Input Process Research Articles

TRMD: a transformer-based reverse design model for quad-band metasurface absorbers

Abstract Metasurfaces have the ability to manipulate electromagnetic waves, which allows for the creation of functions such as perfect absorbers. The goal of a perfect absorber is to achieve high absorption peaks within a specific frequency band. This paper introduces an improved metasurface absorber structure that can achieve efficient absorption in four different frequency bands within the range of 2-9 GHz. In the field of metasurface design, deep learning methods have been recently applied due to their powerful data processing capabilities. However, these methods have primarily used fully connected neural networks and Long Short-Term Memory (LSTM). Despite their capabilities, fully connected networks and LSTM struggle to capture the global information in absorption spectrum data, leading to less accurate predictions. In this study, it was observed that the Transformer model can effectively capture global information using Multi-Head Self-Attention (MHSA) and is not affected by the length of the data. Based on this observation, this paper presents a lightweight model consisting solely of an encoder, achieving a Mean Squared Error (MSE) that is one-twentieth of the State-of-the-Art (SOTA). This model predicts metasurface structure based on target absorption spectra, enabling users to rapidly obtain metasurface absorber structures directly from input absorption spectra. The model consists of two parts: embedding and encoder. The embedding processes input absorption spectra data and adds positional encoding, while the encoder extracts spectral data features. MHSA effectively captures contextual information of absorption spectra, emphasizing key feature information. The final model achieved a MSE convergence of 2 × 10−4 and a coefficient of determination (R 2)value of 0.998, successfully optimizing the design of multi-band metasurface absorbers. Moreover, the predicted results from the model exhibit an absorption spectrum that is highly consistent with the target spectrum.

Implementation of an FPGA-Based System to Process Images and Match Keypoints on High-Resolution Pictures

Processing scenery and finding points of interest is crucial for applications in robotics and aerospace missions. Those areas require efficient and reliable visual input processing. Here, field programmable gate arrays (FPGAs) offer essential advantages, like low power consumption compared to CPUs, performing a large number of calculations simultaneously, and having compact hardware. This paper presents an FPGA system that processes incoming camera data, finds points of interest, and matches them across different images on high-resolution images (2048 × 1088). It is a novel approach to implement the complete image processing pipeline on high-resolution images within the FPGA fabric without additional hardware. For keypoint detection and matching, our work uses a modified SIFT algorithm optimized for FPGA implementation processing and a nearest neighbor-based matching method. It was implemented on a Xilinx Kintex-7 FPGA and partially on a NanoXplore NG-Ultra to evaluate a radiation-hardened FPGA for space applications. On the Kintex-7, the keypoint detection achieves a speed of 33 ms per image, and its features are matched on up to 5 images per second. Judging by the resource utilization of one image processing module on the NG-Ultra, porting the entire system on a radiation-hardened FPGA appears feasible.

Perancangan Sistem Informasi Pendaftaran Rawat Inap pada Layanan Kesehatan Rumah Sakit Umum Daerah Siti Fatimah

The purpose of setting up SIMRS is to improve efficiency, effectiveness, professionalism, performance, and access and services in hospitals. In the preparation of this paper, data was collected through observation methods, interviews, literature studies, and the application of software development models using the waterfall method. The result is the design of a web-based patient registration information system using Laravel and MySQL as a database. Hospitals face several problems, including data redundancy in the input process and less than optimal data presentation, making it difficult to manipulate, change, and delete data. The author hopes that the development of a new information system can simplify and speed up the data input process. The information system designed is expected to make it easier for patients to register online as well as assist admins in the patient registration process, making it easier to make registration reports.

The Effect of CoCoSo Method on the Ranks of Alternatives: A Case Study of Copper Electrical Wire Selection

When using MCDM (Multi-Criteria Decision-Making) methods to rank alternatives, decision makers’ opinions have a huge influence on the ranking results. The decision makers’ opinions can vary depending on the chosen MCDM method, data normalization method, and weighting method. For some MCDM methods, during the application process, users also need to choose the value of a certain coefficient (called the user coefficient). Obviously, the value of the user coefficient depends on users’ opinions, and of course, these opinions can affect the ranking of the alternatives. In this article, the effects of users’ opinions on the ranks of the alternatives when using the CoCoSo (Combined Compromise Solution) method are investigated. Users’ opinions (including the weighting criteria method and the user coefficient) are considered the input of the investigation process. Organizing the investigation of the effects of these two parameters on the ranks of alternatives was applied to the case of copper electrical wire selection. The results show that the users’ opinions have little effect on the ranks of alternatives. This result confirms CoCoSo's outstanding advantage.

Development of a Python Software for the Cardiovascular System Segmentation such as Arteries and Left Ventricle

J. Festas, L.C. Sousa, S.S. Siusa, S.I.S. PintoCardiovascular diseases (CVDs) are the leading cause of death globally, accounting for approximately 17.9 million deaths annually, with coronary artery disease (CAD) as a major contributor. CAD results from the build-up of atherosclerotic plaques, reducing myocardial perfusion and leading to conditions like angina and myocardial infarctions, severely affecting the left ventricle’s function. Advanced diagnostic tools are being developed to enhance the segmentation of cardiovascular structures, improving diagnostic accuracy and treatment outcomes.Accurate segmentation is crucial for analyzing coronary behavior via Computed Tomography Angiography (CTA), a non-invasive alternative to coronary angiography, and for planning surgical interventions. Furthermore, the models retrieved after segmentation can be enlarged simulating hyperemia for further hemodynamic simulations. Similarly, segmenting the left ventricle in dynamic CT scans is vital for assessing cardiac health through measurements like ventricular volume and ejection fraction.However, manual segmentation is time-consuming and suffers from variability, which can lead to significant diagnostic errors [1]. As such, there is a pressing need for more automated tools that enhance accuracy while reducing manual labor.Most common advancements are in the realms of deep learning. These have propelled automated medical image segmentation, achieving results with high efficiency and reliability [2]; however, these require extensive data sets and high variability. In scenarios where such datasets are limited or exhibit low variance, these models risk developing biases or failing to achieve accurate results in cases that are very different then the norm of the training dataset.To mitigate these risks, we are developing a Python-based semi-automatic algorithm that relies on existing open-source libraries, enhancing them with custom developments tailored to our specific needs, while relying on technician input to enhance classical image processing, ensuring adaptability to varied clinical conditions. We are testing this tool with patient data, with results that are very promising: segmentation similarity results of around 85%. We plan to release it as open-source software, encouraging a community-driven approach for continuous improvement and adaptation to clinical needs.The initial tests allows to believe this tool can be a robust and accurate software that can be used in hospital or for investigation purposes. The semi-automatic nature allows for user input and post processing tailoring of the segmentation.[1] Tavakoli, V. et al. (2013) A survey of shaped-based registration and segmentation techniques for cardiac images. Computer Vision and Image Understanding 117, no. 9: 966-989.[2] Litjens, G. et al. (2017) A survey on deep learning in medical image analysis. Medical Image Analysis 42: 60-88.

Enhancing Sentiment Analysis of Indonesian Tourism Video Content Commentary on TikTok: A FastText and Bi-LSTM Approach

Sentiment analysis is a method used to measure public opinion or the emotions of a group of people with similar interests based on their reactions to an event through text, images, videos, or audio on social media. However, such online data presents several challenges that can hinder the sentiment analysis process. These challenges stem mainly from the freedom that users have to post their content. Additionally, irrelevant opinions, often referred to as fake opinions, can also arise. The Bi-LSTM approach processes input sequences bidirectionally, allowing the model to capture information from both previous and subsequent contexts. This method is well-suited for sentiment analysis tasks due to its ability to recognize language nuances and relationships between different parts of the text. This study integrates a Bi-LSTM model with FastText word embeddings to filter out irrelevant opinions considered spam. The dataset consists of 150,351 TikTok comments taken from 100 popular videos related to tourist attractions. The experimental results show that the proposed Bi-LSTM model outperforms other models such as LSTM, CNN, GRU, MD-LSTM, and Peephole LSTM, achieving a test accuracy of 89.18%. Furthermore, when slang word translation is performed to convert slang into formal words, the Bi-LSTM model shows further improvement, with test accuracy reaching 93.10%, again surpassing the baseline models. These results demonstrate the robustness of the proposed method in handling noisy and informal language, thus improving the accuracy of sentiment analysis in the context of social media. This study provides a foundation for future research to improve sentiment analysis by addressing domain-specific challenges such as data imbalance and noise in social media data.

Ground Robots Bring Potential for Efficiency, Safety in FPSO Operations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32872, “Evaluation of the Potential Impact of Ground Robots on FPSO Operations,” by Gustavo L.L. da Silva, Maurício M. Cordeiro Jr., and Maurício Galassi, Petrobras, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ The objective of this paper is to present the operator’s learnings in evaluating the processes by which the asset crew of a floating production, storage, and offloading (FPSO) vessel identifies scenarios for the application of robotics in day-to-day offshore activities. The studies reveal a glimpse of the addressable market size of ground robots for routine offshore verification tasks. An initial analysis suggests that 30–50% of the theoretical equivalent of hours per year could be freed. Introduction In the complete paper, the hypothetical use of ground mobile robots to carry out operational activities is considered. For that goal, the capability of robotic platforms to move along an offshore facility (an FPSO, for the purposes of this paper) has been explored. This includes the ability to overcome obstacles, move up and down stairs or ramps, and walk on uneven floors or gratings while being piloted remotely or even doing so autonomously. Some of the considered equipment may have coupled robotic manipulators that also enable them to interact with the environment, allowing them to open or close doors, turn handles, and activate commands. These robotic platforms, in general, include actuator mechanisms and sensors that enable visual inspections, thermography, dimensional analysis, area mapping, gas measurements, noise and vibration analysis, and other operations requiring specific sensing. Operational Requirements A paramount requirement for a robot to be widely used in an FPSO is its ability to operate in hazardous areas. Robotic platforms for hazardous zones are not widely available. However, some companies are quickly advancing in the development and commercialization of these platforms. A Swiss company has already made available a quadruped robot with high mobilization capacity, including the ability to navigate stairs and carry out autonomous missions that have been preprogrammed. An Austrian company has marketed a caterpillar robot that can carry out inspections autonomously in classified areas. Every such activity will demand thorough analysis to properly define technical and process requirements. On FPSO platforms, the procedure for planning any service takes place at least a few days before its execution, a task already demanding many hours. Subsequently, a permit to work (PTW) is issued to perform the service. Sensors and Actuators for a Robotic System Some of the main data-acquisition or processing sensors used in robotic systems are detailed in the complete paper. In this context, “data” means everything that can be processed online or is capable of producing useful information in the future. Cameras for Acquisition of Images and Videos. These can be very useful in the visual inspection of systems or equipment, the visualization of the workspace, or the generation of input for image processing with the purpose of identifying patterns of interest, segmentation, classification, or other aspects related to computer vision.

Aligning rhetoric with reality: a qualitative analysis of multistakeholder initiatives in the global food system.

Global food system governance increasingly relies on multistakeholder initiatives (MSIs) that aim to include those who are affected by and/or affected by an issue. Multistakeholderism's perceived legitimacy is premised on both its outcomes (output legitimacy) and processes (input legitimacy), the latter in turn based on four key rationales: inclusiveness, procedural fairness, consensual orientation and transparency. To date, evidence on the ineffectiveness of MSI's outcomes undermines its claims to output legitimacy. While individual case study assessments have also raised concerns over their processes, documenting instances of power asymmetries and corporate capture, there has hitherto been no comprehensive assessment of the input legitimacy of multistakeholderism. This work addresses that gap through interviews with 31 participants working either in or on MSIs. Participants noted significant challenges related to input legitimacy, including that (i) inclusion was often based on pre-existing networks of an MSI's founders-most of whom were based in the global North-and risked excluding less well-resourced or marginalized actors; (ii) pre-existing power imbalances, both internal and external to the MSI, considerably influenced its processes and structures; (iii) goal-setting was complicated by conflicts of interest and (iv) reliance on informal processes limited transparency. The similarities in challenges across MSIs indicate that these are not attributable to shortcomings of individual MSIs but are instead indicative of wider system constraints. Rather than rely on multistakeholderism as a 'good' governance norm, our findings add to evidence that MSIs do not meet output legitimacy and signal that the legitimacy of MSIs in their current form should be questioned.

Recommendation system for financial decision-making using Artificial intelligence

The rapid expansion of artificial intelligence (AI) in consumer markets presents challenges, particularly in how cognitive biases influence financial decision-making. These biases can lead to irrational spending, raising ethical concerns about AI’s role in such applications. This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices. The focus is on developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations. The article considers a system for generating personalized financial recommendations based on large language models, which uses transaction history, predicted costs, and anomaly information to generate individual advice. Techniques include using Isolation Forest for identifying atypical financial actions and a combination of ARIMA and LSTM models for budget forecasting. The research also considers integrating these models with large language models (LLMs) to generate personalized recommendations. The methodological part of the work includes an analysis of existing models and their areas of application, defining data types and structures for processing, developing a system that integrates the available models, and testing it. The process of generating recommendations is described, which includes the stages of processing input data, forming context, generating recommendations and evaluating them taking into account user characteristics, such as risk level, financial goals and preferences. The generated recommendations are aimed at optimizing the user's financial behavior and can be adapted to different income levels. Special attention is paid to the ethical aspects of the system, which include ensuring confidentiality, fairness and transparency, as well as the importance of supporting user autonomy in making financial decisions. The system promotes responsible financial behavior by helping to avoid impulsive spending and increasing financial awareness without manipulation or imposing specific decisions.

Meteorology and Climatology at Kazan University: Celebrating the 190th Anniversary of Russia’s Hydrometeorological Service

This article summarizes the history of meteorological and climate research at Kazan University, spanning a period from 1812 to the present day. The distinguished contributions of Professor Adolph Theodor Kupffer to the establishment of Russia’s Hydrometeorological Service in 1834 were highlighted. Major achievements of the Kazan Meteorological School were analyzed, including those concerning global and regional climate change, circulation systems, as well as atmospheric dynamics and structure up to altitudes of 80 km. The influence of weather and climate factors on agriculture, wind industry, heating period, and public health was assessed. The observational data show that annual temperatures in Kazan have increased from 3.1 to 5.7 °C since 1871, with a rise in summer and winter temperatures from 18.1 to 19.7 °С and from –12.6 to –8.7 °C, respectively. The findings suggest that contemporary climate warming in Kazan began earlier (1946) compared to the general trend in the Northern Hemisphere (1970). The annual input of the Northern Hemisphere processes to temperature shifts in Kazan amounts to 63 %, with 27 % in summer and 43 % during winter. Using the CMIP6 climate projections based on anthropogenic factors as a driving force, future air temperature levels in Kazan throughout the 21st century were predicted.The long-term trends in temperature and precipitation across the Middle Volga, Cis-Urals, and the whole of Russia were explored. A general warming trend in recent decades was revealed.The efforts of Kazan meteorologists in advancing the understanding of large-scale atmospheric processes from the Earth’s surface up to 80 km were noted. The agroclimatic and biometeorological conditions of the Volga Federal District, the Republic of Tatarstan in particular, were described.

Non-Gaussian non-stationary wind speed simulation based on time-varying autoregressive model and maximum entropy method

Accurate simulation of non-Gaussian nonstationary wind speeds is a prerequisite for the wind resistant design of some nonlinear structures. Due to its efficiency, the time-varying autoregressive (TVAR) model has been extensively employed for simulating non-Gaussian nonstationary processes. Nevertheless, these simulation techniques based on TVAR exhibit suboptimal performance when confronted with nonstationary and highly non-Gaussian processes. Furthermore, they are unable to replicate the bimodal characteristics of specific wind speeds. This paper presents a new method for simulating univariate non-Gaussian nonstationary wind speeds using the TVAR model and the maximum entropy method. Herein, the connection between the statistical moments of input and output processes in TVAR is firstly derived. Secondly, the maximum entropy method is utilized to reconstruct the probability density function of input process and the time-varying translation function is determined. Finally, the translation process theory is applied to generate the input process, which is then input into the TVAR model to output the non-Gaussian nonstationary wind speed. The numerical results demonstrate that the proposed method exhibits superior simulation accuracy for nonstationary and strongly non-Gaussian wind speed processes. Furthermore, it is capable of capturing the bimodal characteristics of certain hardening non-Gaussian nonstationary wind speeds and possesses a broader range of applications.

Bayesian Optimization Strategies for Electrochemical Technology Development

Traditional design of experiments (DOE) often proves inefficient in technology development, particularly in emerging or rapidly evolving fields. This inefficiency stems from several challenges: a) high-dimensional spaces, which complicate the experimental design because of the exponential increase in the number of experiments; b) resource intensity, since achieving comprehensive coverage of the experimental space may require a multitude of experiments; and c) sensitivity to noise that can undermine the reliability of conclusions and force much repetition to achieve statistical significance. These limitations underscore the need for more adaptable and efficient experimental approaches in the face of rapid technological innovation.In contrast, Bayesian Optimization (BO) [1] can dynamically adjust based on initial findings, prioritize the most promising regions of the experimental space, promising faster and more efficient technology development. At the heart of BO is a probabilistic 'surrogate' model, embodying two main concepts: A Gaussian process (GP) simultaneously models the predicted mean µt(x) and the uncertainty σt(x) at a point x in the input space, given a set of observations (e.g., our prior experimental results) S1t = {(x1; y1), {(x2; y2), ... (xt, yt)}where xt is the process input, and yt is the corresponding output at time t. An acquisition function expresses the most promising setting for the next experiment, based on the predicted mean µt(x) and the uncertainty σt(x). This surrogate model is built and continuously refined using data from previous experiments, enabling informed decision-making for subsequent explorations.Meta’s open-source Ax platform [2] was used as an interface in this work for defining, running, and analyzing Bayesian Optimization (BO) experiments. Thanks to the robust capabilities of BoTorch, Ax effectively abstracts the complexities associated with optimization tasks, reducing the necessity for in-depth exploration of algorithmic intricacies by the user. Both Ax and BoTorch, being Python libraries, required a Python environment, which was established in this work using an Ubuntu installation on the Windows Subsystem for Linux (WSL) feature.The dataset used in this study was originally generated using a one-variable-at-a-time (OFAT) method [3]. Subjecting a rhodium surface in alkaline solution to a repetitive triangular potential sweep over the range 0-1.55 V (RHE) generated a thick, hydrous oxide film which exhibited appreciable charge storage effects at about 1.2–1.5 V, attributed to a bulk redox process enabled by proton transfer. Oxide growth rate depended on variables such as hydroxide ion concentration, potential sweep limits, and temperature, sometimes in a nonlinear manner.The goal of this BO study was to maximize the value of the oxide’s redox charge capacity at a fixed sweep rate of 15.4 V/s for 300 cycles; the previously measured data [3] was used to validate the predicted values. Figure 1 shows a summary of the experimental parameter ranges used here and the creation of an experiment protocol within Ax to optimize parameters towards a specific goal, maximizing charge capacity. Figure 2 shows a GenerationStrategy (GS) user code snippet that interacts with its underlying engine (the “Service API”). A user generated instance of AxClient, a user-friendly interface to the Ax library, uses the GS instance to generate new trials and log trial results.Figure 3 shows historical trial information added to the newly created “Rh_experiment” (Fig. 1) to aid trial prediction. As new trials proceeded, and their information dynamically fed back to the GPEI model, the accuracy of the trials’ predictions improved, the actual Q value reached the maximum values expect from the original data [3]. The variance values also decreased, confirming more accurate predictions.Figure 4 lists historical trial information for a GenerationStrategy with two GenerationSteps, the first containing Sobol, the second GPEI models; the Sobol model in Ax GenerationStrategy is primarily used for initial exploration in a quasi-random manner without assuming any prior knowledge. In the trial examples shown in Fig. 4, the actual Q value reached the expected maximum Q values faster on switching to the GPEI model than in Fig. 3, mainly due to the fortuitous predictions resulting from the Sobol model.This study underscores the effectiveness of BO through its adaptive exploration-exploitation strategy, used here through the Ax platform [2], in streamlining the path from early experimentation to data-driven decision-making in rapid technology development.[1] See, e.g., Greenhill et al. (2020). Bayesian Optimization for Adaptive Experimental Design: A Review. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2966228.[2] https://ax.dev/[3] L.D. Burke and EJ.M. O’Sullivan, J. Electroanal. Chem., 93 (1978) 11-18. Figure 1

Investigation into electrochemical machining of aviation grade inconel 625 super alloy: an experimental study with advanced optimization and microstructural analysis

Purpose This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods. Design/methodology/approach The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO). Findings The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches. Originality/value The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

Multi-criteria decision-making optimization of a two-staged solar power plant for low radiation zone through the social decision units

The effective use of renewable energy has been the most common problem of the last few years. The optimal designs of renewable energy systems are the most crucial issue among these problems. However, the optimal points can differ according to the perspectives. In this study, the effects of the different social aspects were investigated on the optimal design of the two-staged solar power plant operating under low solar radiation. In this regard, three social perspectives were handled to optimize the design parameters, including investor, engineering and consumer sights. Thus, different system designs were formed considering different thermodynamical parameters, solar radiation values, and working fluids. The formed designs were then thermodynamically analyzed through energy and exergy methods and economically analyzed through the net present value (NPV) method to obtain the input and output values to determine the optimal points. Finally, the efficiency analysis technique with output satisficing (EATWOS) analysis was handled for three different social aspects to make a decision on the optimal system. The social aspects were included in the optimization through the analytical hierarchic process (AHP) for inputs and outputs. The results show that the social perspectives have essential effects on the optimal designs of the energy systems. As a result, the system’s energy and exergy efficiency for the investor basis were determined as 20.88 % and 22.42 %, respectively. This system can produce 93.55 MW of power generation with a profit of 103 million US$ at the end of 20 years. The system’s energy and exergy efficiency for the engineering basis were determined as 20.56 % and 22.08 %, respectively. This system can produce 71.17 MW of power generation with a profit of 80 million US$. The system’s energy and exergy efficiency for the consumer basis were determined as 19.65 % and 21.09 %, respectively. This system can produce 88.03 MW of power generation with a profit of 92 million US$.

Milk Value Chain Analysis: The Case of Urban and Peri-Urban Areas of Ambo and Dendi Districts, West Shewa Zone, Oromia National Regional State, Ethiopia

This study was aimed at analyzing value chain of milk in Urban and Peri – Urban areas of Ambo and Dendi districts of West Shewa Zone. The study made use of both primary and secondary data collected from 146 smallholder farmers, 30 consumers, 10 Hotels and Cafeterias and 2 primary milk cooperatives through application of individual interview schedules, focus group discussions and key informant interview. The analysis was made using descriptive statistics and value chain analysis. The value chain analysis revealed that the major actors in the districts milk value chains are input suppliers, milk producers, collectors, processors and consumers. Accordingly, the value chain activities in the chain were input supply, production, processing, marketing and consumption. Four milk marketing channels were identified and relative to all other fresh milk marketing channels, producers get a higher proportion of total gross marketing margin when they sell milk to cooperatives. Profitability analysis result for dairy production indicated that, a dairy producer obtains average gross margin of 83.65per improved cow per day, in the study areas which shows that dairy production was a profitable venture for smallholder dairy producers. Milk value chain in the study areas was hindered by shortage of feeds, continuous increasing price of feeds, poor AI service provision, poor supply of genetically improved cattle breeds, limited availability of credit services, limited extension service provision, poor feeding and animal husbandry practices, low level of market linkage among the value chain actors, absence of milk processing plant and non-market oriented milk production. Therefore, policy aiming at increasing farmers’ access to modern inputs, feed conservation practices, adequate training provision to AI technicians, developing and improving infrastructure, cooperative development and improving extension services are recommended to accelerate the milk value chain’s development in the study areas.

Role of coherence in many-body Quantum Reservoir Computing

Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir’s dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime.

EFL teachers learning to translanguage through loop input: implications for their identity-reconstruction

While numerous training models aim to empower teachers by positioning them as active participants with agency and freedom, they often fall short in providing opportunities for training that seamlessly incorporates both the participants’ own language and English. In this study, we used loop input (process and content aligned) in two languages that exacerbate the process of translanguaging. Loop input involved learning to translanguage by translanguaging. The study was conducted in Türkiye with 12 in-service English as a Foreign language (EFL) teachers. Data were gathered via semi-structured interviews, reflective journals, and in-class observations. Findings showed that loop input-modelled training, due to its reflection and awareness-raising orientations, contributes to in-service EFL teachers’ (INSETs) identity reconstruction in complex manners at the intersection of translanguaging-influenced identities: self-reflective critical teacher, student engagement-oriented teacher, and translanguaging-facilitating teacher. We argue that loop input-modelled translanguaging-embedded training could be a proper model to introduce translanguaging pedagogy.

A free education policy research in Indonesia public senior high school: The evaluation with context input process product model application

A free education policy research in Indonesia public senior high school: The evaluation with context input process product model application

Optimizing laser cladding process parameters for transformation-induced plasticity FeMnCoCr high entropy alloy: A multi-objective approach

A multi-objective optimization approach utilizing the response surface method (RSM) is proposed to determine the optimal processing parameters for laser cladding of Fe50Mn30Co10Cr10 coating. In this paper, a single-variable control method was employed to adjust the laser cladding parameters, and preliminary experiments were conducted to establish the optimization range for the RSM laser cladding parameters. A mathematical prediction model of input processing parameters (laser power, scanning speed and powder feed speed) and output response (dilution rate, W/H, microhardness and porosity) was established by the response surface method. The results indicated that both the dilution rate and microhardness had a positive correlation with laser power and scanning speed, while they were negatively correlated with the powder feed rate. The scanning speed is the most significant factor influencing the porosity of the coating, followed by the powder feed rate. The optimal processing parameters were identified as follows: laser power at 1750 W, scanning speed at 8.0 mm/s, and powder feed rate at 11.5 g/min. The model demonstrated strong agreement with the experimental results. The coating molding quality is exceptional, with an average porosity of 0.07 % and an average microhardness of 208.32 HV0.2. Meanwhile, the coating was composed of HCP phase and FCC phase, with the HCP phase comprising15.6 % and the FCC phase 84.4 %, respectively. Under the optimal processing parameters, the Fe50Mn30Co10Cr10 high-entropy alloy coating demonstrated impressive ductility and strength, with a deformation of 55.93 % and a compressive strength of 2153.7 MPa. The fracture mechanism of Fe50Mn30Co10Cr10 coatings is transcrystalline fracture and toughness fractures.

Differential Intrinsic Firing Properties in Sustained and Transient Mouse αRGCs Match Their Light Response Characteristics and Persist during Retinal Degeneration.

Retinal ganglion cells (RGCs) are the neuronal connections between the eye and the brain conveying multiple features of the outside world through parallel pathways. While there is a large body of literature on how these pathways arise in the retinal network, the process of converting presynaptic inputs into RGC spiking output is little understood. In this study, we show substantial differences in the spike generator across three types of αRGCs in female and male mice, the αON sustained, αOFF sustained, and αOFF transient RGC. The differences in their intrinsic spiking responses match the differences in the light responses across RGC types. While sustained RGC types have spike generators that are able to generate sustained trains of action potentials at high rates, the transient RGC type fired shortest action potentials enabling it to fire high-frequency transient bursts. The observed differences were also present in late-stage photoreceptor-degenerated retina demonstrating long-term functional stability of RGC responses even when presynaptic circuitry is deteriorated for long periods of time. Our results demonstrate that intrinsic cell properties support the presynaptic retinal computation and are, once established, independent of them.