Processing Platform Research Articles

Development of continuous processing platform utilizing aqueous two-phase extraction for purification of monoclonal antibodies

Monoclonal antibody downstream processing typically entails chromatography-based purification processes beginning with Protein A chromatography, accounting for 50% of the total manufacturing expense. Alternatives to protein A chromatography have been explored by several researchers. In this paper, aqueous two-phase extraction (ATPE) has been proposed for continuous processing of monoclonal antibodies (mAbs) as an alternative to the traditional protein A chromatography. The PEG-sulphate system has been employed for phase formation in ATPE, and the mAb is separated in the salt phase, while impurities like high molecular weight (HMW) and host cell proteins (HCPs) are separated in the PEG phase. Following ATPE of clarified cell culture harvest, yield of ≥ 80 % and purity of ≥ 97% were achieved in the salt phase. Considerable (28 %) reduction in consumable cost has been estimated when comparing the proposed platform to the traditional protein A based platform. The outcomes demonstrate that ATPE can be a potentially effective substitute for the traditional Protein A chromatography for purification of mAbs. The proposed platform offers easy implementation, delivers comparative results, and offers significantly better economics for manufacturing mAb-based biotherapeutics.

Research on Management and Optimization of Big Data Computing Engine Based on Cloud Native Technology IT Architecture

In the wave of digital transformation, we are in a data-driven era. With the explosive growth of data volume, traditional big data processing platforms are facing more and more challenges, including insufficient computational efficiency and resource utilization. At the same time, cloud native technology has emerged as a key factor in filling this gap due to its flexibility, automated management, and cross platform compatibility. Although cloud native technology has broad prospects, its application in the field of big data is still in its early stages, and there are still many problems that need to be solved. Among them, the unity of cluster management, the collaboration between operation and development, and the efficiency of technology architecture transformation are the main challenges currently faced. To address these issues, we will conduct in-depth research around cloud native technology, big data computing engine technology, and containerized services, exploring and implementing a big data computing engine management and optimization solution based on cloud native technology. By proposing and implementing cloud native deployment methods, containerized construction methods, and a one-stop big data application management and development platform, the aim is to provide new ideas and methods for solving many problems in traditional big data computing engine management, and to contribute to the development and innovation of the big data field.

Study on Deleveraging and Downshifting of Real Estate Enterprises in the Context of Artificial Intelligence

Abstract With the decrease in the heat of the real estate market, the structural problems of real estate enterprises have become increasingly prominent, which are mainly reflected in high inventory, high leverage and high cost. After analyzing the types and underlying factors of financial risks faced by real estate enterprises, this paper explains the “three red lines” policy of the deleveraging mechanism for real estate enterprises. Based on artificial intelligence technology, an intelligent financial data processing platform has been established, and PCA and K-Means algorithms are used to process financial data. The association rule mining was introduced to analyze the financial data. The deep meaning of the financial data was mined through the weighted Apriori algorithm. The deleveraging strategy of real estate enterprises was established in combination with the financial decision-support process. The application effect of Group W’s deleveraging strategy was analyzed by selecting it as the research object. In 2021, the current ratio was 0.03 times different from the industry average, the asset-liability ratio decreased to 75.69%, and the overall cash balance at the end of the period increased by 11.2 times. After 2018, the “three red lines” indicators were all met, and it successfully ranked among the green enterprises, with the highest cumulative excess rate of return reaching 5.46%. Based on intelligent financial data processing, it can provide decision-making support for real estate enterprises to optimize their structure and realize the deleveraging, downshifting, and efficiency increase of real estate enterprises.

CellShip: An Ambient Temperature Transport and Short-Term Storage Medium for Mammalian Cell Cultures.

Cell culture is a critical platform for numerous research and industrial processes. However, methods for transporting cells are largely limited to cryopreservation, which is logistically challenging, requires the use of potentially cytotoxic cryopreservatives, and can result in poor cell recovery. Development of a transport media that can be used at ambient temperatures would alleviate these issues. In this study, we describe a novel transportation medium for mammalian cells. Five commonly used cell lines, (HEK293, CHO, HepG2, K562, and Jurkat) were successfully shipped and stored for a minimum of 72 hours and up to 96 hours at ambient temperature, after which, cells were recovered into standard culture conditions. Viability (%) and cell numbers, were examined, before, following the transport/storage period and following the recovery period. In all experiments, cell numbers returned to pretransport/storage concentration within 24-48 hours recovery. Imaging data indicated that HepG2 cells were fully adherent and had established typical growth morphology following 48 hours recovery, which was not seen in cells recovered from cryopreservation. Following recovery, Jurkat cells that had been subjected to a 96 hours transport/storage period, demonstrated a 1.93-fold increase compared with the starting cell number with >95% cell viability. We conclude that CellShip® may represent a viable method for the transportation of mammalian cells for multiple downstream applications in the Life Sciences research sector.

Elastic Gateway SoC design: A HW-centric architecture for inline In-Vehicle Network processing

The concepts of future mobility such as autonomous, connected, electric and shared vehicles are bringing a huge revolution to the automotive sector. We are seeing technologies typical from data centers fully embedded into vehicles, shifting from a mechanical-centric product, to an electronics-centric one. All the sensors and actuators embedded in vehicles need to exchange data in real time, in a safe and reliable way. As a result, the field of in-vehicle network (IVN) processing is currently an active research area. In previous work, we derived the requirements of future vehicle network processors and analyzed the state of the art of network processing platforms. From our study we concluded that there is currently no solution available capable of fulfilling all the requirements with the right level of performance. Now, in this work, we evaluate the novel Elastic Gateway (eGW) architecture which aims at fulfilling this gap, advancing towards future Gateway System/Network on Chip (SoC/NoC) solutions. Elastic Gateway SoC concept aims at synthesizing a scalable and future proof architecture embracing all new and already established functions and features demanded in a zonal gateway controller for the new era of mobility. It is composed of a set of configurable IP cores that allow for a full HW-based datapath implementation targeting good enough Quality of Service (QoS) and the minimum possible latency. We provide details of the internal architecture and how the different technologies required in future IVNs are integrated in eGW. With this, we are able to show how eGW meets the requirements of future network processing devices, enabling thus the current revolution.

A Multidimensional Data Collection and Edge Computing Analysis Method

With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis.

Redox-Mediated Enantioselective Interactions in Chiral Metallopolymers Possessing Supramolecular Chirality

Synthetic chiral platforms can be a powerful platform for enantioselective interactions, especially when coupled with redox-mediated electrochemical processes. Redox metallopolymers represent a distinct class of versatile functional materials and have been extensively studied for a wide scope of applications such as membranes, self-assembly, hydrogels, and redox-mediated ion separation. However, many studies have focused on molecularly-selective binding and release of achiral molecules, with limited studies on chirality strategies for enantioselective interactions. Chiral organic anions comprise an important class of biologically-relevant molecules, and their separation and sensing have received intense attention. Here, we introduce new chiral redox-polymers with chiral ferrocene (Fc) building blocks, that can serve as an electro-responsive enantioselective platform for a variety of amino acids and pharmaceutical carboxylates, taking advantage of supramolecular chirality for enhancing electrochemical recognition. Two chiral redox-metallopolymers were designed and synthesized based on Ugi’s amine-inspired chiral monomers containing an amide (poly-1) or amine group (poly-2), and their enantioselective recognitions toward amino acids and pharmaceutical carboxylates were investigated by redox potential shifts using square wave voltammetry. The half potential shift of both poly-(S)-1 and poly-(S)-2 increased more with increasing D-tryptophan concentration than the other enantiomer, L-tryptophan, while the half potential of poly-(R)-1 and poly-(R)-2 shifted more with L-tryptophan than with D-tryptophan, proving the enantioselective interaction of both redox-metallopolymers. Nuclear Overhauser Effect spectroscopy (2D NOESY) and solid-state circular dichroism revealed the emergence of supramolecular chirality of the polymers resulting from the intramolecular interaction between the ferrocene and the alkyl group in the backbone, and the steric hindrance of the ferrocene within the polymer. The half-peak potential shift demonstrated the remarkable enhancement of enantioselective recognition with tryptophan (Trp) and naproxen, provided by the chiral polymers possessing the supramolecular chirality over the single-site, chiral building block itself. For instance, the two (S) polymers showed the potential shift difference 2.4-2.8 times higher than the corresponding monomers upon the binding of Trp enantiomers. The enantioselective potential shift of the chiral metallopolymer electrodes could determine enantiomeric excess (%ee) of Trp over the range of 0–100%ee. Investigation on solvent polarity and pH effect revealed that the enantioselective mechanism of the chiral redox polymers was attributed to the subtle balance between hydrogen bonding and π-π interaction. We also identified the importance of aromatic groups on the analytes for increasing enantioselectivity of the chiral redox polymers. This work highlights the potential of chiral redox-metallopolymers as platforms for electrochemically-modulated enantioselective processes toward a variety of amino acids and pharmaceutical carboxylates. Figure 1

Direct Observation of Solid-Phase Crystallization of an Amorphous Complex Oxide in the Transmission Electron Microscope

Many complex metal oxides possess intricate structure-function relations, resulting in a wide range of tunable functional properties. The introduction of disorder through amorphization provides additional opportunities for tuning the properties, particularly in the area of electrochemical energy storage and conversion [1]. Consequently, amorphous and partly crystalline complex oxides have garnered growing interest in recent years, with two primary goals: to harness their unique functional properties or to employ them as a chemically-flexible, far-from-equilibrium, thin-film processing platform that offers novel synthesis opportunities [2] due to the minimal energy input required to initiate crystallization and other structural relaxation processes.Relevant to both these aims, the processing-structure-function relationship for amorphous complex oxides is examined by in situ solid-phase crystallization (SPC) in the (scanning) transmission electron microscope ((S)TEM), focusing on perovskite-like (ABO3) mixed ion-electron conducting (MIEC) amorphous complex oxides which make promising low-temperature solid-oxide cell (SOC) electrodes [3] due to the widely observed disorder-mediated oxygen surface-exchange [4]. Key aspects of MIEC amorphous complex oxide processing, such as the evolution of structure and chemistry with temperature and the corresponding effect on conductivity are detailed in the amorphous La0.8Sr0.2MnO3-δ (LSM) system. Further, approaches to direct amorphous complex oxide processing towards desirable synthesis products based on these insights are discussed.Here, we elucidate down to the atomic scale the development of structural characteristics such as porosity, crystallized fraction, crystallite tension, size, curvature, and surface termination, with the aim to establish approaches to tune surface functionality in partly crystalline MIECs by heating or electron beam irradiation [5]. In situ electrical measurements are used to correlate these changes with electrical behavior, revealing that the onset of crystallization is correlated with an increase in electronic conductivity by small polaron hopping, attributed to oxygen-metal-oxygen bond alignment. Further, in the absence of epitaxial constraints, an immiscibility-driven self-organization of amorphous heterointerfacial structures manifests by spinodal decomposition, that is likely to be broadly relevant in low-temperature processing of amorphous complex oxides [6]. Thus, processing from an amorphous complex oxide precursor could be a method to establish uniformly-dispersed nanoscale heterointerfaces of tunable characteristic length scale throughout a thin film volume, while also enabling direct tuning of the porous mesostructure by electron beam irradiation. Overall, case studies of amorphous complex oxide crystallization are limited to relatively few chemical systems and processing conditions. Thus, extending this body of work to explore a broader processing space, and extracting fundamental insights to establish process-structure-function relationships are prerequisites to realizing the full potential of partly crystallineMIEC systems, and provides guidance for processing amorphous complex oxides generally. Acknowledgments The authors acknowledge the UC Irvine School of Engineering new faculty setup funds, the National Science Foundation CAREER award (DMR-2042638) and the UC Irvine Materials Research Institute (IMRI). References S. Yan ... H. Xia, Small (2019)D. Prakash, ... F. Cavallo, Small (2022).J. Zhang ... Y. Chen, Advanced Functional Materials (2022)T. Chen ... N. Perry, ACS Applied Materials & Interfaces (2019)J. Wardini ... W. Bowman (submitted)J. MacManus-Driscoll, Advanced Functional Materials (2010)

A comprehensive systematic review: Impact of Land Use/ Land Cover (LULC) on Land Surface Temperatures (LST) and outdoor thermal comfort

Globally, urbanization-driven changes in Land Use and Land Cover (LULC) jeopardize Land Surface Temperatures (LSTs) and the outdoor thermal comfort of urban residents. While urban heat stress's impact on outdoor thermal comfort garners ample scholarly attention, research on the parallel influence of LULC remains limited. This paper comprehensively reviews prevalent tools for collecting LULC data, criteria for evaluating LST and outdoor thermal comfort, correlation measures and mitigation strategies. Focused on peer-reviewed publications from the last 25 years, our data is drawn from Scopus, Web of Science, Google Scholar and Science Direct.Our findings reveal Landsat and geographic information systems (GIS) as predominant datasets and processing platforms for LULC and LST information delineation. Common outdoor thermal comfort indices include the Universal Thermal Climate Index (UTCI), Physiologically Equivalent Temperature (PET) and Surface Urban Heat Island Intensity (SUHII). Validation employs Normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and Pearson's correlation. Geographically, Asia and Africa feature prominently in studies, with Europe comparatively underrepresented. We also highlight key LULC mitigation strategies, including augmenting vegetation in moderate climates, effective albedo management in warmer climates, and optimizing urban layouts for enhanced outdoor thermal comfort.

Quality-integrated diagnostic platform for aerospace complex product assembly processes

Addressing the challenge of tracing and quantifying quality issues that emerge during the assembly process of intricate aviation products, this paper introduces a Quality-Integrated Diagnostic Platform (QIDP). The platform effectively merges machine learning algorithms with the Fuzzy Analytical Hierarchy Process (FAHP) to provide comprehensive solutions for complex aviation product quality assessment. Firstly, the viability of amalgamating assembly quality management with the random forest diagnostic algorithm is presented. Secondly, leveraging the attributes inherent to the intricate assembly process, alongside algorithmic model development and the underpinning of an information technology platform, a novel architecture is introduced for diagnosing the sources of quality issues within the aviation complex component assembly process. Then, building upon the architectural framework, this paper elucidates the design and implementation procedure of the diagnostic algorithm that amalgamates the FAHP with the random forest technique. To illustrate, the bushing assembly process within the aerospace sector is taken as an example. Finally, grounded in the model framework and fusion algorithm, the Assembly Process QIDP is conceived and actualized. The platform seamlessly integrates data provisioning and retrieval components for initial deployment within the aerospace cluster. These components encompass data acquisition, data processing, algorithmic construction, and the visualization of diagnostic outcomes. The achieved diagnostic results effectively fulfill expert evaluation criteria and offer a swift, pragmatic, and intelligent resolution for addressing quality fluctuations within the assembly process of aerospace enterprises.

Rescheduling serverless workloads across the cloud-to-edge continuum

Serverless computing was a breakthrough in Cloud computing due to its high elasticity capabilities and fine-grained pay-per-use model offered by the main public Cloud providers. Meanwhile, open-source serverless platforms supporting the FaaS (Function as a Service) model allow users to take advantage of many of their benefits while operating on the on-premises platforms of organizations. This opens the possibility to deploy and exploit them on the different layers of the cloud-to-edge continuum, either on IoT (Internet of Things) devices located at the Edge (i.e. next to data acquisition devices), in on-premises clusters closer to the data sources (i.e. Fog computing) or directly on the Cloud.This paper presents two strategies to mitigate the overload that disparate data ingestion rates may cause in low-powered devices at the Edge or Fog layers. To this end, it is proposed to delegate and reschedule serverless jobs between the different layers of the cloud-to-edge continuum using an open-source platform for event-driven file processing. To demonstrate the performance of these strategies, a use case for fire detection is proposed that includes processing in the Fog via minified Kubernetes clusters located near the Edge, in the private Cloud via on-premises elastic clusters and, finally, in the public Cloud by using the AWS (Amazon Web Services) Lambda FaaS service. The results indicate that these strategies can mitigate overloads in use cases involving processing across the cloud-to-edge continuum by coordinating several layers of computing resources.

An Integrated Voice Recognition and Natural Language Processing Platform to Automatically Extract Thoracolumbar Injury Classification Score Features From Radiology Reports

Many predictive models for estimating clinical outcomes after spine surgery have been reported in the literature. However, implementation of predictive scores in practice is limited by the time-intensive nature of manually abstracting relevant predictors. In this study, we designed natural language processing (NLP) algorithms to automate data abstraction for the thoracolumbar injury classification score (TLICS). We retrieved the radiology reports of all Mayo Clinic patients with an International Classification of Diseases, 9th or 10th revision, code corresponding to a fracture of the thoracolumbar spine between January 2005 and October 2020. Annotated data were used to train an N-gram NLP model using machine learning methods, including random forest, stepwise linear discriminant analysis, k-nearest neighbors, and penalized logistic regression models. A total of 1085 spine radiology reports were included in our analysis. Our dataset included 483 compression, 401 burst, 103 translational/rotational, and 98 distraction fractures. A total of 103 reports had documented an injury of the posterior ligamentous complex. The overall accuracy of the random forest model for fracture morphology feature detection was 76.96% versus 65.90% in the stepwise linear discriminant analysis, 50.69% in the k-nearest neighbors, and 62.67% in the penalized logistic regression. The overall accuracy to detect posterior ligamentous complex integrity was highest in the random forest model at 83.41%. Our random forest model was implemented in the backend of a web application in which users can dictate reports and have TLICS features automatically extracted. We have developed a machine learning NLP model for extracting TLICS features from radiology reports, which we deployed in a web application that can be integrated into clinical practice.

Development and evaluation of a program based on a generative pre-trained transformer model from a public natural language processing platform for efficiency enhancement in post-procedural quality control of esophageal endoscopic submucosal dissection.

Post-procedural quality control of endoscopic submucosal dissection (ESD) is emphasized in guidelines. However, this process can be tedious and time-consuming. Recently, a pre-training model called generative pre-trained transformer (GPT) on a public natural language processing platform has emerged and garnered significant attention, whose capabilities align well with the post-procedural quality control process and have the potential to streamline it. Therefore, we developed a simple program utilizing this platform and evaluated its performance. Esophageal ESDs were retrospectively included. The manual quality control process was performed and act as reference standard. GPT's prompt was optimized through multiple iterations. A Python program was developed to automatically submit prompt with pathological report of each ESD procedure and collect quality control information provided by GPT. Its performance on quality control was evaluated with accuracy, precision, recall, and F-1 score. 165 cases were involved into the dataset, of which 5 were utilized as the prompt optimization dataset and 160 as the validation dataset. Definitive prompt was achieved through seven iterations. Time spent on the validation dataset by GPT was 13.47 ± 2.43min. Accuracies of pathological diagnosis, invasion depth, horizontal margin, vertical margin, vascular invasion, and lymphatic invasion of the quality control program were (0.940, 0.952) (95% CI), (0.925, 0.945) (95% CI), 0.931, 1.0, and 1.0, respectively. Precisions were (0.965, 0.969) (95% CI), (0.934, 0.954) (95% CI), and 0.957 for pathological diagnosis, invasion depth, and horizontal margin, respectively. Recalls were (0.940, 0.952) (95% CI), (0.925, 0.945) (95% CI), and 0.931 for factors as mentioned, respectively. F1-score were (0.945, 0.957) (95% CI), (0.928, 0.948) (95% CI), and 0.941 for factors as mentioned, respectively. This quality control program was qualified of post-procedural quality control of esophageal ESDs. GPT can be easily applied to this quality control process and reduce workload of the endoscopists.

A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms

Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. Cellular Genetic Algorithms (cGAs) have been competitive at difficult single objective combinatorial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.

Observing inter-well and intra-well oscillations in buckled nanomechanical systems enabled by image processing

The scanning electron microscope (SEM) recordings of dynamic nano-electromechanical systems (NEMS) are difficult to analyze due to the noise caused by low frame rate, insufficient resolution, and blurriness induced by applied electric potentials. Here, we develop an image processing platform enhanced by the physics of the underlying system to track the motion of buckling NEMS structures in the presence of high noise levels. The algorithm is composed of an image filter, two data filters, and a nonlinear regression model, which utilizes the expected form of the physical solution. The method was applied to the recordings of a NEMS beam about 150 nm wide, undergoing intra- and inter-well post-buckling states with a transition rate of approximately 0.5 Hz. The algorithm can track the dynamical motion of the NEMS and capture the dependency of deflection amplitude on the compressive force on the beam. With the help of the proposed algorithm, the transition from inter-well to intra-well motion is clearly resolved for buckling NEMS imaged under SEM.

Manipulating Coherent Exciton Dynamics in CsPbI3 Perovskite Quantum Dots Using Magnetic Field.

Lead halide perovskite quantum dots (QDs) have recently emerged as a promising material platform for quantum information processing owing to their strong light-matter interaction and relatively long-lived optical and spin coherences. In particular, the coherence of the fine-structure bright excitons is sustainable up to room temperature and can be observed even at an ensemble level. Here modulation of the polarization of these excitons in CsPbI3 QDs and manipulation of their time-domain coherent dynamics using a longitudinal magnetic field are demonstrated. The manipulation is realized using femtosecond quantum beat spectroscopy performed with both circularly- and linearly-polarized pulses. The results are well captured by the density of matrix simulation and are picturized using a Bloch sphere. This study forms the basis for preparing arbitrary coherent superpositions of excitons in perovskite QDs for an array of quantum technologies under near-ambient conditions.

SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams

Recently, there has been an explosive generation of streaming data in various fields such as IoT and network attack detection, medical data monitoring, and financial trend analysis. These domains require precise and rapid analysis capabilities by minimizing noise from continuously generated raw data. In this paper, we propose SPinDP (Stream Purifier in Distributed Platform), an open source-based high-speed stream purification platform, to support real-time stream purification. SPinDP consists of four major components, Data Stream Processing Engine, Purification Library, Plan Manager, and Shared Storage, and operates based on open-source systems including Apache Storm and Apache Kafka. In these components, stream processing throughput and latency are critical performance metrics, and SPinDP significantly enhances distributed processing performance by utilizing the ultra-high-speed network RDMA (Remote Direct Memory Access). For the performance evaluation, we use a distributed cluster environment consisting of nine nodes, and we show that SPinDP’s stream processing throughput is more than 28 times higher than that of the existing Ethernet environment. SPinDP also significantly reduces the processing latency by more than 2473 times on average. These results indicate that the proposed SPinDP is an excellent integrated platform that can efficiently purify high-speed and large-scale streams through RDMA-based distributed processing.

Shifting to Open and Distance Learning (ODL) During Pandemic Era: Learning Experiences of the Undergraduate Students at UiTM Cawangan Kelantan

The outbreak of COVID-19 has urged the education providers to transform their method of teaching and learning into Open and Distance Learning (ODL). The sudden implementation of these online learning may cause some negative experiences for students as there are several issues that may impede them during the learning process. Hence, the aim of this study is to assess the university students’ learning experiences on ODL implementation during pandemic era using UiTM Cawangan Kelantan as a case study. A total of 2217 students participated in the study to share their experiences about ODL learning experiences. The data were analysed using descriptive statistics techniques to check on the students’ perceptions, the challenges of ODL faced by them and their most preferred online platform for ODL. The findings reveal that majority of the students gave neutral response on the effectiveness of ODL implementation. As overall, most of the students preferred live chat such as WhatsApp and Telegram as a platform for the learning process. Some negative feedback were given by the students such as not effective for the students with limited facilities, problem with internet coverage and difficult to adapt and perform well through ODL implementation. The findings of the study can be used by the higher education providers to improvise the online learning mechanism in future.

Blockchain Voting: A Step Towards Inclusive Democracy

Abstract: The online voting system that incorporates blockchain technology aims to transform the voting process by introducing upgraded trust, transparency and security. Through the utilization of a decentralized blockchain network, the proposed system ensures that each vote is securely recorded and verified, thereby establishing an immutable and tamper-resistant voting mechanism. One of the fundamental feature of the system is eligible voter verification. By leveraging blockchain's distributed nature, the system can verify the identity and eligibility of each voter, ensure that eligible individuals can only participate in the voting system. This authentication process adds an additional layer of security and helps prevent fraudulent activities. Secure vote casting is another crucial component of the proposed voting system. Through the utilization of blockchain, each and every vote is encrypted and securely stored in a decentralized manner. This ensures that votes cannot be altered or manipulated, providing a high level of integrity to the voting process. Additionally, the system emphasizes transparent result tallying. The decentralized nature of blockchain technology allows for the public verification of votes, enabling anyone to audit the voting process. This transparency enhances trust among voters, as it provides them with the ability to independently verify the accuracy of the election results. Overall, the proposed online voting system, empowered by blockchain technology, offers a transparent, secure and tamper-resistant solution for conducting elections. By leveraging the benefits of blockchain, such as decentralization and immutability, the system addresses the challenges faced by traditional voting systems and provides a more reliable and trustworthy platform for the democratic process. It has the potential to enhance the integrity of elections, increase voter turnout, and foster trust in the electoral system.

Development of production and environmental platforms for the European aggregates and minerals industries

Both quantification and communication of environmental impacts can create the foundation for improved sustainable production and decision-making. Without widespread information about site-specific environmental impacts, stakeholders cannot make a well-informed decision based on the local system boundaries. With organization-wide digital platforms, different aspects of production and management can be integrated and customized based on the organization's needs. This paper aims to give an overview of the development of three different process and environmental platforms, their purposes, system structure, system constraints and the applied framework. These respective platforms are being developed separately for the Swedish aggregates industry, the European aggregates industry and the European critical raw materials sector. The initial demonstrator can integrate site-specific information with a simulation-based allocation of resources and generate Life-Cycle Assessment and Environmental Product Declaration reports harmonized with the EN 15804 standard and the associated Product Category Rules.