Input Components Research Articles

SISTEM EVALUASI PELANGGARAN LALU LINTAS NTMC POLRI DENGAN ALGORITMA FUZZY LOGIC BERBASIS PYTHON

This research aims to build a traffic violation system using a fuzzy logic algorithm based on the Python language. By utilizing input components to receive traffic violation data and output components to produce traffic understanding level values. This method is used to process data based on traffic violations, system test results show the system's accuracy. To confirm the system's capabilities in traffic violations provides a baseline assessment of the level of accurate understanding of police law. The implications of this system show its potential as an effective detection tool for measuring existing legal violations from various levels of a person's understanding, especially in the legal field. Taking this research into account could provide a strong foundation for the development of similar systems in a variety of future comprehension evaluation contexts

Analysis Of The Application Of Electronic Medical Record In RUMKITAL Dr. Mintohardjo With The Measures Of Effectiveness Method

The phenomenon of the industrial revolution 4.0 accompanied by the rapid development of information technology has had a positive impact on the development of hospital management information systems. Currently, manual medical records are deemed no longer effective in relation to the community's need for quality, fast, precise, accurate and complete health services. Electronic Medical Record enables service providers to track patient data more quickly, identify visits, prevent and screen, help diagnose and manage disease so as to improve the quality of health services. This research was conducted to measure the effectiveness of the application of the Electronic Medical Record in Rumkital Dr. Mintohardjo by measuring the data input component, data integration and data security is assessed from the Usability, Flexibility and Efficiency of each component. Data collection was obtained from interviews and questionnaires from informants and respondents, namely officials and health workers at Dr. Rumkital. Mintohardjo regarding the application of Electronic Medical Records. The data obtained were then processed using the Measures of Effectiveness (MoE) method to measure the effectiveness of the application of the Electronic Medical Record. The hypothesis of this research is that the more effective the application of the Electronic Medical Record, the higher the quality of health services at Dr. Hospital. Mintohardjo and proven through data processing using the Measures of Effectiveness method. This study found the results that the effectiveness of the application of the Electronic Medical Record in Rumkital Dr. Mintohardjo has an MoE value of 0.676, it is included in the effective category.
 
 Keywords Electronic Medical Record, Measures Of Effectiveness, Rumkital Dr Mintoharjo

Energy input, habitat heterogeneity and host specificity drive avian haemosporidian diversity at continental scales.

The correct identification of variables affecting parasite diversity and assemblage composition at different spatial scales is crucial for understanding how pathogen distribution responds to anthropogenic disturbance and climate change. Here, we used a database of avian haemosporidian parasites to test how the taxonomic andphylogenetic diversity and phylogenetic structure of the genera Plasmodium, Haemoproteus and Leucocytozoon from three zoogeographic regions are related to surrogate variables of Earth's energy input, habitat heterogeneity (climatic diversity, landscape heterogeneity, host richness and human disturbance) and ecological interactions (resource use), which was measured by a novel assemblage-level metric related to parasite niche overlap (degree of generalism). We found that different components of energy input explained variation in richness for each genus. We found that human disturbance influences the phylogenetic structure of Haemoproteus while the degree of generalism explained richness and phylogenetic structure of Plasmodium and Leucocytozoon genera. Furthermore, landscape attributes related to human disturbance (human footprint) can filter Haemoproteus assemblages by their phylogenetic relatedness. Finally, assembly processes related to resource use within parasite assemblages modify species richness and phylogenetic structure of Plasmodium and Leucocytozoon assemblages. Overall, our study highlighted the genus-specific patterns with the different components of Earth's energy budget, human disturbances and degree of generalism.

AN OPTIMIZED RESIDUAL NETWORK FOR EMOTION RECOGNITION BASED ON A MULTI-FEATURES FUSION TECHNOLOGY AND ELECTROENCEPHALOGRAPHY SIGNALS

Presently, a significant number of students enrolled in colleges and universities encounter mental health challenges, including academic stress and difficulties in interpersonal relationships. These factors contribute to a decline in mental well-being and necessitate prompt intervention and assistance. The automation of mental health identification in students greatly benefits from the implementation of intelligent emotion recognition systems. Through the analysis of students’ emotional states, educational institutions can gain a deeper understanding of students’ mental well-being, enabling them to promptly identify and address any issues and offer more complete care and support to students. This paper proposes a method for automated emotion recognition that is driven by data. The method is developed based on the provided background information. The proposed approach incorporates a feature fusion mechanism and an attention mechanism into the ResNet-34 model. This integration enhances the model’s capability to analyze intricate details and subsequently improves its classification performance when applied to electroencephalography (EEG) signals. This paper introduces several key innovations. First, an optimization technique is applied to the input component of the model, enabling multifeature fusion. Second, an attention mechanism is incorporated after the residual network module, enabling the model to prioritize parts that contribute to classification and enhance feature extraction. Finally, the network parameters are optimized using both softmax loss and center loss functions. The findings from the analysis of the sentiment EEG public data SJTU Emotion EEG Dataset (SEED) indicate that the proposed sentiment recognition approach not only enhances the classification performance of the model on the sentiment EEG data but also improves the stability of the results. This paper presents a novel approach that enables automatic and efficient recognition of students’ emotions on commonly used platforms. The findings of this study hold significant implications for mental health assessment and detection in real-life production settings, offering substantial reference value in this domain.

Management of Implementing Clean and Healthy Living Behaviorin Households in Manisa Village

The current policy that regulates clean and healthy living behavior is the Republic of Indonesia Minister of Health Regulation Number 2269/Menkes/Per/XI/2011 concerning guidelines for fostering clean and healthy living behavior. Clean and healthy living behavior is a government priority program through community health centers and is an output target in implementing health development. The lowest coverage of healthy households in the Manisa Health Center Working Area is in the existing Manisa Village. The aim of the research is to determine the management of implementing clean and healthy living behavior in the household setting. This type of research is qualitative research. The informants for this research, managers of the community health center's clean and healthy living behavior program, community health center leaders, cadres, community leaders, religious leaders and the public, were taken using purposive sampling. Data collection was carried out through in-depth interviews, documentation review and Focus Group Discussions. The results of the research showed problems with the input component, the health promotion staff at the community health center had never received training, the funding allocation was very small, and the supporting facilities for Promkes were limited to print media. In the process, planning has not been implemented in an integrated manner, the organization and implementation of health promotions has not been carried out optimally, and monitoring is only based on the results of surveys of household clean and healthy living behavior. It is known that the output component of implementing clean and healthy living behavior in household management is still below the target. Implementation of management of clean and healthy living behavior in household arrangements is not as expected. Therefore, it is necessary to improve the quality of implementing staff, allocate funds and infrastructure, as well as improve management in implementing clean and healthy living behavior in the household structure.

Gas Path Fault Diagnosis of Turboshaft Engine Based On Novel Transfer Learning Methods

Abstract Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM) based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy.

Analysis of Qualitative Studies on ANC Services During the Covid 19 Pandemic at PMB Ana Hasanah

Background : In this COVID-19 pandemic situation, there are many restrictions on almost all routine services including maternal and newborn health services. Pregnant women are advised to continue to routinely carry out pregnancy checks during the pandemic even though there are service modifications (social distancing). Purpose of Writing: To obtain in-depth information about ANC services during the Covid-19 pandemic at PMB Ana Hasanah. Research Methods: The research used is a qualitative approach with the type of Rapid Assessment Procedures (RAP) research. The informants of this study consisted of 8 pregnant women who made antenatal care visits at PMB Ana Hasanah. While the key informants were husbands or families of pregnant women and Ana Hasanah PMB officers. Research Results: input components, human resources are sufficient and according to standards. Funds are available and sourced from clinic revenue turnover. SOPs are available which refer to the RI Ministry of Health Guidelines. The facilities and infrastructure are complete. For the process component, midwives' independent practice has carried out the flow of antenatal care services and health protocols according to the guidelines, but level 3 PPE is still limited. For the output component, the scope of ANC services has decreased significantly during the pandemic. For the outcome component, pregnant women are satisfied with antenatal care services, but some pregnant women do not have routine checks. Conclusions and Suggestions: The results of this study are expected to provide information to the Mandiri Midwife Practice as a place for maternal and neonatal health services so that they can provide the best service to pregnant women during the pandemic both online and offline so that monitoring of pregnant women during the COVID-19 pandemic can run well.

Wearable and interactive multicolored photochromic fiber display

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent, wearable interactive devices. However, significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight hardware. Here, we introduce a mass-produced, wearable, and interactive photochromic fiber that provides uniform multicolored light control. We designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the fiber. The impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission direction. In addition, we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single fiber. We prepared fibers on mass-produced kilometer-long using the thermal drawing method. The fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as needed. This work provides a new perspective and inspiration to the existing field of fiber display interaction, paving the way for future human–machine integration.

Pengelolaan Obat di Unit Farmasi RSUD Tengku Sulung

Pharmaceutical service is a direct and responsible service to patients related to pharmaceutical preparations with the intention of achieving definite results to improve the quality of life of patients. The purpose of this study was to obtain in-depth information on drug management in the Pharmacy Unit of RSUD Tengku Sulung. The research method used descriptive qualitative with purposive sampling with the number of informants 7 people. Data collection through in-depth interviews, direct observation and document review using content analysis. The results, on the input component obtained information that the lack of human resources, facilities and infrastructure are inadequate and there is no depot and SIM RS. Policies have been made in the form of Drug Management Guidelines in the Pharmacy Unit. In the process component, information is obtained that selection is based on the hospital formulary and national formulary, planning using the combination method, procurement using e-catalog and direct purchase, receipt is carried out by checking and adjusting the order letter, order invoice, and the drug received, storage based on dosage form, classification of types of drugs, medical devices, B3 and arranged alphabetically with the FIFO and FEFO systems, distribution using individual prescribing and Floor Stock systems, deletion is carried out by involving third parties, control is carried out by controlling drug inventory and quality, administration based on recording, reporting and financial administration. The output component obtained by the availability of drugs is still not effective and efficient. In conclusion, the pharmaceutical supply management system in the Pharmacy unit of RSUD Tengku Sulung is not in accordance with the established standards. Suggestions, the need for additional human resources, the fulfillment of infrastructure such as depot procurement and SIMRS, monitoring and evaluation, improved communication during drug selection and budget planning in the fulfillment of facilities and infrastructure.

Facet analysis in data envelopment analysis: some pitfalls of the CRS models

The nonparametric estimator of the Extended Facet reference technology for the Constant Returns to Scale case has attracted some attention, because the associated production frontier by construction does only include strongly efficient faces of maximal dimension, or strongly efficient faces that are part of such strongly efficient faces of maximal dimension. The strongly efficient faces of maximal dimension are denoted Full Dimensional Efficient Facets (FDEFs). The identification of such strongly efficient facets is facilitated by removing all inefficient and all strongly efficient but not extreme efficient DMUs from the estimation procedure of the technology set. Any face that i) is passing through the origin and with (s + m − 1) linear independent extreme efficient observed DMUs positioned on it and ii) with a normal vector with strict positive (strict negative) output (input) components, is a FDEF, where s (m) is the number of outputs (inputs). It is, however, not correct that every face that satisfies only i) is a FDEF. We denote a face (a subface) that satisfies only condition i) but not condition ii) for an AP-face (an AP-subface). It is proved that a radial projection of any output input combination in the estimated EXFA technology set is positioned on the strongly efficient frontier if and only if i) no AP-(sub)faces exist, ii) a regulaty condition RC1 is satisfied and only dual multiplier constraints corresponding to extreme efficient DMUs are included in the estimation. A test for the fulfillment of the condition that no AP-(sub)faces exist is provided.

Classifying flexible measures in two-stage network DEA and DEA-RA using novel slacks-based models

In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures.

An event‐triggered subspace predictive control method for partially unknown linear parameter‐varying systems

AbstractThis article develops a novel event‐triggered subspace predictive control (ET‐SPC) method for discrete‐time linear parameter‐varying (LPV) systems with partially unknown system parameters. An event‐triggered law is developed to alleviate the heavy load of data transmission and computation in the conventional subspace predictive control methods for the LPV systems. The design of the event‐triggered law relies on the input‐to‐state stability (ISS) theory and applies the robust stability condition based on linear matrix inequalities (LMIs), which guarantees the stability of the proposed method. Consequently, the input component can be directly transmitted to the system without receding horizon optimization when the event‐triggered law evaluates that the system satisfies the stability condition. The simulation results illustrate its satisfactory performance, which indicates its potential application involving a considerable amount of data transmission and computation such as the network control systems (NCSs).

Visualization Tool for Obsolescence Management: Reducing Supply Chain Risk for C5ISR Systems

Obsolescence is a challenge for Command, Control, Communications, Computers, Cyber, Intelligence, Surveillance and Reconnaissance (C5ISR) systems within the Army. These challenges include lower availability of components in the supply chain, schedule delays, and higher costs. Proactive obsolescence management in lieu of reactive obsolescence management is essential to assist with understanding and mitigating obsolescence challenges. This article presents a visualization tool for evaluating the risk of obsolescence to a system’s components. The tool was developed and deployed using the R statistical software program. The visualization tool focuses on the areas of component design life, procurement lead time, cost growth, and the number of manufacturers producing the component. These obsolescence properties are combined in the visualization tool to measure the level of obsolescence risk exposure from the supply availability of the components. The visualization tool was first demonstrated using simulations of 10 systems based on a mix of risk levels with respect to the obsolescence properties. The visualization tool allows a user to input component level data for their system and compare it with two reference systems: the best case when all variables are low risk (i.e., proactive) and the worst case when all variables are high risk (i.e., reactive). The reference systems are included to help practitioners quickly understand their systems level of obsolescence risk from the supply availability of the components. The visualization tool was then validated with publicly available data for a drone and a user study that demonstrated improved accuracy, duration, and confidence in assessing obsolescence risk.

Minimal Controllability Problem on Linear Structural Descriptor Systems With Forbidden Nodes

We consider a minimal controllability problem (MCP), which determines the minimum number of input nodes for a descriptor system to be structurally controllable. We investigate the “forbidden nodes” in descriptor systems, denoting nodes that are unable to establish connections with input components. The three main results of this work are as follows. First, we show a solvability condition for the MCP with forbidden nodes using graph theory such as a bipartite graph and its Dulmage–Mendelsohn decomposition. Next, we derive the optimal value of the MCP with forbidden nodes. The optimal value is determined by an optimal solution for constrained maximum matching, and this result includes that of the standard MCP in the previous work. Finally, we provide an efficient algorithm for solving the MCP with forbidden nodes based on an alternating path algorithm.

A Multi-Level Task Framework for Event Sequence Analysis.

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies.

Low-Rank and Sparse Decomposition for Low-Query Decision-Based Adversarial Attacks

Deep learning models are susceptible to contrived adversarial examples, even in the decision-based black-box setting where the attacker has access to the model’s decisions only. Developing more efficient and practical attacks help in better understanding the limitations of deep models. It is important that attacks are crafted with limited queries to avoid suspicion. Since the required number of queries increase with dimensions, low-dimensional embeddings are attractive. This low query budget constraint is a bottleneck for learning-based and data-driven attacks which rely heavily on querying the model. We propose LSDAT, an image-agnostic non-data-driven decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) of images to dramatically reduce the queries and improve fooling rates compared to existing methods. LSDAT crafts perturbations in the low-dimensional subspace formed by the sparse component of the input image and that of a target adversarial image to obtain query-efficiency. A viable perturbation is obtained by traversing the path between the input and adversarial sparse components. Theoretical analyses are provided to justify the functionality of LSDAT. Unlike other competitors (e.g., FFT), LSD works directly in the image domain to guarantee that non-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> constraints, such as sparsity, are satisfied. LSDAT offers better control over the number of queries and is computationally efficient as it performs sparse decomposition of the input and adversarial images only once to generate all queries. Four variants of LSDAT are presented for different scenarios including a pure black-box attack where no queries are allowed. We demonstrate ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> bounded attacks with LSDAT to evince its efficiency compared to baseline attacks in diverse low-query budget scenarios. LSDAT obtains 15 to 20% improvement in fooling ResNet-50 while using far fewer queries than competing methods in a similar setting.

Pathology of teacher recruitment and selection processes and providing policy recommendations for its improvement

The comprehensive teacher system as one of the subsystems of education, like any other system consists of three main components of input, process, and output, each of which, for its part, has an effective and constructive role in the survival and development of this system. The purpose of this study was to analyze the challenges and problems of teacher recruitment process and to provide policy recommendations for its optimization. To do so, adopting a Grounded Theory method, Semi-structured interviews were used as the main instrument for data collection. Using purposive sampling, 15 academic experts, specialists and managers in the field of human resource management and teacher education and 4 student-teachers were selected and participated in this study. Based on the research background and lived experiences of participants, the basic questions of the interview protocol were prepared and finally, with the opinion of experts, its validity was confirmed. After preparing and sending the interview protocol to the participants, the interviews were conducted. Analysis of the results and findings revealed that there are six shortcomings of: 1-policy making-institutional; 2-cognitive-attitudinal; 3-futuristic; 4-executive; 5-legal; and 6-methodological in teacher recruitment and selection. After the formation of the focus group, policy recommendations were provided in accordance with these shortcomings, which were presented in four categories of recommendations focusing on the areas of theory, policy-making and intermediate management, middle management and planning, and executive management. This study rich theoretical, methodological, and practical implications for researchers and policy makers involved in teacher education.

Communication-efficient ADMM using quantization-aware Gaussian process regression

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents' local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, due to data quantization, the GP algorithm is modified to account for the introduced quantization noise statistics. We further improve our approach by introducing an orthogonalization process to the quantizer's input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer's introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution's accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents.

Qualitative research: Utilization of the mobile application of national health insurance (JKN) to increase the effectiveness of services

Background: Sundari General Hospital has implemented an online registration system as a solution to the problem of patient backlogs. Patient registration via the national health insurance mobile (M-JKN) application at Sundari General Hospital has yet to reach the set target.Purpose: To analyze patient registration through the national health insurance mobile application at Sundari General Hospital.Method: Qualitative research uses a case study approach. The research was conducted from April to July 2023. The informants in the study were 10 people. The research instrument uses a list of questions as an interview guide. The information-gathering process was carried out using in-depth interviews, observation and documentation.Results: Showed that the input component has been running well, but there are still some obstacles, such as training for officers, inadequate patient infrastructure, and socialization to patients. The patient registration process has been carried out according to the SOP, but there are still many patients who need help understanding the use of the M-JKN application. The output component needs to be optimized due to the low participation of patients who register through the M-JKN application.Conclusion: Patient registration via the national health insurance mobile application at Sundari General Hospital has been going well, although there are still obstacles, and utilization is still low. However, it has been quite effective in reducing the buildup of patient queues.

Design and Development of Smart Agricultural Greenhouse

Food insecurity across the globe has necessitated the need to optimize crops productivity through automation and Internet of Things (IoT). This research was carried out to develop a smart greenhouse system where the soil nutrient level, air temperature and soil moisture content can be closely monitored through sensors and the Internet. The sensors – major input components of the structure – sent information to a NodeMCU ESP8266 microcontroller for interpretation, configuration and necessary actions by the output components of the smart structure. The output components of the smart structure are the liquid-crystal display (LCD), water pump, fan, heater and relay modules, while the C++ programming language was used. Remarkably, the intelligence aspect of the smart greenhouse is built on the smart algorithm. Based on the performance evaluation of the various system units, the irrigation, cooling, heating and fertilization units have an accuracy of 85%, 90%, 90% and 85% respectively. Interestingly, the performance rating of the prototype was very encouraging, which makes this smart system a reliable material to combat global food insecurity.