Poligami dalam Hukum Islam dan Hukum Positif Indonesia Serta Urgensi Pemberian Izin Poligam di Pengadilan Agama

Penulisan artikel ini bertujuan untuk mengetahui dasar hukum berpoligami dalam hukum islam maupun hukum positif di Indonesia serta mengetahui bagaimana urgensi pemberian izin berpoligami di Pengadilan Agama. Dalam tulisan ini menggunakan pendekatan yuridis normatif dengan berbagai teori interpretasi. Pengadilan Agama merupakan lembaga peradilan dibawah Mahkamah Agung yang sangat penting dalam menangani permasalahan mengenai sengketa yang berhubungan dengan agama Islam. Mulai dari perkawinan, kewarisan, wasiat, hibah, wakaf, zakat, infak, sedekah, sampai ekonomi syariah menjadi tugas dan wewenang dari Pengadilan Agama yang sesuai dengan Pasal 49 dan 50 UU No.7 Tahun 1989 tentang Pengadilan Agama yang telah diamandemen dengan UU No.3 Tahun 2006. Dalam Pasal 4 ayat (1) UU No. 1 Tahun 1974 tentang Perkawinan, apabila seorang suami ingin beristri lebih dari seorang maka wajib mengajukan permohonan kepada Pengadilan di daerah tempat tinggalnya (yaitu Pengadilan Agama). Diatur pula dalam pasal-pasal berikutnya dalam pengajuan poligami harus memenuhi syarat-syarat yang sudah ditentukan menurut UU Perkawinan. Pengaturan tentang poligami di hukum positif seakan mempersulit suami untuk poligami, sedangkan hukum islam sendiri tidak terlalu mempersulit seorang suami untuk poligami. Oleh karena itu kedua hukum ini harus saling sinkron agar tidak menimbulkan suatu permasalahan dalam perkawinan khususnya poligami

The Influence of Electric Fields on the Ordering of Lipid Monolayers and Lipid-Protein Binding

This research is devoted to studies of the influence of an externally controlled electric potential difference on Gibbs monolayers of 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine (SOPC), as well as its influence on the binding of the C2 domain of cytosolic phospholipase-A2 (cPLA2α-C2) protein to the SOPC monolayer. X-ray reflectivity, molecular dynamics (MD) simulations and electrochemical methods are used to gather microscopic and macroscopic information about the ordering, orientation, and binding configuration of the SOPC. SOPC monolayer assembles at the interface between an aqueous electrolyte solution and an organic electrolyte solution with 1,2-dichloroethane (DCE) as the solvent. The area per lipid of SOPC for different potential differences between the interface is determined from interfacial tension measurements. Cyclic voltammetry confirms the result and provides evidence that the interfacial behavior of SOPC monolayers is reversible. X-ray reflectivity measurements are analyzed to determine the interfacial electron density profile, including the thicknesses of the phosphocholine (PC) head group region and the SOPC hydrocarbon chain regions of the monolayer. MD simulation at fixed area per lipid shows small differences in the orientation of SOPC molecules under electric field. This indicates that the primary effect in the experiments is due to the changing area per lipid as a function of electric potential difference, and the subsequent re-orientation of the lipid to accommodate the change in interfacial density. X-ray reflectivity analysis on cPLA2α-C2 domains bound to the SOPC monolayer on the interface between water and DCE provides information on the angular orientation and penetration depth of the domains. The best-fit orientations and penetration of X-ray reflectivity curves for different potential difference are provided, and the result suggests an increase in the electron density of tailgroup layer due to both protein and DCE penetration into this region. Under very high potential difference (0.38 V), a significant change in reflectivity curve is observed, which could be caused by a decrease of the density of cPLA2α-C2 and lipid on the interface

Nondirected Copper-Catalyzed Sulfoxidations of Benzylic C–H Bonds

A copper-catalyzed sulfoxidation of benzylic C–H bonds by nondirected oxidative C­(sp³)-H activation was developed. The process proceeds via sulfenate anions, which are generated by base-triggered elimination of β-sulfinyl esters and benzyl radicals. The functional group tolerance is high, and the product yields are good

Comparative Analysis of Risky Behaviors of Electric Bicycles at Signalized Intersections

<div><p><b>Objective:</b> The primary objective of this study was to compare the risky behaviors of e-bike, e-scooter, and bicycle riders as they were crossing signalized intersections.</p><p><b>Methods:</b> Pearson's chi-square test was used to identify whether there were significant differences in the risky behaviors among e-bike, e-scooter, and bicycle riders. Binary logit models were developed to evaluate how various variables affected the behaviors of 2-wheeled vehicle riders at signalized intersections. Field data collection was conducted at 13 signalized intersections in 2 cities (Nanjing and Kunming) in China.</p><p><b>Results:</b> Three different types of risky behaviors were identified, including stop beyond the stop line, riding in motorized lanes, and riding against traffic. Two-wheeled vehicle riders’ gender and age and traffic conditions were significantly associated with the behaviors of 2-wheeled vehicle riders at the selected signalized intersections.</p><p><b>Conclusions:</b> Compared to e-bike and bicycle riders, e-scooter riders are more likely to take risky behaviors. More specifically, they are more likely to ride in motorized lanes and ride against traffic.</p></div

Synthesis of Dendronized Polymers by a “<i>n</i> + 2” Approach

A series of dendronized polymers (DP) were synthesized by attaching second-generation dendron onto the preformed DP core of generation 1–3 (<i>n</i> + 2 approach). The obtained DP were compared to the reference DP prepared by the conventional graft-from (<i>n</i> + 1) approach using GPC, UV–vis, ¹H NMR, and AFM analyses. The former two analyses showed that the newly prepared DP tend to have a more significant decrease in dendron integrity as the generation increases; however, the striking similarities from the latter two analyses suggest that the properties of the new samples do not much deviate from the reference DP by the graft-from approach. The advantages of the “<i>n</i> + 2” approach over the “<i>n</i> + 1” approach include shorter reaction time, higher yield, and easier purification. On top of these, it has the power of surpassing some selected postpolymerization steps, which are required for the conventional graft-from approach and may be detrimental in some occasions

Image_1_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.jpeg

Background/purposeSevere lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.MethodsThis retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir 9 cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology.ResultsA total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable analysis, G4 ALC nadir was associated with poor overall survival (OS) [hazard ratio (HR), 3.91; 95% confidence interval (CI), 1.34–11.38, p = 0.01]. Seven significant factors [Eastern Cooperative Oncology Group (ECOG) performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy (GTV_N volume), body volume, and maximum dose of planning target volume receiving at least 55 Gy (PTV_5500 Dmax)] were obtained by elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model’s predicting ability in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed.ConclusionsThis study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment approaches.</p

Image_2_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.jpeg

Background/purposeSevere lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.MethodsThis retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir 9 cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology.ResultsA total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable analysis, G4 ALC nadir was associated with poor overall survival (OS) [hazard ratio (HR), 3.91; 95% confidence interval (CI), 1.34–11.38, p = 0.01]. Seven significant factors [Eastern Cooperative Oncology Group (ECOG) performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy (GTV_N volume), body volume, and maximum dose of planning target volume receiving at least 55 Gy (PTV_5500 Dmax)] were obtained by elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model’s predicting ability in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed.ConclusionsThis study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment approaches.</p

Table_1_A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer.docx

Background/purposeSevere lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.MethodsThis retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir 9 cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology.ResultsA total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable analysis, G4 ALC nadir was associated with poor overall survival (OS) [hazard ratio (HR), 3.91; 95% confidence interval (CI), 1.34–11.38, p = 0.01]. Seven significant factors [Eastern Cooperative Oncology Group (ECOG) performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy (GTV_N volume), body volume, and maximum dose of planning target volume receiving at least 55 Gy (PTV_5500 Dmax)] were obtained by elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model’s predicting ability in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed.ConclusionsThis study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment approaches.</p

Selective Allylic Oxidation of Cyclohexene Catalyzed by Nitrogen-Doped Carbon Nanotubes

Carbon nanotubes (CNTs) and nitrogen-doped CNTs (NCNTs) were systematically investigated as metal-free catalysts in the selective allylic oxidation of cyclohexene using molecular oxygen as oxidant in the liquid phase. High cyclohexene conversion (up to 59.0%) and 620.1 mmol g^–1 h^–1 mass-normalized activity were obtained for NCNTs, competing with the state-of-the-art metal catalysts. The positive effect of nitrogen dopant on the performance of CNTs was demonstrated, with respect to the aspects of enhancing activity and increasing selectivity of 2-cyclohexen-1-one, allowing for a ketone/alcohol ratio of 3.7 at 59% conversion. The unique catalytic role of NCNTs was attributed to their capability to promote the radical chain propagation via stabilizing peroxyl and cycloxyl radicals, which boosted the further conversion of 2-cyclohexen-1-ol toward 2-cyclohexen-1-one as well

Synergistic Engineering of Defects and Architecture in a Co@Co₃O₄@N-CNT Nanocage toward Li-Ion Batteries and HER

The design and synthesis of hollow and porous nanostructured electrode materials is an effective strategy to improve the electrochemical performance of lithium-ion batteries and the hydrogen evolution reaction (HER). Herein, we synthesize hollow and porous Co@Co3O4 nanoparticles embedded in N-doped CNTs (N-CNTs) with rich surface defects through a two-step calcination strategy. The formation mechanism is explored. The influence of oxygen vacancies regulated by the nanoscale Kirkendall effect on the electrochemical performance of the electrode is elucidated. The Co@Co3O4@N-CNTs exhibit remarkable activity for catalyzing the HER with a low onset overpotential of 296 mV (a low Tafel slope of 116.2 mV dec–1), much better than Co3O4@N-CNTs (315 mV for overpotential and 124.2 mV dec–1 for Tafel slope). Significantly, the Co@Co3O4@N-CNTs deliver a high discharge capacity of 990 mA h g–1 after 600 cycles at 0.1 A g–1. Our nanostructure strategy can provide new insights into the strategy for high-rate and highly stable energy storage systems