Market Applications Research Articles

Strategi Pengembangan UMKM Dengan Ide Kreatif Melalui Program Kuliah Kerja Nyata Di Kelurahan Kolo

The Real Work Lecture (Kuliah Kerja Nyata or KKN) at STIE Bima is a community empowerment activity carried out by students and Field Supervisors (DPL). This KKN program at STIE Bima took place from August 18 to October 18, 2023, in the Kolo Sub-District. It is undeniable that the Kolo Sub-District has natural potential that needs to be developed through tourism and marine resources by promoting Micro, Small, and Medium Enterprises (MSMEs) and creative industries to be more innovative. The method used in the implementation of the KKN at STIE Bima is through participatory approaches and community involvement in interactions and data collection with MSME actors at the Kolo Sub-District Office. The results obtained from this KKN program include the enhancement of the local community's knowledge in Kolo Sub-District in improving MSMEs and creative industries, thus creating new job opportunities for the community through the application of digital marketing and providing local tourism needs to attract both domestic and international tourists.

Preparation and research of new black zirconia ceramics

The existing black zirconia has problems such as uneven color development, poor stability, expensive raw materials, and harm to the human body and the environment. In order to solve the above problems, this paper intends to use NiAl2O4, NiTiO3, Fe2O3 as chromophore, zirconia as a matrix, and a solid-phase method is used to prepare high-performance black zirconia ceramics. The method avoids the introduction of toxic elements, and at the same time, it is more economical in the selection of color-developing pigments. The experimental results show that black zirconia ceramics with uniform color, continuous adjustment and high temperature stability can be obtained. When the sintering temperature is lower than 1450 °C and the color material mixing ratio is 10 wt.%, the mechanical properties and optical properties of the obtained samples optimum, the overall reflectance of the sample is less than 10 wt.%, which meets the conditions for market application. These black ceramics can be widely used in high-tech fields such as mobile phone backplanes, photovoltaic industry and high-end decorative materials, and have broad application prospects.

Standardising Patient Engagement in Drug Development

Initiatives to increase patient engagement in drug development have recently been accompanied by growing calls for standardisation due to considerable uncertainties about how to best perform patient engagement and use it in drug marketing applications. We focus on materials developed by the Patient Focused Medicines Development (PFMD), a multi-stakeholder group founded in 2015, to investigate what these materials seek to standardize on patient engagement in drug development and what visions of patient engagement are being constructed by them. We take a material-semiotic approach, whereby the materials analysed are seen as influential actors, which can work upon and transform issues of concern. The findings indicate that these materials seek to standardise a new beginning for the drug development trajectory, which they (re)locate to the patients’ needs and preferences, and long-term relationships between researchers and patients developed through specific methods. A new type of patient is thus envisioned, while researchers and patient organisations are ascribed more complex roles.

Modeling the Impact of Different Policies on Electric Vehicle Adoption: An Investigative Study

Electric Vehicles (EVs) emerge as a crucial solution for alleviating the environmental footprint of the transportation sector. However, fostering their widespread adoption demands effective, targeted policies. This study introduces a versatile model, amalgamating stakeholders and policies and leveraging local data with broader market applicability. It delineates two key EV adopter groups—innovators and imitators—shedding light on their evolving impact on adoption trends. A pivotal feature of the model is the factoring of EV attractiveness, comprising Life-Cycle Cost (LCC), Driving Range, Charging Time, and infrastructure availability, all of which are expected to improve with the fast technological advancement of EVs. Financial policies, notably subsidies, prove potent in boosting EV adoption but fall short of targeted sales due to imitator lag. In response, a pragmatic solution is proposed: a government-led EV acquisition of 840 EVs, coupled with a 20% subsidy on new EV purchases and a 20% tax on new ICEV purchases, potentially realizing a 30% EV sales target by 2035. Future research avenues may delve into behavioral dynamics prompting imitators’ adoption, optimizing EV infrastructure strategies, and assessing the socio-economic impacts of EVs. Interdisciplinary approaches hold promise for enriched insights for effective EV integration policies.

Supplementary Influence Maximization Problem in Social Networks

Due to important applications in viral marketing, influence maximization (IM) has become a well-studied problem. It aims at finding a small subset of initial users so that they can deliver information to the largest amount of users through the word-of-mouth effect. The original IM only considers a singleton item. And the majority of extensions ignore the relationships among different items or only consider their competitive interactions. In reality, the diffusion probability of one item will increase when users adopted supplementary products in advance. Motivated by this scenario, we propose a supplementary independent cascade (IC) and discuss the supplementary IM problem. Our problem is NP-hard, and the computation of the objective function is #P-hard. We notice that the diffusion probability will change when considering the impact of its supplementary product. Therefore, the efficient reverse influence sampling (RIS) techniques cannot be applied to our problem directly even though the objective function is submodular. To address this issue, we utilize the sandwich approximation (SA) strategy to obtain a data-dependent approximate solution. Furthermore, we define the supplementary-based reverse reachable (SRR) sets and then propose a heuristic algorithm. Finally, the experimental results on three real datasets support the efficiency and superiority of our methods.

Identifying High-Priority Populations for Dietary Salt Reduction Using the TARPARE Model: A Cross-Sectional Study.

The application of social marketing to public health activities has recently garnered attention. This study aimed to identify high-priority segments for salt reduction interventions using the TARPARE model in a rural Japanese city. This cross-sectional study used convenient sampling. Scored indicators of the TARPARE model were used, and data was collected from 1874 adults to establish the priority order of target segments for promoting salt reduction. The model considers the total number of individuals (T), at-risk persons (AR), persuasibility (P), accessibility (A), resources required (R), and equity (E). For T, the salt content was evaluated using spot urine, and the salt check sheet was scored for each segment according to sex and age in the city's population. AR was the individuals who were overweight. P was determined by the behavior modification stage of salt reduction, unknown recommended salt reduction goals, and lack of knowledge in at least one aspect of salt reduction. A and R were characterized by communication with family or friends and family support, respectively. E was considered an education up to high school. The average aggregated score was 19.9 (standard deviation = 14.0), with higher scores in segments that consistently subjectively restrict salt intake but have a high objective salt intake. The highest priority was given to women in their 60s (49.3 points) and 70s (54.4 points). This study identified high-priority populations for salt reduction in a rural city in Japan. Our priority segments offer guidance for efficient and targeted interventions for salt reduction initiatives.

Mechanical Characterizations on biobased Sheet Molding Compound (SMC) developed for battery boxes.

In this work, performed within FENICE project (www.fenice-composites.com), semifinished materials for the mass production of fire-resistant battery box in the automotive. SMC were developed using PFA (polyfurfuryl alcohol) resin as the matrix and both long and short basalt fibers as reinforcement in a SMC and impregnation pilot line. Mechanical characterization is used to compare prepreg based on fabric impregnation and SMC and measure basic data needed for modelling. Short fiber reinforced composites are cheaper and more isotropic than those reinforced with fabrics, making them more interesting for mass market application in the automotive, such as electrical cars. The semifinished material production must be optimised, and basic mechanical data must be measured, to be used as input into FEM of the components to be produced.

PERANCANGAN APLIKASI FOOD MARKET BERBASIS ANDROID PADA USAHA DIDIMSUM

The rise of the online food business is undeniably due to the increasing needs and lifestyles of the people. The existence of busy work makes people order food from outside because it is easier, more practical, and efficient, and payment transactions can be done through E-wallets or bank transfers with a payment gateway payment system. Didimsum is a home-based business engaged in frozen food. The current problem is that in ordering using WhatsApp, and payments are still in cash, customers find it difficult to get the latest menu information. This research aims is to design an Android-based food market application for Didimsum to make it easier to reach customers, improve brand image, speed up the ordering process, and update menus and prices. The research was conducted with a field research methodology to obtain information directly from the Didimsum frozen food business. The techniques in data collection are observation, interviews, and document studies. The result is an Android-based Food Market Application system that can receive orders and payments from customers so that they are well-recorded and produce order and payment reports more quickly and accurately.

Exploiting the Value of Active and Multifunctional Façade Technology through the IoT and AI

In recent years, the interest in multifunctional façade (MF) technology has increased significantly. Recent advances in the integration of active and passive technologies have led to a new concept of building skins with highly flexible and decentralized control. Such an approach is considered capable of tackling environmental challenges and enhancing indoor environmental quality (IEQ). Integrated HVAC systems, dynamic blinds, and renewable energy systems can drastically increase façade responsiveness and efficiency. Although the technical feasibility of active and MF technology has already been demonstrated, market applications are still limited. The goal of this paper is to define the state of the art of MFs and clarify how the integration of IoT technologies, supported by AI, can increase market interest by fully exploiting the value of these systems. Indeed, recent advances in the IoT and data analysis tools are opening up attractive scenarios in optimization process. Starting with an overview of the most interesting EU-funded projects, this paper presents a MF case study in which IoT infrastructures are fully integrated. The prototype, realized within the MEZeroE Horizon project, stimulates a debate on future trajectories (and gaps) for the marketability of MF.

Guest editorial: Artificial intelligence application and future research directions in interactive marketing

Guest editorial: Artificial intelligence application and future research directions in interactive marketing

Deep learning-based fruit quality classification for customer service

Consumers always select and buy good quality fruits and vegetable. Selection criteria depend upon the freshness, shape, appearance, color, aroma and sturdiness at the first go. The taste and the shelf life come after that. As fruits play main role in day to day life, the grading of fruits is necessary in evaluating agricultural produce. The present existing technology are also used for fruit quality managing purpose but they are not more effective. There are some disadvantages like less reliability, less efficiency and less accuracy. In this paper, we would like to present a design and integration fruit quality classification solution for customer service. The purpose of this integration is to develop a service to classify the quality of fruits for customer in applications in agriculture, market or logistic.

Training in Marketing Strategies through Digital Marketing for Scaling Up Businesses of Silver Craft SMEs in Banda Aceh City

In today's digital era, digital marketing strategies are crucial, particularly for Small and Medium Enterprises (SMEs). The primary objective of this training is to equip participants with essential knowledge and skills for implementing effective digital marketing strategies, with the ultimate goal of enhancing the business scale of SMEs. Conducted over two days, from October 9 to 10, 2023, the training emphasized introducing and teaching various digital marketing applications, such as Facebook and Instagram. It also provided guidance on creating engaging content for marketing their products. By the end of the training, the participant, CV Taufik Perak, had successfully established digital marketing accounts on several social media platforms.

PEGylated porcine-human recombinant uricase: A novel fusion protein with improved efficacy and safety for the treatment of hyperuricemia and renal complications.

The growing prevalence of hyperuricemia necessitates the urgent development of more potent treatments. This study aimed to develop, optimize, and evaluate the safety and efficacy of porcine-human recombinant uricase (PHRU) both in vitro and in vivo. The study employed gene editing of PHRU through site-directed mutagenesis, with recombinant proteins expressed in vitro utilizing Escherichia coli. The polyethylene glycol (PEG) approach was employed to augment uricase stability and diminish immunogenicity. The pharmacokinetics and pharmacodynamics of PHRU were tested in vitro and in Sprague Dawley rats. Successful expression of the fusion protein in E. coli and the development of the PEGylated drug were achieved. In vitro experiments confirmed the efficacy of PEG-PHRU in degrading uric acid, with PEGylation not markedly affecting the biological activity of PHRU. Animal studies revealed that PEG-PHRU significantly lowered plasma uric acid levels and mitigated hyperuricemia-induced renal damage in rats. Both drug metabolism and pharmacokinetics exhibited favorable characteristics without observable adverse effects in experimental animals. This novel fusion protein shows the potential for ameliorating hyperuricemia and related renal complications, highlighting it as a promising drug candidate with substantial market applications.

Comparative Analysis of NPV and IIRR in Financial Market Applications

This paper discusses the comparison between two popular investment rules: Net Present Value (NPV) and Internal Rate of Return (IRR). This study aims to determine which investment rule is more appropriate in financial market. This study is more suitable for people who don’t know much about NPV and IRR but need to use them to evaluate something. This study analyses the advantages and limitations of both NPV and IRR, using factors such as cash flow, discount rate and project scale. In addition, this study also lists examples and practical applications of these investment rules to illustrate which investment rules are more suitable for which situations. By studying the advantages and disadvantages of NPV and IRR, this study aims to help people choose the most appropriate investment rule to make informed investment decisions in financial markets. This study has important practical significance for participants in financial markets to make project decisions.

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies

AbstractBlockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

Audio-Based Emotion Recognition Using Self-Supervised Learning on an Engineered Feature Space.

Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels. Self-supervised learning (SSL) is a family of methods which can learn despite a scarcity of supervised labels by predicting properties of the data itself. To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU- MOSEI)'s acoustic data. Unlike prior papers that have experimented with raw acoustic data, our technique has been applied to encoded acoustic data with 74 parameters of distinctive audio features at discrete timesteps. Our model is first pre-trained to uncover the randomly masked timestamps of the acoustic data. The pre-trained model is then fine-tuned using a small sample of annotated data. The performance of the final model is then evaluated via overall mean absolute error (MAE), mean absolute error (MAE) per emotion, overall four-class accuracy, and four-class accuracy per emotion. These metrics are compared against a baseline deep learning model with an identical backbone architecture. We find that self-supervised learning consistently improves the performance of the model across all metrics, especially when the number of annotated data points in the fine-tuning step is small. Furthermore, we quantify the behaviors of the self-supervised model and its convergence as the amount of annotated data increases. This work characterizes the utility of self-supervised learning for affective computing, demonstrating that self-supervised learning is most useful when the number of training examples is small and that the effect is most pronounced for emotions which are easier to classify such as happy, sad, and angry. This work further demonstrates that self-supervised learning still improves performance when applied to the embedded feature representations rather than the traditional approach of pre-training on the raw input space.

A Critical Analysis on Transformation of Renewable Energy to Green Chemicals: Opportunities and Challenges

AbstractThe abundant natural resources and rapidly falling prices to generate and store renewable energy create a remarkable opportunity for a new group of manufacturing industries to emerge. These technology pathways use abundant or waste resources to produce green chemicals and fuels like green hydrogen (H2), green ammonia (NH3), and green synthetic hydrocarbons (HCs). Integrating chemical processes and renewable energy can complete the carbon loop and bring substantial decarbonization along with economic opportunities around the globe. An evidence‐based and industry‐focused critical review of technologies to produce green chemicals and fuels from renewable energy is presented. It also discusses the market size and applications for these emerging industries and presents their development status, benefits, and challenges to commercialization. Green hydrogen production from renewable energy turns out to be the initial and key stage for all these technological advancements and is indispensable for their techno‐economic viability. Other environmentally friendly feedstocks, such as nitrogen (from the air) and wastes such as CO2 (from industrial flue gas), can produce green chemicals. Besides environmental benefits, several other benefits of producing green chemicals from renewable energy are identified. These include but are not limited to (i) accelerating the economy for renewable energy and hydrogen generation, (ii) savings in energy, costs, and natural resources, and (iii) creating millions of jobs. A perspective on opportunities to develop the green chemical industry and assist academia, industry, and policymakers is provided.

Commercial Egg Replacers in Pound Cake Systems: A Comprehensive Analysis of Market Trends and Application.

Replacing eggs without influencing pound cakes' texture, appearance, and taste is challenging. Ovalbumin, the major protein in egg white, contributes to the structures of cakes by providing SH Groups that form a firm gel during baking. However, there is a shift in the consumers' behaviour regarding health, well-being, animal welfare standards, and environmental concerns. To meet upcoming trends and consumer needs, 102 egg replacement products were launched globally to the best of the authors' knowledge, with 20 of them advertised as suitable for baking applications. Ten locally available commercial egg replacers with a range of protein contents were chosen and applied in a pound cake model system to evaluate their functionality by evaluating cake and cake batter quality. Three different categories of egg replacements were chosen: replacers containing no protein (R1-R3), a low amount of protein (1-10 g/100 g; R4-R5), and a high amount of protein (>10 g/100 g; R6-R10). Those were compared to three control cakes containing powdered whole egg, fresh egg, and liquid whole egg. All the analysed egg replacers significantly differed from the control cakes, including low-protein egg replacement R4. Despite R4 achieving the highest specific volume (1.63 ± 0.07 mL/g) and comparable texture values, none of the examined egg replacers compared favourably with the egg control cakes regarding appearance, physical and textural properties, and nutritional value.

Study on the Performance Mechanism of Polyformaldehyde Glycol Ether Polymer for Crude Oil Recovery Enhancement.

The demand for energy continues to increase as the global economy continues to grow. The role of oilfield chemicals in the process of oil and gas exploration, development, and production is becoming more and more important, and the demand is rising year by year. The support of national policies and the formulation of environmental protection regulations have put forward higher requirements for oilfield chemical products, which has promoted the innovative research and development and market application of oilfield chemicals. Polyformaldehyde glycol ether polymer (PGEP) is simple to synthesize, easily biodegradable, green and environmentally friendly, and in line with the development trend of chemicals used in oil and gas development. The interfacial tension performance of PGEP after compounding with different surfactants can reach as low as 0.00034 mN/m, which meets the requirements of the oilfield (interfacial tension ≤ 5 × 10-3 mN/m). The best oil washing efficiency performance of PGEP compounded with different surfactants reached 78.2%, which meets the requirements of the oilfield (oil washing efficiency ≥ 40%). The fracturing fluid drainage efficiency of PGEP after compounding with different surfactants reaches 22%, which meets the requirements of the oilfield (drainage efficiency ≥ 15%). The surface interfacial tension of the system remains constant after the concentration exceeds 0.2% and decreases with lower concentrations. The drainage efficiency increases with increasing concentrations in the range below 0.6%. It was determined that PGEP can be used as a surfactant instead of fatty-alcohol ethoxylates (FAE) in oilfield development.

A Fish Meat Freshness Detector for IoT-Based Seafood Market Applications

A Fish Meat Freshness Detector for IoT-Based Seafood Market Applications