Innovative Model Research Articles

The Heterogeneous Impact of Market Size on Innovation: Evidence from French Firm-Level Exports

Abstract We analyze how demand conditions faced by a firm in its export markets affect its innovation decisions. We exploit exogenous firm-level export demand shocks and find that firms respond by patenting more; furthermore, this response is driven by the subset of initially more productive firms. The patent response arises two to five years after the shock, highlighting the time required to innovate. In contrast, the demand shock raises contemporaneous sales and employment for all firms regardless of their productivity. This skewed innovation response to common demand shocks arises naturally from a model of endogenous innovation and competition with firm heterogeneity.

Beyond green borders: an innovative model for sustainable transportation in supply chains

Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multi-objective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model’s performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions.

Research on Consumer Willingness to Purchase Time-honored Brand Products Based on TPB Theory in the New Media Environment

In the context of the digital economy, utilizing new media for marketing and brand promotion has become the direction of digital transformation for China’s time-honored brand enterprises. This paper takes consumer research data as a sample and based on the theory of planned behavior (TPB), establishes a mechanism model for the impact of consumer willingness to purchase China’s time-honored brand products in the new media environment. The conclusion of this paper is as follows: First, subjective norms become the most important factor influencing consumer attitudes. Secondly, the level of new media marketing for time-honored brands can significantly influence consumer attitudes and promote the conversion of consumer purchase intention. Finally, perceived behavioral control can significantly influence the attitude of consumers, but it cannot significantly affect their behavior. This paper provides policy recommendations for improving the competitiveness and marketing level of China’s time-honored brands in the new media environment from the aspects of social media construction, new media operation, promoting product innovation and model innovation.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

INNOVATION MODEL OF HUMAN RESOURCES TRAINING AND DEVELOPMENT FOR THE HOTEL INDUSTRY FROM THE ASEAN STANDARD FRAMEWORK: A CASE IN NAKHON RATCHASIMA PROVINCE, THAILAND

From the current global changes and the rapid development of innovation and technology, personnel development according to the previous approach may be insufficient for work. It is necessary to increase the skills of the workforce and improve work efficiency for higher quality. This research aims to study the training and development of human resources in the hotel industry in Nakhon Ratchasima Province at present, to study innovations in training and human resource development in the hotel industry and the ASEAN hotel industry standard framework, and to propose the innovation model for training and development of human resources in the hotel industry from the ASEAN standard framework. The findings suggest implementing the innovation model of resources training and development in the hotel industry by integrating modern technology and training techniques into use in developing staff in various forms that are in accordance with the ASEAN standard framework and hotel service standards to increase the hotel service quality and staff performance. The quantitative approach was employed by using structural equation modeling for path analysis. The data was collected by using questionnaires from 400 samplings from hotel staff in Nakhon Ratchasima Province. The model is consistent with empirical material in statistical significance. It can confirm that seven significant factors directly affect service performance which are job rotation, blended learning, practice training, classroom training, study tour for learning, learning through playing games, and teaching respectively and the new model was proposed for innovative development and training for the hotel staff. This research proposes an innovation process for hotels to implement the use of technology and techniques for human resources training and development. It can increase staff service performance, lead to positive guest experience and satisfaction, and lead to hotel revenue. However, the potential, the different hotel contexts and characteristics, and its organizational culture should be considered for the implementation.

Towards green evolution in urban Egypt: assessing Al Rehab City through LEED-ND and BREEAM-communities frameworks

AbstractThis article provides a comprehensive analysis of the application of green neighborhood rating systems, specifically Leadership in Energy and Environmental Design-Neighborhood Development (LEED-ND) and Building Research Foundation Environmental Assessment Method (BREEAM)-Communities, in the context of Cairo, Egypt, focusing on Al Rehab City, a prominent urban area in New Cairo. The study begins by providing a background on Al-Rehab City, explaining its strategic planning, urban design, and demographic dynamics. It then delves into the current state of sustainable urban planning in Egypt, highlighting the challenges and opportunities that exist in this rapidly urbanizing context. The core of the article includes a comparative analysis of LEED-ND and BREEAM communities, assessing their adaptability and relevance to the Egyptian urban landscape, especially Al Rehab City. It explores many sustainability issues such as smart location, neighborhood pattern and design, green infrastructure, sustainable transportation, energy efficiency, water conservation, waste management, and community engagement. The discussion extends to examine how each of these aspects of sustainability applies to Al Rehab City, complemented by case studies and examples of current sustainable practices in Cairo. Furthermore, the article identifies specific local challenges in implementing these assessment systems, such as economic constraints, policy constraints, and cultural factors. It also suggests opportunities for improvement, including the potential for public–private partnerships, policy reforms, community engagement, technological innovation, and sustainable urban development modeling. In conclusion, the article provides insight into the future prospects of green urban planning in Cairo. The potential impact of adopting comprehensive assessment systems is discussed, with an emphasis on the role of Al Rehab City as a potential model for sustainable urban development in Egypt and the wider MENA region. The article aims to contribute to the discourse on sustainable urban planning, and provide a roadmap for integrating global sustainability standards into the unique context of Egyptian cities.

An improved BiGAN model for anomaly detection in finance

Financial systems play a pivotal role in shaping contemporary society, and the detection of financial anomalies holds immense significance in mitigating the adverse repercussions of market uncertainties on the global economy. In this context, this study presents an innovative LSTM-GANs model, specifically crafted to enhance the detection of anomalies in financial stock markets. The model introduces an "Anomaly Score" as a pivotal metric, which is computed through a combination of factors such as Reconstruction Loss, Latent Space Distance, and Discriminator Score. This composite score provides a quantitative assessment of the anomaly level within the financial data. By applying a predefined threshold to this Anomaly Score, the model efficiently identifies and flags anomalies. In a world where financial markets are increasingly complex and prone to unexpected events, the ability to detect and respond to anomalies swiftly is paramount. This novel LSTM-GANs model offers a promising approach to bolster the accuracy and effectiveness of financial anomaly detection, thereby contributing to the stability and resilience of global financial systems.

Social work collective leadership in a large urban hospital during an era of unprecedented transformational change

This Department of Social Work Services (the Department) is one of the largest hospital social work departments in the country, with almost 600 staff, in the largest of eight hospitals and one medical school of a major health system. The Department’s senior directors (three current and one former) share its innovative and effective collective leadership model that was initially developed in response to a confluence of unprecedented complex events stemming from the Covid-19 pandemic. The experiences of these co-leaders, an evaluation of the model, future internal considerations as its applicability to other health care settings will be discussed.

CHALLENGES AND SUCCESSES IN RURAL ELECTRIFICATION: A REVIEW OF GLOBAL POLICIES AND CASE STUDIES

Rural electrification is a critical aspect of sustainable development, aiming to bridge the energy gap in remote and underserved areas. This paper provides a comprehensive review of global policies and case studies related to challenges and successes in rural electrification. The challenges in rural electrification are multifaceted and often rooted in economic, geographic, and social factors. Limited financial resources, vast and difficult terrains, and sparse population density pose significant hurdles. Additionally, socio-cultural aspects and community dynamics influence the acceptance and sustainability of electrification projects. Understanding and addressing these challenges is crucial for the success of rural electrification initiatives. On the policy front, various nations and international organizations have implemented diverse approaches to tackle these challenges. This review examines the effectiveness of different policy frameworks, exploring their impact on the speed and sustainability of rural electrification. Policies encompass financial incentives, regulatory frameworks, and technology deployment strategies, with an emphasis on fostering public-private partnerships. The paper also delves into case studies from different regions, providing insights into both successful and unsuccessful attempts at rural electrification. Examining these cases helps identify common patterns, lessons learned, and best practices that can inform future initiatives. Successful projects often involve a combination of innovative financing models, community engagement, and the integration of renewable energy sources tailored to local conditions. This paper underscores the importance of a holistic and context-specific approach to rural electrification. Global policymakers, practitioners, and stakeholders can draw valuable lessons from both challenges and successes to refine strategies and enhance the impact of rural electrification initiatives worldwide. As the world continues to strive for universal energy access, this review contributes to the ongoing dialogue on shaping effective policies and practices in the realm of rural electrification.
 Keywords: Rural Electrification, Global Policies, Renewable Energy, Energy Financing, Review.

Exploring the ageing methylome in the model insect, Nasonia vitripennis

BackgroundThe ageing process is a multifaceted phenomenon marked by the gradual deterioration of cellular and organismal functions, accompanied by an elevated susceptibility to diseases. The intricate interplay between genetic and environmental factors complicates research, particularly in complex mammalian models. In this context, simple invertebrate organisms have been pivotal, but the current models lack detectable DNA methylation limiting the exploration of this critical epigenetic ageing mechanism.This study introduces Nasonia vitripennis, the jewel wasp, as an innovative invertebrate model for investigating the epigenetics of ageing. Leveraging its advantages as a model organism and possessing a functional DNA methylation system, Nasonia emerges as a valuable addition to ageing research.ResultsWhole-genome bisulfite sequencing unveiled dynamic alterations in DNA methylation, with differentially methylated CpGs between distinct time points in both male and female wasps. These changes were associated with numerous genes, enriching for functions related to telomere maintenance, histone methylation, and mRNA catabolic processes. Additionally, other CpGs were found to be variably methylated at each timepoint. Sex-specific effects on epigenetic entropy were observed, indicating differential patterns in the loss of epigenetic stability over time. Constructing an epigenetic clock containing 19 CpGs revealed a robust correlation between epigenetic age and chronological age.ConclusionsNasonia vitripennis emerges as a promising model for investigating the epigenetics of ageing, shedding light on the intricate dynamics of DNA methylation and their implications for age-related processes. This research not only expands the repertoire of ageing models but also opens avenues for deeper exploration of epigenetic mechanisms in the context of ageing.

Abstract 4380: Role of kindlin-2 in prostate cancer progression and metastasis

Abstract Prostate cancer (PC) is the leading type of cancer in men accounting for more than 25% of all newly reported cancer cases and remains the second leading cause of cancer-related deaths in men in USA in 2023. The success of early treatments has resulted in a 98% 5-year survival rate for early-stage PC. However, resistance to androgen ablation can lead to metastatic castration-resistant prostate cancer (mCRPC), with a significant drop in the survival rate to ~30%. The metastatic cascade involves changes in adhesive, metabolic and signaling properties of PC cells, allowing them to survive, proliferate and thrive in their new microenvironment. Kindlin-2 (K2), a cytoskeletal adapter protein, is highly expressed in cancer, including PC cells and tumors, contributing to their tumorigenic properties. The present study focuses on three K2 functions relevant to PC metastasis: 1)Reduction in K2 levels, achieved through CRISPR/Cas9, profoundly affected the tumorigenic properties of PC cell lines LNCaP (androgen dependent), DU145, and PC3 (both androgen independent), influencing colony formation, invasion, and adhesion to integrin ligands. While reduction in K2 was observed, K1 and K3 levels in these cell lines was not altered by K2KO. 2)The E3 ubiquitin ligase, Parkin, known for its role in Parkinson’s disease, has emerged as a tumor suppressor inhibiting various cancer hallmarks. Our recently published studies showed that Parkin induces ubiquitination of specific lysines in K2, leading to the hypothesis that Parkin acts as a tumor suppressor by targeting K2 for degradation. Stable over-expression of Parkin inhibited adhesion to fibrinogen (Fg), while loss of function Parkin mutants failed to do so, supporting a role of Parkin in K2 turnover and its impact on mitochondrial dynamics. We are currently investigating the effect of K2-lysines mutants as well as loss of function Parkin mutants on the progression and metastasis of PC tumors in vivo.3)K2's established function is the regulation of integrin activation, affecting adhesion receptors' transition from low to high affinity/avidity states. The K2-integrin axis is pivotal for understanding oncogenic responses, and its role in the prostate microenvironment in PC progression and metastasis is under exploration using our newly established K2-floxed mouse. The K2-floxed mice are currently being crossed with mice harboring prostate-epithelial specific cre under control of the probasin (Pbsn) promoter. These animal studies will shed light on K2's contribution to PC development and progression in vivo, providing insights into its role in the prostate microenvironment. To sum up, this study emphasizes the crucial role of K2 in the tumorigenic properties of PC cells and tumors. By incorporating CRISPR/Cas9 and innovative mouse models, the study seeks to shed the lights on the mechanisms through which K2 contributes to PC development and progression. Citation Format: Lamyae El Khalki, Wei Wang, Neelum A. Yousaf Zai, Justin Szpendyk, Elzbieta Pluskota, Katarzyna Bialkowska, Edward F. Plow, Lucia R. Languino, Dario C. Altieri, Khalid Sossey-Alaoui. Role of kindlin-2 in prostate cancer progression and metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4380.

Micro-Engineered Heart Tissues On-Chip with Heterotypic Cell Composition Display Self-Organization and Improved Cardiac Function.

Advanced in vitro models that recapitulate the structural organization and function of the human heart are highly needed for accurate disease modeling, more predictable drug screening, and safety pharmacology. Conventional3DEngineered Heart Tissues (EHTs) lack heterotypic cell complexity and culture under flow, whereas microfluidic Heart-on-Chip (HoC) models in general lack the 3D configuration and accurate contractile readouts. In this study, an innovative and user-friendly HoC model is developed to overcome these limitations, by culturing human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs), endothelial (ECs)- and smooth muscle cells (SMCs), together with human cardiac fibroblasts (FBs), underflow, leading to self-organized miniaturized micro-EHTs (µEHTs) with a CM-EC interface reminiscent of the physiological capillary lining. µEHTs cultured under flow display enhanced contractile performance and conduction velocity. In addition, the presence of the EC layer altered drug responses in µEHT contraction. This observation suggests a potential barrier-like function of ECs, which may affect the availability of drugs to the CMs. These cardiac models with increased physiological complexity, will pave the way to screen for therapeutic targets and predict drug efficacy.

Spatiotemporal characteristics and driving factor analysis of embodied CO2 emissions in China's building sector

Since the embodied emissions of the building sector play a pivotal role in the total emission profile of developing countries, represented by China, it is crucial to uncover its emission characteristics and the underlying drivers. Most existing studies focus on building operational emissions, and there is a lack of in-depth studies on embodied emissions, especially in spatiotemporal heterogeneity and spatial correlation. Thus, this study calculates embodied emissions of the building sector in China's 29 provinces during 2011–2020 and develops an innovative factor analysis model that integrates the extended STIRPAT model, network analysis, and spatial Durbin model to explore the influence mechanisms of various factors on emissions from local, spillover, overall and temporal perspectives. The empirical results reveal notable spatiotemporal differences and spatial dependence across regions. The area has consistently been the most crucial driver. The building performance demands are increasingly influencing emissions through direct and spillover effects. The slowdown decarbonization of the material industry diminishes its impact. Limited technical improvement and regional shifts of carbon-intensive enterprises have weakened the technological emission reduction effects. Therefore, regulating ineffective construction, discouraging excessive building performance, implementing demand-side material efficiency strategies, bolstering interregional coordination, and developing low-carbon technologies are keys to cutting emissions.

«ОТКРЫТЫЕ ИННОВАЦИИ» - ПАРАДИГМА СОВРЕМЕННОГО ИННОВАЦИОННОГО МЕНЕДЖМЕНТА

High innovation potential in any country is the key to the development of the entire economy. To date, there has been a significant reduction in Russia's partnerships with Western European countries in most areas of activity, including cooperation in the field of innovation. The author believes that the most effective way to develop innovation management is the open innovation paradigm. The article analyzes the indicators of the practical application of open innovations at the corporate level. The main and most significant factors hindering the implementation of the open innovation model in Russian practice are identified, and promising directions for its development are outlined.

A two-stage decision model for sustainable-resilient supplier selection and order allocation under uncertain environment

PurposeThe paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.Design/methodology/approachFirst, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.FindingsThe paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.Originality/valueThe paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

A study on the innovative model of foreign language teaching in universities using big data corpus

This paper aims to explore unsupervised cross-lingual word representation learning methods with the specific task of acquiring a bilingual translation lexicon on a monolingual corpus. Specifically, an unsupervised cross-lingual word representation co-training scheme based on different word embedding models is first designed and outperforms the baseline model. In this paper, we adeptly tackles the obstacles encountered in higher education foreign language teaching and underscores the necessity for inventive teaching methods, and design and implement a linear self-encoder-based principal component acquisition scheme for the interpoint mutual information matrix obtained from a monolingual corpus. And on top of this, a collaborative training scheme based on linear self-encoder for cross-language word representation is designed to improve the learning effect of cross-language word embedding. The results of the study show that the most obvious rise in the pre and post tests of the experimental class in the practical application of the foreign language teaching model based on the method of this paper is the word sense guessing, which rose by 23.12%. Sentence meaning comprehension increased by 23.39%, main idea by 16.61%, factual details by 15.47%, and inferential judgment by 10.28%. Thus, the feasibility of the unsupervised cross-linguistic word representation learning collaborative training method is further verified.

The innovation journey and crossroads of sustainability, resilience and human-centeredness: a systematic literature review

Purpose This study aims to undertake a comprehensive analysis of innovation models, tracing their evolution from Innovation 1.0 to Innovation 4.0 and introducing the concept of Innovation 5.0. It explores the intersection between innovation models and the principles of sustainability, resilience and human-centeredness, providing insights into their implications for Industry 5.0, and their potential to foster a resilient ecosystem amidst challenges and multiple crisis. Design/methodology/approach To achieve this objective, the authors used a systematic literature review approach, considering academic articles on Innovation 4.0, Industry 5.0 (specifically in the context of innovation) and helix models of innovation. The authors conducted thematic analysis and content analysis, followed by keyword co-occurrence analysis, enabling us to systematically synthesize and interpret the relevant literature. Findings The results conclude that Innovation 5.0 is a new paradigm for innovation that fosters broader societal engagement, and emphasizes sustainability, resilience and human-centeredness. Innovation 5.0 is evolving, but it has the potential to transform the way we produce, consume and live. Using insights from the sextuple helix model, this research leverages media and ICT as sixth helix vital role of knowledge sharing, digital transformation, innovation ecosystem and next industrial revolution in this process. Originality/value This study contributes to the ongoing discourse on exploring Innovation 5.0 through the sextuple helix model, offering a fresh perspective on innovation models and their collaborative potential. Its contribution lies in providing practical insights into the transition to Innovation 5.0, emphasizing the need for sustainability, regulatory support and awareness while also offering clear recommendations for future research.

Regulatory Performance of Tourist Hunting and Legal Game Meat Trade in Tanzania

Recently, the government of Tanzania has legalized the sale of game meat under special rules namely the Wildlife Conservation (Game Meat Selling) Regulations of 2020 as an effort to curb illegal hunting. The regulations mention tourist hunting as one of the sources of commercial game meat. Other sources include resident hunting; wildlife farms/ranches/zoos; and culling, cropping, and control of problem animals. In this paper we examine the performance of tourist hunting, legal game meat in the country, and its regulatory framework from the perspectives of key stakeholders, including both public and private or non-state stakeholders. We use a variant of Likert’s methods of summated ratings and the cumulative ordinal regression model to analyse and compare the opinions of stakeholders. We found that there are still challenges, especially related to sources of wild animals. Operators of game meat facilities find it difficult to source game meat from tourist hunting companies and the latter are not willing to offer the meat to licensed operators of game meat shops. Our results support the hypothesis that authorization of the game meat trade can serve as one of the boldest conservation tools to mitigate illegal poaching if well-planned and regulated. The results of the cumulative ordinal regression model predict an improvement of 42% in the performance of regulatory framework per unit improvement in the procedures for application and registration of game meat selling facilities (p = 0.010). The paper recommends leveraging stakeholders' active engagement and strengthening cooperation between game meat value chain actors and law-enforcement authorities. An innovative participatory model for enforcing game meat sanitary and safety regulations is also recommended to avoid potential health risks to consumers. It underscores the importance of institutional capacity building, awareness creation, and adequate funding in the fight against illegal hunting and game meat trade. In order for the country to be able to achieve sustainable game meat trade in the country, the challenge of limited wild animal sources has to be addressed, possibly by supporting the establishment of privately owned wild animal farms, ranches, and zoos, this also has its own challenges that need to be addressed too.

Harvard Medical School Global Mental Health: Trauma and Recovery Course: What is the Global Impact? Three Year’s Results

Purpose: This paper describes and documents an innovative blended learning Global Mental Health: Trauma and Recovery certificate training course. This course combines a two-week face-to-face training in Orvieto, Italy with a five-month follow-up online virtual training as a learning experience for global health care practitioners. Continuing medical education (CME) accreditation is offered upon completion. This course utilized an innovative blended learning model with a community of practice approach, a combination of lectures and discussions, and online in-depth group case study discussions. Methodology: Data was collected by self-reported anonymous evaluation by participants of three continuous years of the CME Global Mental Health: Trauma and Recovery certificate training course sponsored by Harvard Medical School. One hundred fifty-five participants (n= 39 in 2011; n = 57 in 2012; n=59 in 2013) underwent a pre- and post-course evaluation to determine sustained confidence in performing medical and psychiatric care to traumatized patients and communities, as well as to determine their learning of the Global Mental Health Action Plan (GMHAP). Results: Over the course of three independent years, a total of 155 participants were evaluated. There was evidence for significant improvement in their confidence levels in all clinical areas (diagnosis; treatment of trauma; use of psychotropic medication) when comparing baseline to completion of the six-month course. All ten dimensions of the GMHAP and nine medical and psychiatric aspects of treatment revealed significant improvement in confidence levels. Regression analysis also indicated similar results after the adjustment of demographic covariates. Physicians and participants with mental health and social work background had significantly higher confidence. Participants who were MD’s or psychiatrists had higher confidence in most of the categories of confidence except for self-care, understanding culture, collaboration, and policy and financing. The model showed no difference in learning based upon gender and level of development of country of origin. Conclusion: The evaluation of this blended learning CME program provides evidence of significant enhancement of clinical practice and planning skills in health care practitioners working with highly traumatized patients and communities worldwide. This successful training over the past 18 years has gone far to achieve the health and mental health capacity building as requested by the Ministers of Health from post-conflict societies in the historic Rome meeting in 2004.

A New Model for Building Energy Modeling and Management Using Predictive Analytics: Partitioned Hierarchical Multitask Regression (PHMR)

Buildings are major consumers of energy, accounting for a significant proportion of total energy use worldwide. This substantial energy consumption not only leads to increased operational costs but also contributes to environmental concerns such as greenhouse gas emissions. In the United States, building energy consumption accounts for about 40% of total energy use, highlighting the importance of efficient energy management. Therefore, accurate prediction of energy usage in buildings is crucial. However, accurate prediction of building energy consumption remains a challenge due to the intricate interaction of indoor and outdoor variables. This study introduces the Partitioned Hierarchical Multitask Regression (PHMR), an innovative model integrating recursive partition regression (RPR) with multitask learning (hierML). PHMR adeptly predicts building energy consumption by integrating both indoor factors, such as building design and operational variables, and outdoor environmental influences. Rigorous simulation studies illustrate PHMR’s efficacy. It outperforms traditional single-predictor regression models, achieving a 32.88% to 41.80% higher prediction accuracy, especially in scenarios with limited training data. This highlights PHMR’s robustness and adaptability. The practical application of PHMR in managing a modular house’s Heating, Ventilation, and Air Conditioning (HVAC) system in Spain resulted in a 37% improvement in prediction accuracy. This significant efficiency gain is evidenced by a high Pearson correlation coefficient (0.8) between PHMR’s predictions and actual energy consumption. PHMR not only offers precise predictions for energy consumption but also facilitates operational cost reductions, thereby enhancing sustainability in building energy management. Its application in a real-world setting demonstrates the model’s potential as a valuable tool for architects, engineers, and facility managers in designing and maintaining energy-efficient buildings. The model’s integration of comprehensive data analysis with domain-specific knowledge positions it as a crucial asset in advancing sustainable energy practices in the building sector.