Product Model Research Articles

Time Series Modeling and Forecasting of Finger Millet Cultivation Area, Production and Productivity in Chhattisgarh, India: The Box Jenkins Methodology

Chhattisgarh has taken important steps towards promoting millets cultivation and improving the livelihood of farmers. To increase millets production in Chhattisgarh, the state government launched the Millet Mission in September 2021. This mission has been started with a view to make Chhattisgarh the ‘millet hub of India’. The present study was conducted on time series modelling and forecasting of finger millet crops in Chhattisgarh India using Box Jenkins methodology and used historical data on currently cultivated area, production and yield of finger millet crops. The time series data was collected from 2001 to 2023, and analysis of the study was carried out using path analysis and Box Jenkins ARIMA model; and among various 20 models the best and suitable ARIMA (0, 1, 2), (3, 1, 1) and (2, 0, 4) model was selected based on AIC, BIC, MAPE, RMSE, MAE. With the help of the selected appropriate model, the cultivation area, production and yield of finger millet cultivation in Chhattisgarh was forecasted for the year 2024 to 2030. But marvellous, diminishing and fluctuating trend was observed in finger millet cultivation area and production over the forecast period. Whereas increasing and stochastic trend was observed in finger millet yield over the forecast period.

How stem size variations in forest stands influence harvester productivity and the use of productivity models

ABSTRACT Stem size has the greatest effect on harvester productivity, and stem sizes vary in a forest stand. How these within-stand variations influence harvester productivity is normally not considered in studies or predictions of productivity. This study suggests reasons as to why the current production and/or application of productivity models are prone to bias from stem size variations in a stand, irrespective of whether models were developed from tree-based or stand-based studies. Moreover, it also provides empirical data on the stand stem size variation’s influence on stand-based modeling of harvester productivity. Data from 11 harvesters in 347 final fellings and four harvesters in 80 thinnings were used. The mean productivity was 26.7 and 11.0 m3/PMh5 in final felling and thinning, respectively, and the mean stem size explained most of the observed variation. The productivity in final felling decreased with increased levels of stand stem size variation, as well as with increases in the proportion of broadleaf trees in the stand. For thinnings, productivity increased with increases in the proportion of pine trees in the stand, but there was no significant effect of stand stem size variation or other tested factors. The results show that stand stem size variation is a relevant factor to consider when modeling and predicting harvester productivity.

Tensor product model transformation-based reinforcement learning neural network controller with guaranteed stability

Despite the success of neural network (NN) controllers, the stability of closed-loop NN control systems is still a major challenge. This paper proposes a constrained reinforcement learning (RL) algorithm to design optimal NN controllers for a class of nonlinear dynamical systems while guaranteeing the stability of the control systems. By representing a controlled system as a tensor product (TP) model, a sufficient stability condition for a closed-loop NN control system is derived, which is then employed as a training constraint for the NN controller throughout the training process to ensure the stability in each training step. By showing the existence of a Lyapunov function, which is also directly obtained during the training process, the stability can be theoretically confirmed. Simulation studies on various nonlinear dynamical systems illustrate that the NN controllers trained with the proposed constrained RL algorithm outperform linear–quadratic–regulator controllers and yield roughly the same performance index values as those trained with an unconstrained RL algorithm, indicating that the imposed constraint does not significantly affect the training performance. Most importantly, while simulation results show that all controllers provide closed-loop stability, only the NN controllers trained with the proposed constrained RL algorithm theoretically guarantee stability.

Analysis of the Impact of Luxury Marketing Methods on Customer Consumption Psychology

Our research object is Herms, a world-renowned luxury brand, especially its brand strategy, distribution and purchase system, and the psychological characteristics of brand consumers. We use a variety of research methods for analysis, including an in-depth study of Herms' brand history, product lines, and marketing strategies through literature. Using the 4P marketing theory model of product, price, location and promotion, it makes a comprehensive analysis of Hermes' marketing strategy. Finally, a questionnaire about Hermes consumers was used to conduct detailed data analysis and interpretation. It is found that the success of Hermes is largely due to its unique brand strategy and marketing strategy. Although the distribution system is controversial, for Herms, its existence is exactly in line with its brand positioning, and it has successfully attracted some wealthy consumers who are willing to pay for luxury experiences.

The National Organization of Nurse Practitioner Faculties Leadership Mentoring Program: Ten years of positive outcomes

BackgroundNursing leadership programs can have a positive impact on organizations and communities. Health equity in nursing requires leaders who parallel the population demographics. PurposeThis work evaluated the National Organization of Nurse Practitioner Faculties Leadership Mentoring Program (LMP) 10 years from its inception. Lessons learned from this evaluation can inform leadership initiatives in nursing and other health professions. MethodThis cross-sectional evaluation applied the Context, Inputs, Process, and Products model to gauge effectiveness of the LMP. All 48 participants were invited to participate. ResultsThirty-two survey respondents, a 67 % response rate, demonstrated a 767 % increase in extramural scholarship collaborations and promotion to associate and full professor at 43 % and 90 %, respectively. Academic leadership positions to department chair, assistant/associate dean, and dean increased 200 %, 167 %, and 100 %, respectively. Seventy-seven percent of program participants are engaged on boards and committees at local, state, national, and international levels. These roles significantly impact legislative, policy, advocacy, and regulatory efforts, signifying the LMP's influence on broader societal and professional domains. ConclusionIdentifying clear program outcomes and metrics for leadership program evaluation can advance diversity, equity, and inclusion efforts. Sustainable funding models for leadership development will have a high return on investment for health professions.

M-ARM: An automated systematic approach for generating new variant design options from an existing product family

Companies with established product families can meet emerging market needs by extending their product families over time, that is, by introducing new product variants sequentially. Starting points for each new design can be generated by integrating design solutions from existing products in the same family. This article introduces an automated systematic approach to derive all possible ways of combining existing designs to produce a new variant with a desired new combination of functionality, based on algorithmic analysis of product models. The approach considers the functions in existing products and how they are realised in specific CAD parts and the geometric features defining them. By automatically generating and evaluating all the options, the new approach helps identify the most efficient way to combine existing parts and features from a product family to generate a new product variant with distinct functionality. The approach has been applied to product families of mechanical and mechatronic consumer products. It was evaluated by manually implementing the automatically-generated redesign instructions in CAD for several product variants, which built confidence in the approach’s accuracy and performance. From the evaluation, opportunities for further work are suggested.

Eval_uasi Program Buku Bergulir Model CIPP untuk Meningkatkan Literasi Membaca Masyarakat di Nagari Saok Laweh Kecamatan Kubung Kabupaten Solok

Buku bergulir’s program implemented in Nagari Saok Laweh, Kubung District, Solok Regency is no longer as effective as it once was. One of the contributing factors is that the program was halted during the COVID-19 pandemic and only resumed in 2022. This has led to several changes, prompting a study aimed at describing the eval_uation results of the buku bergulir’s program using the context, input, process, and product (CIPP) model to improve reading literacy in the community of Nagari Saok Laweh. This research is a qualitative study employing a descriptive method. Informants were selected using the snowball sampling method. Data collection was conducted through interviews and documentation. The data validation technique used in this study is triangulation. Data analysis was carried out through data reduction, data presentation, and conclusion drawing. The results of this study indicate that (1) the context eval_uation of the buku bergulir’s program is highly relevant and necessary in Nagari Saok Laweh, as the program addresses the need for quality reading materials and has great potential to improve community literacy; (2) based on the input eval_uation, it was found that the program is supported by competent human resources, adequate reading materials, an efficient distribution system, and strong support from the government and the community; (3) the process eval_uation of the buku bergulir’s program in Nagari Saok Laweh shows positive results, with excellent community participation and high enthusiasm; (4) through product eval_uation, it was found that the buku bergulir’s program has successfully increased literacy, reading interest, and the quality of life in the community.

Innovative Vehicle Design Processes Based on the Integrated Framework for Abstract Physics Modeling (IF4APM)

In industrial vehicle design processes, most companies have implemented model-based systems engineering (MBSE). As a consequence, design processes are nowadays not driven by documents, but by digital models of the vehicle to be developed and its components. These models exist on different levels of abstraction. The models on the requirements level are already well defined as well as the models of the defined product behavior and product properties. In recent years, the specification of models on the level of product functions was largely clarified, and elaborate frameworks already exist. However, this is not yet true for the level between functions and definite properties; this level can be referred to as "abstract physics". The enormous importance of this level, which, amongst others, can represent the physical effect chains which allow a vehicle component to function, is expressed by several researchers. Several research works aim at specifying models on this level, but, until now, no general consensus can be identified, and the existing model specifications are less appropriate for the early stages of vehicle design. This paper explains an Integrated Framework for Abstract Physics Modeling (IF4APM), which incorporates different perspectives of abstract physics and is suited for the early phases. The explanation is based on typical components of several kinds of vehicles. The main advantages of the proposed approach are the consistent interconnection of abstract product models, the clearness and understandability of the resulting matrices, and the aptitude to be used in the early phases of a vehicle design process.

A Self-Training-Based System for Die Defect Classification

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms’ reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method’s consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios.

A game theoretic approach for tradable white certificates regarding energy rebound and government intervention

In recent decades, many government administrations have tracked energy efficiency programs (EEPs) against environmental concerns. This was done so that it could potentially be useful as a supportive mechanism for smart technologies applied under the smart city concept. To facilitate this, tradable white certificates (TWCs) have been implemented as popular financial instruments used by energy-intensive sectors to boost cleaner production. In this study, we address an industrial EEP development with a TWC instrument as a multi-agent problem. We study this problem for the first time in the context of a supply chain that includes a manufacturer, an energy producer, and household energy consumers. Furthermore, we explore a new monopolistic pricing model for energy services and energy-efficient products, regarding the rebound effect, energy consumption, and social welfare. Additionally, we discuss two revenue-cost-sharing contracts and compare them as contracts using a comprehensive parametric and experimental analysis. The results show that the second proposed contract has some advantages over the first one. However, the second contract leads to less production than the first one does, while at the same time leading to less social welfare. Also, the findings suggest that the second investigated contract is a more appropriate instrument for the obligated parties than the first one when the aim is to improve the performance of TWC schemes. These findings can provide better circumstances for governance to optimize the critical parameters’ level on TWC schemes with the lowest analytical cost.Graphical abstract

Latent variable modeling of scientific impact: Estimation of the Q model parameters with structural equation models

Abstract Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows us to quantify the reliability of researchers’ Q parameter estimates. It can be extended to incorporate person covariates and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus.

A Dynamic Model of Digital Industrial Innovation – Process, Product and Business Model Innovation

A Dynamic Model of Digital Industrial Innovation – Process, Product and Business Model Innovation

When Do Corporate Venture Capital Investments Drive Product and Business Model Innovations?

When Do Corporate Venture Capital Investments Drive Product and Business Model Innovations?

The Digital Economy: Opportunities and Difficulties Isamic Economy's Future

The advent of technical innovation and economic digitalization via social media platforms and shopping applications has an impact on community social and economic activity. The manual, traditional economic system has been replaced by a modern one. A basic definition of a digital economy is any economic output that uses multifunctional internet-based technologies applied in a variety of digital business models for products or services. The expansion of the Islamic economy's involvement in e-commerce must be promoted in order to support the rise of financing via the digital economy and the empowerment that comes with it. One approach that can be used to create halal product innovations that are popular on the digital economy platform is principle-based regulation. With this technology, one would anticipate that. The results of this study demonstrate that initiatives to support innovation, entrepreneurship, and economic expansion may present a chance to increase access, particularly to Islamic economic inclusion. The digital economy is also anticipated to serve as a platform for the exchange of knowledge about how to better public services, finance, or social services, as well as new technologies that could offer all societal levels first-rate, quick, and reasonably priced Islamic economic services while also enhancing the effectiveness and efficiency of economic activity.

Discussion on the product model technology of XPS foam plastic

Discussion on the product model technology of XPS foam plastic

Collaborative Supervision and Evaluation of (Context, Input, Process and Product) in Muhadatsah Sobahiyah Program

This research is based on the importance of supervision and evaluation as part of the program management function. Supervision and evaluation of the program need to be carried out to measure its success and feasibility. The purpose of this study is to find out how the management supervision, and evaluation of the context, input, process and product model in muhadatsah sobahiyah program at Darussalam Gontor for Girls Islamic Boarding School 2. This study uses a qualitative approach with a descriptive type of research. All data is obtained through observation, Semi-structured interviews, and documentation techniques. Triangulation is performed to test the credibility of the data. Participants in this study consisted of 5 language supervisors, three of whom were members of the Language Advisory Council and two were members of the Central Language Improvement. The research results showed that the supervision process was carried out by the deputy caretaker, Language Advisory Council, Central Language Improvement, and the regional language section. Muhadatsah supervision at Gontor for Girls Campus 2 includes internal, repressive, preventive, active and passive supervision as well as formal truth supervision. It was also discovered that supervision of the program was carried out collaboratively. while the evaluation carried out on the program consists of 4 elements, namely context, process input, and product. So far, the muhadatsah program at Islamic Boarding School Darussalam Gontor for Girls Campus 2 has run according to expectations and desired goals. The advice for researchers can then continue research on supervision and evaluation of other learning programs in an institution. In addition, this research can be one of the references for academics in efforts to implement supervision and evaluation of programs, especially the Arabic language education program.

A design of cross-selling products based on frequent itemset mining for coffee shop business

Using a case study at the XYZ coffee shop in Bandar Lampung, Indonesia, this work conducts knowledge extraction on a sales dataset to create a cross-selling model for product bundling advice. Understanding Knowledge extraction was done using a frequent itemset mining technique based on an Apriori algorithm to extract a set of association rules between products. The study implemented a five-stage frequent itemset structure that encompasses business comprehension, data preparation, data exploration, model creation using the Apriori algorithm, and rules evaluation. The framework that comes from this research provides a set of bundling association rules across items for cross-selling strategies that involve many products and complex sales timeframes. Additionally, we recommended sales activities based on loyalty cards to enhance our dataset with consumer attributes and purchasing trends. Based on customized services and tailored offers based on consumers' past purchases and spending patterns, the loyalty card recommendation was created. Consequently, we may target the appropriate clients and items with our marketing initiatives

Utilization of Liquid Organic Fertilizer from Cow and Chicken Manure, Indigofera on Hydroponic Lettuce, and Consumer Perception

Lettuce (Lactuca sativa L.) is a horticultural plant with high nutritional and economic value and good development prospects in Indonesia. However, the high costs of hydroponic fertilizers drive research to find more economical alternatives, such as substitution with Liquid Organic Fertilizer (LOF). This study aims to analyze consumer perceptions of hydroponic lettuce using LOF substitution and understand consumer responses and purchasing behavior toward these products to identify the attributes influencing purchasing decisions for hydroponic lettuce with LOF substitution. The study employs a survey method with purposive sampling, involving 35 regular customers and 15 supermarket shoppers from PT ABA to collect primary data through questionnaires regarding consumer interest in hydroponic lettuce with LOF substitution. Additionally, proximate analysis and nutrient content testing are conducted in the laboratory. Data analysis uses conjoint and discriminant analysis with SPSS Statistics 24 to evaluate consumer responses and the most preferred product model based on nutritional attributes, price, and weight. The study successfully analyzes consumer perceptions of hydroponic lettuce with LOF substitution and finds that consumers have a positive view of this product, mainly due to its better nutritional content and the plant's ability to absorb nutrients optimally. These factors significantly influence purchasing decisions, with the main attributes being price, weight, and nutritional content.

Construction of an Evaluation Indicator System for Civic and Political Applications in Professional Practice Courses Based on CIPP: Taking the VBSE Practical Training Course as an Example

This study takes the virtual business society environment (VBSE) practical training course as a case study and applies the theoretical framework of the context, input, process, product (CIPP) model to construct an evaluation indicator system for the application of civic and politics in professional practice courses. The context evaluation is measured from the support of the VBSE practical training course into course civic and politics, teachers’ cognition, and the integration of course objectives; the input evaluation is measured from the matching degree of teachers’ civic and political competence, and the matching degree of teaching resources; the process evaluation is measured from the degree of implementation of civic and politics teaching and the degree of students’ acceptance; and the product evaluation is measured from the degree of impact of civic and politics teaching.

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.