Primary Challenge Research Articles

Deep Learning-Based Integrated Circuit Surface Defect Detection: Addressing Information Density Imbalance for Industrial Application

In this study, we aimed to address the primary challenges encountered in industrial integrated circuit (IC) surface defect detection, particularly focusing on the imbalance in information density arising from difficulties in data sample collection. To this end, we have developed a new hybrid architecture model for IC surface defect detection (SDDM), based on ResNet and Vision Transformer (ViT). The core innovation of SDDM lies in the integration of the concepts of image information density and dataset information density, effectively identifying and processing areas of high information density through multi-channel image segmentation techniques. The convolution operations performed within each patch of the model help to precisely capture positional information, thereby meticulously differentiating the complex details on the surface defect detection of ICs. We optimized the model to make it more suitable for industrial applications, significantly reducing computational and operational costs. The experimental results confirmed that the improved SDDM model achieved an accuracy rate of 98.6% on datasets with uneven information density, effectively enhancing the productivity of IC packaging and testing companies, particularly in handling datasets with imbalanced information density.

Digital Literacy: Fundamental Competence for Modern Society

This research highlights the significance of digital literacy in our increasingly interconnected modern society through information technology. It addresses the primary challenges faced by modern society in managing information in the digital era, including the spread of fake news and data privacy breaches. The research aims to elucidate the contribution of digital literacy in addressing these challenges faced by modern society and identify its positive impacts in safeguarding data security, combating misinformation, and enabling active participation in the digital community. The research methodology employed a systematic literature review (SLR) to gather, evaluate, and synthesize literature related to modern society and digital literacy. The findings of the research reveal that digital literacy plays a pivotal role in assisting individuals in comprehending, filtering, and assessing information encountered online while safeguarding their privacy in the digital environment. In conclusion, digital literacy is not just a technical skill but also the key to responsibly and wisely harnessing digital opportunities.

Upcycled PVC support layer from waste PVC pipe for thin film composite nanofiltration membranes

Thin-film-composite (TFC) nanofiltration membranes represent the pinnacle of membrane technology in water treatment and desalination. These membranes typically consist of a polyamide (PA) selective layer and a membrane support layer, often constructed from commercially available materials like polyethersulfone (PES). However, there exists an alternative approach that involves the use of different polymers, preferably upcycled waste polymers, as a viable support layer for TFC membranes. Successfully implementing the upcycling of waste plastics into high-value support membranes can make a significant contribution to addressing the issue of plastic waste pollution. One of the primary challenges associated with utilizing upcycled polymers as support layers is the potential impact on the polyamide selective layer of TFC membranes, subsequently affecting their performance in terms of permeability, rejection, and antifouling properties. In this study, we demonstrate the successful fabrication of TFC membranes with a support layer crafted from upcycled waste PVC pipe. We conducted a comprehensive investigation into the effects of upcycled PVC on the structural and physicochemical properties of the polyamide layer. The ultrafiltration (UF) support membranes were fabricated from waste PVC pipe via the nonsolvent induced phase separation (NIPS) method. Subsequently, a polyamide layer was synthesized atop the Upcycled PVC membrane using interfacial polymerization (IP). The physicochemical properties and performance of the TFC membranes with upcycled PVC support layer were compared with membranes with research-grade (RG) PVC and commercial PES support layers. The results unveiled that the TFC membrane with upcycled PVC support layer exhibited higher water permeability (18.2 LMH/bar), in contrast to RG TFC (15.5 LMH/bar) and PES TFC membranes (13.7 LMH/bar). Furthermore, the salt rejection capabilities of the upcycled PVC TFC membrane were competitive and well within an acceptable range.

Asymmetric VR Game Subgenres: Implications for Analysis and Design

This paper identifies subgenres of asymmetric virtual reality (AVR) games and proposes the AVR Game Genre (AVRGG) framework for developing AVR games. We examined 66 games “in the wild” to develop the AVRGG and used it to identify 5 subgenres of AVR games including David(s) vs. Goliath, Hide and Seek, Perspective Puzzle, Order Simulation, and Lifeline. We describe these genres, which account for nearly half of the 66 games reviewed, in terms of the AVRGG framework that highlights salient asymmetries in the mechanics, dynamics, and aesthetics categories. To evaluate the usefulness of the AVRGG framework, we conducted four workshops (two with the AVRGG framework and two without) with novice game designers who generated 16 original AVR game concepts. Comparisons between the workshop groups, observations of the design sessions, focus groups, and surveys showed the promise and limitations of the AVRGG framework as a design tool. We found that novice designers were able to understand and apply the AVRGG framework after only a brief introduction. The observations indicated two primary challenges that AVR designers face: balancing the game between VR and non-VR player(s) and generating original game concepts. The AVRGG framework helped overcome the balancing concerns due to its ability to inspire novice game designers with example subgenres and draw attention to the asymmetric mechanics and competitive/cooperative nature of games. While half of those who used the AVRGG framework to design with created games that fit directly into existing subgenres, the other half viewed the subgenres as “creative constraints” useful in jumpstarting novel game designs that combined, modified, or purposefully avoided existing subgenres. Additional benefits and limitations of the AVRGG framework are outlined in the paper.

Giant Blue Energy Harvesting in Two-Dimensional Polymer Membranes with Spatially Aligned Charges.

Blue energy between seawater and river water is attracting increasing interest, as one of the sustainable and renewable energy resources that can be harvested from water. Within the reverse electrodialysis applied in blue energy conversion, novel membranes with nanoscale confinement that function as selective ion transport mediums are currently in high demand for realizing higher power density. The primary challenge lies in constructing well-defined nanochannels that allow for low-energy barrier transport. This work proposes a concept for nanofluidic channels with a simultaneous dual electrostatic effect that can enhance both ion selectivity and flux. To actualize this, this work has synthesized propidium iodide-based two-dimensional polymer (PI-2DP) membranes possessing both skeleton charge and intrinsic space charge, which are spatially aligned along the ion transport pathway. The dual charge design of PI-2DP significantly enhances the electrostatic interaction between the translocating anions and the cationic polymer framework, and a high anion selectivity coefficient (≈0.8) is reached. When mixing standard artificial seawater and river water, this work achieves a considerable power density of 48.4W m-2, outperforming most state-of-the-art nanofluidic membranes. Moreover, when applied between the Mediterranean Sea and the Elbe River, an output power density of 42.2W m-2 is achieved by the PI-2DP. This nanofluidic membrane design with dual-layer charges will inspire more innovative development of ion-selective channels for blue energy conversion that will contribute to global energy consumption.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

Stress analysis of a large diameter aspheric plastic lens in the variable temperature assisted injection molding process.

The technology known as precision injection molding (PIM) has shown great promise in the large-scale manufacturing of optical plastic lenses. The primary challenge with the PIM process is accurately predicting and reducing residual stress in optical plastic lenses. In this work, the finite element method (FEM) was used to analyze the residual stress distribution in plastic lenses. A three-dimensional model was created using COMSOL software to investigate how residual stress and temperature varied in optical plastic lenses during the packing and cooling stages. Based on the results, variable temperature assisted injection molding experiments were conducted. The results show that the average residual stress in the optical plastic lenses has decreased by 56%, while the minimum and maximum residual stress levels have decreased by 60% and 61%, respectively. Since this method does not require the extra heat treatment of the optical lenses, it offers considerable cost and efficiency benefits.

Analysis of the Development Trends and Key Challenges of Zero Carbon Buildings in Tropical Islands

In the context of global climate change, particularly in tropical island regions, the construction sector, a notable contributor to CO2 emissions, necessitates a concentrated effort towards enhancing energy efficiency and reducing emissions. This study delves into the evolution and the inherent challenges associated with the development of zero-carbon buildings in such environments. It commences with an examination of key global agreements and China's significant progress in its dual carbon strategy, with a specific focus on energy and emission reduction in the construction sector. The discourse then transitions to an in-depth analysis of the current state of zero-carbon building research in tropical islands, spotlighting the advancements made by Hainan's construction industry in the low-carbon transition and the deployment of zero-carbon technologies. The paper further articulates the emergence of zero-carbon buildings as a pivotal trend in future construction practices, underscoring the crucial role of integrated system design in realizing these objectives. Despite their theoretical and practical viability, zero-carbon buildings encounter significant implementation challenges in the unique context of tropical islands. These challenges include the need for precise system integration, the quantification of design parameters' impact on building performance, and the refinement of operational strategies for zero-carbon buildings. The study also highlights the critical need for targeted research and innovation in building materials and technologies, specifically adapted to the distinctive climatic conditions of tropical islands. This comprehensive analysis provides an extensive exploration of zero-carbon buildings in tropical settings, shedding light on the primary challenges and potential solutions, thereby offering valuable insights to foster sustainable growth in the construction sector.

Global primary care research challenges

Healthcare needs are changing globally as people live longer, but not necessarily in better health. Increased population migration is leading to culturally diverse patient populations with more diverse health needs and expectations. The provision of accessible and equitable primary care poses workforce, infrastructure and management challenges for high- and lower-middle income countries that transcend national borders. In this special issue, we describe ‘global primary care’ as primary care training, research and service delivery that places a priority on the needs and overall health of individuals and their communities, to advance health equity worldwide. Given this nascent definition, what should ‘global primary care research’ look like? The following case demonstrates the breadth of physical, mental, and social issues a primary care practitioner must consider when faced with a patient from a marginalised group in an inner-city practice and why training and research must have a focus on equity.

Optimizing Current Injection Technique for Enhancing Resistivity Method

Geo-electrical resistivity methods are widely used in various fields and have significant applications in scientific and practical research. Despite the widespread use of resistivity methods, current injection is a critical step in the process of resistivity methods, and the quality of current injection significantly impacts the accuracy of the resistivity measurements. One primary challenge is optimizing current injection techniques to enhance resistivity methods. The developed current injector model for the resistivity meter instrument enhances performance by increasing the voltage source to 400 Volts, extending measurement coverage. It provides three injection current options, 0.5A, 0.8A, and 1A, for efficient accumulator use, considering electrode distances and estimating earth resistance using Contact Resistance Measurement (CRM) to estimate the earth resistance. CRM mode ensures proper electrode connection before injection, thus improving measurement efficiency. The embedded TTGO LoRa ESP32 SX1276 facilitates wireless communication over 1.5 km, addressing challenges in remote and internet-limited areas. The model demonstrates reliability, validity, and durability in CRM mode and current injection measurement. Regarding reliability, we determine the relative error of the model by carrying out measurements repeatedly. In lab-scale testing, the average Relative Error in CRM mode is 0.65%, and in earth resistance measurement testing, it is 1.58%. These relative errors are below the 2% maximum error applied in the “Supersting”, a commercial resistivity instrument. The model's validity is defined by comparing the model with the measuring instrument; we have absolute error. In lab scale testing, the average Absolute Error in CRM mode is 3.08%, and in earth resistance measurement testing, it is 3.73%. The model's durability is tested by injecting current for a minute. After one minute of current injection, the power resistor component's temperature is stable at 30°C.

A novel extreme adaptive GRU for multivariate time series forecasting

Multivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly enhanced the ability to tackle such problems. However, a primary challenge in time series forecasting comes from the imbalanced time series data that include extreme events. Despite being a small fraction of the data instances, extreme events can have a negative impact on forecasting as they deviate from the majority. However, many recent time series forecasting methods neglect this issue, leading to suboptimal performance. To address these challenges, we introduce a novel model, the Extreme Event Adaptive Gated Recurrent Unit (eGRU), tailored explicitly for forecasting tasks. The eGRU is designed to effectively learn both normal and extreme event patterns within time series data. Furthermore, we introduce a time series data segmentation technique that divides the input sequence into segments, each comprising multiple time steps. This segmentation empowers the eGRU to capture data patterns at different time step resolutions while simultaneously reducing the overall input length. We conducted comprehensive experiments on four real-world benchmark datasets to evaluate the eGRU’s performance. Our results showcase its superiority over vanilla RNNs, LSTMs, GRUs, and other state-of-the-art RNN variants in multivariate time series forecasting. Additionally, we conducted ablation studies to demonstrate the consistently superior performance of eGRU in generating accurate forecasts while incorporating a diverse range of labeling results.

Progress on Electron Transport Layers for Perovskite Solar Cells

The photovoltaic industry is very interested in designing and developing next-generation device architectures using organic-inorganic perovskite hybrid solar cell materials. In fact, perovskites represent one of the most promising materials for high efficiency, low-cost solar cells. This is most apparent in the power conversion efficiency of perovskite solar cells (PSCs) going from 3.8 to 24.2 % in recent years. One of the primary challenges of developing PSC’s however is the realization of an appropriate electron transport layer. As such, this review focuses on recent developments in the electron transport layer (ETL) of perovskite solar cells. It examines and summarises designs, electron transport layers and perovskite active layers for efficient perovskite solar cells. The performance and stability issues with organic-inorganic halide perovskite solar cells are also discussed with some recommendations for additional research on the ETL and perovskite active layer were offered.

Enhancing medical image classification with convolutional neural networks through transfer learning: A comprehensive review

Medical imaging has become crucial in diagnostics, but manual analysis of the vast amount of data is cumbersome. Transfer learning, utilizing techniques like data augmentation, has addressed the challenges faced by deep learning methods like CNNs in this domain, improving classification accuracy. This study offers a comprehensive review of the applications and potential benefits of deep learning and transfer learning in medical image classification while also shedding light on prospective challenges and avenues for future research. After a comprehensive review and analysis of the literature, it can be inferred that transfer learning addresses the primary challenges deep learning faces in the medical field, enhancing its applicability. While deep learning has substantial potential, it grapples with issues of model "black-box" interpretability, where decisions made by the model are hard for clinicians to understand, and potential breaches of individual data security given the sensitive nature of medical records.

STRATEGI DAN TANTANGAN MANAJEMEN TEKNOMEDIA DI LEMBAGA PENDIDIKAN ANAK USIA DINI

Digital learning media management is a crucial skill required by the heads of educational institutions to navigate the digital era. This research aims to depict: (1) the challenges faced by the leaders of Early Childhood Education (PAUD) institutions in Merauke City in managing technomedia in the learning process, and (2) the strategies employed by these leaders to overcome challenges in technomedia management during the learning process. The informants for this study were purposefully sampled from school principals in Merauke City. The primary challenges confronted by school principals in managing digital learning media in Early Childhood Education institutions (PAUD) include the availability of adequate technological resources, the development of teachers' skills in using technology, and the management of student data security and privacy. School principals also need to take into account parental involvement in supporting the use of digital media at home and ensure that technology use remains balanced with the physical and social activities of children. To tackle these challenges, strategies employed by school principals include organizing training and developing teachers' skills in technology use, selecting quality digital learning content, and establishing clear policies and guidelines related to technology use. Additionally, careful monitoring of students' technology use and maintaining a balance between technology use and traditional learning experiences are also emphasized. By implementing these strategies, PAUD school principals can effectively manage digital learning media, enhance the quality of early childhood education, and ensure that technology is used judiciously in the learning process.

Carbon Fiber Composites Recycling Technology Enabled by the TuFF Technology

Carbon Fiber Composites (CFCs) recycling has received increasing interest by the composites industry, but it is still in its early stages as an industry. There are two primary challenges that need to be addressed in order to achieve full property retention during CFC recycling: (1) the ability to recover the fiber content without property loss; and (2) conversion of the recycled, short fiber material back into high-performance CFC structures. The ability to manufacture high fiber volume fraction CFCs with end-of-life products would provide an opportunity to reduce material cost and lifetime-embodied energy. In this paper, recycled, short carbon fibers are processed via solvolysis and converted into high-performance CFCs with fiber volume fraction of ~50% and excellent composite property retention. This is enabled through alignment of the discontinuous, recycled fiber feedstock using the Tailorable universal Feedstock for Forming (TuFF) process. The paper introduces the necessary steps to process the fibers in the wet-laid process and explores the resulting mechanical and microstructural properties. The importance of incoming fiber surface quality and the effect of surface contamination from residue left by the recycling process on both the TuFF process and final composite properties is discussed in detail. A pyrolytic process has been adopted to remove the residue that is a by-product of the recycling process from the incoming recycled fiber surface. The approach presents a promising pathway for the recycling of high-performance CFCs.

A Model for Improving Business Performance – The evidence from Women Entrepreneurs in West Java, Indonesia

Gender is an important issue in modern businesses, especially for women-owned MSEs with insufficient work experience. Research indicates that among the primary challenges female entrepreneurs face are cultural norms, gender roles, and commercial incentives. The study focuses on the challenges faced by women-owned Micro and Small Enterprises (MSEs) in East Java Province, Indonesia, particularly in terms of gender roles and commercial incentives. The research aims to improve business performance in these MSEs by analyzing their situational aspects and developing a conceptual public policy model. The findings suggest that government involvement is crucial for fostering business performance improvement in these MSEs. Women also demonstrate leadership abilities, and strategic assumptions such as business networks, centralized coaching programs, and central government roles can significantly enhance their performance. The model is based on seven major issues affecting MSE business performance, including goods, data, competency, and public policy.

Понятность языка правосудия: опыт эмпирического исследования содержания и синтаксиса судебных решений

For many years, the primary challenges in the legal sphere regarding language in legal proceedings have been the utilisation of national languages of the republics and foreign access to justice. However, the authors of this paper hypothesise that citizens and organisations of Russian origin often require assistance in translating “legal Russian” to “everyday Russian”. This is due to the continuing syntactic complexity found in court decision language despite widespread attempts to simplify the presentation of information. The authors’ previous research into speech practices of laws and bylaws supports this hypothesis. The authors designed research questions to investigate the hypothesis of changes in the comprehensibility of the language used in court decisions over time. Additionally, they aimed to determine whether such changes depend on the type of courts, the category of cases, or the court instance level. Empirical research methods were used to examine not only whole court decisions but also their elements. Moreover, a representative sample exceeding 15,000 texts was studied. The analysis was conducted at different parts in court decisions including introduction, description, reasoning, and operation. Additional fragments such as facts, citations of legal acts, analysis of evidence, and judge’s conclusions were also studied. The methods of computational linguistics confirmed a consistent increase in the syntactic complexity of court decisions over time. According to the author’s methodology, court decisions are among the most complex legal texts. In essence, legal acts citations contribute to the bulk of these texts, making up the largest proportion (40% on average) in the descriptive and reasoning parts of the decision, compared to other semantic fragments. The research enabled the authors to compile a catalogue of detrimental speech practices used in court decisions, alongside recommendations for their rectification.

Analysis of the bonding’s energy in metal-halide perovskites and brief evaluation of meta-GGA functionals TPSS and revTPSS

Metal-halide perovskites, known for their remarkable photovoltaic performance and ease of production, have garnered global attention in material science. Addressing scalability requires tackling the technology’s primary challenge: instability. Crucial insights into the complex chemistry of these materials are imperative for progress. The present study focused on well-known perovskites, namely CsPbI3, CH3NH3PbI3 and HC(NH2)2PbI3. Through both cohesive energy and ICOHP analysis, the chemical bonding of these compounds. Additionally, a comparative evaluation of the functionals of TPSS, revTPSS, HCTH/407, and PBE was made through bandgap determination. The key findings of this study were: i) having confirmed the predominantly ionic nature of lead halide interactions; ii) having pointed out the predominantly covalent nature of the molecules’ constituents binding; iii) having found that the strongest hydrogen bonds are formed by methylammonium; and iv) having nourished the utility of the TPSS meta-GGA functional in calculating the band gap of organic–inorganic perovskites. The results presented here could be important to the understanding and description of metal halide perovskite materials.

Hydraulically Fractured Horizontal Wells: A Technology Poised To Deliver Another Energy-Related Breakthrough of Enormous Scale

_ The development of efficient technologies for drilling and hydraulically fracturing horizontal wells has enabled the US to more than double hydrocarbon production since 2005 (Fig. 1), thereby providing unprecedented levels of energy security for America. America’s doubling of hydrocarbon output has also held down the price of energy worldwide, and by doing so, accelerated global economic growth. And it has helped reduce the greenhouse gas (GHG) intensity of energy production by backing out “dirtier” forms of energy, such as coal. Energy security—economic growth—reduced GHGs vented to the atmosphere: That’s a winning combination. One that America and many other countries have benefitted from immensely. Given the enormous positive contributions, it is worth noting that 20 years ago, few if any in our industry foresaw the immense potential of this technology, seeing it as being only applicable for extracting gas from ultratight reservoirs like the Barnett Shale, if they were aware of the technology at all. This oversight caused many companies to wait too long before deciding to pursue unconventional reservoirs and caused several of the “shale gas” pioneers to be late in recognizing that hydraulically fractured horizontal wells (HFHWs) could also be successfully applied in liquid-rich plays such as the Eagle Ford and Permian Basin. These are plays that today deliver far more value than that derived from the gas-prone reservoirs that comprised the initial suite of targets. And while events have proven beyond a doubt that HFHWs are a powerful tool for economically extracting hydrocarbons from both gas-prone and liquids-rich unconventional reservoirs, it seems likely that many in our industry are overlooking a third significant application of this technology: The use of HFHWs to extract heat from the Earth’s crust that can be utilized to generate electricity. Old Story, New Horizon What makes this third application particularly compelling as an investment opportunity is that the primary physical challenge that needs to be overcome to achieve attractive rates of return is strikingly similar to that which the oil and gas industry had to surmount to make both gas and liquids-rich unconventional reservoirs economic. The key to success in all of these cases boils down to an ability to create via hydraulic stimulation a sufficiently large amount of conductive, connected, fracture surface area. With this, one can reliably expect per-well production rates to be economic given the extremely slow rate at which hydrocarbons—and heat—move through unconventional reservoirs and the hot, dry, basement rocks that contain the bulk of the world’s geothermal resources. That converting from vertical to horizontal well geometries was critical for unlocking the potential of unconventional hydrocarbon reservoirs is now obvious, with this switch having allowed petroleum engineers to increase per-well fracture surface areas by several orders of magnitude. This move increased per-well flow rates by similar amounts (i.e., from subeconomic flow rates from hydraulically fractured vertical wells (HFVWs) to flow rates of thousands of BOE/D from HFHWs.

Comprehensive perspective towards the management of proteinopathies by elucidating protein misfolding and aggregation.

Protein misfolding and aggregation is the phenomenon of the generic propensity of proteins, considered as a dark side of the protein world, and its exact mechanism is still not deciphered. Understanding the complexity of protein aggregation is currently the primary apprehension and challenge in biology and medicine due to their association with various debilitating human proteinopathies and neurodegenerative diseases. The mechanism of protein aggregation, associated diseases, and the development of efficient therapeutic strategies against these diseases are very challenging. These diseases are caused by different proteins, each protein with different mechanisms and consisting of various microscopic phases or events. These microscopic steps are functioning on different timescales during aggregation. Here, we highlighted the different features and current trends in protein aggregation. The study thoroughly recapitulates the various factors influencing, possible causes, types of aggregates and aggregation, their different proposed mechanisms, and the methods used to study the aggregation. Additionally, the formation and elimination of misfolded or aggregated proteins in the cell, the role of the ruggedness of the protein folding landscape in protein aggregation, proteinopathies, and the challenges for their prevention are comprehensively elucidated. A holistic understanding of different aspects of aggregation, molecular steps governing the various features of protein quality control, and crucial queries about the modulation of these processes and their interactions with other systems in cellular protein quality control can be considered conducive to comprehending the mechanism, designing effective approaches towards prevention of protein aggregation, rationalizing the etiology and development of novel strategies against therapy and management of the proteinopathies.