With cities sprawling and populations booming in developing regions, ensuring social sustainability in urban areas has become more urgent. This study investigates the challenges of implementing social sustainability initiatives for cities in developing countries, focusing on Dhaka as a representative city. A mixed-method approach involving structured questionnaire surveys and key informant interviews was used to collect quantitative and qualitative data. The study identified eighteen challenges to implementing social sustainability initiatives in Dhaka city, including a lack of poor urban governance, an inefficient city management system, a lack of political stability, a long delay in the planning approval process, etc. Additionally, experts identified additional challenges that require attention. The study offers empirical evidence to assist government officials, policymakers, and urban planners overcome these challenges and implement social sustainability effectively. To address the identified challenges, the study recommends revisiting the Structure Plan, Urban Area Plan, Detailed Area Plan, and Urban Sector Policy-2011. It highlights the crucial role of community social workers in mitigating specific issues in socially sustainable urban development. Eventually, the study will contribute to the implementation of SDG-11 in the 2030 Agenda for Sustainable Development by bridging the gap between urbanization and socially sustainable cities.
Kajian rintis telah dilakukan di Kuala Lumpur dalam kalangan 30 orang kanak-kanak berumur 10-18 tahun bagi mengumpulkan pandangan mereka mengenai strategi pemasaran kempen anti-merokok. Hasil kajian menunjukkan kempen tersebut kurang berkesan mencegah kanak-kanak daripada merokok. Faktor-faktor yang menyebabkan kempen ini kurang berjaya mengurangkan tabiat merokok dalam kalangan kanak-kanak adalah kanak-kanak tidak merasa takut dengan poster dan gambar penyakit kritikal yang dipaparkan (60%), mereka dengan mudah boleh mendapatkan rokok (66%), serta kekurangan kerjasama semua sektor masyarakat dalam menyokong kempen ini (77%). Strategi pemasaran kempen anti-merokok dari aspek produk, harga, aksesibiliti, promosi, dasar sosial dan perkongsian komuniti perlu diperbaiki dan diberi penekanan seimbang untuk menjamin keberkesanan kempen secara komprehensif.
Kualiti merupakan elemen terpenting dalam proses pengeluaran atau perkhidmatan yang dihasilkan oleh sesebuah organisasi kepada pelanggan. Kualiti perkhidmatan merujuk kepada ukuran bagaimana sesuatu perkhidmatan yang disampaikan sepadan dengan jangkaan pengguna. Kajian ini bertujuan untuk mengenal pasti kualiti perkhidmatan kesihatan awam yang dapat diakses oleh buruh asing. Pengukuran kualiti perkhidmatan kesihatan dalam kajian ini dapat dibahagikan kepada tiga aspek iaitu keadaan perkhidmatan kesihatan, tempoh masa menunggu dan layanan kakitangan hospital terhadap buruh asing. Dapatan kajian menunjukkan sebilangan besar buruh asing berpuas hati dengan kualiti perkhidmatan kesihatan yang diterima oleh mereka.
Cognitive Behavioral Therapy (CBT) is a critical rehabilitation component for teens who involved in sexual offences. CBT restructures their reasoning capacity to control their anti-social behaviour. An in-depth qualitative study was conducted in one of the state’s institutions for girls to investigate the practice of CBT. Ten pregnant out of wedlock teens who were participate in the therapeutic rehabilitation programme were interviewed thoroughly about the practice of CBT. The study found that the practice of CBT in the programme focused only on the religious activities, tend to focus on the vocational programme, the absence of knowledge enhancement programme, no therapy expert to conduct the CBT procedure, and the absence of any set of protocol treatment for therapy. The findings then encourages the study to recommend few interventions that can enhance the implementation of the CBT practice for teens who involved with sex offence.
The return of ex-prisoners who were released from prison into an environment filled with fellow
friends could lead to negative influences such as drug addiction and crime repetition among former
prisoners. This paper has been derived from a doctorate study studying the repeatition of crimes that
occurred among former prisoners in Malaysia. The findings of the study have found that former
prisoners often return to their fellow members due to family absence. This study has been used
qualitative methods by interviewing 16 ex-prisoners identified through the technique of snowball
sampling. The finding revealed that all these former prisoners from different state were concentrated
around the Chow Kit road. Addiction, as a result of invitation process by friends. This situation are
make the study to proven relationship between the influence of friends and drug abused among the
former prisoners.
It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.
Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.
This paper proposes an approach to accurately estimate the impedance value of a high impedance fault (HIF) and the distance from its fault location for a distribution system. Based on the three-phase voltage and current waveforms which are monitored through a single measurement in the network, several features are extracted using discrete wavelet transform (DWT). The extracted features are then fed into the optimized artificial neural network (ANN) to estimate the HIF impedance and its distance. The particle swarm optimization (PSO) technique is employed to optimize the parameters of the ANN to enhance the performance of fault impedance and distance estimations. Based on the simulation results, the proposed method records encouraging results compared to other methods of similar complexity for both HIF impedance values and estimated distances.
In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.