Full Potential Research Articles

Svend Hollensen, Philip Kotler, and Marc O. Opresnik (2023) Social Media Marketing: A Practitioner Approach. 5th edition

Svend Hollensen, Philip Kotler and Marc Oliver Opresnik boldly present the fifth edition of the book as a real guide. In today's digital age, where social media has become an integral part of our daily lives, harnessing its power for business growth is not just an option but a necessity. In their groundbreaking book, "Social Media Marketing: A Practitioner Approach," Marc Opresnik, Philip Kotler, and Svend Hollensen offer an updated comprehensive roadmap to navigate the complex world of social media marketing and achieve unparalleled success. Published in 2023, this book serves as the ultimate strategy guide for businesses seeking to leverage social media to its fullest potential.

THE ROLE OF HR IN PROMOTING DIVERSITY AND INCLUSION IN NON-GOVERNMENT ORGANIZATION

In today's globalized and interconnected world, the discourse surrounding diversity and inclusion has become increasingly pertinent, resonating deeply within organizational landscapes worldwide. Non-Government Organizations (NGOs) in India, serving as critical agents of social change and development, are no exception to this paradigm shift. As these organizations navigate complex societal challenges, the imperative to foster diversity and inclusion within their ranks emerges as a crucial component of their mission and operational ethos. The burgeoning importance of diversity and inclusion within organizations stems from a recognition of the multifaceted benefits they offer. Beyond mere compliance with legal and ethical standards, embracing diversity encompasses harnessing a wide array of perspectives, experiences, and talents within an organization's workforce. Inclusion, on the other hand, entails creating an environment where every individual feels valued, respected, and empowered to contribute their fullest potential. Together, these principles not only foster innovation and creativity but also enhance organizational performance and competitiveness in an ever-evolving global marketplace.

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design.

Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band-gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material-photoreactor synergy. These factors include achieving photogeneration and charge-carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi-dimensional problem necessitates harnessing machine learning techniques to analyze real-world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics-informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network.

Soft sensors have been increasingly applied for quality prediction in complex industrial processes, which often have different scales of topology and highly coupled spatiotemporal features. However, the existing soft sensing models usually face difficulties in extracting the multiscale local spatiotemporal features in multicoupled complex process data and harnessing them to their full potential to improve the prediction performance. Therefore, a multiscale attention-based CNN (MSACNN) is proposed in this article to alleviate such problems. In MSACNN, convolutional kernels of different sizes are first designed in parallel in the convolutional layers, which can generate feature maps containing local spatiotemporal features at different scales. Meanwhile, a channel-wise attention mechanism is designed on the feature maps in parallel to get their attention weights, representing the significance of the local spatiotemporal feature at different scales. The superiority of the proposed MSACNN over the other state-of-the-art methods is validated through the performance evaluation in two real industrial processes.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Tapping the Full Potential of Infrared Spectroscopy for the Analysis of Technical Lignins.

We present an approach to overcome the challenges associated with the increasing demand of high-throughput characterization of technical lignins, a key resource in emerging bioeconomies. Our approach offers a resort from the lack of direct, simple, and low-cost analytical techniques for lignin characterization by employing multivariate calibration models based on infrared (IR) spectroscopy to predict structural properties of lignins (i. e., functionality, molar mass). By leveraging a comprehensive database of over 500 well-characterized technical lignin samples - a factor of 10 larger than previously used sets - our chemometric models achieved high levels of quality and statistical confidence for the determination of different functional group contents (RMSEPs of 4-16 %). However, the statistical moments of the molar mass distribution are still best determined by size-exclusion chromatography. Analyses of over 500 technical lignins offered also a great opportunity to provide information on the general variability in kraft lignins and lignosulfonates (from different origins). Overall, the effected savings in analysis time (>7 h), resources, and required sample mass combined with non-destructiveness of the measurement satisfy key demands for efficient high-throughput lignin analyses. Finally, we discuss the advantages, disadvantages, and limitations of our approach, along with critical insights into the associated chemical-analytical and spectroscopic challenges.

A novel method for indian vehicle registration number plate detection and recognition using CNN

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

Predisposition in Dynamic Covalent Chemistry: The Role of Non-Covalent Interactions in the Assembly of Tetrahedral Boronate Cages.

Directional bonding strategies guide the design of complex molecular architectures, yet challenges arise due to emergent behavior. Rigid structures face geometric constraints and sensitivity to mismatches, hindering the efficient assembly of molecular organic cages (MOCs). Harnessing intramolecular non-covalent interactions offers a promising solution, broadening geometrical possibilities and enhancing adaptability to boost assembly yields. However, identifying these interactions remains challenging, with their full potential sometimes latent until final assembly. This study explores these challenges by synthesizing boronic acid tripods with varied oxygen positions at the tripodal feet and investigating their role in assembling tetrahedral boronate MOCs. Our results reveal substantial differences in the assembly efficiency among tripods. While the building blocks with oxygen in the benzylic position relative to the central aromatic ring form the MOCs in high yields, those with the oxygen atom directly bound to the central aromatic ring, only yield traces. Through X-ray crystallography and DFT analyses, we elucidate how intramolecular interactions profoundly influence the geometry of the building blocks and cages in a relay-like fashion, highlighting the importance of considering intramolecular interactions in the rational design of (supra)molecular architectures.

Personality Traits and the Growth of Emotional Intelligence. A Systematic Evaluation

Emotional intelligence (EI) and personality traits are inherent factors that shape individuals' perception, cognitive processing, and behavioral responses to emotional stimuli. This study examines the complex interplay between personality traits and emotional intelligence (EI), investigating how certain personality traits can impact the formation and expression of EI. By employing a blend of empirical investigation and theoretical examination, we evaluate the correlation between fundamental aspects of an individual's personality, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism, and their emotional intelligence (EI) capacities, including self-awareness, self-regulation, motivation, empathy, and social skills. This study utilized fourteen articles acquired through a systematic search using the keywords "emotional intelligence and personality" in the Scopus and PubMed databases. An individual's personality plays a significant role in shaping their emotional intelligence, which in turn has a profound impact on multiple domains of human endeavor. Initial findings indicate that specific personality traits may facilitate developing emotional intelligence (EI), whereas others may pose obstacles that necessitate focused interventions. Acknowledging the interaction between personality and emotional intelligence (EI) presents novel opportunities for comprehensive personal growth, emphasizing the significance of customized approaches encompassing both aspects. This study emphasizes the need for a comprehensive comprehension of human behavior, advocating for approaches that accommodate the distinct interplay between an individual's personality and emotional intelligence. These strategies aim to enhance personal and interpersonal outcomes to their fullest potential.

Towards the privileging of care-experienced children and young people’s educational and other ‘life chances’ within social work practice and education

Care-experienced children and young people frequently face adverse ‘life chances’ when compared to their peers. Their life-course trajectories typically include numerous personal, structural and culturally determined challenges set from a young age. Social workers in the UK now play a minimal role in direct support for young people and are instead encouraged to focus on short-term priorities, safeguarding investigations and monitoring ‘risky’ working-class parents. This article considers some explanations and evidence offered for educational and other inequalities experienced by care-experienced children and young people, and highlights specific issues regarding ongoing neoliberal reforms of social care. Case examples relating to criminal justice, asylum-seeking children and sexuality are then briefly discussed. The conclusion draws from evidence to identify some recommendations that may help improve care-experienced children and young people’s full learning potential. This includes moving away from the current neoliberal-inspired short-term focus placed on managing risk and towards the provision of more contextual and meaningful support.

Optimized parameter extraction techniques for enhanced performance evaluation of organic solar cells

The global energy landscape is in the midst of a transformative shift, compelled by the urgent need to reduce our reliance on fossil fuels and embrace eco-friendly alternatives. Organic photovoltaics (OPVs) have emerged as a promising alternative, offering the distinct advantage of performing well in low-light conditions, including indoor environments. Extensive research and development efforts are dedicated to realizing the full potential of OPVs as adaptable, cost-effective, and environmentally friendly solar energy solutions. This paper conducts a thorough examination of the intricate characterization of organic solar cells, with a specific emphasis on crucial parameters like power conversion efficiency, open-circuit voltage, and fill factor. The study utilizes a single diode model to simulate these cells' behavior, employing a meticulous process for parameter extraction. This method leverages Origin software and Python programming, incorporating open-source packages to ensure robust validation. This systematic and rigorous approach significantly enhances our comprehension of OPVs and plays a substantial role in optimizing their performance. In essence, this research represents a significant step forward in advancing sustainable energy technologies, laying a foundation for a greener and more environmentally conscious future.

Automating financial reporting with natural language processing: A review and case analysis

In the evolving landscape of financial reporting, the integration of Natural Language Processing (NLP) emerges as a beacon of innovation, promising to redefine the paradigms of accuracy, efficiency, and compliance. This paper embarks on a scholarly expedition to explore the transformative potential of NLP within the realm of financial disclosures, navigating through the intricate interplay of technological advancements and regulatory frameworks. The study meticulously analyzes the application of NLP techniques in automating financial reporting, unraveling the complexities of implementation and the multifaceted challenges therein through a qualitative research design. Through a comprehensive review of the literature and empirical data, the paper illuminates the efficacy of NLP in enhancing the precision and reliability of financial reports while also delving into stakeholders' perceptions regarding its adoption. The findings reveal a significant improvement in reporting efficiency and accuracy, underscored by the strategic importance of addressing implementation hurdles and regulatory considerations. The study culminates in a set of cogent recommendations, advocating for the development of a robust framework for NLP applications in financial reporting, alongside a clarion call for ongoing research into sophisticated NLP models and scalable solutions. In essence, this paper not only charts a course for the future integration of NLP in financial reporting but also stands as a testament to the indelible impact of technological innovation on the financial industry. It beckons the academic and professional communities to forge a collaborative path towards realizing the full potential of NLP, thereby ushering in a new era of transparency and insight in financial disclosures.

Integrating AI in education: Opportunities, challenges, and ethical considerations

Integrating Artificial Intelligence (AI) in education presents a promising frontier with manifold opportunities, yet it also poses significant challenges and necessitates ethical considerations. This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks. However, it also addresses the challenges pertaining to implementation, including issues related to accessibility, data privacy, and the digital divide. The opportunities afforded by AI in education are vast and transformative. AI-driven technologies have the capacity to adapt instruction to individual learning styles, thereby enhancing student engagement and academic outcomes. Additionally, AI-powered tools can automate administrative tasks, allowing educators to allocate more time to meaningful interactions with students. Moreover, AI holds promise in facilitating the creation of immersive learning environments through virtual reality and augmented reality applications, enriching the educational experience. Nevertheless, the integration of AI in education presents ethical considerations that warrant careful examination. Concerns regarding data privacy and security arise as educational institutions collect and analyze vast amounts of student data. Moreover, there are apprehensions about the potential for AI algorithms to perpetuate biases or reinforce inequalities if not implemented with conscientious oversight. Furthermore, questions surrounding the ethical use of AI in assessing student performance and making consequential decisions underscore the importance of establishing transparent and accountable practices. While the integration of AI in education offers unprecedented opportunities for innovation and improvement, it is imperative to navigate the associated challenges with diligence and ethical foresight. By addressing these challenges thoughtfully, stakeholders can harness the full potential of AI to cultivate equitable, inclusive, and effective educational environments.

Assessing the role of LNG in global carbon neutrality efforts: A project management review

This paper review explores the role of LNG in the context of global carbon neutrality efforts. It examines various aspects such as the current state of LNG usage, its environmental impacts, technological advancements, regulatory frameworks, and the challenges and opportunities associated with its integration into the energy mix. Through a comprehensive analysis, this review aims to provide insights into how project management principles can facilitate the effective implementation of LNG projects to support the transition towards a carbon-neutral future. The transition to a carbon-neutral global economy is an imperative challenge of the 21st century, necessitating a profound shift in energy systems and practices. At the forefront of this transition, liquefied natural gas (LNG) has emerged as a potential bridge fuel, offering a pathway towards decarburization. This project management review delves into the multifaceted role of LNG within the context of global carbon neutrality efforts, examining its current usage, environmental implications, technological advancements, regulatory frameworks, and the associated challenges and opportunities. Through a comprehensive analysis, this review aims to shed light on how project management principles can effectively facilitate the implementation of LNG projects to support the transition towards a carbon-neutral future. LNG's emergence as a potential bridge fuel stems from its relatively lower carbon intensity compared to other fossil fuels. As nations strive to reduce greenhouse gas emissions and meet their commitments under the Paris Agreement, LNG presents an opportunity to mitigate climate change while addressing energy security and economic development objectives. However, a nuanced understanding of LNG's role requires a thorough examination of various dimensions. Firstly, the current state of LNG usage underscores its growing significance in the global energy landscape. With LNG trade reaching record highs in recent years, countries are increasingly turning to LNG to meet their energy needs. Major LNG-producing nations such as Qatar, Australia, and the United States have invested in liquefaction infrastructure, enabling the export of LNG to markets around the world. Additionally, LNG is utilized across diverse sectors, including power generation, industrial processes, transportation, and residential heating, highlighting its versatility and applicability. Despite its potential benefits, LNG is not without environmental impacts. The extraction, production, and transportation of natural gas, the primary component of LNG, can result in methane emissions, a potent greenhouse gas. In conclusion, LNG has emerged as a potential bridge fuel in the transition towards a carbon-neutral global economy. Its relatively lower carbon intensity, versatility, and scalability make it a viable option for reducing greenhouse gas emissions while meeting energy demand. However, realizing the full potential of LNG requires careful consideration of its environmental impacts, technological advancements, regulatory frameworks, and associated challenges. By applying project management principles, stakeholders can effectively navigate the complexities of LNG projects and contribute to the achievement of global carbon neutrality goals.

Integrating IoT with Virtual Healthcare: A theoretical framework for enhancing accessibility and efficiency in the U.S. healthcare sector

This paper presents an open source platform aimed at revolutionizing healthcare delivery in the United States by significantly reducing the cost of virtual healthcare software. The proposed platform addresses the pressing challenges of accessibility and efficiency in the U.S. healthcare sector through cloud native backend solutions and mobile apps driven by modular design and IoT-enabled devices. By leveraging IoT technology, virtual healthcare platforms can provide real-time monitoring, remote consultations, and personalized care, thereby improving patient outcomes and reducing healthcare costs. The platform emphasizes nationwide scalability, inclusivity, and a transition towards a proactive, patient-centric healthcare model. By leveraging the opensource community, modular design and a plugin architecture, it aims to enable feature extensibility beyond what is foreseeable today, promote interoperability, ensure data privacy and security, and promote technology adoption among healthcare providers and patients. The proposal presented in this paper lays the foundation for the implementation aimed at harnessing the full potential of IoT and virtual healthcare to enhance accessibility and efficiency in the U.S. healthcare sector, ultimately advancing the quality of care and well-being of the population.

Inbound and Outbound Tourism in China: A Perspective Article

The tourism industry in China has undergone significant changes due to globalization, technology, and changing consumer preferences. This perspective article, based on a systematic literature review, examines examines trends, challenges, and opportunities in inbound and outbound tourism in China, focusing on the industry's dynamism. Both inbound and outbound tourism have been negatively impacted by the COVID-19 pandemic, which has prompted new strategies and environmental adaptations. China has changed its priorities within the country to encourage domestic travel and support local firms. The rise in internationalisation and the growing middle class in China have led to an unprecedented rise in outbound travel. Chinese travellers are affecting travel habits and spending all around the world in the tourism sector. Language barriers, cultural differences, and safety concerns are some of the challenges that come with travelling abroad. To reach its full potential, China needs to adapt to the shifting global environment and take into account the specifications and preferences of both inbound and outgoing tourists.

Unlocking Amides: A General Method for the Self-Immolative Release of Amide-Containing Molecules.

The controlled liberation of molecules from a constructed framework is a subject of profound interest across various chemical fields. It allows for the masking of a molecule's properties and precise deployment upon a single controllable release event. While numerous methodologies have been developed for amines, alcohols, and thiols, approaches for utilising amides as payload-release handles are still in their early stages of development, despite the prevalence of amides in therapeutic compounds and materials. Herein, is presented a comprehensive strategy for the controlled and selective release of a diverse range of amides with stable linkers. The versatility of this approach is demonstrated by its successful application in the targeted release of various amide-containing drugs in their natural form via the use of commonly used trigger motifs, such as dipeptides or glycosides. As a proof of concept, the FDA-approved antibiotic linezolid has been successfully converted into a prodrug form and released selectively only in the presence of the trigger event. Significantly, in its prodrug state, no activity against Mycobacterium tuberculosis was exhibited. Linezolid's full potential was achieved only upon controlled release, where an equipotent efficacy to the free linezolid control was demonstrated, thus emphasising the immense potential of this method.

Disentangling the relationship between institutional influence, transformational leadership and performance in higher educational institutions in India: a three-way interaction model

PurposeThe present study aims to investigate the relationship between institutional influence and performance, mediated through transformational leadership (TL) and moderated by barriers, situational factors, communication and implementation.Design/methodology/approachUsing a structured survey instrument, data were collected from 370 faculty members from 31 higher educational institutions in southern India. After checking the psychometric properties of the instrument, the authors used Hayes’s PROCESS to test the direct hypotheses and three-way interactions.FindingsThe results revealed that TL mediated the relationship between institutional influence and performance. Further, the findings supported the three-way interactions between (1) institutional influence, barriers and communication positively affecting TL; and (2) TL, situational factors and implementation affecting the performance of faculty members.Research limitations/implicationsThis study underscores the importance of TL for the smooth functioning of higher educational institutions and achieving superior performance, especially in the new normal context after the global pandemic.Practical implicationsThis study makes several significant recommendations to administrators in higher educational institutions, in addition to contributing to the vast literature on TL. The study suggests that administrators must invest resources in developing TL skills so that employees reach their fullest potential and contribute to achieving organizational goals. In addition, leaders in organizations need to exercise a transformational style to combat the new normal post-pandemic academic environment.Originality/valueThis study provides new insights into the importance of TL style and institutional influence to enhance performance. To the best of our knowledge, the conceptual model developed and tested the first of its kind in India, significantly contributing to theory and practice.

The dawn of personalized multi-omics: Detecting disease before you know it

Recent advancements in omics techniques have enabled deep profiling of an individual’s molecular makeup. The wealth of data produced offers insights into genetic predispositions, early disease markers, and personalized treatment strategies. However, the full potential of omics data emerges when combined into longitudinal and personal multi-omics space. Another interesting venue is the inclusion of continuous monitoring of physiological parameters through wearable technology. Wearable health devices, including smartwatches and biosensors, provide real-time data on heart rate, oxygen saturation, sleep patterns, activity levels, and much more. By integrating with omics data, wearables offer a comprehensive view of an individual’s health, allowing for early detection of deviations from normalcy. This convergence allows for the prediction and prevention of diseases at the individual level and provides a powerful monitoring tool in clinical and drug developmental settings. This review explores the fusion of omics and wearable technology, envisioning their synergy as a catalyst for a transformative shift in modern healthcare. Their merging enables predictive and personalized medicine. As these technologies continue to evolve, their translation into routine clinical practice holds the promise of a healthier future for all. Provided herein is a step-by-step vision for how longitudinal personalized multi-omics, combined with wearable devices, will guide proactive healthcare and transform drug discovery in translational medicine.

Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning

Partial label learning (PLL) refers to the classification task where each training instance is ambiguously annotated with a set of candidate labels. Despite substantial advancements in tackling this challenge, limited attention has been devoted to a more specific and realistic setting, denoted as instance-dependent partial label learning (IDPLL). Within this contex, the assignment of partial labels depends on the distinct features of individual instances, rather than being random. In this paper, we initiate an exploration into a self-distillation framework for this problem, driven by the proven effectiveness and stability of this framework. Nonetheless, a crucial shortfall is identified: the foundational assumption central to IDPLL, involving what we term as partial label knowledge stipulating that candidate labels should exhibit superior confidence compared to non-candidates, is not fully upheld within the distillation process. To address this challenge, we introduce DIRK, a novel distillation approach that leverages a rectification process to DIstill Reliable Knowledge, while concurrently preserves informative fine-grained label confidence. In addition, to harness the rectified confidence to its fullest potential, we propose a knowledge-based representation refinement module, seamlessly integrated into the DIRK framework. This module effectively transmits the essence of similarity knowledge from the label space to the feature space, thereby amplifying representation learning and subsequently engendering marked improvements in model performance. Experiments and analysis on multiple datasets validate the rationality and superiority of our proposed approach.