Potential For Improving Efficiency Research Articles

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Enhancing energy efficiency in Bangladesh’s readymade garment sector: the untapped potential of LED lighting retrofits

Purpose Previous studies emphasized the substantial energy-saving potential of light emitting diode (LED) lighting systems, especially in the clothing industry. However, the specific quantification of energy conservation potential in industrial factories, particularly in Bangladesh’s readymade garment (RMG) sector, remains unexplored. The purpose of this study is to investigate the potential energy savings and efficiency improvements of lighting systems in Bangladesh’s RMG sector using LED technology. Design/methodology/approach Understanding and optimizing energy consumption is crucial in the RMG sector because this sector contributes significantly to the country’s export earnings. For this, an RMG factory was surveyed and possible lighting system retrofitting was estimated and compared. Findings The adoption of energy-efficient lighting options, particularly LED, could decrease the current lighting energy usage from 15% to 7.5% in Bangladesh. First, this study reveals, that the reduction of annual energy consumption was determined to be 18,220 kWh due to the retrofitting of the lighting system with LED tube. Second, it conducts real-time measurements to assess the suitability of in-building lighting systems, providing insights into the current scenario. Lastly, it evaluates the economic and environmental benefits of the proposed lighting system in the RMG industries. Due to the retrofitting of the lighting system, the reduction of equivalent CO2 gas emissions was found to be 119.896 tCO2. Originality/value For the first time, this study explored the potential for enhancing energy-efficient lighting system design through retrofitting in the RMG industry, with a focus on Bangladesh. By addressing these aspects, this study aims to contribute to the advancement of energy efficiency and conservation efforts in the RMG sector, ultimately fostering sustainable industrial development in Bangladesh and beyond.

Enhanced efficiency of carbon based all perovskite tandem solar cells via cubic plasmonic metallic nanoparticles with dielectric nano shells

This study investigates a carbon-based all-perovskite tandem solar cell (AP-TSC) with the structure ITO, SnO₂, Cs₀.₂FA₀.₈Pb(I₀.₇Br₀.₃)₃, WS₂, MoO₃, ITO, C₆₀, MAPb₀.₅Sn₀.₅I₃, PEDOT: PSS, Carbon. The bandgap configuration of the cell is 1.75 eV/1.17 eV, which is theoretically limited to 36% efficiency. The effectiveness of embedding cubic plasmonic metallic nanoparticles (NPs) made of Gold (Au) and Silver (Ag) within the absorber layers to eliminate the requirement for thicker absorber layers, decrease manufacturing costs and Pb toxicity is demonstrated in our analysis. This analysis was conducted using 3D Finite Element Method (FEM) simulations for both optical and electrical calculations. Prior to delving into the primary investigation of the tandem structure, a validation simulation was conducted to demonstrate the accuracy and reliability of the simulations. Notably, the efficiency mismatch observed during the validation simulation, specifically in relation to the incorporation of metallic nanoparticles (NPs), amounted to a mere 0.01%. To mitigate the potential issues of direct contact between metallic NPs and perovskite materials, such as increased thermal and chemical instability and recombination at the NP surface, a 5 nm dielectric shell was applied to the NPs. The incorporation of cubic core-shell Ag NPs resulted in a 15.32% enhancement in short-circuit current density, from 16.39 mA/cm² to 18.90 mA/cm², and a 15.68% increase in overall efficiency, from 26.9 to 31.12%. This research paves the way for the integration of core-shell metallic NPs in AP-TSCs, highlighting a significant potential for efficiency and stability improvements. In a dedicated section the band alignment of the sub-cell was addressed. Additionally, a thermal investigation of the proposed tandem structure was conducted, demonstrating the robustness of the proposed AP-TSC. Finally, the sensitivity analyses related to input parameters and the challenges associated with large-scale fabrication of the proposed AP-TSC were extensively discussed.

Deep Learning for Solving Partial Differential Equations: A Review of Literature

Partial Differential Equations (PDEs) are fundamental in modeling various phenomena in physics, engineering, and finance. Traditional numerical methods for solving PDEs, such as finite element and finite difference methods, often face limitations when applied to high-dimensional and complex systems. In recent years, deep learning has emerged as a promising alternative for approximating solutions to PDEs, offering potential improvements in both efficiency and scalability. This paper provides a comprehensive review of the literature on deep learning-based methods for solving PDEs, focusing on key approaches such as Physics-Informed Neural Networks (PINNs), deep Galerkin methods, and neural operators. These methods leverage the expressiveness of neural networks to capture underlying physics while avoiding the curse of dimensionality associated with classical techniques. We explore the theoretical foundations, advantages, and limitations of these deep learning models, along with their applications in diverse fields like fluid dynamics, quantum mechanics, and financial modeling. Additionally, this review examines recent advancements in hybrid models that combine traditional numerical methods with deep learning approaches to enhance accuracy and stability. Through this review, we highlight key trends and open challenges in the field, paving the way for future research at the intersection of deep learning and computational mathematics.

Accessibility of digital healthcare in 2023: insights from a Dutch national survey study

Abstract Background Digital technology is reshaping healthcare, offering potential improvements in accessibility, efficiency and quality. However, to realize potential benefits, ensuring equal access for all is crucial. We investigated digital healthcare usage and studied differences in use across age groups and educational backgrounds in the Netherlands. Methods Surveys were conducted among general healthcare users (n = 837, any citizen living in the Netherlands with access to healthcare) and healthcare users with chronic conditions (n = 1437). We studied their digital skills (can you independently use a computer, tablet or smartphone; digital applications; email and apps?) and their use of digital selfcare (did you use websites for health-related information or treatment?). Findings are stratified for educational level (low/medium/high) and age (15 -39, 40-64, over 65 years). Data collection occurred between April and August 2023, focusing on the twelve months preceding the survey. Results The majority of respondents use digital devices independently (range: 78-91%), but these percentages vary between subgroups, from 48-65% for users with lower education vs 86-99% for users with higher education. Similar differences exist across age groups: 98-100% of users aged 15-39 use a computer, tablet or smartphone, in contrast with 59-78% in the over 65 years age group. Similarly, the use of websites for health information is 22-31% among lower educated users to 68-70% among higher educated users; and 36-48% among those ≥65 years to 73-75% among respondents aged 15-39. Conclusions Substantial variation exists in the use of digital tools and digital selfcare across ages and educational levels. These findings underscore the urgency of addressing inequalities in access to digital healthcare, serving as a call to action for policymakers, healthcare providers, and developers to collectively strive for an accessible (digital) healthcare system for all. Key messages • Differences in use of digital tools and digital selfcare among people of different ages and educational levels warrants the need for co-creation and tailored development and implementation. • Differences in use of digital tools and digital selfcare among people of different ages and educational levels warrants the need for co-creation and tailored development and implementation.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Advancing the deployment and information management of direct air capture: A solution enabled by integrating consortium blockchain system

Direct air capture (DAC) is a critical and emerging Negative Emissions Technology (NET) that directly removes CO2 from the atmosphere, significantly contributing to climate change. However, the deployment and management of large-scale DAC faces challenges such as collections and analysis of energy consumption data, intricate device and system management, emission prediction and operation strategy, precise carbon footprint tracking, etc. This paper proposes the integration of blockchain technology with DAC systems to address these challenges, utilizing blockchain's inherent properties of immutability, security, and transparency. The implementation strategy includes the development of a DAC consortium blockchain system, leveraging a consensus mechanism11Consensus mechanism: The programming and process used in blockchain systems to achieve distributed agreement about the ledger's state or the state of a data set. (Source: Investopedia), ECDSA encryption22Elliptic Curve Digital Signature Algorithm (ECDSA): In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA) which uses elliptic-curve cryptography. As with elliptic-curve cryptography in general, the bit size of the private key believed to be needed for ECDSA is about twice the size of the security level, in bits. (Source: Wikipedia), IoT33Internet of things (IoT): describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communication networks. (Source: Wikipedia) integration, and digital signatures. Preliminary modeling of the proposed system suggests potential improvements in operational efficiency and a reduction in data inaccuracies. The proposed system underscores the system's ability to streamline identity verification, improve data collection accuracy, and facilitate secure, confidential information sharing among DAC stakeholders. By enhancing the efficiency and reliability of DAC operations, this approach supports the scalable and effective deployment of NETs in the global effort to combat climate change. Future research will focus on empirical validation through pilot projects and simulations to further substantiate these claims.

Experimental Study of Turbulent Premixed Flames of Gas-to-Liquids (GTL) Fuel in a Fan-Stirred Combustion Bomb

This study experimentally investigates the turbulent flame speeds (St) of Gas-to-Liquids (GTL) fuel and a 50/50 blend with diesel across turbulence intensities (u') ranging from 0.5 to 3.0 m/s and equivalence ratios (Φ) from 0.7 to 1.3. Experiments were conducted using a cylindrical fan-stirred combustion bomb at an initial temperature (Ti) of 463 K and atmospheric pressure under near homogeneous and isotropic turbulence (HIT) conditions. A pressure transducer measured the St of the propagating GTL flame. High-speed imaging reveals that changes in flame brightness are associated with variations in flame temperature and soot incandescence. Compared to diesel, stoichiometric GTL and the 50/50 diesel-GTL blend showed peak combustion pressure reductions of 8.9% and 4.9%, respectively. Rich diesel fuel and lean GTL exhibited higher pressure rise rates, flame propagation, and St due to their Lewis numbers (Le) being less than one, enhancing flame-turbulence interaction. At u` of 0.5, 1.5, and 3.0 m/s, St for GTL increased by approximately 3.6%, 5.3%, and 2.8%, respectively, when compared to diesel, with these increases observed at Φ < 1.1. Transition from wrinkled to corrugated flamelets with increasing u` supports models predicting flame stability and potential industrial combustion efficiency improvements. Comparisons with numerical St results using the Zimont Turbulent Flame Speed Closure (Zimont TFC) model showed good agreement at lower (u' = 0.5 m/s) and mid-range (u' = 2.0 m/s) turbulence intensities but discrepancies at higher intensities (u' = 3.0 m/s), highlighting the need to refine numerical models for more accurate predictions across all turbulence levels.

Billet Size Optimization for Hot Forging of AISI 1045 Medium Carbon Steel Using Zener-Hollomon and Cingara-McQueen Model

This study investigates the effects of initial billet size variations on material flow behavior in hot forging processes, aiming to optimize the forging process using validated predictive models. Material and high-temperature compressive tests inform mathematical models, while simulations are conducted via the finite element method (FEM). Results align with the Zener-Hollomon and Cingara-McQueen approaches. The Arrhenius model predicts AISI 1045 steel flow stress with an R2 of 0.968 and an average absolute relative error (AARE) of 7.079%. The Cingara-McQueen equation achieves an R2 of 0.997 and an AARE of 2.960%. Reducing billets size from 260 mm to 230 mm decreases the material usage by up to 11.5%, while maintaining workpiece integrity. Experimental and simulated loads exhibit an AARE of about 2.69%, thereby indicating potential cost and efficiency improvements in hot forging processes.

A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.

Injectable PLGA Microscaffolds with Laser-Induced Enhanced Microporosity for Nucleus Pulposus Cell Delivery.

Intervertebral disc (IVD) degeneration is a leading cause of lower back pain (LBP). Current treatments primarily address symptoms without halting the degenerative process. Cell transplantation offers a promising approach for early-stage IVD degeneration, but challenges such as cell viability, retention, and harsh host environments limit its efficacy. This study aimed to compare the injectability and biocompatibility of human nucleus pulposus cells (hNPC) attached to two types of microscaffolds designed for minimally invasive delivery to IVD. Microscaffolds are developed from poly(lactic-co-glycolic acid) (PLGA) using electrospinning and femtosecond laser structuration. These microscaffolds are tested for their physical properties, injectability, and biocompatibility. This study evaluates cell adhesion, proliferation, and survival in vitro and ex vivo within a hydrogel-based nucleus pulposus model. The microscaffolds demonstrate enhanced surface architecture, facilitating cell adhesion and proliferation. Laser structuration improved porosity, supporting cell attachment and extracellular matrix deposition. Injectability tests show that microscaffolds can be delivered through small-gauge needles with minimal force, maintaining high cell viability. The findings suggest that laser-structured PLGA microscaffolds are viable for minimally invasive cell delivery. These microscaffolds enhance cell viability and retention, offering potential improvements in the therapeutic efficiency of cell-based treatments for discogenic LBP.

Augmented reality for point-of-care ultrasound-guided vascular access in pediatric patients using Microsoft HoloLens 2: a preliminary evaluation.

Conventional ultrasound-guided vascular access procedures are challenging due to the need for anatomical understanding, precise needle manipulation, and hand-eye coordination. Recently, augmented reality (AR)-based guidance has emerged as an aid to improve procedural efficiency and potential outcomes. However, its application in pediatric vascular access has not been comprehensively evaluated. We developed an AR ultrasound application, HoloUS, using the Microsoft HoloLens 2 to display live ultrasound images directly in the proceduralist's field of view. We presented our evaluation of the effect of using the Microsoft HoloLens 2 for point-of-care ultrasound (POCUS)-guided vascular access in 30 pediatric patients. A custom software module was developed on a tablet capable of capturing the moving ultrasound image from any ultrasound machine's screen. The captured image was compressed and sent to the HoloLens 2 via a hotspot without needing Internet access. On the HoloLens 2, we developed a custom software module to receive, decompress, and display the live ultrasound image. Hand gesture and voice command features were implemented for the user to reposition, resize, and change the gain and the contrast of the image. We evaluated 30 (15 successful control and 12 successful interventional) cases completed in a single-center, prospective, randomized study. The mean overall rendering latency and the rendering frame rate of the HoloUS application were 139.30ms and 30 frames per second, respectively. The average procedure completion time was 17.3% shorter using AR guidance. The numbers of puncture attempts and needle redirections were similar between the two groups, and the number of head adjustments was minimal in the interventional group. We presented our evaluation of the results from the first study using the Microsoft HoloLens 2 that investigates AR-based POCUS-guided vascular access in pediatric patients. Our evaluation confirmed clinical feasibility and potential improvement in procedural efficiency.

Synergizing human insight and machine learning: A dual-lens approach to uncovering healthcare research and innovation outcomes

Many healthcare organisations have extensive documentation detailing the processes behind their various research and innovation projects. Analysing this data can provide valuable insights into why some projects succeed without major issues, others encounter and overcome problems, and some ultimately fail. This study introduces an approach that combines narrative interviews and Natural Language Processing (NLP) to identify patterns associated with innovation project outcomes. We analysed 618 documents from 67 projects provided by ZonMw, a major Dutch healthcare research funder, and conducted 32 narrative interviews across seven cases of healthcare innovation projects. By using narrative interviews to inform and pre-train a text embedding model, we demonstrate the potential to create a proxy for human judgement, allowing for a more natural identification of contextual patterns in project documentation. The findings indicate that successful projects are more likely to adopt a proactive approach to role changes and uncertainty (due to ambiguous laws and regulations) and to allow flexibility, which enhances stakeholder engagement, compared to failed projects. However, while we were able to conduct descriptive analysis to gain these insights, significant interpretation is still required to fully understand the findings. Our study makes two primary contributions: first, it offers a new approach for future research on the factors that determine project success or failure, closely aligning with Structuration Theory. Additionally, it suggests potential efficiency improvements in theory development by enabling multiple pattern configurations within Grounded Theory. Second, it offers practical strategies for organisations to more effectively capture and use contextual information in their project documentation for future success.

Letter to the editor: Prospective analysis of STRATAFIX™ symmetric PDS plus suture for fascial closure in spinal surgery: a pilot study.

Spine surgery is essential for restoring alignment, stability, and function in patients with cervical spine injuries, especially when instability, pain, deformity, or progressive nerve damage is present. Effective wound closure is vital in these procedures, aiming to promote rapid healing, reduce infection risks, enable early mobilization, and ensure satisfactory cosmetic results. However, there is limited evidence on the optimal wound closure technique for posterior spine surgery, highlighting the need for innovative approaches. A study by Glener et al. evaluated the effectiveness of STRATAFIX™ Symmetric barbed sutures compared to traditional braided absorbable sutures in spinal surgery. In a randomized trial involving 20 patients, the STRATAFIX™ group demonstrated a shorter mean closure time and significantly fewer sutures used, though without a statistically significant reduction in closure time. No significant differences were observed in postoperative complications between the groups during a six-month follow-up. While the findings suggest potential cost savings and efficiency improvements with STRATAFIX™, the study's small sample size and short follow-up period limit its generalizability. Furthermore, AI-based models, such as the Xception deep learning model, show promise in improving suture training accuracy for medical students, which could enhance surgical outcomes and reduce complications. Despite the promising results, further research with larger sample sizes, extended follow-up periods, and multi-center trials is necessary to validate the effectiveness of barbed sutures like STRATAFIX™ in neurosurgery. The integration of AI in surgical training and continued exploration of innovative techniques are essential to advancing the field and optimizing patient care in spinal surgery.

Optimised path planning using Enhanced Firefly Algorithm for a mobile robot.

Path planning is a crucial element of mobile robotics applications, attracting considerable interest from academics. This paper presents a path-planning approach that utilises the Enhanced Firefly Algorithm (EFA), a new meta-heuristic technique. The Enhanced Firefly Algorithm (FA) differs from the ordinary FA by incorporating a linear reduction in the α parameter. This modification successfully resolves the constraints of the normal FA. The research involves experiments on three separate maps, using the regular FA and the suggested Enhanced FA in 20 different runs for each map. The evaluation criteria encompass the algorithms' ability to move from the initial location to the final position without experiencing any collisions. The assessment of path quality relies on elements such as the distance of the path and the algorithms' ability to converge and discover optimum solutions. The results demonstrate significant improvements made by the Enhanced FA, with a 10.270% increase in the shortest collision-free path for Map 1, a 0.371% increase for Map 2, and a 0.163% increase for Map 3, compared to the regular FA. This work highlights the effectiveness of the Enhanced Firefly Algorithm in optimising path planning for mobile robotics applications, providing potential improvements in navigation efficiency and collision avoidance.

Formation of Vibration Fields for a Mechatronic Platform Driven by Dual Asynchronous Motors

This paper investigates the formation of vibration fields in a mechatronic setup driven by dual induction motors, relying on the controlled synchronization of unbalanced rotors. The proposed algorithm enables precise control over rotor speeds and phase shifts. Experimental results from a multi-resonance vibration laboratory setup demonstrate this approach’s ability to form the vibration fields. The ability to control these fields is crucial for applications such as vibratory transportation and the mixing of bulk materials. The results obtained can ensure a diverse picture of the complex trajectories of motion for various points of the platform, primarily in the screens, making various useful effects for vibration technologies. Additionally, the practical value of this research is that in the case of double synchronous mode the ordinate of the lower point of the trajectory is lower than in the case of single synchronous mode, which improves the efficiency of unloading and prevents congestion formation. The experimental data highlight the practical advantages and potential improvements in efficiency and reliability offered by this method.

Manajemen Rantai Pasok Digital dalam Budidaya Jangkrik: Peluang dan Tantangan

Digital supply chain management (MRPD) has become an important focus in the context of insecticide farming such as cicada cultivation. This study aims to explore the opportunities and challenges of MRPD implementation in cicada cultivation. The aim is to identify potential improvements in operational efficiency and risk management through the integration of digital technology in the cricket supply chain. The study is scheduled to be conducted for six months, with the initial phase of research focusing on literature analysis and conceptual framework, followed by primary data collection through interviews and survey. The subjects of the study include cricket farmers, feed suppliers, distributors, and end consumers. The research methods used are qualitative and quantitative approaches to gain an in-depth understanding of stakeholders' perspectives and experiences. The results of the study are expected to provide valuable insights into the benefits and constraints of implementing MRPD in cicada cultivation, as well as practical recommendations for industry stakeholders.

Management and Business Analysis of Common Carp (Cyprinus carpio) at the Aquaculture Installation (IPB) Pandaan

The growing global population increases the demand for protein, making the aquaculture and fisheries industries vital. This study aims to optimize goldfish hatchery management at the IPB Pandaan, East Java, to improve production and seed quality. The research was conducted in January-February 2024 at the IPB Pandaan Fisheries Breeding Installation, using observation and interview methods. Primary data were obtained from interviews with business actors and stakeholders, while secondary data were from literature studies. Data analysis includes production management, water quality, feed, and financial evaluations such as R/C Ratio, BEP, and PP to assess performance and potential efficiency improvements in the goldfish hatchery business. Goldfish hatchery management at IPB Pandaan includes pond preparation, broodstock selection, spawning with temperature and water quality control, egg hatching, feeding, nursery, and seed harvesting. Water quality is maintained with temperatures of 23.9-30.5 ºC, pH 7.3-8.9, and DO 3.2-6.3 ppm. Total investment is Rp 37,500,000, with fixed monthly costs of Rp 3,335,414 and operational costs of Rp 500,000. The total revenue is Rp 7,784,000, and the profit is Rp 3,752,752. An R/C Ratio of 1.93 indicates a profitable business, with a payback period of 16.39 months and a BEP production price of Rp 14,497 per cup.

Microwave-enhanced preparation of finned-FER zeolites and their application in the skeletal isomerization of FAME

The skeletal isomerization of fatty acid methyl esters (FAME) is vital for producing branched-chain esters and is a great tool to improve the cold flow properties of biodiesel. Ferrierite (H-FER) zeolite is an effective catalyst for facilitating this reaction; however, its efficiency can be limited by site accessibility and pore structure. Thus, our research developed a novel method for producing finned-FER zeolites by a seed-mediated approach that applies microwave heating with H-FER as seeds. The main focus was to examine how varying synthesis conditions, particularly microwave heating time, influenced the physical and chemical properties of the produced zeolites; we showed that the optimized condition was achieved with 6 h of microwave heating, revealing well-defined finned crystals and improved characteristics. Finally, the study evaluated the catalytic performance of these finned-FER zeolites. The Friedel-Crafts alkylation reaction, serving as a model reaction, was employed to assess the performance of the materials. We also accessed the performance of these materials in the same reaction in the presence of triphenylphosphine, offering a deeper understanding of the real active sites and their accessibility. Furthermore, the isomerization reactions of methyl oleate were explored to evaluate the practical applicability of the synthesized zeolites. The results revealed that branched isomers exceeding 70 % yield were achieved at 89 % conversion for the best catalyst at a temperature of 250 °C and 20 bar of H2 for 6 h. Thus, this research proposes a more efficient method for synthesizing finned-FER zeolites and systematically analyzes their characteristics, highlighting potential improvements in catalytic efficiency and sustainability.