<p class="Abstrak">Pasien kanker memiliki kebutuhan yang kompleks mulai dari masalah fisik, psikologis, sosial dan spiritual. Keluarga yang merawat pasien kanker disebut<em> family car</em><em>egiver</em>. Seorang <em>family caregiver</em> membantu mengatasi hampir semua permasalahan yang dialami pasien baik saat dirawat di rumah maupun di rumah sakit. Keluarga mengalami suka dan duka dalam merawat pasien. Dalam merawat pasien dengan penyakit kronis, bukan hanya pasien tetapi kesejahteraan dan kualitas hidup <em>family car</em><em>egiver</em> juga penting. Oleh karena itu sangat penting untuk mengetahui bagaimana beban family caregiver dan faktor-faktor yang mempengaruhi beban keluarga dalam merawat pasien.  Beban <em>family car</em><em>egiver</em> dapat diukur menggunakan <em>Caregiver Reaction Assesment</em> (CRA), yang direpresentasikan oleh beberapa faktor. Dengan memahami hubungan kausal antara faktor-faktor beban keluarga, diharapkan dapat membantu untuk mengidentifikasi bagaimana beban <em>caregiver</em> bersumber dan berdampak. Untuk itu, penelitian ini bertujuan untuk mengidentifikasi hubungan kausal antara faktor-faktor yang berhubungan dengan beban family caregiver dalam merawat pasien. Penelitian ini menggunakan algoritma pemodelan kausal bernama <em>Stable Specification Search for </em><em>Cross-sectional Data with </em><em>Latent </em><em>Variable</em> (S3C-<em>Latent</em>) untuk mendapatkan model kausal antara faktor-faktor beban <em>family car</em><em>egiver</em> yang relevan. Dari hasil analisis  pemodelan  didapatkan ada 3 faktor yang memiliki hubungan kausal dan 2 faktor memiliki hubungan asosiasi. Gender memiliki hubungan kausal yang stabil terhadap kesiapan kesehatan dan kesiapan dalam merawat<em>.</em> Sedangkan faktor kesiapan merawat mempengaruhi faktor aktivitas <em>family caregiver</em>, selain itu faktor keuangan memiliki hubungan asosiasi yang kuat dengan faktor aktivitas dan hubungan keluarga. Pemodelan kausal ini dapat digunakan sebagai acuan bagi tenaga kesehatan dalam pelayanan kesehatan yang lebih tepat, efisien, dan efektif di dalam menangani permasalahan beban <em>caregiver</em><em>.</em></p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><span lang="IN">Cancer patients have complex needs ranging from physical, psychological, social</span>,<span lang="IN"> and spiritual problems. Families who</span> take<span lang="IN"> care for cancer patients are called family caregivers. A family caregiver helps </span>to <span lang="IN">overcome almost all problems experienced by </span>the <span lang="IN">patients both while being treated at home and in the hospital. Families experience joy and sorrow in caring for patients. In treating patients with chronic diseases, not only the patient but the family caregiver's well-being and quality of life are also important. Therefore, it is very important to know how the family caregiver's burden is and the factors that affect the family burden in caring for patients. Caregiver family burden can be measured using Caregiver Reaction Assessment (CRA), which is represented by several factors. By understanding the causal relationship between family burden factors, it is hoped that it can help to identify how the caregiver burden is sourced and impacted. Therefore, this study aims to identify the causal relationship</span>s<span lang="IN"> between factors related to the burden on family caregivers in caring for patients. This study uses a causal modeling algorithm called Stable Specification Search for Cross-sectional Data with Latent Variable (S3C-Latent) to obtain a causal model between the relevant caregiver family load factors. The results of modeling analysis showed that there are 3 factors </span>which<span lang="IN"> have a causal relationship and 2 factors have an association relationship. Gender has a stable causal relationship to health readiness and readiness to care</span>, Moreover, t<span lang="IN">he caring readiness factor affects the family caregiver activity factor, </span>and the <span lang="IN">financial factor </span>has <span lang="IN">a strong association with the activity factor and family relationships. This causal modeling can be used as a reference for health workers so as to give health services which are precise, efficient, and effective in dealing with caregiver burden problems.</span></em></p>

To meet changing demands in the business process, multiple services have to be combined. Service Oriented Architecture offers Service Composition mechanisms, using which various services can be combined or composed according to user requirements. The services are combined in static and dynamic manner. The dynamic service composition approaches are moving towards goal driven technique which has gained more attention in recent days. Goal driven approaches reduce the users work by making the system to act dynamically according to the users requirements from the available set of services. The objective of the work mainly focuses on how to provide automated business process to meet the varying needs of the user. To explain the above mentioned approach travel domain is taken into consideration. Hence the outcome of our work in travel domain is to ensure that the goal driven service composition mechanism provides a complete travel trip plan.

Livestock plays very important economic, social and cultural roles in the well being of rural communities across the world. Quality environmental conditions, automation and monitoring are the key necessities of running a good and profitable livestock farm. Air quality, temperature of the surroundings and humidity play a major role while deciding the fan speeds of the exhaust System used in all aspects of livestock farming. Another important part of livestock production is increasing incubation speeds of eggs by performing artificial incubation. It is a requirement to maintain the temperature at a constant value in this system. This paper describes two mutually exclusive Fuzzy Logic algorithm-based systems to automate the exhaust system and an artificial egg incubator. The other important part of a livestock farm is production of milk and milk products. It is required to monitor the health of cows by overseeing their activities at any point of time. This can be done by determining and monitoring the activities performed by the cow. This paper describes a simple Deep Learning Model to classify the activities of a cow broadly as standing, walking or grazing. The Exhaust and the Incubator system are controlled and monitored using Internet of Things (IOT) System using a native web application developed using the Flask framework.

No abstract