conversations around teaching and learning mathematics to CDS evolved through their participation in the after-school field experience, and how they connected the content discussed in their mathematics methods course with their interactions with the CDS in the after-school. Data were collected over a period of one academic year from the following sources: (a) audio recordings of the PSTs' interactions with the students during the after-school sessions, (b) PSTs' after-school field-notes, (c) audio-recordings of weekly methods course meetings, and (d) other document data (e.g., PST and student generated artifacts, PSTs' written course assignments). The findings of this study reveal that the PSTs benefitted greatly through their participation in the after-school field experience. Specifically, after-school participation promoted a deeper understanding of the content pedagogy concepts introduced in the mathematics methods course and gained a greater appreciation for how students learn mathematics with understanding. Further, even though many of PSTs' assumptions about the mathematical abilities of CDS were challenged and PSTs began to view CDSs' cultural and linguistic backgrounds as resources (rather than obstacles) for learning, some PSTs still held negative stereotypes about CDS and teaching and learning mathematics to CDS in particular. Insights gained through this study contribute to a better understanding of how informal mathematics learning contexts may provide a valuable context for pre-service teacher's learning to teach mathematics to CDS.

Keywords: after-school mathematics program, pre-service mathematical education of teachers, qualitative methods, situated socio-cultural theory, teaching culturally diverse students

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Authors: Steven W. Carruthers

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The purpose of the present research is to equate two test forms as part of a study to evaluate the educational effectiveness of the ARTé: Mecenas art history learning game. The researcher applied Item Response Theory (IRT) procedures to calculate item, test, and mean-sigma equating parameters. With the sample size n=134, test parameters indicated “good” model fit but low Test Information Functions and more acute than expected equating parameters. Therefore, the researcher applied equipercentile equating and linear equating to raw scores and compared the equated form parameters and effect sizes from each method. Item scaling in IRT enables the researcher to select a subset of well-discriminating items. The mean-sigma step produces a mean-slope adjustment from the anchor items, which was used to scale the score on the new form (Form R) to the reference form (Form Q) scale. In equipercentile equating, scores are adjusted to align the proportion of scores in each quintile segment. Linear equating produces a mean-slope adjustment, which was applied to all core items on the new form. The study followed a quasi-experimental design with purposeful sampling of students enrolled in a college level art history course (n=134) and counterbalancing design to distribute both forms on the pre- and posttests. The Experimental Group (n=82) was asked to play ARTé: Mecenas online and complete Level 4 of the game within a two-week period; 37 participants completed Level 4. Over the same period, the Control Group (n=52) did not play the game. The researcher examined between group differences from post-test scores on test Form Q and Form R by full-factorial Two-Way ANOVA. The raw score analysis indicated a 1.29% direct effect of form, which was statistically non-significant but may be practically significant. The researcher repeated the between group differences analysis with all three equating methods. For the IRT mean-sigma adjusted scores, form had a direct effect of 8.39%. Mean-sigma equating with a small sample may have resulted in inaccurate equating parameters. Equipercentile equating aligned test means and standard deviations, but resultant skewness and kurtosis worsened compared to raw score parameters. Form had a 3.18% direct effect. Linear equating produced the lowest Form effect, approaching 0%. Using linearly equated scores, the researcher conducted an ANCOVA to examine the effect size in terms of prior knowledge. The between group effect size for the Control Group versus Experimental Group participants who completed the game was 14.39% with a 4.77% effect size attributed to pre-test score. Playing and completing the game increased art history knowledge, and individuals with low prior knowledge tended to gain more from pre- to post test. Ultimately, researchers should approach test equating based on their theoretical stance on Classical Test Theory and IRT and the respective  assumptions. Regardless of the approach or method, test equating requires a representative sample of sufficient size. With small sample sizes, the application of a range of equating approaches can expose item and test features for review, inform interpretation, and identify paths for improving instruments for future study.

Keywords: effectiveness, equipercentile equating, IRT, learning games, linear equating, mean-sigma equating

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Christiane Paulus

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Post-Islamism assumes that the Quran should not be viewed in terms of what Lyotard identifies as a ‘meta-narrative'. However, its socio-ethical content can be viewed as critical of power discourse (Foucault). Practicing religion seems to be limited to rites and individual spirituality, taqwa. Alternatively, can we build on Muhammad Abduh's classic-modern reform and develop it through a postmodernist frame? This is the main question of this study. Through his general and vague remarks on the context of the Quran, Abduh was the first to refer to the historical and cultural distance of the text as an obstacle for interpretation. His application, however, corresponded to the modern absolute idea of authentic sharia. He was followed by Amin al-Khuli, who hermeneutically linked the content of the Quran to the theory of evolution. Fazlur Rahman and Nasr Hamid abu Zeid remain reluctant to go beyond the general level in terms of context. The hermeneutic circle, therefore, persists in challenging, how to get out to overcome one’s own assumptions. The insight into and the acceptance of the lasting ambivalence of understanding can be grasped as a postmodern approach; it is documented in Derrida's discovery of the shift in text meanings, difference, also in Lyotard's theory of différend. The resulting mixture of meanings (Wolfgang Welsch) can be read together with the classic ambiguity of the premodern interpreters of the Quran (Thomas Bauer). Confronting hermeneutic difficulties in general, Niklas Luhmann proves every description an attribution, tautology, i.e., remaining in the circle. ‘De-tautologization’ is possible, namely by analyzing the distinctions in the sense of objective, temporal and social information that every text contains. This could be expanded with the Kantian aesthetic dimension of reason (critique of pure judgment) corresponding to the iʽgaz of the Coran. Luhmann asks, ‘What distinction does the observer/author make?’ Quran as a speech from God to the first listeners could be seen as a discourse responding to the problems of everyday life of that time, which can be viewed as the general goal of the entire Qoran. Through reconstructing koranic Lifeworlds (Alfred Schütz) in detail, the social structure crystallizes the socio-economic differences, the enormous poverty. The koranic instruction to provide the basic needs for the neglected groups, which often intersect (old, poor, slaves, women, children), can be seen immediately in the text. First, the references to lifeworlds/social problems and discourses in longer koranic passages should be hypothesized. Subsequently, information from the classic commentaries could be extracted, the classical Tafseer, in particular, contains rich narrative material for reconstructing. By selecting and assigning suitable, specific context information, the meaning of the description becomes condensed (Clifford Geertz). In this manner, the text gets necessarily an alienation and is newly accessible. The socio-ethical implications can thus be grasped from the difference of the original problem and the revealed/improved order/procedure; this small step can be materialized as such, not as an absolute solution but as offering plausible patterns for today’s challenges as the Agenda 2030.

Keywords: postmodern hermeneutics, condensed description, sociological approach, small steps of reform

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Authors: Turky Al-Qahtani, Roustem Miftahof

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Introduction: Under common assumptions of excitabi-lity, morphological (cellular) homogeneity, and spatial structural anomalies added as required, it has been shown that biological systems are able to display travelling wave dynamics. Being not self-sustainable, existence depends on the electrophysiological state of transmembrane ion channels and it requires an extrinsic/intrinsic periodic source. However, organs in the body are highly multicellular, heterogeneous, and their functionality is the outcome of electro-mechanical conjugation, rather than excitability only. Thus, peristalsis in the gut relies on spatiotemporal myoelectrical pattern formations between the mechanical, represented by smooth muscle cells (SM), and the control, comprised of a chain of primary sensory and motor neurones, components. Synaptically linked through the afferent and efferent pathways, they form a functional unit (FU) of the gut. Aims: These are: i) to study numerically the complex dynamics, and ii) to investigate the possibility of self-sustained myoelectrical activity in the FU. Methods: The FU recreates the following sequence of physiological events: deformation of mechanoreceptors of located in SM; generation and propagation of electrical waves of depolarisation - spikes - along the axon to the soma of the primary neurone; discharge of the primary neurone and spike propagation towards the motor neurone; burst of the motor neurone and transduction of spikes to SM, subsequently producing forces of contraction. These are governed by a system of nonlinear partial and ordinary differential equations being a modified version of the Hodgkin-Huxley model and SM fibre mechanics. In numerical experiments; the source of excitation is mechanical stretches of SM at a fixed amplitude and variable frequencies. Results: Low frequency (0.5 < v < 2 Hz) stimuli cause the propagation of spikes in the neuronal chain and, finally, the generation of active forces by SM. However, induced contractions are not sufficient to initiate travelling wave dynamics in the control system. At frequencies, 2 < v < 4 Hz, multiple low amplitude and short-lasting contractions are observed in SM after the termination of stretching. For frequencies (0.5 < v < 4 Hz), primary and sensory neurones demonstrate strong connectivity and coherent electrical activity. Significant qualitative and quantitative changes in dynamics of myoelectical patterns with a transition to a self-organised mode are recorded with the high degree of stretches at v = 4.5 Hz. Increased rates of deformation lead to the production of high amplitude signals at the mechanoreceptors with subsequent self-sustained excitation within the neuronal chain. Remarkably, the connection between neurones weakens resulting in incoherent firing. Further increase in a frequency of stimulation (v > 4.5 Hz) has a detrimental effect on the system. The mechanical and control systems become disconnected and exhibit uncoordinated electromechanical activity. Conclusion: To our knowledge, the existence of periodic activity in a multicellular, functionally heterogeneous biological system with mechano-electrical dynamics, such as the FU, has been demonstrated for the first time. These findings support the notion of possible peristalsis in the gut even in the absence of intrinsic sources - pacemaker cells. Results could be implicated in the pathogenesis of intestinal dysrythmia, a medical condition associated with motor dysfunction.

Keywords: complex dynamics, functional unit, the gut, dysrythmia

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Authors: Keith Schofield, Garry R. Prentice

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The perceptions of the work an academic does, and the environment in which they do it, contributes to the professional identity of that academic. In turn, this has implications for the level of involvement they have in their job, their satisfaction, and their work product. This research explores academic identities in British and Irish institutions and considers the complex interplay between identity, practice, and participation. Theoretical assumptions made in this paper assert that meaningful work has positive effects on work pride, organisational commitment, organisational citizenship, and motivation; when employees participate enthusiastically they are likely to be more engaged, more successful, and more satisfied. Further examination is given to the context in which this participation happens; the nature of institutional process, management, and relationships with colleagues, team members, and students is considered. The present study follows a mixed-methods approach to explore work satisfaction constructs in a number of academic contexts in the UK and Ireland. The quantitative component of this research (Convenience Sample: 155 academics, and support/ administrative staff; 36.1% male, 63.9% female; 60.8% academic staff, 39.2% support/ administration staff; across a number of universities in the UK and Ireland) was based on an established emotional labour model and was tested across gender groups, job roles, and years of service. This was complimented by qualitative semi-structured interviews (Purposive Sample: 10 academics, and 5 support/ administrative staff across the same universities in the UK and Ireland) to examine various themes including values within academia, work conditions, professional development, and transmission of knowledge to students. Experiences from both academic and support perspectives were sought in order to gain a holistic view of academia and to provide an opportunity to explore the dynamic of the academic/administrator relationship within the broader institutional context. The quantitative emotional labour model, tested via a path analysis, provided a robust description of the relationships within the data. The significant relationships found within the quantitative emotional labour model included a link between non-expression of true feelings resulting in emotional labourious work and lower levels of intrinsic motivation and higher levels of extrinsic motivation. Higher levels of intrinsic motivation also linked positively to work pride. These findings were further explored in the qualitative elements of the research where themes emerged including the disconnection between faculty management and staff, personal fulfilment and the friction between the identities of teacher, researcher/ practitioner and administrator. The implications of the research findings from this study are combined and discussed in relation to possible identity-related and emotional labour management-related interventions. Further, suggestions are made to institutions concerning the application of these findings including the development of academic practices, with specific reference to the duality of identity required to service the combined teacher/ researcher role. Broader considerations of the paper include how individuals and institutions may engage with the changing nature of students-as-consumers as well as a recommendation to centralise personal fulfillment through the development of professional academic identities.

Keywords: academic work, emotional labour, identity friction, mixed methods

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Authors: Ichiro Takahashi

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One of the major assumptions of mainstream macroeconomics is the path independence of capital stock. This paper challenges this assumption by employing an agent-based approach. The simulation results showed the existence of multiple "quasi-steady state" equilibria of the capital stock, which may cast serious doubt on the validity of the assumption. The finding would give a better understanding of many phenomena that involve hysteresis, including the causes of poverty. The "market-clearing view" has been widely shared among major schools of macroeconomics. They understand that the capital stock, the labor force, and technology, determine the "full-employment" equilibrium growth path and demand/supply shocks can move the economy away from the path only temporarily: the dichotomy between the short-run business cycles and the long-run equilibrium path. The view then implicitly assumes the long-run capital stock to be independent of how the economy has evolved. In contrast, "Old Keynesians" have recognized fluctuations in output as arising largely from fluctuations in real aggregate demand. It will then be an interesting question to ask if an agent-based macroeconomic model, which is known to have path dependence, can generate multiple full-employment equilibrium trajectories of the capital stock in the super-long run. If the answer is yes, the equilibrium level of capital stock, an important supply-side factor, would no longer be independent of the business cycle phenomenon. This paper attempts to answer the above question by using the agent-based macroeconomic model developed by Takahashi and Okada (2010). The model would serve this purpose well because it has neither population growth nor technology progress. The objective of the paper is twofold: (1) to explore the causes of long-term business cycle, and (2) to examine the super-long behaviors of the capital stock of full-employment economies. (1) The simulated behaviors of the key macroeconomic variables such as output, employment, real wages showed widely diversified macro-economies. They were often remarkably stable but exhibited both short-term and long-term fluctuations. The long-term fluctuations occur through the following two adjustments: the quantity and relative cost adjustments of capital stock. The first one is obvious and assumed by many business cycle theorists. The reduced aggregate demand lowers prices, which raises real wages, thereby decreasing the relative cost of capital stock with respect to labor. (2) The long-term business cycles/fluctuations were synthesized with the hysteresis of real wages, interest rates, and investments. In particular, a sequence of the simulation runs with a super-long simulation period generated a wide range of perfectly stable paths, many of which achieved full employment: all the macroeconomic trajectories, including capital stock, output, and employment, were perfectly horizontal over 100,000 periods. Moreover, the full-employment level of capital stock was influenced by the history of unemployment, which was itself path-dependent. Thus, an experience of severe unemployment in the past kept the real wage low, which discouraged a relatively costly investment in capital stock. Meanwhile, a history of good performance sometimes brought about a low capital stock due to a high-interest rate that was consistent with a strong investment.

Keywords: agent-based macroeconomic model, business cycle, hysteresis, stability

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Authors: Marina Nechayeva, Malgorzata Marciniak, Vladimir Przhebelskiy, A. Dragutan, S. Lamichhane, S. Oikawa

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This presentation is a progress report on a faculty-student research collaboration at CUNY LaGuardia Community College (LaGCC) aimed at designing a small horizontal axis wind turbine optimized for the wind patterns on the roof of our campus. Our project combines statistical and engineering research. Our wind modeling protocol is based upon a recent wind study by a faculty-student research group at MIT, and some of our blade design methods are adopted from a senior engineering project at CUNY City College. Our use of genetic algorithms has been inspired by the work on small wind turbines’ design by David Wood. We combine these diverse approaches in our interdisciplinary project in a way that has not been done before and improve upon certain techniques used by our predecessors. We employ several estimation methods to determine the best fitting parametric probability distribution model for the local wind speed data obtained through correlating short-term on-site measurements with a long-term time series at the nearby airport. The model serves as a foundation for engineering research that focuses on adapting and implementing genetic algorithms (GAs) to engineering optimization of the wind turbine design using Blade Element Momentum Theory. GAs are used to create new airfoils with desirable aerodynamic specifications. Small scale models of best performing designs are 3D printed and tested in the wind tunnel to verify the accuracy of relevant calculations. Genetic algorithms are applied to selected airfoils to determine the blade design (radial cord and pitch distribution) that would optimize the coefficient of power profile of the turbine. Our approach improves upon the traditional blade design methods in that it lets us dispense with assumptions necessary to simplify the system of Blade Element Momentum Theory equations, thus resulting in more accurate aerodynamic performance calculations. Furthermore, it enables us to design blades optimized for a whole range of wind speeds rather than a single value. Lastly, we improve upon known GA-based methods in that our algorithms are constructed to work with XFoil generated airfoils data which enables us to optimize blades using our own high glide ratio airfoil designs, without having to rely upon available empirical data from existing airfoils, such as NACA series. Beyond its immediate goal, this ongoing project serves as a training and selection platform for CUNY Research Scholars Program (CRSP) through its annual Aerodynamics and Wind Energy Research Seminar (AWERS), an undergraduate summer research boot camp, designed to introduce prospective researchers to the relevant theoretical background and methodology, get them up to speed with the current state of our research, and test their abilities and commitment to the program. Furthermore, several aspects of the research (e.g., writing code for 3D printing of airfoils) are adapted in the form of classroom research activities to enhance Calculus sequence instruction at LaGCC.

Keywords: engineering design optimization, genetic algorithms, horizontal axis wind turbine, wind modeling

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Authors: Rigas Kotsakis, Nikolaos Tsigilis, Vasilis Grammatikopoulos, Evridiki Zachopoulou

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During the last decade, Greece has faced an unprecedented financial crisis, affecting various aspects and functionalities of Higher Education. Besides the restricted funding of academic institutions, the students and their families encountered economical difficulties that undoubtedly influenced the effective completion of their studies. In this context, a fairly large number of students in Alexander campus of International Hellenic University (IHU) delay, interrupt, or even abandon their studies, especially when they come from low-income families, belong to sensitive social or special needs groups, they have different cultural origins, etc. For this reason, a European project, named “Umbrella”, was initiated aiming at providing the necessary psychological support and counseling, especially to disadvantaged students, towards the completion of their studies. To this end, a network of various academic members (academic staff and students) from IHU, namely iMentor, were implicated in different roles. Specifically, experienced academic staff trained students to serve as intermediate links for the integration and educational support of students that fall into the aforementioned sensitive social groups and face problems for the completion of their studies. The main idea of the project is held upon its person-centered character, which facilitates direct student-to-student communication without the intervention of the teaching staff. The backbone of the iMentors network are senior students that face no problem in their academic life and volunteered for this project. It should be noted that there is a provision from the Umbrella structure for substantial and ethical rewards for their engagement. In this context, a well-defined, stringent methodology was implemented for the evaluation of the extent of the problem in IHU and the detection of the profile of the “candidate” disadvantaged students. The first phase included two steps, (a) data collection and (b) data cleansing/ preprocessing. The first step involved the data collection process from the Secretary Services of all Schools in IHU, from 1980 to 2019, which resulted in 96.418 records. The data set included the School name, the semester of studies, a student enrolling criteria, the nationality, the graduation year or the current, up-to-date academic state (still studying, delayed, dropped off, etc.). The second step of the employed methodology involved the data cleansing/preprocessing because of the existence of “noisy” data, missing and erroneous values, etc. Furthermore, several assumptions and grouping actions were imposed to achieve data homogeneity and an easy-to-interpret subsequent statistical analysis. Specifically, the duration of 40 years recording was limited to the last 15 years (2004-2019). In 2004 the Greek Technological Institutions were evolved into Higher Education Universities, leading into a stable and unified frame of graduate studies. In addition, the data concerning active students were excluded from the analysis since the initial processing effort was focused on the detection of factors/variables that differentiated graduate and deleted students. The final working dataset included 21.432 records with only two categories of students, those that have a degree and those who abandoned their studies. Findings of the first phase are presented across faculties and further discussed.

Keywords: higher education, students support, economic crisis, mentoring

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Authors: Ajay Ivan

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This paper focuses on the cultural politics of health-promotional and disease-preventive pedagogic practices in the context of the national tobacco control programme in India. Tobacco consumption is typically problematised as a paradox: tobacco poses objective health risks such as cancer and heart disease, but its production, sale and export contribute significantly to state revenue. A blanket ban on tobacco products, therefore, is infeasible though desirable. Instead, initiatives against tobacco use have prioritised awareness creation and behaviour change to reduce its demand. This paper argues that public health communication is not, as commonly assumed, an apolitical and neutral transmission of disease-preventive information. Drawing on Michel Foucault’s concept of governmentality, it examines such campaigns as techniques of disciplining people rather than coercing them to give up tobacco use, which would be both impractical and counter-productive. At the level of the population, these programmes constitute a security mechanism that reduces risks without eliminating them, so as to ensure an optimal level of public health without hampering the economy. Anti-tobacco pedagogy thus aligns with a contemporary paradigm of health that emphasises risk-assessment and lifestyle management as tools of governance, using pedagogic techniques to teach people how to be healthy. The paper analyses the pictorial health warnings on tobacco packets and anti-tobacco advertisements in movie theatres mandated by the state, along with awareness-creation messages circulated by anti-tobacco advocacy groups in India, to show how they discursively construct tobacco and its consumption as a health risk. Smoking is resignified from a pleasurable and sociable practice to a deadly addiction that jeopardises the health of those who smoke and those who passively inhale the smoke. While disseminating information about the health risks of tobacco, these initiatives employ emotional and affective techniques of persuasion to discipline tobacco users. They incite fear of death and of social ostracism to motivate behaviour change, complementing their appeals to reason. Tobacco is portrayed as a grave moral danger to the family and a detriment to the vitality of the nation, such that using it contradicts one’s duties as a parent or citizen. Awareness programmes reproduce prevailing societal assumptions about health and disease, normalcy and deviance, and proper and improper conduct. Pedagogy thus functions as an apparatus of public health governance, recruiting subjects as volunteers in their own regulation and aligning their personal goals and aspirations to the objectives of tobacco control. The paper links this calculated management of subjectivity and the self-responsibilisation of the pedagogic subject to a distinct mode of neoliberal civic governance in contemporary India. Health features prominently in this mode of governance that serves the biopolitical obligation of the state as laid down in Article 39 of the Constitution, which includes a duty to ensure the health of its citizens. Insofar as the health of individuals is concerned, the problem is how to balance this duty of the state with the fundamental right of the citizen to choose how to live. Public health pedagogy, by directing the citizen’s ‘free’ choice without unduly infringing upon it, offers a tactical solution.

Keywords: public health communication, pedagogic power, tobacco control, neoliberal governance

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: Alejandra Martin-Dominguez, Javier Canto-Rios, Velitchko Tzatchkov

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Electrocoagulation is a water treatment technology that consists of generating coagulant species in situ by electrolytic oxidation of sacrificial anode materials triggered by electric current. It removes suspended solids, heavy metals, emulsified oils, bacteria, colloidal solids and particles, soluble inorganic pollutants and other contaminants from water, offering an alternative to the use of metal salts or polymers and polyelectrolyte addition for breaking stable emulsions and suspensions. The method essentially consists of passing the water being treated through pairs of consumable conductive metal plates in parallel, which act as monopolar electrodes, commonly known as ‘sacrificial electrodes’. Physicochemical, electrochemical and hydraulic processes are involved in the efficiency of this type of treatment. While the physicochemical and electrochemical aspects of the technology have been extensively studied, little is known about the influence of the hydraulics. However, the hydraulic process is fundamental for the reactions that take place at the electrode boundary layers and for the coagulant mixing. Electrocoagulation reactors can be open (with free water surface) and closed (pressurized). Independently of the type of rector, hydraulic head loss is an important factor for its design. The present work focuses on the study of the total hydraulic head loss and flow velocity and pressure distribution in electrocoagulation reactors with single or multiple concentric annular cross sections. An analysis of the head loss produced by hydraulic wall shear friction and accessories (minor head losses) is presented, and compared to the head loss measured on a semi-pilot scale laboratory model for different flow rates through the reactor. The tests included laminar, transitional and turbulent flow. The observed head loss was compared also to the head loss predicted by several known conceptual theoretical and empirical equations, specific for flow in concentric annular pipes. Four single concentric annular cross section and one multiple concentric annular cross section reactor configuration were studied. The theoretical head loss resulted higher than the observed in the laboratory model in some of the tests, and lower in others of them, depending also on the assumed value for the wall roughness. Most of the theoretical models assume that the fluid elements in all annular sections have the same velocity, and that flow is steady, uniform and one-dimensional, with the same pressure and velocity profiles in all reactor sections. To check the validity of such assumptions, a computational fluid dynamics (CFD) model of the concentric annular pipe reactor was implemented using the ANSYS Fluent software, demonstrating that pressure and flow velocity distribution inside the reactor actually is not uniform. Based on the analysis, the equations that predict better the head loss in single and multiple annular sections were obtained. Other factors that may impact the head loss, such as the generation of coagulants and gases during the electrochemical reaction, the accumulation of hydroxides inside the reactor, and the change of the electrode material with time, are also discussed. The results can be used as tools for design and scale-up of electrocoagulation reactors, to be integrated into new or existing water treatment plants.

Keywords: electrocoagulation reactors, hydraulic head loss, concentric annular pipes, computational fluid dynamics model

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Authors: Antonis Skouloudis, Konstantinos Evangelinos, Walter Leal-Filho, Panagiotis Vouros, Ioannis Nikolaou

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Organizational resilience capacity to extreme weather events (EWEs) has sparked a growth in scholarly attention over the past decade as an essential aspect in business continuity management, with supporting evidence for this claim to suggest that it retains a key role in successful responses to adverse situations, crises and shocks. Small and medium-sized enterprises (SMEs) are more vulnerable to face floods compared to their larger counterparts, so they are disproportionately affected by such extreme weather events. The limited resources at their disposal, the lack of time and skills all conduce to inadequate preparedness to challenges posed by floods. SMEs tend to plan in the short-term, reacting to circumstances as they arise and focussing on their very survival. Likewise, they share less formalised structures and codified policies while they are most usually owner-managed, resulting in a command-and-control management culture. Such characteristics result in them having limited opportunities to recover from flooding and quickly turnaround their operation from a loss making to a profit making one. Scholars frame the capacity of business entities to be resilient upon an EWE disturbance (such as flash floods) as the rate of recovery and restoration of organizational performance to pre-disturbance conditions, the amount of disturbance (i.e. threshold level) a business can absorb before losing structural and/or functional components that will alter or cease operation, as well as the extent to which the organization maintains its function (i.e. impact resistance) before performance levels are driven to zero. Nevertheless, while it seems to be accepted as an essential trait of firms effectively transcending uncertain conditions, research deconstructing the enabling conditions and/or inhibitory factors of SMEs resilience capacity to natural hazards is still sparse, fragmentary and mostly fuelled by anecdotal evidence or normative assumptions. Focusing on the individual level of analysis, i.e. the individual enterprise and its endeavours to succeed, the emergent picture from this relatively new research strand delineates the specification of variables, conceptual relationships or dynamic boundaries of resilience capacity components in an attempt to provide prescriptions for policy-making as well as business management. This study will present the development of a flood resilience capacity index (FRCI) and its application to Greek SMEs. The proposed composite indicator pertains to cognitive, behavioral/managerial and contextual factors that influence an enterprise’s ability to shape effective responses to meet flood challenges. Through the proposed indicator-based approach, an analytical framework is set forth that will help standardize such assessments with the overarching aim of reducing the vulnerability of SMEs to flooding. This will be achieved by identifying major internal and external attributes explaining resilience capacity which is particularly important given the limited resources these enterprises have and that they tend to be primary sources of vulnerabilities in supply chain networks, generating Single Points of Failure (SPOF).

Keywords: Floods, Small & Medium-Sized enterprises, organizational resilience capacity, index development

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Authors: Dorota Winnicka-Jasłowska, Małgorzata Jastrzębska, Jan Kaczmarczyk, Beata Łaźniewska-Piekarczyk, Piotr Skóra, Beata Kobiałko, Agata Kołodziej, Błażej Mól, Ewelina Lasyk, Karolina Brzęczek, Michał Król

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Creating economical, comfortable, and healthy buildings which respect the environment is a necessity resulting from legal regulations, but it is also a response to the expectations of a modern investor. Developing the concept of a residential house based on the 4E and the 2+2+(1) IDEAs is a complex process that requires specialist knowledge of many trades and requires adaptation of comprehensive solutions. IDEA 4E assumes the use of energy-saving, ecological, ergonomics, and economic solutions. In addition, IDEA 2+2+(1) assuming appropriate surface and functional-spatial solutions for a family at different stages of a building's life, i.e. 2, 4, or 5 members, enforces certain flexibility of the designed building, which may change with the number and age of its users. The building should therefore be easy to rearrange or expand. The task defined in this way was carried out by an interdisciplinary team of students of the Silesian University of Technology as part of PBL. The team consisted of 6 undergraduate and graduate students representing the following faculties: 3 students of architecture, 2 civil engineering students, and 1 student of environmental engineering. The work of the team was supported by 3 academic teachers representing the above-mentioned faculties and additional experts. The project was completed in one semester. The article presents the successive stages of the project. At first pre-design studies were carried out. They allowed to define the guidelines for the project. For this purpose, the "Model house" questionnaire was developed. The questions concerned determining the utility needs of a potential family that would live in a model house - specifying the types of rooms, their size, and equipment. A total of 114 people participated in the study. The answers to the questions in the survey helped to build the functional programme of the designed house. Other research consisted in the search for optimal technological and construction solutions and the most appropriate building materials based mainly on recycling. Appropriate HVAC systems responsible for the building's microclimate were also selected, i.e. low, temperature heating, mechanical ventilation, and the use of energy from renewable sources was planned so as to obtain a nearly zero-energy building. Additionally, rainwater retention and its local use were planned. The result of the project was a design of a model residential building that meets the presented assumptions. A 3D VR spatial model of the designed building and its surroundings was also made. The final result was the organization of an exhibition for students and the academic community. Participation in the interdisciplinary project allowed the project team members to better understand the consequences of the adopted solutions for achieving the assumed effect and the need to work out a compromise. The implementation of the project made all its participants aware of the importance of cooperation as well as systematic and clear communication. The need to define milestones and their consistent enforcement is an important element guaranteeing the achievement of the intended end result. The implementation of PBL enables students to the acquire competences important in their future professional work.

Keywords: architecture and urban planning, civil engineering, environmental engineering, project-based learning, sustainable building

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Authors: Daniele Lerede, Gianvito Colucci, Matteo Nicoli, Laura Savoldi

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The European Green Deal is the first internationally agreed set of measures to contrast climate change and environmental degradation. Besides the main target of reducing emissions by at least 55% by 2030, it sets the target of accompanying European countries through an energy transition to make the European Union into a modern, resource-efficient, and competitive net-zero emissions economy by 2050, decoupling growth from the use of resources and ensuring a fair adaptation of all social categories to the transformation process. While the general purpose to allow the realization of the purposes of the Green Deal already dates back to 2019, strategies and policies keep being developed coping with recent circumstances and achievements. However, general long-term measures like the Circular Economy Action Plan, the proposals to shift from fossil natural gas to renewable and low-carbon gases, in particular biomethane and hydrogen, and to end the sale of gasoline and diesel cars by 2035, will all have significant effects on energy supply and demand evolution across the next decades. The interactions between energy supply and demand over long-term time frames are usually assessed via energy system models to derive useful insights for policymaking and to address technological choices and research and development. TEMOA-Europe is a newly developed energy system optimization model instance based on the minimization of the total cost of the system under analysis, adopting a technologically integrated, detailed, and explicit formulation and considering the evolution of the system in partial equilibrium in competitive markets with perfect foresight. TEMOA-Europe is developed on the TEMOA platform, an open-source modeling framework totally implemented in Python, therefore ensuring third-party verification even on large and complex models. TEMOA-Europe is based on a single-region representation of the European Union and EFTA countries on a time scale between 2005 and 2100, relying on a set of assumptions for socio-economic developments based on projections by the International Energy Outlook and a large technological dataset including 7 sectors: the upstream and power sectors for the production of all energy commodities and the end-use sectors, including industry, transport, residential, commercial and agriculture. TEMOA-Europe also includes an updated hydrogen module considering its production, storage, transportation, and utilization. Besides, it can rely on a wide set of innovative technologies, ranging from nuclear fusion and electricity plants equipped with CCS in the power sector to electrolysis-based steel production processes and steel in the industrial sector – with a techno-economic characterization based on public literature – to produce insightful energy scenarios and especially to cope with the very long analyzed time scale. The aim of this work is to examine in detail the scheme of measures and policies for the realization of the purposes of the Green Deal and to transform them into a set of constraints and new socio-economic development pathways. Based on them, TEMOA-Europe will be used to produce and comparatively analyze scenarios to assess the consequences of Green Deal-related measures on the future evolution of the energy mix over the whole energy system in an economic optimization environment.

Keywords: European Green Deal, energy system optimization modeling, scenario analysis, TEMOA-Europe

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Authors: Roseanne Sharon Borromeo, Mariana Carvalho, Mariia Karizhenskaia

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Healthcare providers need to comprehend cultural diversity for optimal patient-centered care, especially near the end of life. Although a universal method for navigating cultural differences would be ideal, culture’s high complexity makes this strategy impossible. Adding cultural humility, a process of self-reflection to understand personal and systemic biases and humbly acknowledging oneself as a learner when it comes to understanding another's experience leads to a meaningful process in palliative care generating respectful, honest, and trustworthy relationships. This study is a systematic review of the literature on cultural humility in palliative care research and best practices. Race, religion, language, values, and beliefs can affect an individual’s access to palliative care, underscoring the importance of culture in palliative care. Cultural influences affect end-of-life care perceptions, impacting bereavement rituals, decision-making, and attitudes toward death. Cultural factors affecting the delivery of care identified in a scoping review of Canadian literature include cultural competency, cultural sensitivity, and cultural accessibility. As the different parts of the world become exponentially diverse and multicultural, healthcare providers have been encouraged to give culturally competent care at the bedside. Therefore, many organizations have made cultural competence training required to expose professionals to the special needs and vulnerability of diverse populations. Cultural competence is easily standardized, taught, and implemented; however, this theoretically finite form of knowledge can dangerously lead to false assumptions or stereotyping, generating poor communication, loss of bonds and trust, and poor healthcare provider-patient relationship. In contrast, Cultural humility is a dynamic process that includes self-reflection, personal critique, and growth, allowing healthcare providers to respond to these differences with an open mind, curiosity, and awareness that one is never truly a “cultural” expert and requires life-long learning to overcome common biases and ingrained societal influences. Cultural humility concepts include self-awareness and power imbalances. While being culturally competent requires being skilled and knowledgeable in one’s culture, being culturally humble involves the sometimes-uncomfortable position of healthcare providers as students of the patient. Incorporating cultural humility emphasizes the need to approach end-of-life care with openness and responsiveness to various cultural perspectives. Thus, healthcare workers need to embrace lifelong learning in individual beliefs and values on suffering, death, and dying. There have been different approaches to this as well. Some adopt strategies for cultural humility, addressing conflicts and challenges through relational and health system approaches. In practice and research, clinicians and researchers must embrace cultural humility to advance palliative care practices, using qualitative methods to capture culturally nuanced experiences. Cultural diversity significantly impacts patient-centered care, particularly in end-of-life contexts. Cultural factors also shape end-of-life perceptions, impacting rituals, decision-making, and attitudes toward death. Cultural humility encourages openness and acknowledges the limitations of expertise in one’s culture. A consistent self-awareness and a desire to understand patients’ beliefs drive the practice of cultural humility. This dynamic process requires practitioners to learn continuously, fostering empathy and understanding. Cultural humility enhances palliative care, ensuring it resonates genuinely across cultural backgrounds and enriches patient-provider interactions.

Keywords: cultural competency, cultural diversity, cultural humility, palliative care, self-awareness

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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