Proposed System Research Articles

Development of an IoT-based Real-Time Temperature and Humidity Monitoring System for Factory Electrical Panel Rooms

Internet of Things (IoT) based systems are used for monitoring the status of inventory and components including variables such as temperature and humidity, in order to maintain their quality under ideal conditions. These IoT devices can also provide real-time monitoring of quality of components, identifying any defects and issuing alerts if quality criteria are not upheld throughout the manufacturing process.This study presents the development and implementation of an IoT-based Real-Time Temperature and Humidity Monitoring System specifically designed for Industrial Electrical Panel Rooms. In the backdrop of industrial automation advancements, there is a significant need for real-time monitoring systems to ensure efficiency and safety. The proposed system establishes the capabilities of the IoT to offer a network of interconnected devices that can communicate, share data, and provide real -time feedback. The system's methodology consists of three pivotal stages: data acquisition using the "DHT22" temperature sensor, data transmission through both wired and wireless means, and user alert mechanisms that trigger email notifications when predefined temperature thresholds are exceeded. The system was tested in an actual factory environment, demonstrating its potential to mitigate risks associated with humidity disturbances and overheating in electrical panel rooms. The research highlights the significance of IoT in enhancing industrial safety and operational efficiency.

Novel compound multistable stochastic resonance weak signal detection

Abstract The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resonance (NCMSR) model by combining the Woods–Saxon (WS) and tristable models. The influence of the parameters of the NCMSR systems on the output response performance is studied under different α stable noises. Meanwhile, the adaptive synchronization optimization algorithm based on the proposed model is employed to achieve periodic and non-periodic signal identifications in α stable noise environments. The results show that the proposed system model outperforms the tristable system in terms of detection performance. Finally, the NCMSR model is applied to 2D image processing, which achieves great noise reduction and image recovery effects.

K-Means-Based DNN Algorithm for a High Accuracy VLP System

In this paper, a positioning algorithm based on the combination of K-means clustering and deep neural networks (DNNs) is first presented for multiple light emitting diodes (LEDs) integrated with visible light positioning (VLP) systems. We extracted the maximum value from the collected optical power of LEDs, utilizing the ratio of each optical power to this maximum optical power as the input training data. The experimental results demonstrate that the proposed algorithm outperformed the conventional DNN algorithm in terms of anti-jamming capability and positioning accuracy. In addition, the positioning accuracy of the proposed system reached a millimeter level, which is the highest experimental VLP accuracy, to the best of our knowledge.

Development of an indoor corridor localization system based on a WCL method with corridor area selection and distance compensation

SummaryIn this paper, a received signal strength indicator (RSSI)‐based weighted centroid localization (WCL) method for indoor corridor scenarios is developed and tested. The contribution of the proposed system is that, to scope the area of an unknown target position, the possible corridor area where the target node should be located is automatically selected, and only reference nodes deployed in such an area are applied for position estimation. Additionally, to improve the estimation precision, distance values converted from measured RSSIs and used for the WCL are also compensated to alleviate the RSSI variation problem caused by physical environments. Therefore, our methods can save a number of reference nodes to be used, while localization accuracy can also be achieved. Experiments in the corridor environment using a 2.4 GHz IEEE 802.15.4 wireless sensor network with four reference nodes deployed in the test field dimension of 22 m × 9.3 m have been performed. Experimental results demonstrate that the proposed method, which requires only two reference nodes, outperforms the original WCL method with four reference nodes by 53.053%, as indicated by average localization errors. The results also reveal that the proposed method provides error distances lower than the WCL method for all test target positions, while all estimated positions fall within the corridor areas by 100%.

Application of Biomaterials in the Development of Hydrogel-Forming Microneedles Integrated with a Cyclodextrin Drug Reservoir for Improved Pharmacokinetic Profiles of Telmisartan.

Telmisartan (TEL) is a promising antihypertensive agent among other angiotensin receptor blockers. However, its oral application is limited by its poor water solubility. This study presents the successful utilization of biomaterial-based hydrogel-forming microneedles integrated with a direct compressed tablet reservoir (HFMN-DCT) for the transdermal delivery of telmisartan in the treatment of hypertension. The combination of PVP, PVA, and tartaric acid was used in the HFMN formulation. A range of cross-linking temperatures and times were employed to optimize the characteristics of the HFMN. The HFMN exhibited excellent swelling capacity, mechanical strength, and insertion properties. Additionally, the poorly soluble characteristic of TEL was improved by the inclusion complex formulation with β-cyclodextrin (βCD). Phase solubility analysis showed an Ap-type diagram, indicating a higher-order complex between TEL and βCD, with respect to βCD. A ratio of TEL:βCD of 1:4 mM demonstrates the highest solubility enhancement of TEL. The inclusion complex formation was confirmed by FTIR, XRD, DSC, and molecular docking studies. A significantly higher release of TEL (up to 20-fold) from the inclusion complex was observed in the in vitro release study. Subsequently, a DCT reservoir was developed using various concentrations of sodium starch glycolate. Essentially, both the HFMN and DCT reservoir exhibit hemocompatibility and did not induce any skin irritation. The optimized combination of the HFMN-DCT reservoir showed an ex vivo permeation profile of 83.275 ± 2.405%. Notably, the proposed system showed superior pharmacokinetic profiles in the in vivo investigation using male Wistar rats. Overall, this study highlights the potential of HFMN-DCT reservoir systems as a versatile platform for transdermal drug delivery applications.

Fruit Disease Detection using Image Processing

These days within the agrarian assiduity we've a good reference in fruit field. As we know that India’s frugality is truly dependent upon the agrarian assiduity, so an advanced field is must necessary and important. That is why growers want the homemade process of covering the fruits from crop till its growing phase. Purely homemade observing will not give reasonable results and also consumes a lot of your time, hence proposing an resourceful smart husbandry approach. That helps growers to descry conditions in crops. The proposed work uses image processing ways like preprocessing to enhance the quality of an image and segmentation to partition the images also feature birth to prize some useful information and type to classify the diseases. Fruit conditions are a major problem in profitable losses and product in the agrarian assiduity worldwide. In this paper, an image processing approach is proposed for relating fruit conditions. conditions in fruit cause ruinous problem in profitable losses and producting agricultural assiduity worldwide. According to the Sri Lankan terrain, treatment details are taken by the growers from the field officers. But it can take a multitudinous days. So, this proposed system can be used to identify fruit conditions snappily and automatically. This proposed approach is composed of the following main way; Preprocessing, point birth, Bracket, Training &Testing. Our experimental results express that the proposed result can significantly support accurate discovery and automatic type of fruit diseases. Paper demonstrates the system for discovery of fruit complaint. Present days as there's restrictive interest for husbandry assiduity, successful development and enhanced product of fruit is abecedarian and imperative. For this reason agronomists bear homemade observing of fruits. But all the time primer observing will not give satisfactory results and they generally bear guidance from master. So there's demand for proposing an effective cultivating system which helps for better product and advancement with truly lower mortal trouble, Image processing strategies are employed for performance of proposed system. For image segmentation Four point vectors are used in the proposed system those are colour, morphology, texture and structure of hole on the fruit. The system utilizes two image databases, one for training of formerly stored complaint images and one further for performance of query images. For matching of patterns and identification of fruit conditions GLCM or CNN generality is used.

Chiral monomer template for designing Low-Driving-Field blue phase liquid crystals

Blue phase liquid crystals (BPLC) are known to be useful for various non-trivial applications due to their nanoscale self-assembled cubic crystalline structures. Even though the BPLC exhibits an efficient optically isotropic phase due to the double helical confinement of liquid crystal (LC) molecules within a few hundred-nanometer range, this hinders the orientation of LC molecules along the applied field direction. Consequently, the BPLC demands larger driving fields to operate, degrading the device’s performance in real-world applications. In this context, we provide a chiral monomer template for BPLC that decreases the driving field by 52 % while the electro-optical responses remain unaffected. Furthermore, our approach provides higher thermal stability, with a broader BPLC phase range (42 °C), including near ambient temperature. As this approach potentially overcomes the fundamental challenges of BPLC, the proposed system could be useful for next-generation display devices.

Thermo-economic optimization of an artificial cavern compressed air energy storage with CO2 pressure stabilizing unit

In recent years, the attention of engineers has been increasingly attracted to the compressed air energy storage with artificial cavern as it frees the conventional system from the dependence of salt cavern, greatly reducing the limiting factors of project location. However, the current issues are how to enhance the reliability and safety of the artificial cavern due to the cyclical pressure loading and to cut down the cost of cavern construction. The present study overtures an isobaric system for doing this via designing pressure stabilizing unit utilizing the CO2 phase change process. The specific volume of saturated liquid is an order of magnitude lower than that of the saturated gas and this characteristic contributes much to reduce the cavern size. The system thermodynamics and economics are calculated numerically and the findings are discussed to identify whether the additional complexity of isobaric gas storage are worth to do. It is recommended that the air storage pressure, CO2 storage pressure and CO2 liquefaction pressure should be positioned in sequence at 6.5 MPa, 6 MPa and 9 MPa as the optimal design conditions. In this case, the system efficiency is 69.92 %, the levelized cost of storage is 0.1332 $/kWh, the dynamic payback period is 7.26 years and the investment return ratio is 1.58. Compared to the isochoric system with equivalent efficiency, the air storage pressure in proposed system is highly reduced with just 0.65 times of original value. More importantly, the constant pressure in the cavern operation process significantly upgrades its reliability and safety. The size of cavern volume is narrowed with a decline rate of 31.86 % and the system levelized cost of storage scales back with an amplitude of 0.0138 $/kWh. To be matched with, the investment return ratio is increased by 54.9 %.

Novel renewable seawater desalination system using hydrogen as energy carrier for self-sustaining community

This study proposes a novel renewable seawater desalination system in which hydrogen is utilized as an energy carrier to balance the fluctuating electricity supply and demand and provide heat to the desalination system. The proposed system employs a water‑hydrogen nexus framework to achieve an efficient operation for providing electricity, potable water, and green hydrogen. The optimal system configuration that considers seasonal variations in renewable energy production is determined by using a heuristic algorithm. It can sustain flexible and standalone operations with less curtailment. The results demonstrate that the system has an average efficiency of 38.9 %, which is higher than the results reported in previous studies. The daily differences in system efficiency are primarily caused by the solar and water electrolysis systems. Curtailment rates of 5 % to 14 % during the operation are owing to the energy storage capacity and supply-demand balance. A higher utilization rate of water electrolysis and fuel cell systems is beneficial for profitability. However, the present utilization rate ranges from 40 %–60 % for water electrolysis systems and <20% for fuel cell systems. Thus, an integrated net-zero system of water electrolysis and fuel cells is proposed, which can be used as a substitute for traditional fossil-based desalination plants.

Multipoint Energy-Balanced Laser-Ultrasonic Transducer Based on a Thin-Cladding Fiber.

This study proposes a novel multipoint transducer system by utilizing the single-mode-multimode-thin-cladding fiber (SMTC) structure. This structure leverages the disparity in mode field diameter between the multimode fiber (MMF) and thin-cladding fiber (TCF) to generate high-amplitude ultrasonic signals safely and efficiently. The fabricated transducer exhibits signal amplitudes 2-3-fold higher compared to conventional laser-ultrasonic transducers. Simulation analysis investigates the impact of the length of the MMF and the diameter of the TCF on coupling efficiency. The coupling efficiency of individual transducer units can be accurately controlled by adjusting the length of the MMF. A three-point energy-balanced laser-ultrasonic transducer system was achieved, with improved energy conversion efficiencies, and the optimal thickness of candle soot nanoparticles (CSNPs) is experimentally determined. Additionally, we carried out experiments to compare the performance of the proposed SMTC-based transducer system under different material conditions using two different photoacoustic materials: graphite-epoxy resin and candle soot nanoparticle-polydimethylsiloxane (CSNP-PDMS) composite. CSNPs, as a cost-effective and easy-to-prepare composite material, exhibit higher photoacoustic conversion efficiency compared to graphite-epoxy resin. The proposed system demonstrates the potential for applications in non-destructive testing techniques.

Security Enabled New Term Weight Measure Technique with Data Driven for Next Generation Mobile Computing Networks

In the field of ASIC and FPGA, Machine Learning (ML) techniques play a major role and become predominant for accurate results for different applications like big data analysis and automotive electronics, and driverless vehicles which are required speed and power savings. Due to increasing the demand for higher accuracy, low power, low area consumption, and higher throughput for the complexity of the designs in the latest technology, the proposed system is fulfilling these demands in ASIC and FPGA domains, reconfigurable hardware architecture has been proposed it consists of an ML-based Support Vector Machine (SVM), high-speed AHB protocol and Floating point (FP) operations and also the system has the flexibility to communicate with I2C and I2S protocols. In order to increase throughput with minimal latency, the proposed architecture with AHB protocol and AHB to APB bridge is incorporated between the fabric dynamically reconfigurable multi-processor (FDPM) and peripherals along with security algorithms using SHA-256bits and AES. In order to perform ML-based applications, the proposed system is incorporated double-precision floating point (DPFP) arithmetic operations. The overall proposed architecture is developed in Verilog HDL and quality checking using the LINT tool and Clock Domain Crossing (CDC) using Spyglass tool and synthesized using DC compiler for ASIC and Vivado Design Suite 2018.1 for FPGA implementation and verification. The entire design is interfaced with the Zynq processor and SDK tool to verify data transfer between hardware and software. The obtained results show the generated custom accelerator is able to compute any complex ML classifiers for a larger amount of data. The obtained results are compared with existing state-of-art results and found that 18 % improvement in throughput, a 21 % improvement in power consumption savings, and a 34 % reduction in latency.

Steady-state analysis of a hybrid power supply system using an induction generator with a shunt AC/DC converter

Hybrid power supply systems (HPSSs) are considered as a good option for electric power supply of remotely located from the grid consumers due to significant fuel savings compared to diesel sets. Quick development and improvement of HPSSs may be achieved using specialized methodologies and programs. In the paper a schematic diagram is proposed and operation principles of a 400 V / 50 Hz HPSS were developed. The system’s main component is the master generating unit of the hydropower plant using a 250 kW induction generator (IG). The voltage of the system is controlled by the controller of the AC/DC power converter. The electrical frequency of the system is controlled by the speed controller of the hydropower turbine. A wind turbine, an energy storage system and a regulated dump load are connected to the IG through the AC/DC converter. Goal. The paper aims to develop a methodology for steady state performance analysis of the hydraulic turbine driven isolated IG operating in parallel through an AC/DC power converter with additional sources and consumers of active power. Methodology. The methodology for evaluation of performance characteristics of the IG operating in the proposed system has been developed. The methodology is based on the equivalent circuit of the system, equations of active and reactive power balance in the system and the superposition method. Results. The equations of frequency, voltage and power regulators of the system are given. The performance characteristics of the IG operating in the system supplying resistive and RL load in «constant voltage – constant frequency» mode are obtained. Novelty. The developed methodology is innovative in taking into account the control algorithms of the system. The comparative analysis of the IG’s performance operating in the stand-alone generating unit and in the generating unit connected to the proposed system is performed. Practical value. The developed methodology can be used for development and performance improvement of hybrid AC power systems.

Interface Control and Status Monitoring of Electronic Information Equipment based on Nonlinear Data Encryption

An advanced electronic information equipment interface control and status monitoring system is proposed to ensure the fairness, objectivity, and security of information while identifying responsibility for traffic accidents. Through an in-depth analysis of the system's security requirements and the current landscape of information security technology, a robust security strategy is developed for each crucial system stage. A PC-based platform is developed for efficient data acquisition, secure processing, reliable transmission, and fortified storage, focusing on implementing nonlinear data encryption methods. Performance evaluation of the system involved rigorous testing using files ranging from 3MB to 10MB. The results of the proposed system revealed a significant improvement in the system's overall speed and efficiency, showcasing an average performance enhancement of one quarter compared to the original platform. The proposed system demonstrated an impressive 15% to 30% increase in processing speed, establishing its capability to ensure data integrity protection during information transmission, facilitate accurate identification of data recording equipment post-accident, and safeguard the security of stored data. The developed electronic information equipment interface control and status monitoring system effectively addresses critical challenges associated with ensuring data integrity and security in traffic accident investigations.

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Towards sustainable energy Carriers: A solar and Wind-Based systems for green liquid hydrogen and ammonia production

Power-to-fuel energy systems have an essential need for decarbonizing energy carriers. This study proposes a solar and wind energy-based system for producing liquid hydrogen and ammonia as energy carriers. The proposed system caters to urban requirements encompassing electricity, cooling, heating, and freshwater. Three different scenarios, only liquid hydrogen, only ammonia, and dual production, are considered and compared. An artificial neural network is employed for prediction and computational time reduction, coupled with a genetic algorithm for system optimization. For a case in which 40 % of the net power is used in the electrolyzer, and the system simultaneously produces liquid hydrogen and ammonia, the overall energy and exergy performance of the system are 56.78 % and 44.69 %, respectively. In this case, the system has the capability of producing 13.2 MW of net power, 0.057 kg/s of liquid hydrogen, 0.162 kg/s of cold ammonia, 3.63 kg/s of freshwater, and 3.4 MW cooling load. The exergy analysis of the system reveals that the Rankine cycle and the electrolyzer have the highest exergy destruction, at 27 % and 21 %, respectively. Using the Technique for Order Preference by Similarity to Ideal Solution method, the optimal liquid hydrogen rate, ammonia production rate, overall exergy efficiency, and net power are determined as 0.026 kg/s, 0.151 kg/s, 51.38 %, and 16.52 MW respectively are recognized as optimal values for dual production scenario.

Vehicle Theft Detection and Locking System using GSM and GPS

A vehicle tracking system is very useful for tracking the movement of a vehicle from any location at any time. An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of popular technology that combines a smartphone with an Arduino UNO. This easy to make and inexpensive compared to others. The designed in vehicle device works using Global Positioning System (GPS) and Global System for Mobile Communication(GSM) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle those positions is to be determined and tracked in real time. A vehicle tracking system is an electronic device installed in a vehicle to enable the owner or a third party to track the vehicle's location. This paper proposed to design a vehicle tracking system that works using GPS and GSM technology, which would be the cheapest source of vehicle tracking and it would work as anti-theft system. It is an embedded system which is used for tracking and positioning of any vehicle by using Global Positioning System (GPS) and Global system for mobile communication (GSM). An Arduino UNO is used to control the GPS receiver and GSM module. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time interval. The GSM module is used to transmit andupdate the vehicle location to a database. This paper gives minute by minute update about vehicle location by sending SMS through GSM modem. This SMS contain latitude and longitude of the location of vehicle. Arduino UNO gets the coordinates from GPS modem and then it sends this information to user in text SMS. GSM modem is used to send this information via SMS sent to the owner of the vehicle. Location is displayed on LCD. And then Google map displays location and name of the place on cell phone. Thus, user able to continuously monitor a moving vehicle on demand using smartphone and determine the estimated distance and time for the vehicle to arrive at a given destination

Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset

In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.

Real-Time Jaundice Detection in Neonates Based on Machine Learning Models

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.

STRATEGIES AND PUBLIC PARTICIPATION IN SOLID WASTE MANAGEMENT IN PORT LOKO CITY

To create a framework for long-term, sustainable solid waste management, this paper examines the Port Loko municipalitys solid waste management plan and public involvement. It sets out: to examine the waste management tactics employed by the Port Loko City Council; to evaluate the methods used for solid waste management transportation; to investigate whether dustbins are available for collecting solid waste throughout the city; to determine whether land fill is available; to evaluate the degree of public involvement in the procedures related to solid waste management; to create a framework for long-term, sustainable solid waste management. The primary means of addressing the objectives was by means of semi-structured interviews and discussions with diverse stakeholders and members of the municipalitys community. The current solid waste management system was examined in the study, along with input from the general public, to determine its advantages and disadvantages. It was found that Port Loko Citys current solid waste management system is not sustainable. The separation of waste is not provided for. Waste is not being collected or transported in an appropriate or sufficient manner. Officially, recycling and composting of waste are not permitted. People are not involved in the decision-making process regarding solid waste because the majority of the waste is disposed of in open landfills. Additionally, the study examined the recently suggested solid waste management system. It was discovered that the recently suggested system, which includes a framework for a two-stream waste segregation system, is superior to the current system. The current waste collection and transportation system is not as good as the proposed one. A key component of the plan is the composting of biodegradable waste. But to create a solid waste management system that is sustainable, important issues were left out of the proposed system

Novel cool roofing technology system with sustainable design for attic temperature reduction

The penetration of increasing solar gain towards the building deteriorates the building's thermal quality and raises cooling demand. In particular, the roof receives the most solar gain. The roof system reformation is urged to improve the roof system's heat capacity and conserve building energy. This paper introduced a novel cool roofing system to replace the traditional roof system. The proposed novel cool roofing applications merged: (i) lightweight foam concrete, (ii) hollow concrete slab, (iii) white cooling paint and (iv) active reflective insulation system powered by solar energy. The discovery of the proposed system was evaluated with experimental chambers exposed to the outdoor environment. Under intense sunlight, the internal roof surface was 30.6 % cooler (50.5 °C to 35.0 °C). The mean attic temperature achieved was 33.9 °C under a field where the daily maximum recorded surrounding temperature is 43.0 °C. In contrast with a bare roof at 41.9 °C, the proposed design demonstrated a 19.2 % lower solar gain, achieving a sub-ambient structure. Besides that, the cooling demand simulation generated similar cooling demand savings (20 %). Significantly, this comprehensive cool roofing system exhibited a considerable and constructive outcome in cooling the buildings by achieving sustainable development to create a better and more environmental future for all.