Efficient Implementation Research Articles

Low-carbon and high-efficiency nanosheet-enhanced CO2 huff-n-puff (HnP) for heavy oil recovery

CO2 huff-n-puff (CO2-HnP) process is a promising technique for enhanced heavy oil recovery. The huff stage of this process involves the formation of foamy oil flow, characterized by dispersed CO2 bubbles within heavy oil, which plays a pivotal role in both oil recovery and CO2 sequestration. Maintaining the stability of foamy oil is crucial for the efficient implementation of CO2-HnP processes. Herein, ultrathin plate-shaped nanosheets are employed as stabilizers for foamy oil. Carboxyl-alkyl composite Janus nanosheets (SANs) are synthesized using a bottom-up approach. Comprehensive characterizations illustrate that SANs, owing to their amphiphilic and Janus nature, efficiently adsorb at the CO2-oil interface, thereby modifying interfacial properties and enhancing foamy oil stability even at low concentration (0.005 wt%). The incorporation of SANs extends the duration of foamy oil effect, resulting in a substantial enhancement of oil recovery efficiency from 30.7% to 39.4%, and an increase in CO2 storage factor from 6.5% to 7.8%, compared to conventional CO2-HnP processes. Micromodel experiments and molecular dynamics simulations show that SANs strengthen foamy oil stability by increasing interfacial activity and reducing CO2 diffusion rate. Overall, the multifunctional capabilities of SANs in interfacial modification offer promising prospects for enhanced oil recovery and optimizing CCUS efficiency.

The role of government integrity in the impact of environmental taxes on comparative advantage in environmental goods and climate change in emerging markets

The goal of this study is to examine how environmental taxes influence the comparative advantage in environmental products and carbon emissions within emerging economies. To gain a better understanding, we examine whether this impact changes depending on the level of government integrity. This study utilizes panel autoregressive distributed lag (ARDL) techniques and a two-step generalized method of moments (GMM) dynamic panel model to ensure accuracy. The results indicate that increased environmental taxes mitigate the comparative advantage in environmental goods for emerging markets. However, for countries with high levels of government integrity, higher environmental taxes enhance their competitive edge in environmental goods. Additionally, our findings show that although a rise in environmental taxes is associated with higher carbon emissions, raising such taxes results in a reduction in carbon emissions for emerging economies with solid government integrity. These findings suggest that robust political institutions are crucial in promoting the comparative advantage of emerging markets in environmental goods and mitigating climate change. In the absence of substantial confidence in political or governmental institutions, the efficient implementation of climate taxes poses considerable challenges. Furthermore, we observe that an increase in the comparative advantage of environmental goods results in a decrease in carbon emissions.

HARNESSING GROUP THEORY FOR SIMPLIFIED ELECTRIC CIRCUIT ANALYSIS

Electric circuit analysis is fundamental to advancing modern technology, yet traditional methods often become complex and inefficient when applied to large-scale or intricate networks. Group theory, a branch of abstract algebra renowned for its capacity to exploit symmetry, has shown promise as a solution to this challenge. By applying group theoretical principles to circuit analysis, particularly through symmetry operations and transformation groups, engineers can simplify complex systems, reducing computational demands and revealing deeper insights into circuit behavior. Through systematic symmetry-based simplifications, group theory can decrease the number of equations required by up to 40% in certain configurations, enhancing both analytical efficiency and practical implementation. However, challenges include the mathematical complexity inherent to group theory, the limitations in analyzing highly irregular or nonlinear circuits, and the need for compatible computational tools. Recommendations include the integration of group theoretical approaches into standard circuit analysis software, focused training for electrical engineers, and further research to address the application of these methods in emerging technologies such as quantum and neuromorphic computing. Therefore, this review aims to elucidate the practical potential of group theory in transforming electric circuit analysis, presenting a streamlined approach that balances theoretical rigor with engineering feasibility.

Investigation of the Knowledge Level of Hospital Managers About Strategic Management Tools and Their Use

Objectives: Effective and efficient implementation of all elements of strategic management is important for health organizations to achieve their future goals. This study aims to examine the knowledge levels of hospital managers about strategic management tools, their usage of these tools, and the priorities and needs in tool selection. Materials and Methods: The population of the study is the managers working in private hospitals in Antalya Province. 90 managers constitute the sample of the study. The data were collected by face-to-face questionnaire method. The questionnaire includes sociodemographic characteristics form and statements to measure the level of knowledge and use of strategic management tools. Mann Whitney U and Kruskal-Wallis tests were applied to examine the differences between groups, and Spearman Correlation Analysis was applied to examine the relationships between variables. SPSS.25 Package Program was used to analyze the data obtained. Statistical significance was accepted at 0.05 level. Results: 59% of the participants were female, 63% had a bachelor's degree, and the majority (41%) were middle managers. Those who did not receive managerial training and strategic management training were the majority (54% and 71%, respectively). When the knowledge level of the managers about strategic management tools was analyzed, mission and vision (98.9%) received the highest score, and value chain analysis (73.3%) received the lowest score. It was observed that the priorities of the administrators in the selection of strategic management tools were different between the groups according to gender, level of education, and type of school graduated from. In addition, statistically significant relationships were found between the priorities in the selection of strategic management tools and age, professional experience and years of working in the organization. Conclusion: Managers have limited knowledge and use of strategic management tools and there are differences in the selection of strategic management tools according to their sociodemographic status.

Impact of Human Resource Policy Implementation on Service Delivery at the Office of the Director of Public Prosecutions, Western Region, Kenya

Efficient implementation of policies has a crucial function in improving service delivery in the public and private domains. The success of policy execution directly impacts how much the policy contributes to enhancing service delivery. To achieve high-quality, efficient, and timely service delivery, the Directorate has adopted a strategy of revamping, retooling, and continuous learning. The Office of the Director of Public Prosecutions (ODPP) follows the Excellence Charter and adheres to the ODPP Strategic Commitments for the period 2020-2023. In this context, the researcher focused on examining the impact of human resource policy implementation on service delivery at the Office of the Director of Public Prosecutions, Western Region, Kenya. Implementation Theory and Organizational Culture Theory guided the study. Descriptive research design was utilized. The target population was 198 individuals working within the ODPP Western Region of Kenya. Individual respondents were selected randomly from Legal, Administration, and Support Staff in region. Data collection was done through questionnaires distributed to staff and document analysis was conducted in the resource centres available in the region. Face-to-face interviews with selected County Heads were carried out by the researcher. Descriptive statistics, such as means, percentages, and frequencies, was used in the research's quantitative and qualitative approaches to analyze the data using SPSS. The findings indicated that human resource has a significant impact on service delivery at the ODPP, Western Region, Kenya. The results upon Analysis of Variance (ANOVA) established human resource policy implementation had an r of 0.265, β = 0.252, t= 3.786, p= 0.05. It was concluded that Policy Implementation had a statistically significant impact on service delivery in the ODPP. The study recommends that ODPP should enhance HR policy implementation and service delivery in the Western Region by improving communication, offering regular training, fostering open dialogue, establishing a robust monitoring framework, deploying dedicated HR Officers, and ensuring transparent and unbiased career progression and training opportunities. The study will be part of references materials for all ODPP Staff, stakeholders, policy makers and other institutions in Kenya as an insightful tool in advancement of efficacy and efficiency in policy implementation.

Determining Factors to Develop Periodic Plan, Strategic Plan and Medium-Term Expenditure Framework in Local Governments in Nepal

This research article explores the factors influencing the development and implementation of periodic plan, strategic plans, and the Medium-Term Expenditure Framework (MTEF) in local governments of Nepal, particularly focus on Bharatpur Metropolitan City. The study adopts a qualitative research design, employing document analysis, focus group discussions, and interviews with key stakeholders, including government officials, local communities, NGOs and civil societies. The findings identify key determinants: public responsible governance; efficiency, financial integrity, and systematic implementation of programs; proper resource allocation; and risk identification and management. Major findings emphasize the importance of public responsible governance, which includes public voice, participation, and shared responsibility in decision-making processes. Additionally, the study highlights the necessity of efficiency, financial integrity, and systematic implementation of programs for effective service delivery. Proper resource allocation and risk management are also crucial, ensuring that development projects meet community needs and sustainable goals. These elements collectively contribute to the successful governance and development of local governments in Nepal. The research also highlights the need for further studies, particularly in other regions of Nepal, using diverse methodologies to strengthen the understanding of local-level planning and its implications. Int. J. Soc. Sc. Manage. Vol. 11, Issue-4: 88-94.

Deep learning-based spike sorting: a survey.

Objective.Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.Approach.Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e., models that detect spikes and extract features or do classification within a single network, are included.Main Results.Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on ASICs and FPGAs. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.Significance.This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.

Dynamics of high-dimensional quantum systems coupled to a harmonic bath. General theory and implementation via multiconfigurational wave packets and truncated hierarchical equations for the mean-fields.

Modeling the dynamics of a quantum system coupled to a dissipative environment becomes particularly challenging when the system's dimensionality is too high to permit the computation of its eigenstates. This problem is addressed by introducing an eigenstate-free formalism, where the open quantum system is represented as a mixture of high-dimensional, time-dependent wave packets governed by coupled Schrödinger equations, while the environment is described by a multi-component quantum master equation. An efficient computational implementation of this formalism is presented, employing a variational mixed Gaussian/multiconfigurational time-dependent Hartree (G-MCTDH) ansatz for the wave packets and propagating the environment dynamics via hierarchical equations, truncated at the first or second level of the hierarchy. The effectiveness of the proposed methodology is demonstrated on a 61-dimensional model of phonon-driven vibrational relaxation of an adsorbate. G-MCTDH calculations on 4- and 10-dimensional reduced models, combined with truncated hierarchical equations for the mean fields, nearly quantitatively replicate the full-dimensional quantum dynamical results on vibrational relaxation while significantly reducing the computational time. This approach thus offers a promising quantum dynamical method for modeling complex system-bath interactions, where a large number of degrees of freedom must be explicitly considered.

Novel insights into revealing the intrinsic degradation mechanism of ciprofloxacin by Chlorella sorokiniana: Removal efficiency, pathways and metabolism

Microalgae-mediated biodegradation of antibiotics has attracted increasing interest. However, the exact degradation mechanism behind it remains elusive, which is crucial for improving its efficiency and large-scale implementation. Therefore, this study aims to thoroughly investigate how Chlorella sorokiniana (C.sorokiniana) degrades ciprofloxacin (CIP) from the molecular level to elucidate its complicated degradation mechanism. Results show that C.sorokiniana exhibited an impressive removal rate of 38.05–74.29 % for CIP, the synergistic effect of biosorption and biodegradation was the main removal mechanism of CIP. The content of EPS increased by 11.84–103.31 %, acting as a carrier to increase the bioavailability of CIP. Enzyme activity analysis identified POD, CYP450, and GST as the major catalytic enzymes responsible for the biotransformation process of CIP with increases of 6–41.2 %, 18.78–102.1 % and 3.53–58.15 %, respectively. Molecular docking further revealed that the binding affinity of 146.24 μM, 27.71 μM and 16.33 μM was observed for POD,CYP450, and GST, respectively, indicating the high metabolic efficiency of CIP by CYP450. Finally, DFT calculations elucidated the enzyme-mediated biodegradation pathway of CIP, including hydroxylation, decarboxylation, and ring opening. These findings are expected to further fill the knowledge gap on the catalysis mechanism of CIP degradation by enzymes and improve the practical application of microalgae to antibiotic wastewater.

School as a Safe Space through Gender-Responsive Basic Education Policy

Quality education promotes gender-responsive policies. Yet, this is not always the case in developing countries like the Philippines. There are gaps in policy implementation, and safety issues faced by LGBTQIA+ learners persist. Purpose. This study focuses on teachers’ and learners’ perception of Gender-Responsive Basic Education Policy (GRBEP) implementation and the safety of lesbian, gay, bisexual, transgender, queer, intersex, asexual, and expanding identities (LGBTQIA+) learners in public and private schools. Hence, it determined the GRBEP implementation level and LGBTQIA+ learners' safety level. No significant correlation between policy implementation and learners’ safety, and no significant difference between public and private schools’ policy implementation and learners’ safety were assumed. The minimal studies and the urgent need to ensure a gender-responsive and safe learning environment underscore the significance of this research. Methodology. Validated questionnaires were distributed to 744 learners and teachers: 401 from private schools and 343 from public schools. The data collected were analyzed using mode, percentage, Pearson’s product-moment correlation, and independent samples test. Results. Learners and teachers perceived a “high level of implementation” of GRBEP in schools. LGBTQIA+ learners were generally “safe” in schools. The implementation level of Gender-Responsive Basic Education Policy correlates with the safety level of LGBTQIA+ learners in school, as it was statistically significant (p=.026 < α=0.05). There is no substantial evidence of a difference between public and private schools’ implementation level of GRBEP (p = 0.544 > α=0.05). There is not enough evidence of a significant difference between public and private schools’ safety levels of LGBTQIA+ learners (p = 0.188 > α=0.05). Conclusion. An efficient GRBEP implementation by schools ensures a working educational policy. LGBTQIA+ learners are safe in the learning environment. Public and private schools must strengthen awareness campaigns, address misconceptions and overlooked realities, conduct real-time monitoring, evaluation, and feedback mechanisms, as well as foster partnerships and linkages to uphold a safe space for every learner. With these actions, policymakers and school administrators can work towards creating a true quality and inclusive education that supports the well-being and success of all learners, regardless of their sexual orientation, gender identity, or gender expression.

Multi-disciplinary team approach for pediatric hemimegalencephaly: Insights from a single institutional case series.

Recent genetic studies have revealed that hemimegalencephaly (HME) is a multi-system disorder associated with germline or mosaic variants within the PI3K-mTOR-GATOR1 signaling pathways. Patients with HME typically develop drug-resistant epilepsy necessitating extensive evaluation, hemispherectomy, and long-term management. We describe the role of a multidisciplinary team (MDT) for the diagnosis and management of recent patients with HME at UCLA who underwent hemispherectomy. Genetic evaluation identified nine patients with the following variants: NPRL3 x2 germline, PIK3CA mosaicism x4, MTOR mosaicism x1, AKT3 mosaicism x1, unknown x1. Each patient's MDT comprised 4-9 specialties. One child with a MTOR variant had persistent epilepsy after hemispherectomy, but addition of everolimus resulted in an 80% decrease in seizure frequency. Another child with hemihypertrophy and PIK3CA mosaic variant was offered targeted PIK3CA inhibitor treatment, alpelisib, for overgrowth. A third child with germline NPRL3 variant inherited from their unaffected mother resulted in a sibling being diagnosed with the variant who later developed seizures secondary to focal cortical dysplasia. The implementation of a MDT offers essential guidance for families affected by HME, encompassing prognostication, surveillance, and therapeutic strategies. Identifying the etiology of HME can facilitate the development of targeted treatments and enable timely genetic counseling. PLAIN LANGUAGE SUMMARY: Hemimegalencephaly (HME) is a complex brain disorder caused by genetic changes. It often leads to severe epilepsy that doesn't respond to standard treatments and frequently requires surgery. In this case series, nine patients with HME were identified and found to have genetic mutations in key growth-regulating genes. A multidisciplinary team model was developed to facilitate patients' care. For example, one patient's seizures improved with surgery, another with a new targeted medication, and another received treatment for symptoms of overgrowth. This team approach provides comprehensive care for patients and can lead to efficient care coordination and implementation of novel therapies.

LSTM-CRP: Algorithm-Hardware Co-Design and Implementation of Cache Replacement Policy Using Long Short-Term Memory

As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discrimination of complex access patterns. Therefore, this paper presents the LSTM-CRP algorithm as well as its efficient hardware implementation, which employs the long short-term memory (LSTM) for access pattern identification at run-time to guide cache replacement algorithm. LSTM-CRP first converts the address into a novel key according to the frequency of the access address and a virtual capacity of the cache, which has the advantages of low information redundancy and high timeliness. Using the key as the inputs of four offline-trained LSTM network-based predictors, LSTM-CRP can accurately classify different access patterns and identify current cache characteristics in a timely manner via an online set dueling mechanism on sampling caches. For efficient implementation, heterogeneous lightweight LSTM networks are dedicatedly constructed in LSTM-CRP to lower hardware overhead and inference delay. The experimental results show that LSTM-CRP was able to averagely improve the cache hit rate by 20.10%, 15.35%, 12.11% and 8.49% compared with LRU, RRIP, Hawkeye and Glider, respectively. Implemented on Xilinx XCVU9P FPGA at the cost of 15,973 LUTs and 1610 FF registers, LSTM-CRP was running at a 200 MHz frequency with 2.74 W power consumption.

A Review of Strategies and Technologies for Sustainable Decentralized Wastewater Treatment

The traditional model of centralized wastewater treatment is facing substantial strain due to a confluence of global challenges. Consequently, it is imperative to evaluate the impediments and potential advantages associated with the deployment of decentralized wastewater (DW) treatment technologies and systems. Decentralized wastewater (DW) treatment represents a sustainable approach to managing and purifying wastewater across both urban and rural settings. This literature review provides a detailed examination of current advancements and challenges associated with DW treatment technologies. It specifically addresses their operational efficiency, long-term sustainability, and practical implementation across diverse environments. This review critically analyzes recent studies that highlight innovative methodologies, including the deployment of constructed wetlands, anaerobic digestion processes, and predictive models enhanced by artificial intelligence. A critical focus is placed on the ecological and economic advantages of source separation and resource recovery from wastewater streams. The issue of emerging contaminants, such as microplastics, antibiotics, and steroids, is also discussed, emphasizing the continued need for innovation in treatment technologies. Findings from various life cycle assessments are presented to illustrate the environmental impact and feasibility of decentralized systems relative to centralized alternatives. This comprehensive analysis offers valuable insights into the future trajectories of wastewater treatment research and implementation.

Computational screening of umami tastants using deep learning.

Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for identifying novel tastants. In this study, we have devised a virtual screening pipeline for identifying highly potent umami tastants from large molecular databases. We curated the most extensive classification dataset containing 439 umami and 428 non-umami molecules and trained a transformer-based architecture to differentiate between the two classes, achieving 93% accuracy. Additionally, we built a neural network model for predicting the potency of umami compounds, the first effort of its kind. The classification and potency prediction models were combined with similarity analysis and toxicity screening to build an end-to-end virtual framework for the rational discovery of novel tastants. We applied this framework to the FooDB database containing around 70,000 molecules as an illustrative use case for screening potent umami compounds. The screened molecules were validated using molecular docking with the umami taste receptor. This study demonstrates the potential of data-driven methods in discovering new tastants from structural and chemical features of molecules and proposes an efficient implementation for industrial applications.

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

Ultrafast diffraction algorithms for light-in-flight holography

Digital light-in-flight (LIF) holography is an ultrafast imaging technique capable of single-shot simultaneous 3D and femtosecond time resolution acquisitions of light pulse propagation. However, the numerical diffraction algorithms used to model light on femtosecond timescales are currently limited in scope, accuracy, and efficiency. We derive an analytical model capable of modeling LIF hologram formation for various optical setup configurations, able to model 3D objects and precisely account for the limited temporal coherence of the signal. We design an efficient algorithmic implementation and validate the system in numerical simulations and with an experimental LIF holographic recording setup. We report ultrafast numerical diffraction over 10,000 times faster than the reference technique, with higher accuracy and capable of modeling 3D samples, thereby broadening its application domain.

An efficient hardware implementation of SHA-3 using 3D cellular automata for secure blockchain-based IoT systems

Abstract This paper introduces an innovative hardware implementation of the Secure Hash Algorithm (SHA-3) using three-dimensional cellular automata (3D-CA) for secure blockchain-based IoT systems. Our approach optimizes the SHA-3 algorithm by leveraging the parallel processing capabilities of 3D-CA, addressing the computational challenges in resource-constrained Internet of Things (IoT) environments. Our proposed design is implemented on various Field Programmable Gate Arrays (FPGA) circuits. The obtained results demonstrate the potential of our design to address the demands of blockchain-based IoT systems effectively. Furthermore, a comparative analysis reveals that our hardware implementation offers competitive performance compared to previous works. To ensure security and validate output randomness, the design undergoes rigorous evaluation using the Strict Avalanche Criterion (SAC) and the NIST Statistical Test Suite (STS). The results demonstrate that the proposed architecture exhibits high sensitivity to input changes while maintaining robust resistance against cryptanalytic attacks.

Development of a fuzzy analytic hierarchy process model, sensitivity analysis, and addressing barriers to additive manufacturing implementation in small- and medium-sized enterprises (SMEs)

Additive manufacturing (AM) is gaining popularity worldwide due to its potential to revolutionize manufacturing processes, improve product customization, and reduce waste. Nevertheless, small- and medium-sized enterprises (SMEs) encounter major challenges when implementing AM technologies. The objective of this research is to utilize the fuzzy analytic hierarchy process (FAHP) to identify and prioritize the critical challenges that impede the implementation of AM for SMEs. The study employs a two-stage methodology. First, a thorough literature review and expert discussions identify 15 major barriers to AM adoption. Second, a systematic model is proposed to establish a ranking of these barriers, which is based on the FAHP. The results are validated using sensitivity analysis to ensure the robustness of the findings. Additionally, potential solutions to overcome these barriers are discussed. The research indicates that the two most significant barriers for SMEs are the high costs of equipment and the scarcity of skilled designers. The model that has been presented is a valuable resource for SME managers and practitioners, as it allows them to make strategic and informed decisions that facilitate the efficient implementation of AM. This research is groundbreaking in its comprehensive analysis of the prioritization and ranking of AM implementation barriers, with a particular emphasis on SMEs. It offers essential insights for future research and practical applications.

Digital Technologies for Promoting Construction and Demolition Waste Management: A Systematic Review

The increasing concern about the environment has led to the necessity of ensuring efficient Construction and Demolition Waste Management (C&DWM) in the built environment. Despite the extensive research on C&DWM, the industry still faces significant challenges, including inefficiencies, high costs, and environmental impacts. Meanwhile, incorporating digital technologies (DTs) has emerged as a way to eradicate the challenges of C&DW. In response to the knowledge gap, in this research, we conducted a systematic literature review (SLR), incorporating bibliometric, text-mining, and content analysis to meet the research objectives. In total, 126 papers were retrieved from the Scopus database and transferred into VOSviewer to conduct the bibliometric analysis. The findings identified seven specific DTs, namely, blockchain, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Robotics, Computer Vision (CV), and Building Information modeling (BIM). This study demonstrates that these technologies play a significant role in promoting efficient C&DWM in the construction industry. The study’s implication lies in its potential to guide industry stakeholders and policymakers in promoting the use of DTs and overcoming the barriers to their adoption, thereby facilitating more efficient and sustainable C&DWM practices. Finally, the findings of our research indicate possible future research directions for promoting DTs for C&DWM and eradicating the barriers to efficient implementation.