Keberkesanan kaedah pembelajaran berasaskan masalah dalam meningkatkan kemahiran berfikir aras tinggi dan menyelesaikan masalah dalam kalangan pelajar

Aspirasi negara yang diilhamkan dalam Wawasan 2020 ialah mencapai taraf negara maju. Hal ini telah meletakkan kepentingan yang sangat tinggi terhadap pendidikan sebagai pemacu untuk mencapai matlamat menjadi sebuah negara maju yang mampu mendepani cabaran dan permintaan ekonomi yang dipacu oleh sains dan teknologi, seperti yang telah digariskan dalam Pelan Pembangunan Pendidikan Malaysia. Pembelajaran Berasaskan Masalah (PBM) adalah satu pendekatan pengajaran berasaskan masalah sebenar, yang melibatkan penggunaan pelbagai kemahiran untuk menyelesaikannya. Kajian kuasi-eksperimental ini bertujuan mengkaji keberkesanan kaedah PBM dalam meningkatkan Kemahiran Berfikir Aras Tinggi (KBAT) dan kemahiran menyelesaikan masalah pelajar. Kajian ini mengambil masa selama lapan minggu. Instrumen yang digunakan dalam kajian ini ialah soalan ujian pra-pasca, senarai semak dan soal selidik. Kajian ini melibatkan 71 orang responden dari Sekolah Menengah Kebangsaan Tun Ismail yang terbahagi kepada dua kumpulan, iaitu kumpulan rawatan dan kumpulan kawalan. Kesemua responden ini ialah kumpulan pelajar yang mengambil mata pelajaran sains tingkatan empat. Data yang diperoleh dianalisis secara deskriptif dan inferensi menggunakan perisian Statistical packages for Social Science Version 21.0 (SPSS). Hasil dapatan kajian ini membuktikan kaedah PBM berjaya meningkatkan Kemahiran Berfikir Aras Tinggi (KBAT) dan kemahiran menyelesaikan masalah pelajar. Pelajar juga menunjukkan persepsi positif terhadap kaedah PBM ini. Dapatan kajian ini menggambarkan teknik pengajaran dan pembelajaran yang berbeza, menarik serta berkesan dari kaedah tradisional mampu meningkatkan kemahiran pelajar

Reduction of bacteria in storage system of sewage effluents

The present work is aimed to investigate the linear regression model of total coliform (TC), faecal coliform (FC) and enterococci (ENT) responses in the storage system of sewage effluents at different temperatures (room temperature 25 ± 2 °C, 55 and 65 °C). Five litres (v/v) of sewage effluent samples was stored at room temperature (25 ± 2 °C) for 1, 2, 3 and 4 weeks. In order to investigate the response of bacteria to the storage system at thermal conditions, the sewage samples were stored at the temperatures of 55 and 65 °C in a water bath shaker for 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110 and 120 min, respectively. The results indicated that the storage system at room temperature significantly (p < 0.01) effected the reduction of TC (33%), FC (36.6%) and ENT (47.8%). Moreover, sewage effluent met WHO guidelines after two weeks of storage period at room temperature. The storage system at 55 and 65 °C had more significant influence (p < 0.01) on TC, FC and ENT. The reductions for TC, FC and ENT were 49.6 versus 64, 47.7 versus 83.2% and 47.4 versus 57.3%, respectively. ENT (Gram-positive bacteria) exhibited more resistance to the storage system at 65 °C than TC and FC (both are Gram-negative bacteria). This might be due to the differences in the cell wall structure. It can be concluded that the storage system of sewage effluents has a significant potential for the reduction of indicator bacteria

Artificial intelligence a new paradigm for distributed sensor networks on the internet of things: a review

The confluence ofArtificial Intelligence(AI)with the Internet of Things (IoT) has created new opportunities for distributed sensor networks in a variety of sectors. The potential of AI as a new paradigm for distributed sensornetworks in the IoT is investigated in this review article, with an emphasis on the convergence of architecture, methodologies, platforms, sensors, devices, energy approaches, communication and networking, and applications. An analysis was conducted to examine the existing research in this field using a comprehensive literature review, revealing notable advancements,and indicating the need for further investigation. Furthermore, Moreover, suggestions and ideas are presented regarding potential innovation within the sector, with a particular emphasis on the imperative for further research and development. Our findings highlight AIoT's transformational ability in allowing more efficient and intelligent sensor networks, with implications for smart homes, healthcare, environmental monitoring, and industrial automation. The potential for harnessing the power of AI holds the key to unlocking novel opportunities and addressing the challenges inherent in dispersed sensor networks within the realm of the Internet ofThings (IoT). This research advances our understanding of AIoT convergence and lays the groundwork for future breakthroughs in this interesting topic