Local Processing Research Articles

Series of advanced SLAM processes and applications

Simultaneous Localization and Mapping (SLAM) is an increasingly important field in robotics. SLAM enables the processes of localization and mapping to run simultaneously by using methods like feature extraction, feature matching, extended Kalman Filter, and probability distribution models, allowing robots to have more autonomy and greater efficiency. SLAM has been applied in industry robots, autonomous vehicles, augmented reality, UAVs, humanoid robots, and planetary rovers. With an increasing number of applications of SLAM, the demand for higher-performance SLAM algorithms has driven innovation and advancement in the field every year. SLAM is a highly promising algorithm for the future of robotics, but problems like uncertainty, correspondence, and time complexity prevent the full use of SLAM in robotics applications. It is essential to analyze SLAM in all aspects to apply it to future work. This article provides a detailed insight into the SLAM process, considers previous advancements and current problems, and discusses the future of SLAM.

The Adaptation of Global Norms The 1951 Refugee Convention in Indonesia

This article aims to explain and answer the research question: Why did the localization of the global norms of the 1951 Refugee Convention in Indonesia lead to the issuance of Presidential Regulation No. 125 of 2016? Indonesia has been a transit area for refugees since 1979 and is the third country in ASEAN with the highest number of refugees. However, Indonesia has never formally and officially adopted the 1951 Refugee Convention. To answer the research question, this study applies the concept of norm localization proposed by Acharya (2004) as the framework for the analysis. This concept explains that the localization process of global norms might lead to three possible outcomes: acceptance of the norms, adaptation and modification of the norms, or complete rejection of the norms. The adaptation of the global norms is the result of adjusting some elements of the norm that are considered more appropriate to the local situations. In this article, we argue that the consideration of humanitarian values, national interests, and domestic challenges are the three most significant domestic factors that influence the process of norm localization in Indonesia. This article discusses how the three factors influenced the process of policy/decision-making on how Indonesia should deal with its refugee issues. In conclusion, we mainly argue that Presidential Regulation No. 125 of 2016 is the result of a complex adaptation process of the UN 1951 Refugee Convention, which involved multidimensional issues, as well as a multi-actor that represents various interests.

Интернационализация текста в парадигме лингвистическихитранслатологическихисследований: глобальное и предлокализационное направления интернационализации

The research deals with linguistic and extralinguistic features of the internationalization process of multimedia products text materials. The term internationalization is considered in the paper within the framework of the translation activity “GILT processes” (processes of globalization, internationalization, localization and translation). The article describes the current state of linguistic and translatological studies of the internationalization process. The paper presents the definition of the internationalization concept. The range of procedures carried out in the process of internationalization is defined. The paper describes the most significant technical procedures performed on the product during internationalization. The paper provides some characteristic linguistic transformations caused by the tasks of internationalization. The research paper offers several examples of using internationalization in the video game industry, the film industry and in creating multilingual and global versions of websites. As a result of the conducted research, two directions of internationalization were identified: pre-localization and global ones. The pre-localization direction of internationalization is to create an intermediate version of the product (mainly in English), which serves to simplify localization (following internationalization) into other languages. This approach significantly reduces the cost of creating a multilingual version of the product. The global direction of internationalization is to create a global version of this product in English. It is assumed that users of the global version can be not only native English speakers, but also those who speak it as a foreign language. Due to the wide spread of English as a foreign language in the world, the creation of an internationalized English version of the product allows companies to enter the global market with significantly lower financial costs and in a shorter timeframe than in the case of creating a multilingual version of the same product.

Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)

Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includesfour steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor startsbroadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possiblelocation where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing themeasuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchorsaccording to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts ininitialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it forfuture use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational modeattaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, withlower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar MeanAbsolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer amongthe neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared toMTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers aneffective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressingthe correlation between wireless device positions and signal intensities to improve the localization process. Theobtained results and provided justification emphasize the significance and value of the model in the field oflocalization in WSNs. The model represents a valuable contribution to the development of localization techniquesand improving their accuracy to meet the needs of various applications. The model opens up opportunities for itsutilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disastermanagement, enhancing its practical applications and practical significance.

EAIS-Former: An efficient and accurate image segmentation method for fruit leaf diseases

Fruit leaf disease segmentation is an essential foundation for achieving accurate disease diagnosis and identification. However, shadows caused by folded leaves and serrations on leaves can lead to difficulty in extracting edge features, affecting the accuracy of leaf segmentation. In addition, the varying shapes and blurred boundaries of disease spots can further lead to poor segmentation performance of spots. To address the above problems, this work proposes a method called EAIS-Former by combining the advantages of global modeling of Transformer, local processing and positional coding of convolutional neural network (CNN) for accurate segmentation in fruit leaf disease images. Dual scale overlap (DSO) patch embedding is designed to effectively extract multi-scale disease features by dual paths to alleviate omission of lesions. Ultra large convolution (ULC) Transformer block is customized for performing positional encoding and global modeling to efficiently extract global and positional features of leaves and diseases. Skip convolutional local optimization (SCLO) module is proposed to optimize the local detail and edge information and improve the pixel classification ability of the model so that the segmentation results of leaves and spots can be finer and more tiny spots can be extracted. Double layer upsampling (DLU) decoder is built to efficiently fuse the detail information with the semantic information and output the accurate segmentation results of leaves and spots. The experimental results show that the proposed method reach 99.04%, 98.64%, 99.24%, 99.42%, 98.59% and 98.58% intersection over union (IoU) for leaf segmentation on apple rust, pomegranate cercospora spot, mango anthracnose, jamun fungal disease, apple alternaria blotch and apple gray spot datasets, respectively. The IoU of lesion segmentation achieve 94.47%, 94.54%, 83.83%, 86.60%, 89.59% and 88.76%, respectively. In contrast to DeepLabv3+, the accuracy of disease segmentation is raised by 5.25%, 5.15%, 5.55%, 7.64%, 7.04% and 9.35%, respectively. Compared with U-Net, the proposed method improves the accuracy of disease spot segmentation by 4.3%, 4.44%, 5.26%, 9.42%, 5.87% and 6.53% under the six fruit leaf test sets, respectively. In addition, total parameters and FLOPs of the proposed method are only 18.44% and 8.47% of U-Net, respectively. Therefore, this study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation, which provides a sufficient basis for the accurate analysis of fruit leaves and diseases.

Unlocking the potential of Tanzania's cashew processing industry: A comparative analysis with Côte d’Ivoire and Mozambique

Cashew processing is a vital contributor to economic growth in Tanzania, renowned for its cashew nut production. However, despite the country’s historical prominence in cashew production, only 5% of raw cashews undergo local processing, resulting in substantial economic losses. This paper explores the challenges and opportunities within Tanzania’s cashew processing industry, focusing on limited local processing capacity, inefficient tools, raw material availability, market access, and government support. Comparative insights from Côte d’Ivoire and Mozambique further enrich our understanding of the cashew sector. It recommends increasing local processing capacity, enhancing technical expertise through training, ensuring a reliable supply of high-quality raw materials, improving market access via information systems, and strengthening government support with tailored policy frameworks. Notably, the paper emphasizes regional collaboration among African cashew-producing nations as a crucial strategy for collective growth and knowledge exchange. By drawing upon their experiences, this study aims to provide guidance for policymakers, industry stakeholders, and investors to revitalize and sustain Tanzania’s cashew processing sector.

Genome-wide identification of U-box gene family and expression analysis in response to saline-alkali stress in foxtail millet (Setaria italica L. Beauv).

E3 ubiquitin ligases are central modifiers of plant signaling pathways that regulate protein function, localization, degradation, and other biological processes by linking ubiquitin to target proteins. E3 ubiquitin ligases include proteins with the U-box domain. However, there has been no report about the foxtail millet (Setaria italica L. Beauv) U-box gene family (SiPUB) to date. To explore the function of SiPUBs, this study performed genome-wide identification of SiPUBs and expression analysis of them in response to saline-alkali stress. A total of 70 SiPUBs were identified, which were unevenly distributed on eight chromosomes. Phylogenetic and conserved motif analysis demonstrated that SiPUBs could be clustered into six subfamilies (I-VI), and most SiPUBs were closely related to the homologues in rice. Twenty-eight types of cis-acting elements were identified in SiPUBs, most of which contained many light-responsive elements and plant hormone-responsive elements. Foxtail millet had 19, 78, 85, 18, and 89 collinear U-box gene pairs with Arabidopsis, rice, sorghum, tomato, and maize, respectively. Tissue specific expression analysis revealed great variations in SiPUB expression among different tissues, and most SiPUBs were relatively highly expressed in roots, indicating that SiPUBs may play important roles in root development or other growth and development processes of foxtail millet. Furthermore, the responses of 15 SiPUBs to saline-alkali stress were detected by qRT-PCR. The results showed that saline-alkali stress led to significantly differential expression of these 15 SiPUBs, and SiPUB20/48/70 may play important roles in the response mechanism against saline-alkali stress. Overall, this study provides important information for further exploration of the biological function of U-box genes.

Performance optimisation techniques for Conflict-free Replicated Data Types (CRDT)

The research relevance is determined by the need for distributed data handling optimisation that does not cause conflicts during replication, since the introduction of such types of data leads to an increase in the need for their optimisation and performance. The study aims to develop techniques to improve the efficiency of various types of data with conflict-free replication. To achieve this goal, the methods of analysis and experimentation were used. The study results demonstrate that the use of certain optimised methods of conflict-free data types in replication leads to a significant improvement in efficiency and reduction of resource costs. The results show that caching and precomputation, asynchronous synchronisation, and local processing are the most effective methods for improving the efficiency of Conflict-free Replicated Data Types. It is established that the choice of a specific method for improving the performance of Conflict-free Replicated Data Types should be justified and based on a careful analysis of the requirements and characteristics of a particular distributed system. This takes into account user needs, data criticality, edit frequency, etc. The research includes the development and optimisation of synchronisation algorithms that significantly improve the performance of these types of data in distributed systems. The work includes simple software implementations for detailed analysis and study of methods for optimising the performance of data types in conflict-free replications. The introduction of distributed computing has made it possible to optimise the processes of data replication and synchronisation, which helps to reduce overheads and improve system performance. It is concluded that the study is of practical importance since the use of optimised conflict-free data types with replication in real distributed systems can improve their efficiency and reliability. The practical significance of the study lies in the possibility of applying optimised methods of data types with conflict-free replication in real distributed systems, which will increase their performance and ensure efficient operation under conditions of high load and limited resource potential

Flexible implementation of modulated localisation microscopy based on DMD.

Localisation microscopy of individual molecules allows one to bypass the diffraction limit, revealing cellular organisation on a nanometric scale. This method, which relies on spatial analysis of the signal emitted by molecules, is often limited to the observation of biological objects at shallow depths, or with very few aberrations. The introduction of a temporal parameter into the localisation process through a time-modulated excitation was recently proposed to address these limitations. This method, called ModLoc, is demonstrated here with an alternative flexible strategy. In this implementation, to encode the time-modulated excitation a digital micromirror device (DMD) is used in combination with a fast demodulation approach, and provides a twofold enhancement in localisation precision. Layout: Nowadays, we can use an optical microscope to observe how proteins are organised in 3D within a cell at the nanoscale. By carefully controlling the emission of molecules in both space and time, we can overcome the limitations set by the diffraction limit. This allows us to pinpoint the exact location of molecules more precisely. However, the usual spatial analysis method limits observations to shallow depths or causing low distortion of optical waves. To overcome these restrictions, a recent approach introduces a temporal element to the localisation process. This involves changing the illumination over time to enhance the precision of localisation. This method, known as ModLoc, is showcased here using a flexible and alternative strategy. In this setup, a matrix of micrometric mirrors, working together with a fast demodulation optical module, is used to encode and decode the time-modulated information. This combination results in a twofold improvement in localisation precision.

A Robust Approach for Licence Plate Detection Using Deep Learning

Intelligent transport systems must be developed due to the rising use of vehicles, particularly cars. In the field of computer vision, the identification of a vehicle's licence plate (LP) has been crucial. Various methods and algorithms have been used for the detection process. It becomes challenging to find similar photos, nevertheless, because the features of these plates change depending on colour, font, and language of characters. The research proposes a powerful deep learning framework based on feature extraction using convolutional neural networks and localization using canny-edge detection. Three steps make up the model's operation. An improved approach integrating the usage of bilateral filters and Canny edge detection is used for the processes of segmentation and localization. Further, a CNN architecture is used to extract features from images and classify the presence of licence plates in unseen vehicles. If present, the stage is followed by recognition of numbers written on the plates. An extensive experimental investigation takes place using three datasets namely Stanford Cars, Car Licence Plate Detection dataset and Indian Licence Plates Database. The attained simulation outcome ensures a superior performance over existing techniques in a significant way.

Visible Light Induced Exciton Dynamics and trans-to-cis Isomerization in Azobenzene Aggregates: Insights from Surface Hopping/Semiempirical Configuration Interaction Molecular Dynamics Simulations.

Assemblies of photochromic molecules feature exciton states, which govern photochemical and photophysical processes in multichromophoric systems. Understanding the photoinduced dynamics of the assemblies requires nonadiabatic treatment involving multiple exciton states and numerous nuclear degrees of freedom, thus posing a challenge for simulations. In this work, we address this challenge for aggregates of azobenzene, a prototypical molecular switch, performing on-the-fly surface hopping calculations combined with semiempirical configuration interaction electronic structure and augmented with transition density matrix analysis to characterize exciton evolution. Specifically, we consider excitation of azobenzene tetramers in the nπ* absorption band located in the visible (blue) part of the electromagnetic spectrum, thus extending our recent work on dynamics after ππ* excitation corresponding to the ultraviolet region [Titov, J. Phys. Chem. C2023, 127, 13678-13688]. We find that the nπ* excitons, which are initially strongly localized by ground-state conformational disorder, undergo further (very strong) localization during short-time photodynamics. This excited-state localization process is extremely ultrafast, occurring within the first 10 fs of photodynamics. We observe virtually no exciton transfer of the localized excitons in the nπ* manifold. However, the transfer may occur via secondary pathways involving ππ* states or the ground state. Moreover, we find that the nπ* quantum yields of the trans-to-cis isomerization are reduced in the aggregated state.

Understanding Acharya’s Concept on Norms Localization: A Lesson Learned from Localization of Transnational Norms on Anti- Female Genital Mutilation in Gorontalo, Indonesia

This paper is a critical review of Amitav Acharya's concept related to the process of global norm internalization. According to Acharya the process leads to three possible outcomes, i.e., the norms being accepted (norm displacement), adapted and modified (localization), or rejected (resistance rejection). This paper analyzes the localization of transnational norms to the domestic sphere, both the process, and its result, focusing on the case study of norm localization on anti-Female Genital Mutilation (FGM) in Gorontalo, Indonesia. In Gorontalo, the global norm of anti-FGM was initially accepted by the people and was implemented as government policies. However, later there was rejection by the people which pressured for the modification of the regulation. This paper attempts to answer the question of "How is the process of localization of transnational norms anti-female genital mutilation in Gorontalo, Indonesia?". Our findings lead to the identification of the various actors, factors and model of localization which are not necessarily similar to what has been proposed by Acharya. This paper hence contributes to the modification of Acharya’s model of norms internalization.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Study of biomarkers p53, Ki-67, Bcl-2, and VEGF in pterygium.

To study the biomarkers present in primary pterygium samples of patients of Indian ethnicity and compare it with the samples obtained from the unaffected conjunctiva of the same eye. A prospective case-control study of 17 eyes in patients above 10 years of age with primary pterygium who underwent pterygium excision using limbal conjunctival autograft technique. The pterygium samples (cases) and conjunctival samples (controls) were sent for immunohistochemical (IHC) staining for the following biomarkers: p53, Bcl-2, Ki-67, and vascular endothelial growth factor (VEGF). The immunohistochemistry of the samples and the controls revealed p53 positivity in 47.05% of pterygium samples and 29.4% of controls ( P < 0.587). Nine cases each in pterygium and control samples were positive for Ki-67 expression. Differences in the staining pattern between the two groups were not statistically significant ( P < 1.000). Bcl-2 positivity was seen in 10 pterygium samples (58.8%) and 12 controls (70.5%), with no statistical difference between the two groups ( P < 0.455). VEGF expression was seen in both epithelial and endothelial cells of the samples and controls, with no statistical difference between the two groups, with P = 1.000 for the epithelial staining and P = 0.637 for endothelial staining. The expression of biomarkers was comparable in both groups. We conclude that pterygium, against common belief, might not be a localized disease process but a global ocular phenomenon where the apparently healthy tissue also has some ongoing disease process at a molecular level.

Left ventricle function and post-transcriptional events with exercise training in pigs.

Standardized exercise protocols have been shown to improve overall cardiovascular fitness, but direct effects on left ventricular (LV) function, particularly diastolic function and relation to post-transcriptional molecular pathways (microRNAs (miRs)) are poorly understood. This project tested the central hypothesis that adaptive LV remodeling resulting from a large animal exercise training protocol, would be directly associated with specific miRs responsible for regulating pathways relevant to LV myocardial stiffness and geometry. Pigs (n = 9; 25 Kg) underwent a 4 week exercise training protocol (10 degrees elevation, 2.5 mph, 10 min, 5 days/week) whereby LV chamber stiffness (KC) and regional myocardial stiffness (rKm) were measured by Doppler/speckle tracking echocardiography. Age and weight matched non-exercise pigs (n = 6) served as controls. LV KC fell by approximately 50% and rKm by 30% following exercise (both p < 0.05). Using an 84 miR array, 34 (40%) miRs changed with exercise, whereby 8 of the changed miRs (miR-19a, miR-22, miR-30e, miR-99a, miR-142, miR-144, miR-199a, and miR-497) were correlated to the change in KC (r ≥ 0.5 p < 0.05) and mapped to matrix and calcium handling processes. Additionally, miR-22 and miR-30e decreased with exercise and mapped to a localized inflammatory process, the inflammasome (NLRP-3, whereby a 2-fold decrease in NLRP-3 mRNA occurred with exercise (p < 0.05). Chronic exercise reduced LV chamber and myocardial stiffness and was correlated to miRs that map to myocardial relaxation processes as well as local inflammatory pathways. These unique findings set the stage for utilization of myocardial miR profiling to identify underlying mechanisms by which exercise causes changes in LV myocardial structure and function.

PANDA: Processing in Magnetic Random-Access Memory-Accelerated de Bruijn Graph-Based DNA Assembly

In this work, we present an efficient Processing in MRAM-Accelerated De Bruijn Graph-based DNA Assembly platform, named PANDA, based on an optimized and hardware-friendly genome assembly algorithm. PANDA is able to assemble large-scale DNA sequence datasets from all-pair overlaps. We first design a PANDA platform that exploits MRAM as computational memory and converts it to a potent processing unit for genome assembly. PANDA can not only execute efficient bulk bit-wise X(N)OR-based comparison/addition operations heavily required for the genome assembly task but also a full set of 2-/3-input logic operations inside the MRAM chip. We then develop a highly parallel and step-by-step hardware-friendly DNA assembly algorithm for PANDA that only requires the developed in-memory logic operations. The platform is then configured with a novel data partitioning and mapping technique that provides local storage and processing to utilize the algorithm level’s parallelism fully. The cross-layer simulation results demonstrate that PANDA reduces the run time and power by a factor of 18 and 11, respectively, compared with CPU. Moreover, speed-ups of up to 2.5 to 10× can be obtained over other recent processing in-memory platforms to perform the same task, like STT-MRAM, ReRAM, and DRAM.

Applicability of non-invasive, digital palpation device to detection of woody breast conditions in chicken breast muscle.

Woody breast (WB) is one of muscle myopathy found in chicken breast, characterized with enlarged size and extremely stiff texture. The WB condition is one of the most prevalent quality issues in the modern poultry industry. WB has been shown to be heritable, but no effective detection method of WB severity in live birds exists for the selection purpose. The objective of this study was to determine potential of a non-invasive, portable digital palpation device as WB detection method that can be used for the selection to estimate the heritability of WB. The physical and functional properties of WB was also investigated in comparison with normal breast (NB). Two hundred ten breast muscles were obtained from a local processing plant one day after harvest and sorted based on WB scoring (1 for NB and 2 and 3 for WB). The samples were subjected to physical and physicochemical analyses, determining biomechanical properties (muscle tone, stiffness, elasticity, relaxation, and creep), pH, color, cooking yield, and texture (firmness and compression energy were used for raw meat and shear force and energy for cooked meat). The least squares means of the following variables were significantly different between WB and NB (p < 0.01): stiffness (603.4 vs 565.8; N/m), and elasticity (1.40 vs 1.55). However, relaxation and creep were not significantly different (p > 0.05). These results collectively showed that biomechanical properties of WB differ from NB. The degree of muscle stiffness in WB can be considered as a trait to be selected. The WB score showed strong negative correlations with cooking yield (-0.77) and cooked L*(-0.74), which means that as the breast becomes harder, the cooking yield decreases, and the color becomes darker after cooking. The WB score showed high correlations with physical and functional characteristics and exhibited strong correlations with the biomechanical properties measured by the device. Therefore, the results indicated that the digital palpation device has potential to detect the WB severity (degree of stiffness) of breast muscle.

Investment Review in Marine Fish Distribution, Baleendah Sub-District, Bandung District, Indonesia: A Case Study

The stable availability of fish as a crucial source of affordable animal protein with commendable nutritional content, contributing significantly to quality protein intake. To provide and ensure a steady supply of raw materials, a marine fish distributor in Baleendah Sub-district, Bandung Regency, is imperative. This distributor, functioning as a fish container (cold storage), acts as a crucial link between major marine fish producers and local fish processing farmers. During abundant fish seasons, the distributor collects fish, supporting prices during limited stock periods. This strategic intervention ensures the continuity of activities for salt-boiled fish processing farmers in Bandung Regency.The key objectives in establishing a marine fish distributor in Baleendah Sub-district, Bandung Regency, include optimizing the operation of distribution facilities and infrastructure. This optimization ensures a consistent supply of raw materials, reducing price fluctuations and ultimately lowering the production cost of processed fish. For the marine fish distribution company to operate sustainably, controlling fixed and variable expenses is crucial. Distributors of marine fish have total fixed and variable costs of IDR 271,950,000. The net result is calculated by deducting the whole fixed and variable costs from the revenue, which comes to IDR 688,050,000. The calculated Revenue/Cost (R/C) Ratio of 3.542 suggests a positive equilibrium between revenue and expenses. This ratio is a crucial financial indicator that shows profitability potential. Furthermore, the payback period—which is projected to be 0.46 years—indicates the amount of time needed to recoup the initial expenditure in relation to the net benefit that is delivered.

Low-latency partial resource offloading in cloud-edge elastic optical networks

In the context of the rapid deployment of IoT, 5G, and cloud computing, numerous emerging applications demand efficient networked computing capacity for task offloading from mobile and IoT users. This paper focuses on the optimization of network resource allocation and reduction of end-to-end (E2E) latency through the strategic decision of whether and where to offload user requests in a cloud-edge elastic optical network (CE-EON). To address this problem, we first formulate the problem into an integer linear programming (ILP) model as an initial solution. Additionally, we introduce several heuristic approaches that leverage the concept of partial resource offloading, specifically based on proportional segmentation (PRO_PS), partial resource offloading based on average segmentation (PRO_AS), all resource offloading (ARO), and all local processing (ALP). Furthermore, we implement a collaborative cloud-edge (CCE) offloading approach as a baseline for comparison. Our results demonstrate that the PRO_PS approach closely approximates the optimal solutions obtained from the ILP model in static scenarios. Moreover, the PRO_PS approach achieves the lowest E2E latency, blocking probability, and optimized network resource allocation in dynamic scenarios. This highlights the effectiveness of the proposed approach in improving system performance and addressing the challenges of CE-EONs.

Localization in 3D Wireless Sensor Networks with Obstacle Consideration

Wireless sensor network (WSN) refers to the network formed by sensor nodes for communicating sensed data over the wireless medium. These sensor nodes are mostly deployed randomly in the target area, and hence estimating the coordinates of these sensor nodes through the localization process is an important activity. Majority of the localization algorithms existing in the literature assume the target area to be an obstacle-free 2D terrain. However, such algorithms are not suitable for real-life scenarios where WSNs are actually deployed on 3D terrains that may also have obstacles that hinder the radio signals from the deployed sensor nodes. Hence, factors such as the height of the terrain, the presence of obstacles, etc., require significant considerations while designing algorithms for WSN. The proposed research presents a localization scheme for 3D WSN where the sensor nodes are randomly deployed in a 3D area having large obstacles. The proposed scheme adopts a distributed approach for calculating the virtual coordinates of the sensors using four beacon nodes. The deployment area is divided into regions, and each node computes its virtual coordinates with respect to the obstacle. Simulation results indicate an average localization error of 6.93 m. The proposed scheme requires very less computational effort and can be easily adapted in different scenarios.