Future Demand Research Articles

Primordial Germ Cells Quality of Chicken Embryo from Post Cryopreservation Blood Samples

The optimal cellular reservoir for the conservation and propagation of endangered poultry is acknowledged to be primordial germ cells (PGCs). This study aimed to evaluate and compare the efficacy of cryopreserving PGCs derived from purified sources and blood samples. The experimental design encompassed two treatment groups, each iterated six replication. Blood samples that had been filled with 500 µL of phosphate-buffered saline without Ca2+ and Mg2+ (PBS) underwent centrifugation at 1200 rpm for six minutes. The supernatant was discarded and the pellet was mixed with 500 µL 10% FBS-PBS and was then subdivided for cryopreservation and PGCs purification. Cryopreservation included the controlled freezing of blood and purified PGCs samples in Nalgene cryogenic vials with the incorporation of banker cells. Following thawing, statistical analyses revealed no significant variance in the mean PGCs count between purified PGCs and blood samples (p > 0.05). The percentages of PGCs mortality and the recovery rate demonstrated statistical significance (p < 0.05). Further investigations indicated that cryopreserving blood samples significantly increased the recovery rate of PGCs while decreasing post-thaw mortality. Morphology of PGCs from blood cryopreservation showed similar morphology to PGCs in general. The potential application of cell-based cryo-banking will contribute to the preservation of poultry genetic diversity for sustainability and adaptation to future poultry demands.

Planning the optimal general nurse graduates: methodology and significance

Global problems with a shortage of nurses and an increasing demand for nursing care raise questions about whether the education system as the primary source of new nurses is in line with future demands. Despite the shortage, the situation in the Czech Republic is not yet critical, but the analysis of the age structure and the small number of graduates indicates a problem on the horizon of 15 to 20 years. The objective of the article is to present the methodology developed to estimate the demand for the profession of a nurse to maintain a functional health care system according to demographic changes and to propose the optimal number of students enrolled in the nursing study program. Authors created the methodology as part of the project “Competent Nurse for the 21st Century: Analysis and proposal for optimisation of nursing education and professional practice”, supported by the Czech Republic Technological Agency. The methodology was certified by the Czech Republic Ministry of Health at the end of 2022.

Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex network

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

INTEGRATING LAND USE ANALYSIS WITH WATER DEMAND ESTIMATION: A CASE STUDY OF PUTRAJAYA, MALAYSIA

The challenges posed by population growth, urbanization, and changing land use patterns on sustainable water resource management are significant. This paper puts forth an integrated framework aimed at assessing future water demand in Putrajaya, Malaysia. The proposed framework combines population projections, estimations of water demand, and analyses of land use activities. Through an examination of demographic trends and land use patterns, the framework predicts population growth and identifies areas with high water demand. Daily water use patterns in homes and businesses (temporal analysis) inform the designing future water infrastructure, incorporating temporal aspects. Statistical and spatial analysis techniques are then utilized to merge these projections with water demand estimations to quantify water requirements in various zones and types of land use. This study has unveiled two daily peaks in water demand, which align with household schedules. Residential areas emerge as the primary consumers of water, displaying an evening peak distinct from the midday peak seen in businesses. The current water demand in Putrajaya is estimated at 94 million litres per day, with domestic usage surpassing non-domestic usage in a ratio of 3:2. Projections based on future land use plans foresee a 19% increase in demand, underscoring the urgency for proactive water management strategies. Spatial analysis has highlighted residential areas as the main users of water, with demand levels varying throughout the city. By comprehending these temporal and spatial patterns, water authorities can strategically target interventions, optimize infrastructure siting, and forecast future demand trends. These proactive measures are essential for securing a sustainable water future for Putrajaya.

Developing Ecotourism Community Based Approach: A Sustainable Tourism Development Project o n Tanjung Surat Island

The Tanjung Surat Island Ecotourism Development Master Plan study was commissioned to support the Johor Southeast Development Authority (KEJORA) in creating a high-impact strategy to develop Tanjung Surat Island into a premier ecotourism destination for both local and international tourists. This study addresses the prevalent community misunderstanding regarding ecotourism development. The main objective of this study is to identify strategic planning through community engagement initiatives spearheaded by KEJORA on Tanjung Surat Island. Quantitative methods, including the distribution of questionnaires to 118 respondents, were employed to meet this objective. In addition to the questionnaires, the study incorporated several other data collection methods to ensure a thorough understanding of the community's perspective. These included structured interviews with key stakeholders, focus group discussions with community leaders, and participatory observation to gather qualitative insights that complemented the quantitative data. The study's findings will provide a comprehensive guide and reference for planning across economic, social, and environmental dimensions, aiding in the development of Tanjung Surat Island's ecotourism sector. This guide will benefit KEJORA and other stakeholders, including the private sector, interested in supporting and shaping the island's ecotourism future. Additionally, the study highlights the critical need for Technical and Vocational Education and Training (TVET) to enhance the quality of existing services, ensuring they meet current and future demands and standards.

Memory and protest in Belgrade: Remembering the 1990s in the mass demonstrations of 2023

The history of the 1990s and of political life in Serbia can be viewed as a history of protest. It was during this decade, in which Slobodan Milošević held power, that an entire generation of activists became part of a process of learning to engage in civil resistance. In waves of massive anti-regime protests as well as in smaller, yet persistent, anti-war protests, they asserted that ‘Belgrade is the World’. This article examines the ways in which practices and themes from these protests have resurfaced more recently, and how this activism is remembered and referenced in the 2020s. Based on data collected in Belgrade during anti-regime protests in 2023, which began as a movement ‘against violence’ (‘ protiv nasilja’), it traces how the memory of protest of the 1990s turned into memory in protest as it appeared in banners, chants, slogans and speeches, as well as in everyday conversation among citizens, some three decades later. Deploying the concept of the memory-activism nexus, the article analyses generational claims in Serbia, the practices of protesters, their hope in activism, their fear of cultivating false expectations and their continuing demands for a better future. It places cultural memory in Serbia in the context of a politics of disappointment, illustrating that memory in protest can function as both a mobilising force and a source of political paralysis.

Selection of Solar-Based Water-Splitting Hydrogen Production and Hydrogen Storage Technologies Using Neutrosophic Analytic Hierarchy Process and Neutrosophic Complex Proportional Assessment

Abstract The production of hydrogen through water-splitting using solar energy has the potential to meet the world’s future energy demand while also addressing the issue of greenhouse gas emissions. To compare and select the best technology among solar thermochemical cycles, photoelectrochemical, and photocatalytic water-splitting in terms of solar-to-hydrogen efficiency, production cost, safety, life expectancy, maintenance cost, and detrimental impact on the environment, this study used a multi-criteria decision analysis hybrid called Neutrosophic Analytic Hierarchy Process (NAHP) and Neutrosophic Complex Proportional Assessment (NCOPRAS). The same method was used to determine the best technology for automobiles among compressed hydrogen storage, metal hydrides, metal-organic frameworks, and chemical storage in terms of gravimetric system capacity, volumetric system capacity, safety, system cost, cycle life, energy efficiency, detrimental impact on the environment, and refueling time. There were 4 experts each in hydrogen storage and hydrogen, specifically from the Philippines, Australia, India, Romania, and Italy. All experts’ judgments have a consistency ratio (CR) < 0.1. The priority criterion for hydrogen production was life expectancy (weighted value (WV) = 0.189), and gravimetric system capacity for hydrogen storage (WV = 0.137). The result showed that among all alternatives for hydrogen production, the solar thermochemical cycle was the best (relative significance value (RSV) = 0.3432) as for hydrogen storage, the best technology among the alternatives was metal hydrides (RSV = 0.2575). A sensitivity analysis was used to check the variability and robustness of the solution.

Cadmium Minimization in Crops: A Trade-Off With Mineral Nutrients in Safe Breeding.

Cadmium (Cd) contamination poses a threat to global crop safety. To address this issue, researchers mainly focused on the Cd, explored mechanism of accumulation to low-Cd breeding technologies and created several low-Cd varieties over the past decades. However, new challenges have emerged, particularly the yield reduction due to disturbances in mineral nutrient balance. The goals of breeding have been transferred from a primary focus on 'low-Cd crops' to 'low-Cd/nutrient-balanced' crops, which means limiting Cd content while maintaining other nutrient elements like iron (Fe), manganese (Mn) and zinc (Zn) at a proper content, thus to meet the future agricultural demands. Here, on a multielement perspective, we reviewed the mechanisms of Cd and mineral nutrient transport system in crops and summarized the research advances in Cd minimization through artificial mutations, natural variations and genetic engineering. Furthermore, the challenge of disruption of mineral nutrients in low-Cd crops was discussed and two potential approaches designing Cd-mineral nutrient-optimized artificial transporters and pyramiding Cd-mineral nutrient-optimized variations were proposed. Aiming at addressing these challenges, these approaches represent promising advancements in the field and offer potential pathways for future research and development in the creation of safe and high-quality crops.

Facile synthesis of transition metal-selenides@CNTs for electrochemical oxygen evolution reactions

The fabrication of carbon based efficient electrocatalysts for hydrogen production during electrochemical water splitting could be a great achievement to fulfill the future renewable energy demand. In this study, one-step hydrothermal process was used to develop composites of multi-metal selenides with carbon nanotubes (MnSe@CNTs, MnCuSe@CNTs, MnCoNiSe@CNTs) as electrocatalysts for OER during electrolytic water splitting. XRD and SEM with EDS analysis confirm the successful fabrication of proposed catalyst and their crystalline morphology. Among the synthesized functionalized CNTs composites, bimetallic selenide composite gives a better electrocatalytic activity with the reduce overpotential, lower Tafel slope and reduced charge transfer resistance in 1 M KOH. By using this catalyst, water oxidation starts at 1.4 V onset potential with a very low overpotential of 245 mV and the Tafel slope was only 107 mV dec−1. Metal based selenides composites with CNTs introduces a lot of active sites, subsequent nanoparticles of MnSe, MnCuSe, & MnCoNiSe, causes to boost the surface area and electrocatalysis towards oxygen evolution reactions.

Plant Factory Speed Breeding Significantly Shortens Rice Generation Time and Enhances Metabolic Diversity

Rice (Oryza sativa L.) plays a pivotal role in global food security, yet its breeding is constrained by its long generation time and seasonality. To enhance rice breeding efficiency and meet future food demands, we have developed a vertical hydroponic breeding system integrated with light-emitting diodes (LEDs) lighting in a closed plant factory (PF), which significantly accelerates rice growth and generation advancement. The results show that indica rice can be harvested as early as after 63 days of cultivation, a 50% reduction compared with field cultivation, enabling the annual harvesting of 5–6 generations within the PF. A hyperspectral imaging system (HSI) and attenuated total reflectance infrared (ATR-IR) spectroscopy were further employed to characterize the chemical composition of the PF- and field-cultivated rice. Metabolomics analysis with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) revealed that, compared with the field-cultivated rice, the PF-cultivated rice exhibited an up-regulation of total phenolic acids along with 68 non-volatile and 19 volatile metabolites, such as isovitexin, succinic acid, and methylillicinone F. Overall, this study reveals the unique metabolic profile of PF-cultivated rice and highlights the potential of PFs to accelerate the breeding of crops such as rice, offering an innovative agricultural strategy to support food security in the face of global population growth and climate change.

Projected Annual Lecanemab Treatment Eligibility in an Irish Regional Specialist Memory Clinic.

The advent of Disease Modifying Therapies (DMTs) for the treatment of Alzheimer's Disease (AD) has the potential to transform the lives of those with early AD. Timely identification of eligible patients is needed to ensure treatments are delivered during a narrow window of therapeutic opportunity. Appropriate clinical service design will hinge on improved understanding of future demands, thus there is a pressing need to investigate patient eligibility in real world clinical cohorts. The primary aim of this study is to assess the eligibility by appropriate use criteria (AUC) for lecanemab therapy in a real-world, undifferentiated clinical patient cohort attending a Regional Specialist Memory Clinic (RSMC), with the secondary aims of determining the proportion of patients with biomarker positive Alzheimer's Disease (AD) who would be eligible for lecanemab therapy by AUC. Clinical trial eligibility criteria were also applied to both groups and discrepancies that exist between eligibility rates explored. A retrospective cohort study of all new patients attending a RSMC from 1st January 2022 to 31st December 2022 was conducted. Data collected included demographic details, outcomes of diagnostic assessments and comorbidities. MRI images, where indicated, were reviewed. Amyloid positivity was defined as either Amyloid and Tau positive (A+T+) or Amyloid positive with a positive P-Tau/Ab42 ratio on cerebrospinal fluid (CSF) testing. Appropriate use criteria (AUC) and clinical trial criteria for lecanemab were applied. Proportion of eligible patients was calculated. Eleven (5.9%) of 188 new patient attenders were eligible (average age 66.7years [SD 8.9], 63.6% female) by AUC, with 26.2% of patients with biomarker positive Alzheimer's Disease eligible for lecanemab therapy. The most common reason for exclusion was a lack of biomarker confirmation of AD pathology followed by cognitive ineligibility (based on defined cognitive testing cut-offs) at the time of referral and/or initial assessment. Only 40.4% of patients had CSF testing for AD biomarkers while almost 20% of the patients with biomarker positive AD were excluded due to lack of a screening MRI in the previous 12months. In this study, the potential eligibility rate by AUC of the entire patient cohort (5.9%) was limited by the small proportion of patients who had CSF testing for AD biomarkers. So while disease-modification with Lecanemab is a welcome therapeutic advance, although only a small proportion of people currently attending specialist services will be eligible. Successful delivery of DMTs will require significant resource allocation and optimisation of referral pathways to facilitate early identification of potentially eligible patients.

Demand-Aware Distributed Pathfinding for Repositioning Vehicles in Shared-use Autonomous Mobility Services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed routing pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

Workforce development in the transport sector amidst environmental change: A conceptual review

This study embarks on an exploratory journey to elucidate the complex interplay between workforce development and environmental sustainability within the transport sector. It underscores the critical importance of nurturing a skilled workforce capable of propelling the sector towards sustainable practices, thereby addressing pressing environmental challenges. To achieve a comprehensive understanding, this review synthesizes a wide array of data sources, adopting a strategic framework for literature search and analysis. The methodology hinges on stringent criteria for literature selection, ensuring the relevance and quality of the information reviewed. Central to the study is the development of a theoretical framework that situates workforce development within the paradigm of sustainable transport. This includes an examination of the architectural underpinnings of sustainable transport systems, the potential for modal shifts to lessen environmental impacts, and the pivotal role of technological and methodological advancements. Moreover, the review highlights innovative practices in workforce training and development that are crucial for meeting future demands. The in-depth analysis sheds light on the multifaceted impacts of workforce development initiatives on achieving sustainable transport objectives, emphasizing the economic, social, and environmental dimensions. It identifies existing gaps in workforce competencies and outlines strategies for skill enhancement. Additionally, the study emphasizes the indispensable role of policy, standards, and stakeholder collaboration in fostering sustainable transport. Conclusively, the study presents strategic recommendations for policymakers, educators, and industry leaders, charting a course for a resilient and sustainable transport workforce. It posits that an integrative approach, marrying workforce development with environmental sustainability, is essential for navigating the future challenges and opportunities within the transport sector.

An evaluative study on parking challenges and future demand forecasting at Kano Farm Center Market, Nigeria.

Parking involves stopping and disengaging a vehicle and leaving it unoccupied. Commercial centers require assistance in providing adequate parking facilities for vehicles to transport people, deliver goods, or conduct business transactions. Failing to meet the expected demand for parking can lead to frustration, safety concerns, and accidents. Our study involved conducting reconnaissance and surveys to assess parking demand and duration in the area. The objective was to provide valuable insights into the current urban transportation system, focusing on parking patterns and proposing a parking system specifically for Kano Farm Center Market. Our findings indicate an estimated annual growth rate of 10%, suggesting that in 15 years, the number of daily parkers will rise to 3,308. This means that more than the existing parking space of 2710 stalls will be required, and we recommend providing a larger area of 300m x 300m. This underscores the importance for urban planners and commercial center managers to invest in expanded and innovative parking solutions to enhance efficiency, safety, and accessibility for all users.

Design of NiPS3/MoS2/rGO hybrid electrocatalyst for electrochemical hydrogen evolution

The design and development of efficient electrocatalysts for hydrogen evolution reaction is presently a pressing task to overcome future energy demands. In the present study a hybrid layered compound NiPS3/MoS2/rGO (NMR) is investigated as an electrocatalyst for hydrogen evolution reaction (HER). The metal trichalcogenidophosphates (MTPs), transition metal dichalcogenides and reduced graphene oxide materials based electrocatalysts are promising material for HER. The hybrid layered compound NMR displays good electrocatalytic activity in acidic medium in comparison to its counterparts MoS2/rGO and NiPS3/rGO with an overpotential and Tafel slope of 152 mV vs RHE and 75 mV/dec, whereas MoS2/rGO and NiPS3/rGO shows an overpotential of 206 mV and 223 mV vs RHE with a Tafel slope value of 146 mV/dec and 154 mV/dec respectively. The hybrid electrocatalyst exhibited high stability up to 1000 cycles with a stable current. The high HER activity of NMR is attributed to synergistic effect of electrocatalysts where P and S of NiPS3 acts as an appropriate hydrogen adsorption sites and catalytically active Mo edge sites of MoS2 with high charge carrier property of rGO contributed to enhanced hydrogen evolution reaction.

Peer Score Presentation in Non-English Department Classes: Investigating Students’ Perceptions to Boost Confidence

English is a required course during the first and second semesters learning in Vocational Higher Education in Indonesia. Recognizing the importance of future career demands, presentation skills are incorporated into the Semester Learning Plans as English for Specific Purposes. This research focuses on students' perspectives regarding English learning through presentations regarding their own field particularly the implementation of peer scoring. Employing a qualitative descriptive approach, the study involved first-semester students at Politeknik Negeri Banjarmasin. One hundred and fifteen students of Banjarmasin State Polytechnic, Indonesia majoring in Mining Engineering (19 students), Civil Engineering (35 students) and Accounting (61 students) responded to a questionnaire, and ten students were interviewed, serving as voluntarily participants. Data collection method included a questionnaire administered via Google Form and interviews for triangulation. Students filled in the questionnaire after scoring their peer’s presentation. In scoring their peers, they used a presentation scoring rubric. Findings indicate a generally positive perception among students towards peer scoring presentations. Despite experiencing anxiety during preparation and delivery of presentation, students expressed high motivation and found the presentation experience rewarding as the scores not only from the lecturer but also from their peers. They worried to score at the beginning, but the scoring rubric help them guide along the process. Some students who commonly do not pay attention to the presenters in the presentation are now fully paying attention because they are in charge to score. Overall, students viewed presentation projects favorably, highlighting their potential to improve speaking performance and be a rater on their peers’ performance. The implication of this research is to highlight students’ ability to deliver a presentation as well as build their confidence in speaking activities.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Design Thinking for Innovative Learning: Crafting a Collaborative Activity Package to Enhance Teamwork Skills in Science Education

This study aims to enhance teamwork skills among lower secondary school science students through a Learning Activity Package (LAP) using the design thinking process. The research evaluates the experiences of science teachers and students to identify areas for improvement in teamwork skills, which is achieved through interviews and observations in the Empathise Define phase. Insights from this phase are used in the Ideate Prototype phase to develop and refine collaborative learning activities, focusing on communication, collaboration, and problem-solving. Rooted in cooperative learning theory and utilising a design thinking framework, the study employs a user-centred approach with tools like personas and user journey maps. The project, through expert evaluations and an iterative design process, aims to contribute significantly to the development of teamwork skills, preparing students for future workforce demands.

Cluster Analysis and Forecasting on Local Shoe Products: Case study for Ventela in Indonesia

Shoes are a secondary need that is in demand by all age groups. Each shoe brand has many models, which means a store must provide complete stock to meet consumer needs. The available models have different purchasing power or demand, which creates difficulties for stores in determining shoe products that are often sold and shoe products that are not in demand by customers. The data used in cluster formation includes three variables recorded and collected from e-commerce transactions, namely Number of Visitors, Number of Buyers, and Total Sales. Based on these variables, Ventela shoe models are grouped into three clusters, namely low-selling, normal and best-selling. Next, the variable number of transactions for Ventela shoe models in the best-selling cluster is taken to be predicted using the Exponential Smoothing method. The forecasts obtained are used to determine future demand to maximize profits. Based on the results of the clustering analysis, it was found that the number of shoe models included in the best-selling cluster was six, including (1) Ventela Ethnic Low All Black, (2) Ventela Ethnic Low Black Natural, (3) Ventela Public Low Black Natural, (4) Ventela Public Low Cream, (5) Ventela Republic Low Black Natural, and (6) Ventela Republic Low White. Referring to the sample of this study, which only spanned less than three years, several shoe models produced a forecast value of zero. It means, based on the forecast results for the next 12 months, there may be no sales.