Improved Method Research Articles

Research Progress and Prospect on Profile Roll Forming Methods and Equipment

Abstract: In recent years, with the development of aerospace, automotive, rolling stock, shipbuilding, military weapons and other industries, the demand for bent and rolled parts is increasing, and the requirements for roll forming accuracy are growing. With the rapid development of coal power, hydropower, nuclear power, wind power, and petrochemical industries, the design and research on roll bending machine and the profile roll forming methods have come into focus. The purpose of this study is to summarize the classification and characteristics of roll bending equipment, analyze the reasons that affect the accuracy of profile bending, and propose specific solutions. This paper summarizes the research status and patents on the existing roll bending equipment, introduces its structural characteristics, differences, and molding effects, and focuses on the difficulties, optimization, and improvement methods of roll bending molding. In this paper, the mechanical structures of the existing roll forming equipment have been analyzed and compared, the effects of different roll forming equipment on the profile bending results have been determined, and the reasons affecting the bending accuracy have been pointed out. By summarizing the patents and research on profile roll forming methods and equipment, the current situation and future research prospects of roll forming equipment are discussed. The roll forming equipment is divided into a roll bending machine, a CNC roll bending machine, and a flexible roll bending machine. Each design can meet different profile processing needs. From the design point of view, the optimization under the given processing profile can achieve relatively high processing accuracy. However, the processing of different profiles will still produce geometric defects, and there is some space for improvement in the structure of roll forming equipment, the materials of processing rollers, and the NC interactive system. Thus, further research on profile roll forming is needed in the future.

Reducing Clostridioides difficile Infections in a Medical Intensive Care Unit: A Multimodal Quality Improvement Initiative.

Clostridioides difficile (C. diff) infection causes significant morbidity for hospitalized patients. A large medical intensive care unit had an increase in C. diff infection rates. The aim of this project was to reduce the C. diff polymerase chain reaction (PCR) test positivity rate and the rate of C. diff PCR tests ordered. Rates were compared between preintervention (July 2017 to December 2019) and postintervention (January 2021 to December 2022) timeframes. Unit leadership led a robust quality improvement project, including use of quality improvement tools such as A3, Gemba walks, and plan-do-study-act cycles. Interventions were tailored to the barriers identified, including standardization of in-room supply carts; use of single-packaged oral care kits; new enteric precautions signage; education to staff, providers, and visitors; scripting for patients and visitors; and use of a C. diff testing algorithm. Statistical process control charts were used to assess for improvements. The average rate of C. diff PCR test positivity decreased from 34.9 PCR positive tests per 10 000 patient days to 12.3 in the postintervention period, a 66% reduction. The average rate of PCR tests ordered was 28 per 1000 patient days in the preintervention period; this decreased 44% to 15.7 in the postintervention period. We found clinically significant improvements in the rate of C. diff infection and PCR tests ordered as a result of implementing tailored interventions in a large medical intensive care unit. Other units should consider using robust quality improvement methods and tools to conduct similar initiatives to reduce patient harm and improve care and outcomes.

Structural damage detection based on structural macro-strain mode shapes extracted from non-stationary output responses

Abstract Long-gauge fiber Bragg grating strain sensors have been widely employed because of their broader measuring range and higher sensitivity. However, current structural damage detection methods using macro-strain modal parameters are based on structural frequency response function or stationary power spectrum density, which are not applicable to non-stationary responses. To overcome this limitation, an improved method is proposed in this paper for structural damage detection based on structural macro-strain responses under unknown multi-point non-stationary excitations. First, a new concept of macro-strain energy spectrum transmissibility (MEST) is proposed using structural non-stationary macro-strain responses, and it is derived that MEST at a certain system pole equals the ratio of macro-strain mode shape. Then, the singular value decomposition technique is adopted for the MEST matrix to identify structural natural frequencies and macro-strain mode shapes. Finally, two damage detection indicators are constructed based on the identified normalized macro-strain mode shape (NMMS). The first indicator is the difference in structural NMMS before and after structural damage. The second one is based on the curvatures of structural NMMS, which can be used for structures without intact baseline. Numerical verifications are conducted to identify beam-type structural damage under multi-point non-stationary excitations or vehicle loads. Five damage scenarios with different measurement noise levels are investigated, and damage detection results validate the effectiveness of the proposed method.

Centrifuge modelling on seismic failure of MSW landfills with high water level

Landfill failure threatens the safety of surrounding cities and causes soil and water pollution. High water levels can usually spur landfill failure. With the presence of earthquake, landfill instability is triggered in a greater possibility. However, the dynamic response and failure mechanism of landfills with high water level subjected to earthquakes are not yet clearly understood, and relevant experimental studies are quite scarce. This study includes a series of seismic centrifuge tests to investigate the seismic response of the landfill at different water levels. An improved method is proposed to prepare synthetic municipal solid wastes (MSWs) with similar static-dynamic properties to the actual MSWs. The failure and evolution mechanism of landfills with high water levels induced by the earthquake is revealed. Significant transitions are observed in the dynamic response of deformation, acceleration, and pore-water pressure as the water level changes. With the rise of water level, the settlement at the top of the landfill decreases first and then increases, while the horizontal displacement at the toe increases slowly, followed by a sudden drop; the acceleration amplification coefficient in the middle of the landfill increases first and then decreases; the dynamic pore-water pressure gradually decreases from positive to negative, but oscillates at the moderate water level. Three failure criteria and a hydro-earthquake coupled failure limit are proposed to demonstrate the combined effect of water level and earthquake on landfill stability. The test results provide a basis to establish seismic failure standards for landfills with different water levels and guide the seismic instability design of landfills with high water level.

An improved method for evaluating fracture density using pulsed neutron capture logging

Fracture density is a critical fracture evaluation parameter for the optimization and production prediction of hydraulic fracturing models. Non-radioactive tracer techniques (NRT) have successfully used a quantitative fracture density method based on neutron-induced gamma ray from formation elements. Furthermore, fracture density can be calculated using the formation capture cross-section before and after hydraulic fracturing based on pulsed neutron capture logging. However, the response sensitivity of the macroscopic scattering cross section to the fractures will decrease when the fracture density is high due to the neutron self-shielding phenomenon. Therefore, an improved fracture density evaluation method is applied, which combines the macroscopic capture cross section and the neutron self-shielding correction factor. In the new method, the peak area of titanium from the captured gamma spectrum was used to obtain a neutron self-shielding correction factor, in order to improve the sensitivity of fracture density determination. Furthermore, the response of capture cross-section variation to fracture density at various tagged proppant concentrations and formation backgrounds was investigated. The findings indicate that the tagged proppant concentration influences the detection limit of fracture density and the sensitivity of fracture density identification. The accurate calculation range of fracture density using the new method has been extended from 5% to 10% under the condition that the tagged proppant concentration is 0.2%. Meanwhile, water salinity significantly impacts capture cross-section variation, while the effects of porosity, lithology, and fluid type on capture cross-section variation are negligible. A simulated fracturing example demonstrates the method's applicability in various measuring environments. The results show that fracture density and height are consistent with the model settings after correcting for water salinity, and the fracture density calculation error is less than 3%. Therefore, our proposed evaluation method for fracture density corrected for the neutron self-shielding effect improves response sensitivity and fracture density calculation accuracy.

Experimental and theoretical studies on deformation characteristics of Geosynthetic-Reinforced Soil (GRS) abutments induced by vertical loads

This study conducted five centrifuge model tests to investigate the deformation characteristics of the Geosynthetics Reinforced Soil (GRS) abutments under vertical loads, considering the setback distance ab and beam seat width B as two major influencing factors. Test results show that a linear correlation existed between the maximum lateral facing displacements DL and the maximum settlements at the top of the GRS abutments Dv. The ab and the B had different effects on the deformation characteristics of the GRS abutments as well as the relationship between the DL and the Dv. The total volumetric strains of the GRS abutments were smaller than 0.3% for all the cases investigated in this study, indicating that it was reasonable to use the assumption of zero-volume change for the deformation calculation of the GRS abutments. This study proposed an improved semi-empirical method to describe the relationship between the DL and the Dv. Centrifuge test results and data collected from the literature were used to validate the improved method. It was concluded that the improved method had the advantage of considering the effects of the ab and the B separately and therefore significantly improved the prediction accuracy of the deformations of the GRS abutments.

Analysis of the Effectiveness of Model, Data, and User-Centric Approaches for Chat Application: A Case Study of BlenderBot 2.0

BlenderBot 2.0 represents a significant advancement in open-domain chatbots by incorporating real-time information and retaining user information across multiple sessions through an internet search module. Despite its innovations, there are still areas for improvement. This paper examines BlenderBot 2.0’s limitations and errors from three perspectives: model, data, and user interaction. From the data perspective, we highlight the challenges associated with the crowdsourcing process, including unclear guidelines for workers, insufficient measures for filtering hate speech, and the lack of a robust process for verifying the accuracy of internet-sourced information. From the user perspective, we identify nine types of limitations and conduct a thorough investigation into their causes. For each perspective, we propose practical methods for improvement and discuss potential directions for future research. Additionally, we extend our analysis to include perspectives in the era of large language models (LLMs), further broadening our understanding of the challenges and opportunities present in current AI technologies. This multifaceted analysis not only sheds light on BlenderBot 2.0’s current limitations but also charts a path forward for the development of more sophisticated and reliable open-domain chatbots within the broader context of LLM advancements.

Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil > yellow moist soil > sandy soil > silt soil > mulching helilu soil> loessal soil> loamy soil > slope loessal soil > arenosol > tillage leaching gray cinnamon soil > calcareous gray cinnamon soil > red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

Stabilization of Pavement Subgrade Clay Soil Using Sugarcane Ash and Lime

Soft to medium clay soil possesses major sources of damages to the pavement layers overlying them because of their potential failure under moisture changes and external heavy traffic load. In such situations, soil stabilization methods can be used to improve the soil properties and satisfy the desired engineering requirements. This study presents the use of sugarcane bagasse ash (SBA) and lime as chemical stabilizers for a clay soil subbase. Sugarcane bagasse ash and lime are used individually and as mixtures at varying percentages to stabilize a clay soil from Taxila, Pakistan. Various geotechnical laboratory tests such as Atterberg limits, compaction test, and California Bearing Ratio (CBR) are carried out on both pure and stabilized soils. These tests are performed at 2.5%, 5%, and 7.5% of either SBA or lime by weight of dry soil. In addition, mixtures of lime and SBA in ratios of 1:1, 2:1, 3:1, 1:2, and 1:3 are used in 5%, 7.5%, and 10% of dry soil weight, respectively. Results indicate that soil improved with 7.5% SBA showed a 28% increase in the liquid limit, while soil mixed with 2.5% lime in combination with 7.5% SBA showed an increase of 40% in the plastic limit. For the plasticity index, the soil mixed with 7.5% SBA showed an increase of 42%. Moreover, 2.5% lime in combination with 2.5% SBA showed the best improvement in soil consistency as this mixture reduced the soil plasticity from high to low according to the plasticity chart. Furthermore, 2.5% SBA in combination with 5% lime demonstrated the largest improvement on the CBR value, which is about a 69% increase above that of the pure soil. Finally, the cost analysis indicates a promising improvement method that reduces pavement cost, increases design life, and mitigates issues of energy consumption and pollution related to SBA as a solid waste material.

Cognitive testing of the Children’s Palliative Outcome Scale (C-POS) with children, young people and their parents/carers

Background: The Children’s Palliative Outcome Scale (C-POS) is being developed using best methodological guidance on outcome measure development, This recommends cognitive testing, an established method of item improvement, prior to psychometric testing. Aim: To cognitively test C-POS within the target population to establish comprehensibility, comprehensiveness, relevance and acceptability. Design: Cross-sectional cognitive interview study following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology and Rothrock guidance on outcome measure development. Cognitive interviews were conducted using ‘think aloud’ and verbal probing techniques. Setting/participants: Children 5–⩽17 years old with life-limiting conditions and parents/carers of children with life-limiting conditions were recruited from 14 UK sites. Results: Forty-eight individuals participated (36 parents; 12 children) in cognitively testing the five versions of C-POS over two to seven rounds. Content and length were acceptable, and all questions were considered important. Refinements were made to parent/carer versions to be inclusive of non-verbal children such as changing ‘share’ to ‘express’ feelings; and ‘being able to ask questions’ to ‘having the appropriate information’. Changes to improve comprehensibility of items such as ‘living life to the fullest’ were also made. Parents reported that completing an outcome measure can be distressing but this is anticipated and that being asked is important. Conclusion: Cognitive interviewing has facilitated refinement of the C-POS, especially for non-verbal children who represent a large proportion of those with a life-limiting condition. This study has enhanced the face and content validity of the measure and provided preliminary evidence for acceptability for use in routine practice.

Choc-Tadalafil Fusion: Unlocking Solubility and Taste Harmony with ß-CD-Infused Medicated Chocolate.

The primary limitations of tadalafil in treating erectile dysfunction are its low solubility and unpleasant bitter taste, which ultimately result in inadequate patient adherence. The present study aimed to develop and characterize a medicated chocolate formulation containing Tadalafil and ß-CD (solubility enhancer) employing the concept of Design of Experiment (DoE) using chocolate as a user-friendly excipient. An inclusion complex was formulated by incorporating the drug into ß-CD using the kneading method for solubility improvement and also as a taste masker for Tadalafil. The ratio of drug: ß-CD inclusion complex was selected based on a phase solubility study. The inclusion complex was molded into a chocolate base and optimized using the DoE approach. Further, drug excipient interaction was evaluated by DSC and FTIR study. Phase solubility study suggested a 1:1 ratio of Tadalafil: β-CD for better solubility. DSC spectra suggested the conversion of crystalline structure into an amorphous state which indicates improvement of the drug solubility. DSC and FTIR studies revealed that there was no significant interaction between drug and excipients. Next, %CDR (cumulative drug release) at 30 min revealed the immediate effect of Tadalafil from chocolate formulation and free drug analysis (an unbound drug with ß-CD) proved reduced bitterness of the drug in the complex. Additionally, the medicated chocolate was found to be stable at room temperature as per stability study. β-CD was found to be a promising multifunctional excipient as a solubility enhancement carrier and taste masker for bitter-tasting drugs.

Reducing IV Antibiotic Duration for Neonatal UTI Using a Clinical Standard Pathway.

Urinary tract infections (UTIs) are the most common bacterial infections in young infants and are traditionally treated with longer intravenous (IV) antibiotic courses. A growing body of evidence supports shorter IV antibiotic courses for young infants. Our primary aim was to decrease the IV antibiotic treatment to 3 days over 2 years for neonates aged 0 to 28 days who have been hospitalized with UTIs. Using quality improvement methods, our primary intervention was to implement a revised clinical pathway recommending 3 (previously 7) days of IV antibiotics. Our primary outcome measure was IV antibiotic duration, and the secondary outcomes were length of stay (LOS) and costs. The balancing measure was readmission within 30 days of discharge. Neonates were identified by using International Classification of Diseases diagnosis codes and excluded if they were admitted to the ICU or had a LOS >30 days. We used statistical process control to analyze outcome measures for 4 years before (baseline) and 2 years after the pathway revision (intervention) in February 2020. A total of 93 neonates were hospitalized with UTIs in the baseline period and 41 were hospitalized in the intervention period. We found special cause variation, with a significant decrease in mean IV antibiotic duration from 4.7 (baseline) to 3.1 days (intervention) and a decrease in mean LOS from 5.4 to 3.6 days. Costs did not differ between the baseline and intervention periods. There were 7 readmissions during the baseline period, and 0 during the intervention period. The implementation of a revised clinical pathway significantly reduced IV antibiotic treatment duration and hospital LOS for neonatal UTIs without an increase in hospital readmissions.

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Fairness Improvement Method Using Explicit Congestion Notification for QUIC

Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.

Implementing Inpatient Social Needs Screening in an Urban Tertiary Care Children's Hospital.

The American Academy of Pediatrics recommends screening for unmet social needs, and the literature on inpatient screening implementation is growing. Our aim was to use quality improvement methods to implement standardized social needs screening in hospitalized pediatric patients. We implemented inpatient social needs screening using the Model for Improvement. An interprofessional team trialed interventions in a cyclical manner using plan-do-study-act cycles. Interventions included a structured screening questionnaire, standardized screening and referrals workflows, electronic health record (EHR) modifications, and house staff education, deliberate practice, and feedback. The primary outcome measure was the percentage of discharged patients screened for social needs. Screening for social needs was defined as a completed EHR screening questionnaire or a full social work evaluation. Process and balancing measures were collected to capture data on screening questionnaire completion and social work consultations. Data were plotted on statistical process control charts and analyzed for special cause variation. The mean monthly percentage of patients screened for social needs improved from 20% at baseline to 51% during the intervention period. Special cause variation was observed for the percentage of patients with completed social needs screening, EHR-documented screening questionnaires, and social work consults. Social needs screening during pediatric hospitalization can be implemented by using quality improvement methods. The next steps should be focused on sustainability and the spread of screening. Interventions with greater involvement of interdisciplinary health care team members will foster process sustainability and allow for the spread of screening interventions to the wider hospitalized pediatric population.

ISight - Navigable Path Detection for Visually Impaired using Semantic Segmentation

Abstract: This project introduces a new educational service that uses machine learning algorithms in Python and the OpenCV library to detect and prevent tampering, providing blind people with better freedom and personal security. Thanks to the integration of real-time detection equipment, the system can identify various hazards in different locations without using conventional equipment. Topics covered include optimizing algorithms for immediate use and maximizing efficiency for portable devices. Python supports rapid development, while OpenCV's modularity enables scalable development. This approach performs existing processes accurately, quickly and efficiently, using flexible methods for continuous improvement. Future developments include the integration of advanced learning models and user insights to provide more accurate feedback and pave the way for new products.

Performance evaluation of MeteoTracker mobile sensor for outdoor applications

Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for high-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offer the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in a fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves to be crucial for increasing the data quality. The results mark an intra-sensor variability of approximately ± 0.5 °C for air temperature and ± 1.2 % for the corrected relative humidity, both of which are within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed to be between ± 0.3 to ± 0.5 °C for air temperature and between ± 3 % and ± 5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.

Theory-based cryopreservation mode of mesenchymal stromal cell spheroids

Cryopreservation of spheroids requires development of new improved methods. The plasma membranes permeability coefficients for water and cryoprotectants determine time characteristics of mass transfer through the cell membranes, and therefore the optimal modes of cells cryopreservation. Here we proposed an approach to cryopreservation of multicellular spheroids which considers their generalized characteristics as analogues of the membranes’ permeability coefficients of the individual cells. We have determined such integral characteristics of spheroids from mesenchymal stromal cells (MSCs) as osmotically inactive volume; permeability coefficients for water and Me2SO molecules and the activation energy of their penetration. Based on these characteristics, we calculated the osmotic behavior of multicellular spheroids under cooling conditions to select the optimal cooling rate. We also determined the optimal cooling rate of spheroids using the probabilistic model developed based on the two-factor theory of cryodamage. From the calculation it follows that the optimal cooling rate of the MSC-based spheroids is 0.75°С/min. To verify the obtained theoretical estimates, we conducted experiments on freezing MSC-based spheroids under different modes. The obtained results of primary viability screening indicate that freezing at a constant linear cooling rate of 0.75–1.0°С/min gives a good result. Theoretical prediction of the spheroid osmotic behavior during cooling provided the basis for experimental verification of varying the temperature to which slow cooling should be carried out before immersion in liquid nitrogen. Slow freezing of spheroids to −40 °C followed by immersion in liquid nitrogen was shown to preserve cells better than slow freezing to −80 °C. Obtained data allow more effective use of MSC-based spheroids in drug screening and regenerative medicine.

Field test of machine-learning based mean radiant temperature estimation methods for thermal comfort-integrated air-conditioning control improvement and energy savings

Advanced control strategies for heating, ventilation, and air-conditioning (HVAC) systems aim to enhance both buildings' energy efficiency and occupant thermal comfort. Despite their potential, real-world demonstration studies on such advanced technologies are still lacking. This study presents a field demonstration of a real-time predicted mean vote (PMV)-based HVAC control strategy in a typical residential building under hot and dry climate conditions. This study introduces an advanced thermal comfort-based controller (TCC) for the PMV-based HVAC control. TCC continually assesses indoor and outdoor thermal conditions and adjusts the HVAC setpoint temperature to optimize real-time energy use while ensuring satisfactory indoor thermal comfort. Machine learning models for the PMV-based HVAC control system are employed to estimate a mean radiant temperature (MRT) point, which is one of the variables used to calculate real-time PMV values. Three machine learning models (i.e., linear regression, regression trees, and artificial neural network) are adopted in this study with non-stationary real-time input values, including times, pre-determined setpoints, and indoor and outdoor temperatures. The developed TCC is installed in a full-scale experimental house in Kuwait, which is under hot and dry climate conditions, to assess the house's indoor thermal comfort and AC energy efficiency performance. Results indicate that the TCC provides better thermal comfort performance compared to the non-TCC case, including up to a 60% improvement in PMV. The proposed TCC controlled by the machine learning methods demonstrates HVAC energy savings potential of over 20% while meeting the desired thermal comfort levels in the experimental building. These findings are expected to be valuable, as they can contribute to reducing cooling energy in residential buildings in hot and dry climate conditions.

Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing, and a modified deep learning MU-NET convolutional neural network

A study revealed that the Siwa Oasis faces high soil salinity, which negatively impacts agricultural areas and crop productivity, despite its significant economic and agricultural importance. The current research proposes an approach to detect and segment salinity and vegetation areas at Siwa Oasis, Egypt, by combining remote sensing and building a deep learning neural network model-based U-NET algorithm to detect salinity change areas, anticipate further degradation, and predict soil quality indicators. To locate changes among the available images, standard image improvement, classification, and change detection methods have been used. We applied a deep learning modified U-Net (MU-NET) algorithm to segment and produce salinity maps. The MU-NET architecture is a two-level nested U-structure merged with a residual U-block (RUb), which consists of an encoder and a decoder. We applied RUb, which consists of several layers and skip connections. Different combinations of the salinity and vegetation indices were added to the original image to improve segmentation precision. The model was validated and trained using actual data samples collected over a 10-year period from the Landsat 8 satellite, which can monitor and analyse present land cover changes. The dataset consisted of 91 OLI and TIRS spectral images. Each one consists of eleven bands with a spatial resolution of 30 m for bands 1 to 7 and 9. A field survey was used as the main source of data for comparing the proposed model's outputs to assess the error rate. The study region is experiencing an increase in soil salinity in all directions, particularly with regard to the spatial distribution of saline soils, not just the quantitative increase in salt-affected soils. These findings supported the acceleration of soil salinization and vegetation death. The proposed model achieved the highest performance results among the other models and literature and was based on applying method 12 using 13 image layers, with the highest accuracies of 91.27% and 90.83% for salinity and vegetation, respectively.