KEPEMIMPINAN HUKUM TUA DALAM PENYELENGGARAAN PEMERINTAHAN DIMASA PANDEMI CORONA VIRUS DISEASE 19 (Suatu Studi di Desa Tokin Kecamatan Motoling Timur)

Kepemimpinan seorang kepala desa pada sebuah desa merupakan sosok sentral dalam penyelenggaraan pemerintahan desa, dimana kepala desa merupakan pemimpin yang dipilih secara demokratis layaknya kepala daerah dan presiden, oleh sebab itu, maju mundurnya sebuah desa terdapat faktor kepemimpinan. Di Indonesia dampak pandemic covid 19 bukan hanya dirasakan pada tingkat kota besar namun dampaknya hingga ke desa-desa. Dampak yang paling parah dirasakan kebanyakan masyarakat, termasuk masyarakat desa, dampak penurunan ekonomi, diakibatkan pembatasan sosial, aktivitas manusia dari satu daerah ke daerah yang lain demi mencegah penyebaran virus. Selain dampak ekonomi, dampak lain yang sangat dirasakan adalah dampak sosial, dimana dengan adanya pandemi tersebut masyarakat diwajibkan menjaga jarak dan kegiatan-kegiatan sosial lainnya seperti ibadah, kegiatan budaya dan perkumpulan dilarang, bahkan menjadi polemik antara masyarakat desa dimana terjadi penutupan jalan-jalan dan lorong yang tidak sedikit menimbulkan konflik sosial berskala kecil. Penelitian ini menggunakan metode Purpusive Sampling, maka informan yang akan dipilih adalah orang-orang yang diharapkan memiliki pengetahuan, pengalaman, dan kompetensi terhadap masalah-masalah yang akan diteliti. Hasil penelitian menunjukkan bahwa kepemimpinan Kepala Desa terpengaruh akibat adanya pandemic covid 19, pengaruh tersebut dirasakan lewat perubahan budaya kerja, kebijakan serta focus kepada penanganan dampak covid 19. Dengan adanya pandemic covid 19, dimensi kemampuan kepala desa Tokin dalan mengarahkan disimpulkan berjalan dengan baik dan mampu menjalankan perannya dengan baik terlihat dari berjalannya kebijakan pemerintah pusat dan daerah di Desa Tokin.Kata Kunci : Kepemimpinan, Hukum Tua, Pemerintahan, Pandemi covid19

Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography–mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC–MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver–operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC

Study of e+e−→ppˉe^+e^- \rightarrow p\bar{p} in the vicinity of ψ(3770)\psi(3770)

Using 2917 $\rm{pb}^{-1}$ of data accumulated at 3.773~$\rm{GeV}$, 44.5~$\rm{pb}^{-1}$ of data accumulated at 3.65~$\rm{GeV}$ and data accumulated during a $\psi(3770)$ line-shape scan with the BESIII detector, the reaction $e^+e^-\rightarrow p\bar{p}$ is studied considering a possible interference between resonant and continuum amplitudes. The cross section of $e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}$, $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p})$, is found to have two solutions, determined to be ($0.059\pm0.032\pm0.012$) pb with the phase angle $\phi = (255.8\pm37.9\pm4.8)^\circ$ ($<$0.11 pb at the 90% confidence level), or $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}) = (2.57\pm0.12\pm0.12$) pb with $\phi = (266.9\pm6.1\pm0.9)^\circ$ both of which agree with a destructive interference. Using the obtained cross section of $\psi(3770)\rightarrow p\bar{p}$, the cross section of $p\bar{p}\rightarrow \psi(3770)$, which is useful information for the future PANDA experiment, is estimated to be either ($9.8\pm5.7$) nb ($<17.2$ nb at 90% C.L.) or $(425.6\pm42.9)$ nb

The Nonlinear Characteristic of Vibration Signal in Milling flutter process

Abstract. In order to analysis the nonlinear characteristics of vibration signal in the milling chatter process, this article uses Lyapunov Exponent and Approximate Entropy to analysis vibration signal of stable milling, vibration signal of chatter development and vibration signal of chatter in varied cutting-depth milling process. Plot the relationship curves of milling parameters with Lyapunov Exponent and Approximate Entropy respectively. The results showed that vibration signal had strong chaos characteristics when the milling chatter developed and occurred, and chaos characteristics of the vibration signal can be regarded as an effective method to identify the development and occurrence of chatter, and it also has guiding function for choosing milling parameter. Intorduction Chatter is a kind of strong self-excited vibration which occurs in the milling process and it is the main factor of restricting cutting efficiency, reducing machining accuracy of workpiece, increasing the fatigue of cutting tools. In milling process, the detected vibration signal may arise multivalued property of response and mutagenicity of state and it has bifurcation and nonlinear characteristics of chaos[1]. If underlying chatter is found by analyzing the law of nonlinear characteristics of vibratio