Hasil untuk "Water supply for domestic and industrial purposes"

Lequn Fu, Yijun Zhong, Xiao Li et al.

Humanoid robots deployed in industrial environments are required to perform load-carrying transportation tasks that tightly couple locomotion and manipulation. However, achieving stable and robust locomotion under varying payloads and upper-body motions is challenging due to dynamic coupling and partial observability. This paper presents a load-aware locomotion framework for industrial humanoids based on a decoupled yet coordinated loco-manipulation architecture. Lower-body locomotion is controlled via a reinforcement learning policy producing residual joint actions on kinematically derived nominal configurations. A kinematics-based locomotion reference with a height-conditioned joint-space offset guides learning, while a history-based state estimator infers base linear velocity and height and encodes residual load- and manipulation-induced disturbances in a compact latent representation. The framework is trained entirely in simulation and deployed on a full-size humanoid robot without fine-tuning. Simulation and real-world experiments demonstrate faster training, accurate height tracking, and stable loco-manipulation. Project page: https://lequn-f.github.io/LALO/

Muhammad Ibrahim Khan, Bivin Pradeep, James Brusey

Typical domestic immersion water heater systems are often operated continuously during winter, heating quickly rather than efficiently and ignoring predictable demand windows and ambient losses. We study deadline-aware control, where the aim is to reach a target temperature at a specified time while minimising energy consumption. We introduce an efficient Gymnasium environment that models an immersion hot water heater with first-order thermal losses and discrete on and off actions of 0 W and 6000 W applied every 120 seconds. Methods include a time-optimal bang-bang baseline, a zero-shot Monte Carlo Tree Search planner, and a Proximal Policy Optimisation policy. We report total energy consumption in watt-hours under identical physical dynamics. Across sweeps of initial temperature from 10 to 30 degrees Celsius, deadline from 30 to 90 steps, and target temperature from 40 to 80 degrees Celsius, PPO achieves the most energy-efficient performance at a 60-step horizon of 2 hours, using 3.23 kilowatt-hours, compared to 4.37 to 10.45 kilowatt-hours for bang-bang control and 4.18 to 6.46 kilowatt-hours for MCTS. This corresponds to energy savings of 26 percent at 30 steps and 69 percent at 90 steps. In a representative trajectory with a 50 kg water mass, 20 degrees Celsius ambient temperature, and a 60 degrees Celsius target, PPO consumes 54 percent less energy than bang-bang control and 33 percent less than MCTS. These results show that learned deadline-aware control reduces energy consumption under identical physical assumptions, while planners provide partial savings without training and learned policies offer near-zero inference cost once trained.

Agnieszka Chmielewska

This paper presents a domestic hot water (DHW) consumption model for multi-family residential buildings that captures demand variability across monthly, daily, and hourly timescales. The model enables both the disaggregation of annual consumption for dynamic simulations and the generation of synthetic yet realistic DHW load profiles when detailed measurements are unavailable. It is developed from a dataset of 42 buildings containing 1376 apartments. The analysis identifies seasonal, weekly, and hourly usage patterns, reflecting the influence of apartment layout, building size, and user behavior under Polish climatic and cultural conditions. The proposed model reproduces seasonal deviations of up to 23%, with average daily demand falling to 77% of the annual mean in August and rising above the yearly average during winter months. It also captures weekly variability, with weekend consumption exceeding weekday levels by more than 16%. On working days, the hourly profile exhibits a clear dual-peak structure, with approximately 18% of daily demand occurring in the morning and up to 45% in the evening, whereas weekends show a flatter distribution without pronounced peaks. These results provide a robust basis for more accurate demand representation in energy modeling, system design, and optimization under local conditions.

Izabela Piegdoń, Barbara Tchórzewska-Cieślak

Shakeel Ahmed Talpur, Muhammad Rashad, Aziz Ahmed et al.

This study investigated fluoride contamination in groundwater and associated health risks in the Badin district of Pakistan. Fifty-seven groundwater samples were analyzed for fluoride, turbidity, iron, and total dissolved solids (TDS). Pollution indices and health risk models were employed to assess contamination levels and potential health impacts. Results showed that 47 % of samples exceeded the WHO fluoride limit of 1.5 mg/L, with a mean concentration of 1.92 mg/L. Spatial analysis revealed high contamination in northern and southern areas. Health risk assessments indicated that children, particularly females, faced the highest risk of fluorosis. TDS, turbidity, and iron levels also exceeded WHO limits in significant portions of the samples. This investigation uniquely combines multiple pollution indicators, spatial analysis, and age-specific health risk assessments, presenting vibrant insights for targeted interventions, policy development, and resource allocation to address this critical public health issue in fluoride-endemic regions.

Opoka William, Beteley Tekola Meshesha, Muhajir Mussa et al.

Abstract Excess fluoride in water is a significant public health concern, causing illnesses such as arthritis, dental fluorosis and skeletal fluorosis. Hydroxyapatite (Ca10(PO4)6(OH)2) has shown a promise in fluoride removal through demineralization during which fluoride ions are effectively replaced by hydroxide ions (OH−). However, performance issues with applying hydroxyapatite (HAp) remain a challenge. This study assessed the efficacy of aluminum hydroxide/hydroxyapatite (Al(OH)3/HAp) composite for fluoride removal from drinking water through batch and column experiments. The composite’s functional groups, crystalline phases, surface area, and the point of zero charge (pzc) were characterized, and its performance was evaluated in both batch and continuous experiments. During batch experiments, a 30% Al (OH)3/HAp ratio exhibited an adsorption capacity of 2.30 mg/g with a fluoride removal efficiency of 92.02%. Kinetic analysis indicated that fluoride adsorption followed a pseudo-second-order model, suggesting chemisorption, while the Redlich–Peterson isotherm confirmed heterogeneous adsorption behavior. Among the tested competing anions, the presence of carbonate ions had the most adverse impact on fluoride removal efficiency. Column experiments using simulated water containing fluoride, bicarbonate and carbonates further demonstrated that an adsorption capacity of 1.28 mg/g was obtained at 15 mL/min flow rate, 10 cm bed depth, and 10 mg/L initial fluoride concentration, with the Clark model best describing the breakthrough data. Breakthrough time decreased with increasing flow rate and initial fluoride concentration but improved with greater bed depth. This study revealed Al(OH)3/HAp as a cost-effective, efficient adsorbent for fluoride removal, addressing critical challenges in water treatment and providing insights into the adsorption behavior and mechanism of Al(OH)3/HAp composite for fluoride removal.

Hichem Yakoub Khemmal, Azzedine Hani, Kaddour Benmarce

Abstract Groundwater is a vital resource, especially in semi-arid regions where water scarcity poses a major challenge. The northern upper Boussellam region, with its unique hydrogeological conditions, requires effective groundwater management for sustainability. This study introduces a novel approach for mapping groundwater recharge potential zones across a 1,100 $$\hbox {km}^2$$ km 2 area using geographic information systems, the analytic hierarchy process, total dissolved solids levels, and the receiver operating characteristic curve. The study incorporates the precipitation data from NASA LaRC POWER to address the spatial and temporal limitations of local meteorological data and enhance model performance. The results revealed a clear spatial distribution of recharge potential, with a maximum of 8.66 observed in the northern mountainous areas and a gradual decrease toward the southern region. A key innovation of this study is the use of total dissolved solids levels for model validation, revealing a strong relationship with recharge dynamics. The receiver operating characteristic curve analysis yielded an area under the curve of 0.835, indicating high model reliability. The approach incorporates easily accessible data, including NASA LaRC POWER, the digital elevation model, Landsat imagery, and total dissolved solids, offering a replicable and adaptable framework, particularly useful in regions with limited hydrogeological data. These findings provide valuable insights into groundwater recharge processes and highlight the importance of total dissolved solids as a practical tool for model validation. The study advances the identification and spatial distribution of groundwater recharge zones, providing a foundation for more effective water resource management and sustainable resource planning in semi-arid regions facing similar challenges.

Ifeoluwa Oluwatosin Kunle-John, Segun P. Michaels, Edith N. Okay

Eleyele Lake has enormous economic importance as it is completely surrounded by various communities which discharge their domestic waste directly into the lake. This alters the physical, chemical and biological characteristics of the lake. It is essential to assess the water for its various usage. Twelve (12) samples were collected from various locations of the lake and analysed. Some physical parameters (electrical conductivity, Total Dissolved Solids, Ph and temperature were determined in-situ. The rest of the sample was taken to the laboratory for various chemical analysis and the results were compared to the WHO standards. The chemical extent of the contamination was determined by the contamination factor, degree of contamination and Geo-Accumulation Index. The physical parameters show that the TDS has an average of 122.2ppm and EC was uniform throughout the various points of reading suggesting that the lake is fresh water. The pH averaged at 72, temperature at 27.2 degrees. The selected trace element falls within the WHO acceptable limits. Their contamination indices showed that Ba, Co, Cs, Cu, Mo, Rb, Sr and Zn are generally less than one depicting their geogenic origin. The high degree of contamination is influenced by high levels of Al and Fe due to human activities and industrial waste disposal and can lead to anemia, osteomalacia (brittle or soft bones), cardiac arrest, stomach problems, nausea, and hemochromatosis. Thus, Eleyele lake is not advisable for consumption.

Sagar Sudhakara, Yuchong Zhang

We consider a multi-retailer supply chain where each retailer can dynamically choose when to share information (e.g., local inventory levels or demand observations) with other retailers, incurring a communication cost for each sharing event. This flexible information exchange mechanism contrasts with fixed protocols such as always sharing or never sharing. We formulate a joint optimization of inventory control and communication strategies, aiming to balance the trade-off between communication overhead and operational performance (service levels, holding, and stockout costs). We adopt a common information framework and derive a centralized Partially Observable Markov Decision Process (POMDP) model for a supply chain coordinator. Solving this coordinator's POMDP via dynamic programming characterizes the structure of optimal policies, determining when retailers should communicate and how they should adjust orders based on available information. We show that, in this setting, retailers can often act optimally by sharing only limited summaries of their private data, reducing communication frequency without compromising performance. We also incorporate practical constraints on communication frequency and propose an approximate point-based POMDP solution method (PBVI/SARSOP) to address computational complexity. Numerical experiments on multi-retailer inventory scenarios demonstrate that our approach significantly improves the cost-service trade-off compared to static information sharing policies, effectively optimizing the schedule of information exchange for cooperative inventory control.

Shuai Li, Chen Yizhe, Li Dong et al.

The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision language-action (VLA) models have been a key paradigm in AI to unify perception, reasoning, and control. Has the performance of the VLA models met the industrial requirements? In this paper, from the perspective of industrial deployment, we compare the performance of existing state-of-the-art VLA models in industrial scenarios and analyze the limitations of VLA models for real-world industrial deployment from the perspectives of data collection and model architecture. The results show that the VLA models retain their ability to perform simple grasping tasks even in industrial settings after fine-tuning. However, there is much room for performance improvement in complex industrial environments, diverse object categories, and high precision placing tasks. Our findings provide practical insight into the adaptability of VLA models for industrial use and highlight the need for task-specific enhancements to improve their robustness, generalization, and precision.

Gcobisa Ndzungu, Caliph Zvinowanda, J. Catherine Ngila

Abstract Poor raw water quality has led science and technology research to explore more environmentally friendly water treatment methods, such as advanced oxidation processes. Ferrate (VI) salts have been regarded as a new green oxidant, cost-effective, and coagulant/flocculent in a single mixing and dosing unit in wastewater treatment. This research aimed to synthesize calcium ferrate (CaFeO4), a benign green chemical that can be utilized to treat water and wastewater through a facile wet oxidation process, with minimal increase in electrical conductivity. This study used the wet oxidation approach to synthesize stable aqueous calcium ferrate in an alkaline media using distilled water. During process optimization, CaFeO4 was synthesized and named 60-CaFeO4, 70-CaFeO4, 80-CaFeO4, and 90-CaFeO4, where the numbers represent the synthesis temperature. The prepared CaFeO4 was characterized using XRD, FT-IR, UV–Vis, TEM, SEM, Raman, and redox titration. FT-IR analysis confirmed the presence of the Fe–O stretching vibrations in all the freeze-dried CaFeO4 powders, signifying the successful preparation of the materials. The physicochemical characteristics of raw water, such as turbidity, color, COD, pH, EC, and TDS, were analyzed before and after treatment with CaFeO4. A jar test was performed to evaluate the reactivity and efficiency of the CaFeO4 toward wastewater treatment through coagulation/flocculation processes. After coagulation using CaFeO4, the pollutants levels were reduced COD (43.56–71.56%) and turbidity (97–99%), signifying the effectiveness of the as-prepared materials. CaFeO4 was successfully produced at various temperatures and was stable enough to be stored at room temperature for over six months without significantly decomposing. Graphical abstract

Samineh Gholami, Arash Shakouri, Jasem Raesi

In this research, the biological adsorption process of lead heavy metal was investigated using brown algae Nizimuddinia zanardini and green algae Ulva rigida. Optimum adsorption conditions by measuring pH, the amount of adsorbent, time, and the concentration of lead ion were investigated. Langmuir, Freundlich, and Temkin isotherms were used to investigate surface adsorption. Adsorption kinetics were investigated according to pseudo-first and second-order kinetic equations. Examining the pH results, it was found that brown algae at pH = 5 and green algae at pH = 6 could reach the maximum adsorption of lead. Examining the time-related results, it was found that the best adsorption time for both algae was 60 min. The results of the adsorbent dosage showed that the two bioadsorbents of brown and green algae have the highest amount of adsorption in the amounts of 0.5 and 0.7 g, respectively, and in the initial concentration of metal ions, the highest efficiency was obtained in the dosage of 2 mg/l. The results of the isotherm model showed that lead adsorption by brown algae corresponds to the Langmuir isotherm and green algae to the Freundlich isotherm. Also, according to the kinetic results, both adsorptions follow second-order kinetics.

Mingxue Meng, Xiao Pu, Siqi Li et al.

Development of rainfed maize (Zea mays L.) is sensitive to fluctuations of environmental conditions, while whether the sensitivity varies across the growth stages is still unclear. Based on a 5-year dataset collected from consecutive observations, this study examined the sensitivities of biomass and yield production of rainfed maize to root zone soil water, air temperature and shortwave radiation at four growth stages in the Sanjiang Plain of Northeastern China under sub-humid cool-temperate climates. The multiple linear regression model was employed to establish functional relations between biomass and yield production and significant explanatory variables. A Monte-Carlo simulation was used to test sensitivities of biomass and yield production to perturbation of a single significant explanatory variable or co-perturbation of multiple significant explanatory variables. Results showed that root zone soil water prevailed over air temperature and shortwave radiation in affecting rainfed maize development for the most time of the growing period. Biomass production was most sensitive to root zone soil water which had positive variance contributions of 70 – 100% at the early and late vegetative stages and a negative variance contribution of −99.4% at the early reproductive stage. Yield production was also sensitive to root zone soil water at the early reproductive stage with a 100% positive variance contribution. Biomass and yield production were most sensitive to air temperature at the late reproductive stage and the positive variance contributions of air temperature were 97.7 – 100%. Shortwave radiation negatively contributed to biomass production by −28.6% at the late vegetative stage. The findings of this study suggest that more attention could be paid to the most sensitive factor at different growth stages of rainfed maize for great biomass accumulation and high grain yield.

Federico Vilaseca, Santiago Narbondo, Christian Chreties et al.

RESUMENUn sólido estudio hidrológico diario es una tarea desafiante en regiones caracterizadas por una alta variabilidad hidro-climática, como Uruguay. Por esta razón, los modelos hidrológicos de base física de diferentes escalas temporales y espaciales (concentrados, semi-distribuidos y distribuidos) han pasado por un largo período de desarrollo y aplicación local. En los últimos años, los modelos basados en datos se están usando con éxito para resolver problemas hidrológicos. Hasta ahora, estos diferentes tipos de modelos se han estudiado individualmente para evaluar su capacidad para simular el proceso diario de precipitación-escorrentía. Este trabajo proporciona una profunda comparación entre un modelo agregado (GR4J), un modelo semi-distribuido (SWAT) y otro basado en datos (Random Forest (RF)) para simular el proceso diario de precipitación-escorrentía de dos cuencas hidrográficas ubicadas en Uruguay (una con reservorio y la otra sin). El rendimiento de cada modelo se analizó comparando numéricamente y gráficamente el caudal observado versus el simulado en términos de correspondencia temporal y cuantiles. En general, RF presenta un mejor rendimiento en comparación con los otros modelos físicamente basados. Sin embargo, carece de la capacidad de generalización que caracterizó a los otros dos enfoques. GR4J y SWAT logran un desempeño similar en nuestros casos de estudio.

Elsayed Mustafa Abu Elgoud, Ahmed Ibrahim Abd-Elhamid, Hisham Fouad Aly

Abstract A straightforward ferrocyanide immobilization on the surface of graphene oxide (GO) was conducted for rapid and efficient adsorption capacity for lanthanum and praseodymium from an aqueous solution. The GO was mixed with 1-methyl imidazole in the presence of epichlorohydrin to form GO-imidazole-Cl and thereafter suspended in a potassium ferrocyanide solution to fabricate GO-imidazole-FeCN. The prepared materials were characterized with different advanced techniques confirming the preparation method. The adsorption ability of GO-imidazole-FeCN towards La(III) and Pr(III) ions was evaluated. Moreover, the adsorption isotherm showed that the sorption process was fitted with the Langmuir isotherm model with a considerable maximum adsorption capacity of 781.25 mg g−1 for La(III) and 862.07 mg g−1 for Pr(III). The thermodynamic studies showed that the adsorption of both metal ions was spontaneous and endothermic. In addition, the adsorbent showed excellent adsorption–desorption behavior over 5 times, suggesting that GO-imidazole-FeCN may be considered a potential candidate for La(III) and Pr(III) removal from different metal ions which present in fission products.

Ahmad Jafarzadeh, Abbas Khashei-Siuki, Ali Shahidi

An accurate estimation of the water requirement of saffron, as the most strategic product in the eastern regions of Iran, is inevitable. Considering the field limitations in measuring the water requirement, applying empirical models has always been of interest. However, since each estimation model has unique strengths and weaknesses, relying only on an experimental model cannot obtain a reliable estimate for water requirements. This study intends to evaluate different combined methods' ability to merge the saffron water requirements simulations and obtain an improved output. Six empirical models and four other combination techniques were applied to get some skilful simulations about saffron water requirements in arid regions. Results indicate that the evapotranspiration prediction under the Abtew method (ABM) has more proficiency, such that its RMSE was 0.13 mm. Also, the different comparative tests show that the outputs of combined techniques such as Multi Model Super Ensemble ‘MMSE’ and Modified MMSE ‘M3SE’ outperform others.

Trevor Dunlap, Yasemin Acar, Michel Cucker et al.

Recent years have shown increased cyber attacks targeting less secure elements in the software supply chain and causing fatal damage to businesses and organizations. Past well-known examples of software supply chain attacks are the SolarWinds or log4j incidents that have affected thousands of customers and businesses. The US government and industry are equally interested in enhancing software supply chain security. On February 22, 2023, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 17 practitioners from 15 companies. The goal of the Summit is to enable sharing between industry practitioners having practical experiences and challenges with software supply chain security and helping to form new collaborations. We conducted six-panel discussions based upon open-ended questions regarding software bill of materials (SBOMs), malicious commits, choosing new dependencies, build and deploy,the Executive Order 14028, and vulnerable dependencies. The open discussions enabled mutual sharing and shed light on common challenges that industry practitioners with practical experience face when securing their software supply chain. In this paper, we provide a summary of the Summit. Full panel questions can be found in the appendix.

Xin Huang, Lin Qiu

Abstract To address the prominent problem of declining runoff in many rivers around the world, studying the law of runoff change and attribution analysis is very important for the planning and management of watershed water resources and has practical significance for solving the imbalance between supply and demand of watershed water resources and maintaining the healthy development of rivers. Three commonly used coupled water-energy balance equations based on Budyko hypothesis are selected to estimate the elasticity coefficient of runoff change to each driving factor, and the contribution rate of different factors to runoff change in the study area is quantified by the total differential method and the complementary method, respectively. The results show that the runoff of Huangfuchuan River basin showed a significant decreasing trend from 1954 to 2015, and the runoff mutation points were 1979 and 1996; in the alteration period I (1979–1996), precipitation was the main factor leading to the runoff reduction in Huangfuchuan River basin, followed by the influence of underlying surface; the contribution rate of underlying surface to runoff alterations ranged from 63.7% to 65.46%; the impact of potential evapotranspiration was slightly smaller. In the alteration period II (1997–2015), the underlying surface played a dominant role in runoff reduction of Huangfuchuan River basin. The contribution rate of the underlying surface to runoff change ranged from 80.21% to 86.34%, followed by precipitation, and the potential evapotranspiration had the least impact. The impact of human activities on the whole watershed increased with the passage of time. The land use change, the overall increase of NDVI (vegetation cover) and the construction of water conservation projects are important reasons for the reduction of runoff in Huangfuchuan River basin.

Jyoti Jain Tholiya, Navendu Chaudhary, Bhuiyan Monwar Alam

Abstract The water supply system in the city of Pune is affected due to the fast and chaotic development in and around the city. The quantity of per capita water supply and hours of supply per day varies substantially across the city. Some central parts of the city benefit from a large availability of water as compared to peripheral areas. This research employed Ordinary Least Squares (OLS) Regression, Geographically Weighted Regression (GWR), and the new version of GWR termed Multi-scale Geographically Weighted Regression (MGWR) models to better understand the factors behind observed spatial patterns of water supply distribution and to predict water supply in newly merged and proposed villages in the Pune city's periphery. Results showed statistical significance of slope; distance from service reservoirs; and water supply hour. MGWR and GWR models improved our results (adjusted R2: 0.916 and 0.710 respectively) significantly over those of the OLS model (adjusted R2: 0.252) and proved how local conditions influence variables. The maps of GWR display how a particular variable is highly important in some areas but less important in other parts of the city. The results from the current study can help decision-makers to make appropriate decisions for future planning to achieve Sustainable Development Goal number 6 (SDG #6), which focuses on achieving universal and equitable access to safe and affordable drinking water for all.

P. Mishra, D. Nandi, P. Sahu et al.

Freshwater supply is critical for domestic, agricultural and industrial purposes. A good supply of clean water is normally obtained from surface and groundwater water bodies. Nonetheless, many localities rely heavily on the latter as the main source of their water resource. Therefore, proper mapping, exploitation and conservation of groundwater resources should become a primary focus in the years to come. In this study, the groundwater samples collected from Bamanghati, Odisha were assigned into three classes (excellent, good and bad) based on the guidelines provided by World Health Organization in 1984. These water quality assignments were completed via a combined approach of hydro-geochemical information and artificial neural network for reconstructing a classifier for groundwater analysis. Here, the probabilistic approach and boosted instance selection method were used to remove inconsistencies in the dataset and to determine the classification accuracy, respectively. Finally, the transmuted dataset is used for kernel estimator-based Bayesian and Decision tree (J48) classification approaches. The findings from the present study confirm that the preprocessing task using statistical analysis along with the combined method of hydro-geochemical attributes-based classification approach is encouraging while the decision tree approach is better than the Bayesian neural network classifier in terms of precision, recall, F-measures, and Kappa statistics.