Management Paradigm Research Articles

Challenges in Hypophosphatasia: Suspicion, Diagnosis, Genetics, Management, and Follow-Up.

Hypophosphatasia (HPP) is a rare genetic disorder caused by loss-of-function variants in the ALPL gene, leading to deficient tissue-nonspecific alkaline phosphatase (ALP) activity. This results in a distinctive biochemical profile marked by low serum ALP levels and elevated pyridoxal-5-phosphate (PLP). The clinical spectrum of HPP ranges from perinatal lethality to asymptomatic cases, presenting significant diagnostic and therapeutic challenges. Diagnosis of HPP relies on identifying the characteristic biochemical signature (low ALP, high PLP), concomitant with skeletal (osteomalacia, rickets, pseudofracture) or extraskeletal (muscle weakness, musculoskeletal pain, dental) manifestations. Current diagnostic frameworks lack uniformity, highlighting the imperative for a standardized diagnostic approach. Molecular genetic testing plays a pivotal role in making the diagnosis of HPP, but difficulties persist in diagnosing milder cases and correlating genotypes with phenotypes. Comprehensive multidisciplinary care is indispensable, with enzyme replacement therapy (ERT) proving efficacious in severe cases and more nuanced management approaches for milder presentations. Overcoming challenges in ERT initiation, treatment response assessment, dose titrations, and long-term surveillance necessitates further refinement of management guidelines. Mild forms of HPP and asymptomatic carriers of pathogenic ALPL variants pose substantial diagnosis and management challenges. Developing consensus-driven guidelines is crucial to enhance clinical outcomes and patient care.

A memetic algorithm for improved joint route selection and split-level management in next-generation wireless communications

The complexity of next-generation wireless communications, especially Beyond 5G and 6G communication systems, will be handled by artificial intelligence-based management paradigms. The joint selection of routes and functional split levels involves critical decisions that network infrastructure providers need to make to support requests from virtual Mobile Network Operators (vMNOs). These decisions comprise the assignment and configuration of physical network resources, which must comply with the specific quality of service restrictions of each vMNO request. Recent work defined a detailed mathematical model for this complex challenge, its formulation as a constrained, discrete optimization problem, and the first algorithmic approaches. It was also found that an evolutionary algorithm delivers higher-quality solutions than an ad-hoc heuristic, and faster running times compared to a well-known commercial solver. This paper introduces a memetic algorithm that exploits the strengths of the former evolutionary method while incorporating several key innovations: a domain-specific recombination operator; a specialized repairing procedure; an enhanced fitness evaluation scheme; and a multiobjective archiving strategy that preserves promising solution trade-offs. We conduct a comprehensive evaluation of the performance and behavior of this proposal, as well as the contribution of each specific design component. The results highlight that our memetic algorithm consistently outperforms previous approaches from the literature, providing better trade-offs in terms of solution quality and the rate at which vMNO requests are successfully fulfilled.

Wind of change: how ChatGPT and big data can reshape the knowledge management paradigm?

PurposeThe new disruption in the form of ChatGPT can be a valuable tool for organizations to enhance their knowledge management and decision-making capabilities. This article explores how ChatGPT can enhance organizations' KM capability for improved decision-making and identifies potential risks and opportunities.Design/methodology/approachUsing existing literature and a small-scale case study, we develop a conceptual framework for implementing artificial intelligence on the internal organizational knowledge base of big data and its integration with a larger knowledge base of ChatGPT.FindingsThis viewpoint conceptualizes integrating knowledge management and ChatGPT for improved organizational decision-making. By facilitating efficient information retrieval, personalized learning, collaborative knowledge sharing, real-time decision support, and continuous improvement, ChatGPT can help organizations stay competitive and achieve business success.Research limitations/implicationsThis is one of the first studies on the integration of organizational knowledge management systems with ChatGPT. This research work proposes a conceptual model on integration of knowledge management with generative AI which can be further tested in actual work settings to check it's applicability and make further modifications.Practical implicationsThe study provided insights to managers and executives who, in collaboration with IT professionals, can devise a mechanism for integrating existing knowledge management systems in organizations with ChatGPT.Originality/valueThis is one of the first studies exploring the linkage between ChatGPT and knowledge management for informed decision-making.

Surgical management of spinal pathologies in the octogenarian: a narrative review.

Optimal management paradigms of spinal pathologies in the octogenarian population are controversial given the higher incidence of comorbidities with concern for poor prognosis and fear of increased complications associated with surgical management. In this narrative review, we aim to detail the complex clinical considerations when approaching odontoid screw fixation/instrumented fusion, spinal decompression, and spinal fusion in the octogenarian. Literature review was conducted via Google Scholar and PubMed databases, with literature selected based on statistical power and clinical relevance to the following pathologies/surgical techniques: odontoid fracture, surgical decompression, and surgical fusion in the octogenarian. The aforementioned pathologies were selected based on prevalence in the advanced-age population in which surgical screening techniques and management remain nonuniform. Preoperative evaluation of the octogenarian patient increasingly includes frailty, sarcopenia, and osteopenia/osteoporosis assessments. In cases of odontoid fracture, conservative management appears to provide beneficial clinical outcomes with lower rates of complication compared to surgery; however, rates of radiographic odontoid fusion are far lower in conservatively managed patients. Regarding surgical decompression and fusion, the presence of comorbidities may be more predictive of outcome rather than age status, with the advent of minimally invasive techniques providing safety and efficacy in the surgical management of this age cohort. Age status may be less pertinent than previously thought in the decision to pursue spinal surgery for odontoid fracture, spinal decompression, or spinal fusion; however, each of these procedures has respective risks and benefits that must be considered within the context of each patient's comorbidity profile.

The impact of R&D classification shifting in high-technology industries

PurposeThis study investigates the motivations and consequences of classification shifting from cost of sales to research and development (R&D) in high-technology industries.Design/methodology/approachThis study conducts a multivariate analysis using logistic and ordinary least squares regression methods on panel data of high-technology firms for the period 1988–2012 to examine the effect of R&D classification shifting on gross margin benchmarks and future performance.FindingsThe results show that R&D classification shifting increases the likelihood of meeting or beating gross margin benchmarks. They also show that firms engaged in R&D classification shifting exhibit lower future R&D productivity, stock returns, and operating performance. The findings indicate that the short-term benefits of achieving gross margin benchmarks are offset by the long-term negative impact of R&D misclassification.Practical implicationsThis paper provides insights that can help regulators develop clearer guidelines for the appropriate classification of R&D costs.Originality/valueMoving beyond the core earnings management paradigm, this study demonstrates the use of R&D classification shifting as a tool to manipulate gross profits and R&D in high-technology industries. Most prior studies focused on the determinants of R&D classification shifting, while few investigated the impact of the practice. The findings in this study provide initial evidence of the consequences of R&D classification shifting for future R&D productivity and firm performance in high-tech industries. Using five methods, this study also validates R&D classification shifting and addresses the alternative explanation of R&D overinvestment.

Adjuvant Therapy for High-Risk Stage II Melanoma: Current Paradigms in Management and Future Directions.

Melanoma is the fifth most common cancer in the United States and accounts for the majority of all skin cancer-related deaths, making it the most lethal cutaneous malignancy. Systemic adjuvant therapy for stage IIB-IV melanoma is now approved for patients who have undergone surgical resection, given the appreciable risk of recurrence and mortality in this patient population. Despite the lower stage, high-risk stage II melanoma (stage IIB/IIC) can often exhibit an even more aggressive course when compared to stage IIIA/IIIB disease, thus justifying consideration of adjuvant therapy in these patients. In this review, we highlight the current standard of practice for the treatment of stage IIB/C melanoma, with a focus on adjuvant therapies supported by published landmark clinical trials, including anti-PD-1 therapy. Notably, adjuvant therapies approved thus far in this patient population have demonstrated an improvement in recurrence-free survival, while their impact on overall survival is pending. Finally, this review highlights currently ongoing trials and future directions for research and treatment possibilities for high-risk clinical stage II melanoma.

A comprehensive review of synbiotics: an emerging paradigm in health promotion and disease management.

Synbiotics are complex preparations of prebiotics that can be selectively utilized by live microorganisms to improve host health. Synbiotics are divided into complementary synbiotics, which consist of probiotics and prebiotics with independent functions, and synergistic synbiotics, which consist of prebiotics that are selectively used by gut microorganisms. Complementary synbiotics used in human clinical trials include Lactobacillus spp. and Bifidobacterium spp. as probiotics, and fructooligosaccharides, galactooligosaccharides, and inulin as prebiotics. Over the past five years, synbiotics have been most commonly used in patients with metabolic disorders, including obesity, and immune and gastrointestinal disorders. Several studies have observed alterations in the microbial community; however, these changes did not lead to significant improvements in disease outcomes or biochemical and hematological markers. The same synbiotics have been applied to individuals with different gut environments. As a result, even with the same synbiotics, there are non-responders who do not respond to the applied synbiotics due to the different intestinal environment for each individual. Therefore, to obtain meaningful results, applying different synbiotics depending on the individual is necessary. Synergistic synbiotics are one solution to circumvent this problem, as they combine elements that can effectively improve health, even in non-responders. This review aims to explain the concept of synbiotics, highlight recent human clinical trials, and explore the current state of research on synergistic synbiotics.

Rancang Bangun Monitoring Tempat Sampah Berbasis Mikrokontroler

Islamic boarding schools, as one of the oldest Islamic educational institutions in Indonesia, play an important role in rural communities. However, Islamic boarding schools often face hygiene problems, especially in waste management. The large number of students produces large volumes of waste, so effective management is needed. The waste management paradigm must now be changed to reduce, recycle and reuse (3R: Reduce, Reuse, Recycle). In the Industry 4.0 era, technology such as microcontroller-based smart trash cans and IoT have become effective solutions. This smart trash can can measure the weight and height of waste in real-time, providing data that can be accessed via smartphone and website, making it easier for waste managers to maintain a clean environment. This smart trash can will be placed at the Denanyar Islamic Boarding School and managed by a non-governmental organization that cares about cleanliness and the use of recycled waste.

ASO Author Reflections: Evolving Paradigms in Axillary Management for Breast Cancer: Insights from the ISPY-2 Neoadjuvant Chemotherapy Trial.

ASO Author Reflections: Evolving Paradigms in Axillary Management for Breast Cancer: Insights from the ISPY-2 Neoadjuvant Chemotherapy Trial.

Dynamic Infrared Radiation Regulator Enabling Positive and Reversible Modulation of Emissivity and Temperature

AbstractThe ongoing advancements and growing adoption of infrared detection technology have spurred a tremendous amount of interest in thermal camouflage technology. Various approaches are employed to develop infrared camouflage materials capable of manipulating emissivity or surface temperature. However, the range of thermal radiation regulation implemented by these materials is still somewhat limited. In this paper, a combined emissivity and temperature regulation strategy that integrates a thermoelectric device (TED) and a thermochromic structure is proposed. By utilizing this strategy, it becomes possible to simultaneously control the surface temperature and the emissivity without needing additional complex excitation. As a concept demonstration, large tunabilities of 0.38 for long‐wave infrared (8–14 µm) emittance and 87 °C for surface temperature are observed, resulting in a prominent tunability of the thermal radiation temperature that is 15.4 °C greater than that of a conventional TED with constant emissivity. This work aims to introduce a new design paradigm for future thermal radiation management and camouflage techniques.

A New Paradigm for Sustainable Supply Chain Management for Business Operation

Background: The rapid developments in society and population, climate change, and environmental challenges are crucial factors impacting business operations and sustainability. To adapt to these changes effectively, businesses need to understand these factors from the dimensions of society, the environment, and the economy. Method: This research investigates these factors by deploying a questionnaire to gather data from 400 participants and employing confirmatory factor analysis as well as structural equation modeling to identify the relationships among these factors. Results: This study confirms the significant role of policy and strategy in sustainable supply chain management for operations in the public sector and institutions. This new perspective allows stakeholders to respond to changes more effectively with sustainable supply chain management while maintaining flexibility in application. Additionally, this study identifies the dimension of institution and policy as another key factor. Conclusions: These findings provide in-depth insights into the key factors of sustainable supply chain management for business operations. These insights can aid in developing strategies and setting targets aligned with sustainable development goals, responding to changes in society, the environment, the economy, as well as in institutions and policy, leading to long-term sustainable business operations.

A CFD simulation platform for surface finishing processes in advanced manufacturing

Products created by additive manufacturing often have surface imperfections that require post-processing operations to remove extraneous material in order to meet design specifications. The usage of computational fluid dynamics (CFD) simulations to predict material removal rates of components, allows practitioners to optimize the setup and usage of post-processing equipment. However, those without in-depth knowledge of CFD or the related specialized software, require an easy-to-use and cost-effective application to manage the computational workflow. The two specific surface finishing applications investigated here, are, abrasive flow machining (AFM) and robotic stream finishing (RSF). In order to satisfy user requirements, a modular, threaded, fault-tolerant and object-oriented project management application, written with the Python programming language and PyQt6 framework, has been developed to conduct surface finishing-related CFD simulations using OpenFOAM®. The advantages of the proposed software are: 1) the modern PyQt6 framework is used to develop a cross-platform and user-friendly application which employs the model-view class architectural paradigm for data management and its display, 2) step-by-step interactive project workflows have been tailored specifically for AFM and RSF simulations, 3) the developed steady-state viscoelastic flow solver for AFM and continuum-based steady-state dense granular flow solver for RSF, offer advantages over those provided by OpenFOAM® and 4) simulation results have been corroborated by experimental data to assess the improved accuracy of material removal prediction of the current software when compared to other commercial applications.

Epidemiology and Management Paradigm of Head and Neck Infections, Including COVID-19 Pandemic Period: A 10-Year Retrospective Study in a Maxillofacial Center of Cluj-Napoca.

Background. The management of odontogenic infections varies across the globe. To shed light on the subject, this study delves into the practices of the Oral and Maxillofacial Department at Cluj-Napoca County Hospital. Material and Methods. This comprehensive retrospective analysis of 10 years of clinical experience covered a range of factors, including demographics, clinical and investigations factors, medical and surgical treatment approaches, and follow-up. Additionally, the study examined the change in trends over the course of the COVID-19 pandemic. Results. While head and neck infection occurrence had a decreasing trend before the COVID-19 pandemic, during the pandemic era the number of patients almost doubled. The infections were prevalent in the submandibular space, teeth being the prevalent cause. Young adults are prone to suffer from odontogenic infections. High levels of C reactive protein, ASA II-IV risk, and hepatic dysfunction indicated a longer time of hospitalization. The majority of antibiograms were negative, and the positive ones indicated Streptococcus and Staphylococcus species as prevalent pathogens. Extra- or intraoral surgical drainage and wide-spectrum antibiotics proved to be the workhorse for odontogenic infections. Conclusion. This study advances our understanding of oral and maxillofacial surgery and offers actionable strategies for improving patient outcomes in similar healthcare institutions worldwide.

Evolving Paradigms in Sepsis Management: A Narrative Review.

Sepsis, a condition characterized by life-threatening organ dysfunction due to a dysregulated host response to infection, significantly impacts global health, with mortality rates varying widely across regions. Traditional therapeutic strategies that target hyperinflammation and immunosuppression have largely failed to improve outcomes, underscoring the need for innovative approaches. This review examines the development of therapeutic agents for sepsis, with a focus on clinical trials addressing hyperinflammation and immunosuppression. It highlights the frequent failures of these trials, explores the underlying reasons, and outlines current research efforts aimed at bridging the gap between theoretical advancements and clinical applications. Although personalized medicine and phenotypic categorization present promising directions, this review emphasizes the importance of understanding the complex pathogenesis of sepsis and developing targeted, effective therapies to enhance patient outcomes. By addressing the multifaceted nature of sepsis, future research can pave the way for more precise and individualized treatment strategies, ultimately improving the management and prognosis of sepsis patients.

Hot and Fresh: Evidence of Climate-Related Suboptimal Water Conditions for Seagrass in a Large Gulf Coast Estuary

Seagrasses have long been a focal point for management efforts aimed at restoring ecosystem health in estuaries worldwide. In Tampa Bay, Florida (USA), seagrass coverage has declined since 2016 by nearly a third (11,518 acres), despite sustained reductions of nitrogen loads supportive of light environments for growth. Changing physical water quality conditions related to climate change may be stressing seagrasses beyond their optimal growth ranges, requiring an assessment to determine if this decline can be linked to climate stress. Three ambient water quality datasets of varying sampling designs and coverage were evaluated to characterize physicochemical environments in Tampa Bay and the potential relationships with seagrass change. Tampa Bay has become hotter and fresher with water temperature increasing by 0.03–0.04 °C per year and salinity decreasing by 0.04–0.06 ppt per year, translating to an increase of 1.3 to 1.7 °C and a decrease of 1.6 to 2.6 ppt over the last 50 years. Additionally, the number of days when temperature was above 30 °C or salinity was below 25 ppt has increased on average across all bay segments by 48 and 37 days, respectively, since 1975. These changes varied spatially and seasonally, with the most dramatic changes observed in the upper bay. Generalized Additive Models provided a weight-of-evidence that recent seagrass declines are somewhat associated with hotter and fresher conditions. Trends in warming and increased precipitation in the region are likely to continue, further creating suboptimal conditions for seagrasses in Tampa Bay. These results should compel resource managers to consider the likelihood that reduced resilience of estuarine resources due to shifting ecological baselines driven by additional climate change drivers will complicate long-standing management paradigms. While conventional management approaches that focus on limiting nutrient loads should be continued, their future effectiveness may be confounded by climate change drivers and warrant additional, complementary interventions and continuous monitoring data to support ecosystem health into the future.

Evaluation and management of knee osteoarthritis.

Knee osteoarthritis (KOA) significantly contributes to the global disability burden, with its incidence expected to escalate by 74.9% by 2050. The urgency to comprehend and tackle this condition is critical, necessitating an updated and thorough review of KOA. A systematic review up to February 26, 2024, has elucidated the principal aspects of KOA's pathogenesis, risk factors, clinical manifestations, and contemporary management paradigms. The origins of KOA are intricately linked to mechanical, inflammatory, and metabolic disturbances that impair joint function. Notable risk factors include age, obesity, and previous knee injuries. Diagnosis predominantly relies on clinical assessment, with radiographic evaluation reserved conditionally. The significance of rehabilitation assessments, informed by the International Classification of Functioning, Disability, and Health framework, is highlighted. Treatment strategies are diverse, prioritizing nonpharmacological measures such as patient education, exercise, and weight management, with pharmacological interventions considered adjuncts. Intra-articular injections and surgical options are contemplated for instances where conventional management is inadequate. KOA stands as a predominant disability cause globally, characterized by a complex etiology and profound effects on individuals' quality of life. Early, proactive management focusing on nonpharmacological interventions forms the cornerstone of treatment, aiming to alleviate symptoms and enhance joint function. This comprehensive review underscores the need for early diagnosis, individualized treatment plans, and the integration of rehabilitation assessments to optimize patient outcomes. Further research is needed to refine prevention strategies and improve management outcomes for KOA patients.

Nontubercular Primary Spinal Epidural Abscess – A Retrospective Study of 11 Culture-Positive Cases

Abstract Background: An epidural abscess that occurs in the absence of contiguous spread from vertebral osteomyelitis or following spinal surgery, spinal anesthesia, or open spinal injuries is considered to be a primary spinal epidural abscess (SEA). The triad of back pain, fever, and progressive neurological dysfunction has long been considered to be the hallmark of primary SEA. Objective: The vast majority of epidural abscesses in our country are either tubercular or associated with pyogenic spondylitis. Primary non tubercular spinal epidural abscesses are rare and also need a different paradigm of management in terms of need for stabilization and duration of antibiotic treatment that we wish to highlight. Material and Methods: Eleven consecutive cases of nontubercular primary SEA were operated by the corresponding author between 2010 and 2019. Their clinical presentation, magnetic resonance imaging (MRI) findings, blood investigations, bacteriological profile, and outcomes were extracted from the patient files and follow-up visits to the outpatient clinic and analyzed 5 years after the last case. Results: All patients had spinal pain at presentation. Seven patients had fever and ten had neurological deficits at presentation. Diabetes and renal failure were the most common comorbidities. All patients had elevated erythrocyte sedimentation rate and C-reactive protein at presentation. The total leukocyte count was not uniformly elevated. One patient who was human immunodeficiency virus positive with a low cell count could not be operated. Diagnosis in his case was established by computed tomography-guided aspirate of the pus. Of the remainder, one patient had no recovery following surgery and two patients had residual deficits. Gram-positive coverage was started empirically in all patients who were operated and based on cultures changed and later extended orally for 4 weeks after discharge till repeat MRI and blood cultures were satisfactory. Conclusions: SEA is a surgical emergency. The majority of cases are caused by Gram-positive bacteria, and many of the patients have comorbidities that need to be addressed. Patients with better preoperative neurological status and those with lumbar location of abscess tend to have a better recovery. Antibiotic treatment must be continued till postoperative radiological and hematological parameters are satisfactory.

Sustained Release Therapies with the Prostaglandin Analogues Intracameral Implants.

The travoprost intracameral implant was recently approved by the US Food and Drug Administration for sustained release medical treatment of open-angle glaucoma in the USA. The approval represents a substantial and progressive step forward in the area of sustained-release glaucoma therapy. Topical intraocular pressure-lowering medications for the treatment of glaucoma are faced with a host of challenges for long-term and usually lifelong care. A changing paradigm in glaucoma management involves first-line interventions with laser modalities, micro-invasive surgeries, and sustained-release treatment platforms. Future needs in the area of sustained-release therapy include a non-prostaglandin drug delivery platform and longer-term treatments that do not require surgical reintervention.

Adjuvant Therapy for Endometrial Cancer in the Era of Molecular Classification

ABSTRACT Endometrial cancer primarily undergoes surgical intervention, with adjuvant treatments such as external beam pelvic radiotherapy, vaginal brachytherapy, chemotherapy, and combined therapy investigated in randomized trials. Treatment decisions hinge on clinicopathological risk factors. Low-risk cases usually require surgery alone, whereas high-intermediate risk often benefit from adjuvant vaginal brachytherapy for enhanced local control with minimal side effects. Recent trials advocate pelvic radiotherapy for high-risk cases, particularly in Stage I–II tumors with risk factors. Chemoradiation proves advantageous for serous cancers and Stage III disease, improving recurrence-free, and overall survival. Molecular studies, notably the Cancer Genome Atlas project, identified four distinct molecular classes, transcending stages, and histological types. These molecular subtypes exhibit a stronger prognostic impact than histopathological characteristics, heralding a shift toward molecular-integrated diagnostics and treatments. Incorporating molecular factors into adjuvant strategies, including targeted therapies, marks a new paradigm in endometrial cancer management, underpinning ongoing research, and clinical trials. This review outlines current adjuvant approaches, underscores the emergence of molecular-integrated risk profiling, and touches on developments in targeted therapy.

GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT

Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical industrial working scenarios, equipment(s) stay in a normal condition for most of its lifespan, leading to a disproportionate training dataset, hampering the prediction accuracy. To overcome the above problems, we propose a data-driven multi-task learning framework aided by a novel least squares recurrent auxiliary classifier generative adversarial network (LS-R-ACGAN). LS-R-ACGAN employs recurrent neural networks (RNNs) in its generator & discriminator networks for multi-variate fault data generation while overcoming the vanishing gradient problem of ACGANs. Post-data-augmentation, a balanced training dataset, trains a multi-task learning model based on a deep gated RNN (DGRU) for joint HA and RUL estimation. Our simulations use the C-MAPSS dataset for testing the proposed approach’s accuracy. The final results showcase improvements by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 3.54% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 2.38% on the RMSE and Score metric over existing state-of-the-art works suggesting its competitiveness and competence for real-world implementations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study attacks multiple presumptions concerning the maintenance of complex systems including, ample availability of fault samples alongside normal samples and solving only one of the problems, i.e., health assessment (HA) or remaining useful life (RUL) estimations rather than both. Popular artificial intelligence (AI) based solutions rely heavily upon the quantity of training data for desired performance outcomes. However, in real-life industrial scenarios, fault instances are rare, inherently resulting in an imbalanced dataset. Additionally, it is both labor-intensive and costly to repeat multiple experiments for gathering required data as the equipment(s) pass through short-lived fault states. Thus conventional AI-based solutions may end up producing biased predictions. This work proposes to solve the imbalanced training dataset problem along with joint HA and RUL estimations, in one solution pipeline, currently unexplored in majority of the existing works in the literature. LS-R-ACGAN generates ample diverse fault samples for reducing the degree of imbalance between normal and fault classes. Subsequently, after data augmentation, the balanced training dataset helps train a multi-task learning model for joint HA and RUL estimations. The proposed framework has been implemented and tested on a benchmark dataset proving its superiority over multiple existing methods in the literature.