KEBIJAKAN PENANGKARAN RUSA TIMOR (Cervus timorensis) OLEH MASYARAKAT (STUDI KASUS DI NUSA TENGGARA BARAT)

Rusa timor (Cervus timorensis) merupakan satwa langka yang keberadannya dilindungi undang-undang. Nusa Tenggara Barat (NTB)merupakan salah satu habitat alam rusa timor. Meskipun begitu, keberadaannya di alam sudah semakin langka akibat maraknya perburuan dan perdagangan liar (illegal hunting & illegal trading ). Untuk mencegah kepunahan rusa timor pemerintah mengeluarkan kebijakan dalam bentuk pemberian ijin penangkaran rusa oleh masyarakat. Kebijakan ini diharapkan dapat mencegah masyarakat melakukan perburuan rusa di alam. Selain itu, masyarakat dapat merasakan manfaatnya secara ekonomi dalam bentuk pemanfaatan satwa rusa, baik untuk dikonsumsi dagingnya maupun sebagai satwa peliharaan. Dengan kebijakan tersebut diharapkan rusa di habitat alaminya akan tetap terjaga bahkan terus bertambah, sementara masyarakat mendapatkan manfaat dalam bentuk peningkatan kesejahteraan ekonomi. Metodologi kajian ini dilakukan dengan mereview kebijakan terkait penangkaran rusa untuk kemudian dikomparasikan dengan implementasi di lapangan. Kajian ini menunjukkan bahwa produk perundangan yang mengatur penangkaran rusa lebih dominan berasal dari pemerintah pusat. Regulasi yang beroperasi pada tingkat tapak lebih bersifat standar teknis yang dikeluarkan BKSDA NTB. Implementasi peraturan penangkaran rusa masih banyak yang belum berjalan. Penyebabnya adalah kelemahan dari sisi fasilitasi dan kontrol oleh BKSDA NTB, dan di sisi lain kurangnya pemahaman masyarakat terhadap prosedur penangkaran. Kurangnya pemahaman masyarakat sendiri disebabkan minimnya sosialisasi dari pihak berwenang (BKSDA NTB). Kedepannya kebijakan penangkaran rusa oleh masyarakat masih sangat potensial untuk dikembangkan karena minat masyarakat sendiri cukup tinggi. Penguatan kelembagaan sangat perlu dilakukan untuk mendukung kebijakan pengembangan penangkaran rusa oleh masyarakat. Kata kunci : rusa timor, kebijakan, penangkaran, masyaraka

Corporate social responsibility: Business responses to coronavirus (COVID-19) pandemic

The global health, economic, and social impacts of the coronavirus (COVID-19) pandemic are growing day by day. Over the past few months, first China, and now the whole world has been grappling with the effects of the COVID-19 pandemic in businesses, employees, customers, communities, and each other. The people worldwide are strongly committed to working together and supporting each other in every way possible during this critical period filled with fear and uncertainty. Grounded on stakeholder theory and corporate social responsibility (CSR) literature, the study attempts to explore business responses to the COVID-19 pandemic to support its vital stakeholders such as employees, customers, communities, and society as a whole through CSR initiatives. The study based on the contemporary phenomenon considered multi-items as data sources such as press releases, newsletters, and letters to shareholders, which were retrieved from the top 25 (the sample) corporations of the 100 Best Corporate Citizens-2019 (the population) in the United States’ respective websites on the internet. The outcomes of this research report that sampled companies show respect to their employees and focus on stewardship relations between corporations and customers and communities during the COVID-19 pandemic. It will have a significant theoretical application and practical implication on business duty to society and future research on CSR as a strong arm to deal with a critical disaster like the COVID-19 pandemic

Effects of gamma radiation on nutritional and microbial quality of Pampus chinensis (Euphrasen 1788)

To evaluate the efficiency of gamma radiation in combination with low temperature Chinese pomfret, Pampus chinensis were preserved by the treatment of different doses of gamma radiation (3, 5 and 8 KGy) at freezing temperature (-20°C) during 90 days of storage period. Quality assessments for fish were carried out at an interval of 15 days during the storage period. Quality assessments were done by organoleptic, chemical (Total Volatile Nitrogen, TVN and Trimethylamine, TMA) and microbiological (Total Bacterial Count, TBC and Total Mould Count, TMC) evaluation. From the analysis of all parameters, maximum shelf-life was observed for irradiated (8 KGy) sample. It remained acceptable up to 75 days and that was the highest duration among 4 types of samples

Effects of irradiation on formaldehyde concentration and nutritional changes of formalin treated fish, Pampus chinensis

Formaldehyde is a very reactive compound capable of interacting with many functional groups of proteins including intermolecular and intramolecular cross-links of the molecules. The formation of cross-linking bonds may induce conformational change in proteins that favor further interaction of functional and hydrophobic groups. Formaldehyde which has been using illegally as a chemical preservative by some fish traders in our country. A study was carried out to determine the effects of irradiation (1.5 KGy) on formaldehyde concentration and nutritional (protein and lipid) changes of formalin (37% formaldehyde) treated fish (fresh) samples and found that the concentration of formaldehyde both in treated samples (0.37% formalin and 0.37% formalin with 1.5 KGy irradiation) were 37.0 µg/gm and 36.75 µg/gm. On the other hand, the amount of protein and lipid in treated samples before radiation (14.56% and 3.49%) and after radiation (14.15% and 3.25%). That means, radiation has no effect on the change of protein, lipid and formaldehyde

Clinical evaluation of chemo-sterilization through histomorphology and hormonal changes in bucks

The study was aimed to evaluate methods for nonsurgical castration of Black Bengal bucks by intra-testicular injection of calcium chloride, sodium chloride, citric acid solutions or sterile deionized water. Twelve healthy bucks were randomly allotted to groups A, B, C, and D, consisting of three bucks each. The local anesthetic, 2% lidocaine hydrochloride, was infused into the spermatic cord of each buck, followed by bilateral intra-testicular injections of 30% calcium chloride (CaCl2), 25% sodium chloride (NaCl), 50% citric acid (C6H8O7) solutions, and sterile deionized water dosed at 2 ml per testis in groups A, B, C and D respectively. To evaluate the efficacy of chemical agents on the inactivation of testes, clinical parameters, changes in scrotal circumference, testicular fine needle aspiration (TFNA), histopathology and serum concentration of testosterone and LH were monitored. A significant decrease in the scrotal circumference was observed between the intra-testicular injection and day 14 in all the bucks. Absence of spermatogenic cells and spermatozoa in the testicular biopsy was observed on day 14 post injection in the bucks, except for one in group C. Histopathology revealed massive destruction of seminiferous tubules and disorganization of the testicular parenchyma. Serum testosterone concentration declined significantly on day 14 compared with day 0. Consequently, the gradual elevation in serum LH concentration was significant. Thus, intra-testicular injections of CaCl2 and NaCl were more effective than C6H8O7 in inducing chemical-based nonsurgical castration

Detection and analysis of wheat spikes using Convolutional Neural Networks

Background Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production. Thus, being able to detect and characterize spikes from images of wheat fields is an essential component in a wheat breeding pipeline for the selection of high yielding varieties. Results We have applied a deep learning approach to accurately detect, count and analyze wheat spikes for yield estimation. We have tested the approach on a set of images of wheat field trial comprising 10 varieties subjected to three fertilizer treatments. The images have been captured over one season, using high definition RGB cameras mounted on a land-based imaging platform, and viewing the wheat plots from an oblique angle. A subset of in-field images has been accurately labeled by manually annotating all the spike regions. This annotated dataset, called SPIKE, is then used to train four region-based Convolutional Neural Networks (R-CNN) which take, as input, images of wheat plots, and accurately detect and count spike regions in each plot. The CNNs also output the spike density and a classification probability for each plot. Using the same R-CNN architecture, four different models were generated based on four different datasets of training and testing images captured at various growth stages. Despite the challenging field imaging conditions, e.g., variable illumination conditions, high spike occlusion, and complex background, the four R-CNN models achieve an average detection accuracy ranging from 88 to 94% across different sets of test images. The most robust R-CNN model, which achieved the highest accuracy, is then selected to study the variation in spike production over 10 wheat varieties and three treatments. The SPIKE dataset and the trained CNN are the main contributions of this paper. Conclusion With the availability of good training datasets such us the SPIKE dataset proposed in this article, deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images. The proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios. It accurately quantifies the differences in yield produced by the 10 varieties we have studied, and their respective responses to fertilizer treatment. We have also observed that the other R-CNN models exhibit more specialized performances. The data set and the R-CNN model, which we make publicly available, have the potential to greatly benefit plant breeders by facilitating the high throughput selection of high yielding varieties

Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions.

In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to convert one-dimensional (1D) acoustic emission (AE) signals from multiple sensors into a two-dimensional (2D) image to capture information under variable operating conditions. In this process, a short-time Fourier transform (STFT) is first applied to the AE signal of each sensor, and the STFT results from the different sensors are then fused to obtain a condition-invariant 2D image of cracks; this scheme is denoted as Multi-Sensors Fusion-based Time-Frequency Imaging (MSFTFI). The MSFTFI images are subsequently fed to the fine-tuned transfer learning (FTL) model built on a convolutional neural network (CNN) framework for diagnosing crack types. The proposed diagnostic scheme (MSFTFI + FTL) is tested with a standard AE dataset collected from a self-designed spherical tank to validate the performance under variable pressure conditions. The results suggest that the proposed strategy significantly outperformed classical methods with average performance improvements of 2.36–20.26%

Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions.

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing's rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy

Quantifying TRM by Modified DCQ Load Flow Method

In the integrated power system network uncertainty can occur at any time. The transmission reliability (TRM) margin is the amount of transmission capacity that guarantees that the transmission network is protected from instability in the operating state of the system. The calculation of the available transfer capacity (ATC) of the transmission reliability margin should be included in a deregulated power system to ensure that the transmission network is safe within a fair range of uncertainties that arise during the power transfer. However, the TRM is conserved as a reliability margin to reflect the unpredictability of the operation of the electric system. Besides, the system operator (SO) utilizes the TRM value during unreliability by adjusting the ATC value some amount up or down to account for errors in data and uncertainty in the model. This paper describes a technique for TRM estimation by modified DCQ load flow method considering VAR transfer distribution factor. The main focus of this study is to get a new approach to determine TRM by incorporating with ATCQ considered reactive power and sensitivity w.r.t ATC considered voltage magnitude. This technique is applied to the IEEE 6 bus system, and results are compared with previous results for validation. The technique leads to more exact and secure estimates of transmission reliability margin