Demands Of Population Research Articles

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents’ movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Operational description of rare diseases: a reference to improve the recognition and visibility of rare diseases

Improving health and social equity for persons living with a rare disease (PLWRD) is increasingly recognized as a global policy priority. However, there is currently no international alignment on how to define and describe rare diseases. A global reference is needed to establish a mutual understanding to inform a wide range of stakeholders for actions. A multi-stakeholder, global panel of rare disease experts, came together and developed an Operational Description of Rare Diseases. This reference describes which diseases are considered rare, how many persons are affected and why the rare disease population demands specific attention. The operational description of rare diseases is framed in two parts: a core definition of rare diseases, complemented by a descriptive framework of rare diseases. The core definition includes parameters that permit the identification of which diseases are considered rare, and how many persons are affected. The descriptive framework elaborates on the impact and burden of rare diseases on patients, their caregivers and families, healthcare systems, and society overall. The Operational Description of Rare Diseases establishes a common point of reference for decision-makers across the world who strive to understand and address the unmet needs of persons living with a rare disease. Adoption of this reference is essential to improving the visibility of rare conditions in health systems across the world. Greater recognition of the burden of rare diseases will motivate new actions and policies to address the unmet needs of the rare disease community.

Data Integration to Support Child Care Business Sustainability: Iowa’s Child Care Connect

Working parents need access to childcare, and the unprecedented COVID-19 pandemic highlighted critical gaps in data system capacities to meet this need. This was particularly acute in the US where many child care providers closed their doors permanently because they could not sustain their business. The state of Iowa estimates losses over $930 million per year to businesses and individuals due to a lack of childcare. Childcare businesses are not sustainable if they cannot fill slots and be consistently paid for care – and families need to be able to find care when they need it to fill those slots. Iowa is addressing this gap by developing a real-time, integrated data solution that includes historically disparate data systems to better identify childcare supply and demand. Through intensive community engagement processes including leadership from state agencies, the development team created a system that includes multiple technology solutions for real-time data sharing; a secure cloud-based system for storage, integration, and analysis; and two public-facing applications that allow data to be aggregated and shared for strategic decision-making as well as used by families to identify available care when and where they need it. This presentation will discuss challenges in cross-sector data linkage with a focus on child care providers, families, and population demand. It will include an overview of the development process, governance structure, and analytic uses of the system. Highlights include identification of real-time vacancies by age group, geography, quality rating, and in relation to estimated potential demand using additional integrated data resources.

Essential competencies for healthcare chaplains: insights from hiring managers and implications for chaplaincy education

This first-of-its-kind study explored the essential competencies healthcare chaplaincy hiring managers (HCHMs) sought for entry-level healthcare chaplains (HCCs). We conducted qualitative interviews with 16 HCHMs and using reflexive thematic analysis (RTA), developed nine key themes: interpersonal skills, teamwork, knowledge of family and group dynamics, understanding of the healthcare system, professionalism, proficiency in spiritual assessment, effective communication, commitment to lifelong learning, and knowledge of various religious and spiritual beliefs, extending beyond one’s own faith tradition. These themes reflected HCCs roles as healthcare professionals who contribute to patient care, interdisciplinary collaboration, and spiritual leadership responsibilities. The study underscored the need for chaplaincy education to integrate healthcare-specific coursework, interprofessional training, cultural and religious humility, and deeper knowledge of diverse belief systems. Developing and incorporating curriculum standards based on these themes could enhance the readiness of HCCs to deliver comprehensive care and meet the dynamic demands of diverse patient populations within today’s healthcare landscape.

Turning the project of dictatorship into an example of democracy? Chances and risks of participation at the Nazi relic Prora

This article explores a participatory process organised to deal with the highly contested former Nazi project Prora on the Baltic island of Rügen. It was carried out on behalf of the German government in 1996. The participatory process was an exceptional way of dealing with a heritage that remains difficult for Germany to this day. Key aspects shifted in Prora as a result of the procedure: away from the ideal of conscientious and careful conservation of the building as a memorial to past dictatorships and towards an active process of highlighting, rejecting and changing qualities of Prora for the construction of a new and alternative future. The main focus shifted from memory work to different present-day demands of the local population and the associated future scenarios for their island. The article argues that during the participatory process Prora was a real-life laboratory in which many things were tried out that were to become important pioneering approaches in heritage management over the next two decades. However, the participatory process in Prora is not only an impressive example of the potential that lies in such approaches; as an experiment it also points to new problems that are associated with them.

Double layer optimization control strategy for distribution network based on population algorithm and demand optimization

Double layer optimization control strategy for distribution network based on population algorithm and demand optimization

Minimisation of losses of consumer quality during storage of cherry fruits under the influence of hydrocooling and protective composition

The need for consumption of fruit products, in particular sweet cherries and cherries, is growing every year in Ukraine. This is driven by national traditions, stakeholder needs, and the population's demand for healthy eating. Cherries are a nutritious fruit that contains such functional ingredients as essential amino acids, vitamins, minerals, dietary fiber, etc. Unfortunately, in terms of long-term storage, cherries quickly lose their quality and have a short harvest and processing period. To increase the shelf life of products, it is advisable to develop and improve methods of storage and processing of cherries. Optimisation of chilling methods for cherries before storage increases the shelf life of fresh fruit and the suitability for fresh consumption. Improving the methods of chilling cherries will help to increase the demand for its fruits by all stakeholders in the European market. The aim of the research was to establish the optimal concentration of acetic and lactic organic acids, which are included in the protective composition for processing cherry fruits, and to determine the level of daily losses of cherry fruits during hydrocooling while maintaining the global optimum point. To establish an effective cooling method, fruit losses caused by diseases of microbiological origin and physiological disorders were determined. During storage, the effect of the concentrations of the protective composition on the indicators of weight loss and the average level of daily fruit loss was studied. A regression model was built and the optimal values of the indicators were determined by stages: a regression model was built on the basis of experimental data and the global optimum point was determined on the basis of the model and a confidence interval for the optimum was built. According to the results of the research, the lowest rate of daily fruit losses at the level of 0,117%-0,121% was recorded during hydrocooling of cherry fruits using organic compositions of lactic and acetic acids in the ratio of their components of 1,25-2,25:1,50-2,25 and 1,75-2,25% with the amount of acetic acid at 1,25%, respectively. The regression analysis showed the optimal value of the average level of daily losses during storage of cherry fruits of the model variety ‘Vstrecha’ – 0,106% at the value of factors x1=2,217 and x2=1,966.

A material flow or life cycle analysis perspective for the Water-Energy-Food nexus assessment of organisations? A comparative study

Nowadays, food production systems play a relevant role as the steady increase of global population and food demand. The water-energy-food (WEF) nexus is a suitable approach to tackle resources management associated with these three pillars recognizing synergies and trade-offs. Different approaches have been used in the literature to measure the WEF nexus, being material flow analysis (MFA) and life cycle assessment (LCA), two of the most proven methodologies. The MFA approach is based on the amount of resources consumed, while using the LCA perspective considers all flows of the system (LCA footprints approach) or considering only the flows associated with water, energy, and food pillars as the inventory data (WEF-LCA approach). This manuscript compares the three mentioned approaches to identify their strengths and weaknesses. To do this, a sample of 100 Spanish dairy farms is analysed, where a single WEF nexus index (WEFni) is obtained using Data Envelopment Analysis. Results show that only four farms achieved a WEFni equal to 100 in all approaches, while the main difference between them is the number and type of resources considered for calculating the WEF nexus, which could imply a partial identification of hotspots of food systems.

Sponsored senior computers never happened – a 20-year perspective on ICT deployment of healthcare at home!

PurposeThe article studies the utilization of ICT within healthcare at home in Sweden overlooking a perspective of 20 + years. Starting point are articles by the authors from 2002 and 2003. These outlined an already feasible ICT supported healthcare at home as a tool to address healthcare’s identified challenges—a growing elderly population and general demand for more healthcare in combination with issues on financing and lack of human resources. Despite early signals the care transition has been unexpectedly slow. Why?MethodsWith the articles as take-off the journey for ICT based healthcare at home up to today is discussed. In the articles five areas considered as critical for a successful transition were pinpointed and are now used to support the authors considerations, reasoning, and conclusions.ResultsResults indicate that technology has never been a limiting factor. Nor has selected medical issues or expected benefits normally been a limitation. Instead limitations and hindrances are found in other areas and activities.ConclusionsTechnology is not the main challenge when it comes to implementation, instead a focus on business models, deployment, and scaling issues is now advocated. Initiatives also must make more use of gained knowledge and experience. Not doing so delays utilization and deployment. The impact on healthcare effects and expected savings may be over-optimistic. A thorough analysis of all associated costs, consequences, investments, and benefits is recommended. Non-digital alternatives must continue to be offered to these who cannot go digital. In communication improved medical outcome and patient satisfaction must be emphasised.

Consumer-consumption-need theoretical model for smart clothing: Construction and empirical study for Chinese silver-haired population

In the era of artificial intelligence, smart clothing, as a product of the interaction between fashion clothing and intelligent technology, has increasingly attracted the attention and affection of enterprises and consumers. However, to date, there is a lack of focus on the demand of silver-haired population’s consumers for smart clothing. To adapt to the rapidly aging modern society, this paper explores the influencing factors of silver-haired population’s demand for smart clothing and proposes a corresponding consumer-consumption-need theoretical model (CCNTM) to further promote the development of the smart clothing industry. Based on literature and theoretical research, using the technology acceptance model (TAM) and functional-expressive-aesthetic consumer needs model (FEAM) as the foundation, and introducing interactivity and risk perception as new external variables, a consumer-consumption-need theoretical model containing nine variables including perceived usefulness, perceived ease of use, functionality, expressiveness, aesthetics, interactivity, risk perception, purchase attitude, and purchase intention was constructed. A questionnaire survey was conducted among the Chinese silver-haired population aged 55–65 using the Questionnaire Star platform, with a total of 560 questionnaires issued. The results show that the functionality, expressiveness, interactivity, and perceived ease of use of smart clothing significantly positively affect perceived usefulness (P < 0.01); perceived usefulness, perceived ease of use, aesthetics, and interactivity significantly positively affect the purchase attitude of the silver-haired population (P < 0.01); perceived usefulness, aesthetics, interactivity, and purchase attitude significantly positively affect the purchase intention of the silver-haired population (P < 0.01); functionality and expressiveness significantly positively affect perceived ease of use (P < 0.01); risk perception significantly negatively affects purchase attitude (P < 0.01). Through the construction and empirical study of the smart clothing consumer-consumption-need theoretical model, this paper hopes to stimulate the purchasing behavior of silver-haired population’s consumers towards smart clothing and enable them to enjoy the benefits brought by scientific and technological advancements, which to live out their golden years in comfort, also, promote the rapid development of the smart clothing industry.

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Leveraging IoT, AI, and ML for Enhanced Decision-Making in Karnataka’s Smart Citie

The rapid urbanization in Karnataka, characterized by increasing population and infrastructure demands, necessitates innovative solutions to ensure sustainable and efficient urban management. Leveraging the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) offers significant potential to enhance the decision-making capabilities of policy makers in Karnataka’s smart cities. This research paper investigates the effectiveness of these technologies in improving urban governance, focusing on real-time data acquisition, predictive analytics, and informed policy decisions. AI and ML are crucial in the analysis and interpretation of the vast amounts of data generated by IoT devices. AI algorithms process this data to identify patterns, anomalies, and trends, while ML models predict future scenarios based on historical data. For instance, predictive analytics can forecast traffic congestion, energy demand, and potential public health crises, allowing policy makers to deploy preemptive measures. In smart city initiatives, AI-driven insights ensure that resources are allocated efficiently, urban planning is optimized, and public services are enhanced. In conclusion, the integration of IoT, AI, and ML holds transformative potential for enhancing decision-making processes in Karnataka’s smart cities. By providing real-time data, predictive insights, and efficient resource management tools, these technologies enable policy makers to address urban challenges proactively and sustainably.

The Contribution of Conservation Agriculture on Environmental Protection in Rwanda

Purpose: The study aimed to assess the contribution of Conservation agriculture on Environmental protection in Rwanda: case of Kayonza District. Materials and Methods: The study reviewed different studies related to Conservation agriculture and Environmental protection and used qualitative and quantitative approaches. The Data were collected using a questionnaire and face-to-face interview on Sample composed by 404 Respondents from Kayonza district. The data collected and analyzed by using Distributive statistics. Findings: The study indicated that CA techniques are contributing in environmental protection and there is a relationship between adopting Conservation Agriculture (CA) techniques and environmental protection. The decrease in organic matter content and high soil erosion are affecting soil fertility and health and one of the main causes of global warming is also intensive chemical fertilization of agricultural crops. In contrast, CA techniques combine minimum soil disturbance, mulching, water holding capacity, reduce soil compaction, reduce soil erosion and improve soil fertility and application of organic fertilizers and re use of crop residues, Study have shown that farmers who adopt CA experience the improvement and there improve. The study underscores the importance of adopting CA techniques to reduce the cost and protect environment. Implications to Theory, Practice and Policy: By adopting CA practices, farmers can contribute to the protection of natural resources, conservation of biodiversity, and mitigation of climate change while maintaining agricultural productivity by promoting sustainable land management practices that conserve soil, water, and biodiversity, mitigate climate change impacts, and reduce environmental degradation associated with conventional agricultural practices. But not all small holder farmers involved in implementing all CA techniques due to different factors, NGOs and Government can collaborate to develop subsidies, grants, and funding programs specifically aimed to promote conservation agriculture. By adopting CA techniques, agriculture in the next decade will have to sustainably produce more food from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands.

The Implementation of “Smart” Technologies in the Agricultural Sector: A Review

The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring.

Threats of tropical cyclone on cropping systems and crop calendar of rice in India: Issues, policy practice gap and adaptation strategies

The Food and Agriculture Organization (FAO) has calculated that by 2050, we will need to grow 60 % more food to provide for a population of 9.3 billion people worldwide. India must increase its yearly food grain production from the current level of 252–333 million tonnes by 2050 in order to fulfil its expanding population demand. India is one of the nations most at risk from climate change due to its geophysical location and variability. This research explored the impact of cyclonic storm surges on crop calendars (‘Aman’ and ‘Boro’ rice) and also suggested adaption strategies during these conditions. This study showed that alteration of contemporary rice sowing and harvesting dates significantly impacts achieving sustained yields, surpassing all other crop management, soil, and other criteria. The origin and landfall of the cyclone of the Bay of Bengal (BoB) in pre- and post-monsoon season impede the goal of optimal rice production in Eastern and south-eastern India. We have estimated that ‘Rabi’ and ‘Kharif” rice production potential will increase by about 29 % with attainable changes to rice sowing and harvesting dates incorporating the severe cyclonic storm (SCS) landfall and rainfall in the study area. Our results also show that transformational benefits in rice yields are only possible in India if the convectional crop calendar changes with the modern scientific crop calendar. Managing the seasonal cropping calendar more effectively can benefit food security, economic success, and climate adaptability rice seed as a method for adaptation to ongoing climate change.

Collaborative nursing education between advanced practice registered nurses

The surge in healthcare demands due to the explosion of growth in the aging adult population demands that academic institutions address enhanced education of Advanced Practice Registered Nurses (APRNs). This is particularly challenging amidst nurse faculty shortages in specialty areas, stemming from factors such as lack of pay competitiveness with clinical positions, and doctoral-prepared nurses seeking faculty positions. Despite efforts to address the shortages, recruitment challenges persist, necessitating innovative approaches. This article explores the interprofessional collaborative teaching between Nurse Anesthesia (NA) and Adult Gerontology Nurse Practitioner (NP) programs, focusing on airway management, sedation techniques, chest X-ray interpretation, and ultrasound skills. To date, this collaboration between NA and NP faculty has proven effective. Both synchronous and online asynchronous teaching have received overwhelmingly positive student feedback. Ongoing communication and collaborations between NA and NP faculty facilitate teaching and educational strategies across programs, sharing faculty expertise, and mitigating reduced faculty numbers. This innovative model benefits faculty and students and provides a platform for firsthand interprofessional collaboration, fostering mutual respect and preparing students for effective interdisciplinary healthcare teamwork.

Carbon Storage Potential and Saturation Behavior of Soils As Influenced by Level of C Input and Soil Types in Indian Sub-Continent

ABSTRACT Agricultural soils have the potential to store C (carbon), thereby improving soil health, and contributing to climate change mitigation. However, the capacity of a soil to hoard C may be limited. We tested the C saturation behavior of agricultural soils belonging to predominant soil orders (Inceptisols, Entisols, Alfisols, Aridisols and Vertisols) in Indian Subcontinent. A mesocosm experiment in open environmental conditions was conducted with six rate of farmyard manure (FYM) addition (0, 2.5, 5.0, 10.0, 20.0 and 40 Mg ha−1) for three and half years and total carbon (TC) was analyzed. Addition of more than 10 Mg FYM ha−1 enhanced TC content of soil with the period of experimentation irrespective of soil orders. Application of C input of the agriculturally recommended addition of FYM in India at 5.0 Mg ha−1 maintained the soil TC content. Without addition of any C input, sometimes there was depletion in native carbon. With higher levels of C additions, all the soils did not show any evidence of C saturation, thereby suggesting their higher potential to additional C storage. Indian agricultural soils could contribute significantly toward achieving the target of “4 per 1000” initiative of the United Nations. Higher organic carbon in soils would make agroecosystems more resilient toward extreme climate events and sustain crops productivity thereby could help to meet the food demand of the ever-increasing population of the country in the years to come.

Drought Stress Tolerance in Rice: Physiological and Biochemical Insights

Rice (Oryza sativa L.), an important food crop, necessitates more water to complete its life cycle than other crops. Therefore, there is a serious risk to rice output due to water-related stress. Drought stress results in morphological changes, including the inhibition of seed germination, reduced seeding growth, leaf area index, flag leaf area, increased leaf rolling, as well as the decrement of yield traits, such as plant height, plant biomass, number of tillers, and 1000-grain yield. Stress also causes the formation of reactive oxygen species (ROS) such as O2−, H2O2, and OH−, which promote oxidative stress in plants and cause oxidative damage. The process of oxidative degradation owing to water stress produces cell damage and a reduction in nutrient intake, photosynthetic rate, leaf area, RWC, WUE, and stomatal closure, which may be responsible for the decrement of the transpiration rate and plant dry matter under decreasing soil moisture. Plants have the ability to produce antioxidant species that can either be enzymatic (SOD, POD, CAT, GPX, APX) or non-enzymatic (AsA, GSH) in nature to overcome oxidative stress. During drought, several biochemical osmoprotectants, like proline, polyamines, and sugars, can be accumulated, which can enhance drought tolerance in rice. To meet the demands of an ever-growing population with diminishing water resources, it is necessary to have crop varieties that are highly adapted to dry environments, and it may also involve adopting some mitigation strategies. This study aims to assess the varying morphological, physiological, and biochemical responses of the rice plant to drought, and the various methods for alleviating drought stress.

Tracking Biomarkers for the Health and Welfare of Aquaculture Fish

Aquaculture production has been growing consistently over the last few decades to meet the increasing animal protein demand of the human population. However, increased production and rearing intensities raise the challenges of guaranteeing fish health and welfare, which is essential to avoid losses and ensure product quality. Biomarkers can provide insights into the fish’s nutritional, physiological, and health status, and aid in the evaluation of early nutritional and physiological imbalances, distress conditions, and pathological diagnosis. The discovery and validation of biomarkers rely mostly on the use of information provided by different parameters, including biochemical, metabolic, or immunologic, as well as several omics, from genomics and transcriptomics to proteomics and metabolomics. In this review, a summary of the main biomarkers used in aquaculture is provided along with an overview of the main omics technologies available for further biomarker research. This review also highlights the need to develop non-lethal biomarkers that can easily and quickly be measured to provide a prompt response to producers.

Uncovering urban water consumption patterns through time series clustering and entropy analysis

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.