The development of a 10.7-MHz fully balanced current-tunable bandpass filter with Caprio technique

Bandpass filters are integral in modern communication systems for selecting specific frequency ranges to ensure interference-free signal transmission and reception. This paper explores various bandpass filter designs, including those using active inductors, transmission-line unit-cells, microstrip open-loop resonators, and dual-port dual-frequency integration antennas. The focus is on the 10.7-MHz bandpass filter, widely used in FM radio and television systems. The study evaluates current-controlled and balanced designs, analyzing their performance, advantages, and drawbacks. Unique trade-offs in terms of linearity, distortion, temperature sensitivity, and component variations are discussed. Additionally, advancements in filter technology and diverse design options are presented. The paper introduces a novel current-balanced, frequency-adjusted bandpass filter to address odd-order noise issues. This filter aims to achieve high linearity, harmonic distortion attenuation, and the elimination of even-order harmonics. Through synthesis, analysis, simulation, and comparison with traditional filters, the proposed design enhances signal quality and efficiency. The fully-balanced current-tunable bandpass filter with the Caprio technique at 10.7 MHz is developed, exhibiting symmetrical characteristics with lower total harmonic distortion. The circuit’s structure is simple and adaptable for integration, validated through consistent simulation results. The study concludes by emphasizing the constant sensitivity of transistor differential amplifier circuits to the center frequency and the linear relationship between center frequency and adjustable bias current. The suggested transistor and capacitor selection criteria contribute to optimizing the circuit’s performance, aligning with the Caprio technique’s recommendations. Overall, this research presents a promising solution for achieving high-quality signal transmission in contemporary communication system

Knowledge claims in European Union energy policies : Unknown knowns and uncomfortable awareness

Altres ajuts: acords transformatius de la UABUnidad de excelencia María de Maeztu CEX2019-000940-MDespite the concerted efforts of the scientific community and politicians to contain greenhouse gas emissions, the CO level in the atmosphere continues to increase monotonically. This raises the question whether the scientific representations and related knowledge claims used to inform energy policy have been incomplete or incorrect. Are there alternative relevant knowledge claims that have been overlooked or ignored in the discussion of energy policies and if so, why? We answer these questions by elaborating three case studies, energy efficiency improvements, liquid biofuels, and decarbonization of electricity, and using a novel procedure for quality checking policy narratives that is based in post-normal science and developed in the EU project Moving Towards Adaptive Governance in Complexity: Informing NEXUS Security (MAGIC). The focus of our approach is on the coherence of the why (concerns or justifications), what ("solution"), and how ("scientific evidence") of energy policies. We show that for all cases studied alternative knowledge claims, mostly derived from the relatively new field of non-equilibrium thermodynamics, would be available for better informing energy policy, but that they are unknown knowns in the chosen framing of the issues. We conclude that the idea that the various concerns identified in EU energy policy can be solved simultaneously is unrealistic. This idea can only persist by virtue of banishing uncomfortable knowledge and the creation of implausible socio-technical imaginaries. When considering different aspects of the problem and integrating different narratives and knowledge claims, a smooth and painless transition to a zero-carbon economy seems unlikely

Social Media Management Strategies for Organizational Impression Management and their Effect on Public Perception

With the growing importance of social media, companies increasingly rely on social media management tools to analyze social media activities and to professionalize their social media engagement. In this study, we evaluate how social media management tools, as part of an overarching social media strategy, help companies to positively influence the public perception among social media users. A mixed methods approach is applied, where we quantitatively analyze 15 million user-generated Twitter messages containing information about 45 large global companies highly active on Twitter, as well as almost 160 thousand corresponding messages sent from these companies via their corporate Twitter accounts. Additionally, we conducted interviews with six social media experts to gain complementary insights. By these means, we are able to identify significant differences between different social media management strategies and measure the corresponding effects on the public perception. (C) 2015 Elsevier B.V. All rights reserved

An assessment of the effectiveness of using data analytics to predict death claim seasonality and protection policy review lapses in a life insurance company

Data analytics tools are becoming increasingly common in the life insurance industry. This research considers two use cases for predictive analytics in a life insurance company based in Ireland. The first case study relates to the use of time series models to forecast the seasonality of death claim notifications. The baseline model predicted no seasonal variation in death claim notifications over a calendar year. This reflects the life insurance company’s current approach, whereby it is assumed that claims are notified linearly over a calendar year. More accurate forecasting of death claims seasonality would enhance the life insurance company’s cashflow planning and analysis of financial results. The performance of five time series models was compared against the baseline model. The time series models included a simple historical average model, a classical SARIMA model, the Random Forest Regressor and Prophet machine learning models and the LSTM deep learning model. The models were trained on both the life insurance company’s historical death claims data and on Irish population deaths data for the 25-74 age cohort over the same observation periods. The results demonstrated that machine learning time series models were generally more effective than the baseline model in forecasting death claim seasonality. It was also demonstrated that models trained on both Irish population deaths and the life insurance company’s historical death claims could outperform the baseline model. The best forecaster was Facebook’s Prophet model, trained on the life insurance company’s claims data. Each of the models trained on Irish population deaths data outperformed the baseline model. The SARIMA and LSTM consistently underperformed the baseline model when both were trained on death claims data. All models performed better when claims directly related to Covid-19 were removed from the testing data. The second case study relates to the use of classification models to predict protection policy lapse behaviour following a policy review. The life insurance company currently has no method of predicting individual policy lapses, hence the baseline model assumed that all policies had an equal probability of lapsing. More accurate prediction of policy review lapse outcomes would enhance the life insurance company’s profit forecasting ability. It would also provide the company with the opportunity to potentially reduce lapse rates at policy review by tailoring alternative options for certain groups of policyholders. The performance of 12 classification models was assessed against the baseline model - KNN, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra Trees, XGBoost, LightGBM, AdaBoost and Multi-Layer Perceptron (MLP). To address class imbalance in the data, 11 rebalancing techniques were assessed. These included cost-sensitive algorithms (Class Weight Balancing), oversampling (Random Oversampling, ADASYN, SMOTE, Borderline SMOTE), undersampling (Random Undersampling, and Near Miss versions 1 to 3) as well as a combination of oversampling and undersampling (SMOTETomek and SMOTEENN). When combined with rebalancing methods, the predictive capacity of the classification models outperformed the baseline model in almost every case. However, results varied by train/test split and by evaluation metric. Oversampling models performed best on F1 Score and ROC-AUC while SMOTEENN and the undersampling models generated the highest levels of Recall. The top F1 Score was generated by the Naïve Bayes model when combined with SMOTE. The MLP model generated the highest ROC-AUC when combined with BorderlineSMOTE. The results of both case studies demonstrate that data analytics techniques can enhance a life insurance company’s predictive toolkit. It is recommended that further opportunities to enhance the predictive ability of the time series and classification models be explored

Applying Machine Learning to Biological Status (QValues) from Physio-chemical Conditions of Irish Rivers

This thesis evaluates and optimises a variety of predictive models for assessing biological classification status, with an emphasis on water quality monitoring. Grounded in previous pertinent studies, it builds on the findings of (Arrighi and Castelli, 2023) concerning Tuscany’s river catchments, highlighting a solid correlation between river ecological status and parameters like summer climate and land use. They achieved an 80% prediction precision using the Random Forest algorithm, particularly adept at identifying good ecological conditions, leveraging a dataset devoid of chemical data

ERAWATCH Country Reports 2012: Portugal

This analytical country report is one of a series of annual ERAWATCH reports produced for EU Member States and Countries Associated to the Seventh Framework Programme for Research of the European Union (FP7). The main objective of the ERAWATCH Annual Country Reports is to characterise and assess the performance of national research systems and related policies in a structured manner that is comparable across countries. The Country Report 2012 builds on and updates the 2011 edition. The report identifies the structural challenges of the national research and innovation system and assesses the match between the national priorities and the structural challenges, highlighting the latest developments, their dynamics and impact in the overall national context. They further analyse and assess the ability of the policy mix in place to consistently and efficiently tackle these challenges. These reports were originally produced in December 2012, focusing on policy developments over the previous twelve months. The reports were produced by independent experts under direct contract with IPTS. The analytical framework and the structure of the reports have been developed by the Institute for Prospective Technological Studies of the Joint Research Centre (JRC-IPTS) and Directorate General for Research and Innovation with contributions from external experts.JRC.J.2-Knowledge for Growt

Family, society and the individual: determinants of entrepreneurial attitudes among youth in Chennai, South India

High rates of youth unemployment across the world have captured the attention of many world organizations and other policy makers. One policy solution that has been proposed to curb these high rates is encouraging youth entrepreneurship. In this paper, we examine the formation of attitudes that are favorable to entrepreneurship using data from 185 business students in South India. We adopt an approach that tests the relative efficacy of two principal factors in the formation of entrepreneurial attitude, i.e., stocks of youth human/social capital and a series of personality traits. Results from a probit model suggest that the youth’s prior labor market experience, the social capital that youth have accumulated through volunteering, and the social connections that parents have made are all highly predictive of pro-entrepreneurial attitudes; personality traits exert less importance. Implications for these findings are discussed for the creation of strategies that can stimulate entrepreneurship among youth as one way to combat high rates of youth unemployment

Comparative analysis of X-Y-Z generation entrepreneurs in a semi-peripheral EU member country : insights from regularized regression techniques

PURPOSE: The aim of our research is to deeply analyze entrepreneurial dynamics across generations X, Y, and Z, enhancing understanding of generational shifts and offering insights for future tailored entrepreneurship policies and development programs. This study serves as a foundation for stakeholders to address the unique challenges and opportunities presented by each generational cohort.DESIGN/METHODOLOGY/APPROACH: In our paper, we conduct a nuanced comparative analysis of entrepreneurs from Generation X, Y, and Z within a semi-peripheral European Union member state, employing Ridge, Lasso, and Elastic Net regression techniques. Utilizing a sophisticated system-level approach, we devised a quint-segment model capable of encapsulating the generational disparities in a comprehensive manner.FINDINGS: Our findings delineate a pronounced polarization within the sector, highlighting a notable intergenerational coexistence particularly between Generations Y and Z. Despite the distinct socio-economic backgrounds and entrepreneurial approaches prevalent amongst these generational cohorts, there emerges a remarkable alignment in self-perception and economic trust between Generation Y and Z entrepreneurs. Conversely, this shared perspective markedly diverges from that held by Generation X individuals, spotlighting a significant generational schism in the appraisal of the business environment and the evolving role of education and training across these generations.PRACTICAL IMPLICATIONS: In light of emergent entrepreneurial paradigms, it is imperative for policymakers and educational institutions to recalibrate, cognizant of Generations Y and Z's proclivity for informal pedagogical modalities and networking. Business support mechanisms, notably incubators, are enjoined to refine their approaches, accentuating Gen Z's predilection for trust-anchored mentorship. Concurrently, investors and governmental entities must reconfigure strategies, attentive to dynamic sectoral and capital sourcing shifts. As workplace ethos undergoes transformation, enterprises should champion inclusivity, with advisory services emphasizing bespoke, trust-centric advisement.ORIGINALITY/VALUE: The paper presents a novel systemic analysis of entrepreneurial dynamics across generations offering fresh insights particularly on the economic and self-perception dimensions of Generations Y and Z in juxtaposition with Generation X. Through a quintsegment model and five predictive models, the study not only corroborates existing literature but also unveils unique intergenerational discrepancies and convergences, thereby enriching the understanding of generational shifts in entrepreneurial realms. This research holds significant implications for shaping future entrepreneurship policies and tailoring business development programs, emphasizing the importance of recognizing generational nuances in the entrepreneurial ecosystem.peer-reviewe