bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of health risks from heavy metals in the creeks feeding Mogan Lake, Türkiye
Authors: Pulatsü Serap , Latifi Dijar
Published: September 2023

The non-carcinogenic and carcinogenic health risks arising from potential exposure to heavy metals pose a negative threat to human health. In this study, heavy metals (Hg, As, Cd, Cr, Pb, Ni, Cu, Zn) analyses were conducted in the waters of four creeks in the Mogan Lake Basin (Sukesen Creek, Başpınar Creek, Yavrucak Creek, Gölcük Creek) under anthropogenic pressure. In the water samples taken from the creeks that also contribute to Mogan Lake, which has significant recreational importance in the basin: a) The non-carcinogenic health risks (HQ) of exposure to heavy metals through ingestion and dermal routes were identified for adults and children, b) The total potential non-carcinogenic health risks for adults and children were determined using the hazard index (HI). The total HI (THI) value was calculated as the sum of individual HIs (HIingestion + HIdermal), c) Carcinogenic health risk (CR) values were calculated for three heavy metals (Cr, Ni and As). According to the findings: a) Ingestion HQ values were found to be higher in adults and children due to As compared to dermal HQ values. According to the HI values, there was a high level of non-carcinogenic health risk in terms of heavy metals in Sukesen, Başpınar, and Gölcük Creeks, varying according to the month and age group. However, for Yavrucak Creek, there has not been appeared to be a non-carcinogenic health risk for adults and children, b) According to the calculated HQdermal and HIdermal values, there was no significant adverse health risk due to dermal exposure for adults and children, c) Children hazard index values were found to be higher than adult hazard index values, highlighting that children were at higher health risk most particularly when it comes to the considered heavy metals, d) The THI values for Sukesen and Başpınar Creeks indicated a significant non-carcinogenic health risk possibility for both adults and children in all sampled months. In Gölcük Creek, a serious non-carcinogenic health risk probability was observed for adults in April and for children during the sampling period, e) The calculated CR values for chromium, nickel, and arsenic indicated that the ingestion pathway poses a higher risk compared to the dermal route, expressing the likelihood of cancer incidence in adults and children. In the context of potential health hazards, to take administrative measures regarding heavy metal contamination, particularly in Başpınar and Sukesen Creeks, is important not only for the protection of public health but also for the sustainability of Mogan Lake.
Shor's Factoring Algorithm and Modular Exponentiation Operators
Authors: Singleton Jr Robert L.
Published: September 2023

We provide a pedagogical presentation of Shor's factoring algorithm, which is a quantum algorithm for factoring very large numbers (of order of hundreds to thousands of bits) in polynomial time. In contrast, all known classical algorithms for the factoring problem take an exponential time to factor such large numbers. Shor's algorithm therefore has profound implication for public-key encryption such as RSA and Diffie–Hellman key exchange. We assume no prior knowledge of Shor's algorithm beyond a basic familiarity with the circuit model of quantum computing. Shor's algorithm contains a number of moving parts, and can be rather daunting at first. The literature is replete with derivations and expositions of Shor's algorithm, but most of them seem to be lacking in essential details, and none of them provide a pedagogical presentation. They require a thicket of appendices and assume a knowledge of quantum algorithms and classical mathematics with which the reader might not be familiar. We therefore start with first principle derivations of the quantum Fourier transform (QFT) and quantum phase estimation (QPE), which are the essential building blocks of Shor's algorithm. We then go on to develop the theory of modular exponentiation (ME) operators, one of the fundamental components of Shor's algorithm, and the place where most of the quantum resources are deployed. We also delve into the number theory that establishes the link between factorization and the period of the modular exponential function. We then apply the QPE algorithm to obtain Shor's factoring algorithm. We also discuss the post-quantum processing and the method of continued fractions, which is used to extract the exact period of the modular exponential function from the approximately measured phase angles of the ME operator. The manuscript then moves on to a series of examples. We first verify the formalism by factoring N=15, the smallest number accessible to Shor's algorithm. We then proceed to factor larger integers, developing a systematic procedure that will find the ME operators for any semi-prime N=p×q (where q and p are prime). Finally, we factor the composite numbers N=21, 33, 35, 143, 247 using the Qiskit simulator. It is observed that the ME operators are somewhat forgiving, and truncated approximate forms are able to extract factors just as well as the exact operators. This is because the method of continued fractions only requires an approximate phase value for its input, which suggests that implementing Shor's algorithm might not be as difficult as first suspected.Quanta 2023; 12: 41–130.
théorie interprétative de Danica Seleskovitch vingt ans après
Authors: Tryuk Małgorzata
Published: September 2023

THE INTERPRETIVE THEORY OF DANICA SELESKOVITCH TWENTY YEARS AFTER: THE QUESTION OF THE DIRECTIONALITY IN CONFERENCE INTERPRETINGThe aim of the present paper is to examine directionality in conference interpreting and the changing concepts of active and passive languages in the practice of interpreting in Poland. The study presents the results of a small-scale survey conducted in Warsaw in Winter 2019/2020 among interpreters, members of national professional associations of interpreters in order to investigate the changing landscape of the profession, the changing roles of interpreters working out of their A language into their B or C languages or between their B and C languages in different settings. Apart of the data gathered in the survey, in-depth interviews were carried out with three interpreters so as to indentify possible time-contigent changes in their perception of directionality in interpreting. The study draws some conclusions concerning the utility of language regime and the training of new generations of conference interpreters.
L’omission et ses avatars
Authors: Anckaert Philippe
Published: September 2023

THE OMISSION AND ITS AVATARS: TERMINOLOGICAL CHALLENGES AND ISSUES IN DESCRIPTIVE TRANSLATION STUDIESIn this study, Ph. Anckaert gives an overview of the terms proposed over the last thirty years by French-speaking terminologists to designate the various forms of non-translation, which are either errors or procedures. It emerges that there is a high degree of terminological uncertainty surrounding the notion of omission. To overcome this difficulty, which is detrimental to the quality of translation studies, the author proposes to abandon the use of the term omission to designate a translation procedure and to use effacement (deletion) as a hyperonym for implicitation, deletion of heterolingualism and other procedures that remain to be identified or validat éd. This ordering of the terminological apparatus is part of the desire to identify mutually exclusive descriptors, which are indispensable for quantitative analysis conceived as the scientific basis for qualitative analysis.
Code and Data Repository for Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives
Authors: Zhang Gongbo , Peng Yijie , Zhang Jianghua , Zhou Enlu
Published: September 2023

This repository contains supporting material for the paper Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives by Gongbo Zhang, Yijie Peng*, Jianghua Zhang, and Enlu Zhou.
Bringing men and masculinities into political science
Authors: Murray Rainbow , Bjarnegård Elin
Published: September 2023

Most politicians are men, yet there is a surprising lack of focus within political science on the causes and consequences of male dominance. This article outlines how political science could benefit from greater engagement with scholarship on men and masculinities. The concept of ‘political masculinities’ has focused on the importance of ‘the political’ to masculinities scholarship; we argue for extending this concept to analyse men and masculinities within political science. We identify insights from scholarship on masculinities that would deepen our understanding of power within formal political arenas. We consider how gender and politics scholarship could benefit from expanding its focus on men. We highlight feminist institutionalism as a tool for bringing masculinities into the study of political institutions. We then offer a framework for taking this research agenda forwards, showing how we can better understand male dominance by thinking about how men access, exercise, maintain and reproduce power.
Cultural Necromancy: Digital Resurrection and Hegemonic Incorporation
Authors: Prewitt Ryan , Accardi Max
Published: September 2023

Abstract: This essay follows the recent discourse on two phenomena: the tendency of hegemony to incorporate subversive cultures, and the digital reanimation of prominent dead people. At the intersection of these phenomena lies what we call “cultural necromancy,” a special case of hegemonic incorporation that aesthetically manipulates the physical presence of a deceased figure in the service of power. This essay explores historical analogues to cultural necromancy and how the digital age has accelerated the process through examples ranging from medieval saints to Lenin’s mausoleum to the Tupac hologram. It then examines cultural necromancy’s implications for counterculture and resistance.
Two-layer reconstruction of Raman spectra in diffusive media based on analytical model in the time domain
Authors: Susnjar Stefan , Martelli Fabrizio , Mosca Sara , Sekar Sanathana Konugolu Venkata , Swartling Jjohannes , Reistad Nina , Farina Andrea , Pifferi Antonio
Published: September 2023

We present and derive an analytical model that describes the migration of Raman scattered photons in two-layer diffusive media, based on the diffusion equation in the time domain. The model is derived under a heuristic approximation that background optical properties are identical on the excitation and Raman emission wavelengths. Methods for the reconstruction of two-layer Raman spectra have been developed, tested in computer simulations and validated on tissue-mimicking phantom measurements data. Effects of different parameters were studied in simulations, showing that the thickness of the top layer and number of detected photon counts have the most significant impact on the reconstruction. The concept of quantitative, mathematically rigorous reconstruction using the proposed model was finally proven on experimental measurements, by successfully separating the spectra of silicone and calcium carbonate (calcite) layers, showing the potential for further development and eventual application in clinical diagnostics.
Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels
Authors: Nordquist Erik , Zhang Guohui , Barethiya Shrishti , Ji Nathan , White Kelli M. , Han Lu , Jia Zhiguang , Shi Jingyi , Cui Jianmin , Chen Jianhan
Published: September 2023

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.
Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023
Authors: Sumi Ayako
Published: September 2023

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed.
The effect of calcium silicate and ground magnesium limestone (GML) on the chemical characteristics of acid sulfate soil
Authors: Azman Elisa Azura , Ismail Roslan , Ninomiya Seishi , Jusop Shamshuddin , Tongkaemkaew Uraiwan
Published: September 2023

Acid sulfate soil characterized by pyrite (FeS2) which produces high acidity (soil pH < 3.5) and release high amount of Al3+ and Fe2+. Application of 4 t ha-1 Ground Magnesium Limestone (GML), is a common rate used for acid sulfate soil by the rice farmers in Malaysia. Therefore, this study was conducted to evaluate the integral effect of ground magnesium limestone (GML) and calcium silicate and to determine the optimal combination on acid sulfate soils in Malaysia. The acid sulfate soils were incubated under the submerged condition for 120 days with GML (0, 2, 4, 6 t ha-1) in combination with calcium silicate (0, 1, 2, 3 t ha-1) arranged in a Completely Randomized Design (CRD). The soil was sampled after 30, 60, 90 and 120 days of incubation and analyzed for soil pH, exchangeable Al, Ca, Mg, K and available Si. A total of 2 out of 16 combinations met the desired soil requirement for rice cultivation. The desired chemical soil characteristics for rice cultivation are soil pH > 4, exchangeable Al < 2 cmolc Kg-1, exchangeable Ca > 2 cmolc kg-1, exchangeable Mg > 1 cmolc kg-1 and Si content > 43 mg kg-1. The combinations are i) 2 t ha-1 calcium silicate + 2 t ha-1 GML, and ii) 3 t ha-1 calcium silicate + 2 t ha-1 GML, respectively. These combination rates met the desired requirement of soil chemical characteristics for rice cultivation. Soil acidity was reduced by a gradual release of Ca2+ and SiO32- from calcium silicate continuously filling the exchange sites and reducing the potential of extra (free) H+ availability in the soil system. Combination of calcium silicate and GML, shows the ameliorative effect with; i) release of Ca, ii) binding of Al3+ making it inert Al-hydroxides and, iii) bind H+ to produce water molecules.
Outcomes of a social media campaign to promote COVID-19 vaccination in Nigeria
Authors: Evans W. Douglas , Bingenheimer Jeffrey B. , Long Michael , Ndiaye Khadidiatou , Donati Dante , Rao Nandan M. , Akaba Selinam , Nsofor Ifeanyi , Agha Sohail
Published: September 2023

The COVID-19 pandemic has been an historic challenge to public health and behavior change programs. In low -and middle-income countries (LMICs) such as Nigeria, there have been challenges in promoting vaccination. Vaccine hesitancy and social norms related to vaccination may be important factors in promoting or inhibiting not only COVID vaccination, but other routine vaccinations as well. The aim of this study was to conduct a national-level quasi-experimental evaluation of a social media based COVID-19 vaccination promotion campaign in Nigeria run in 2022. We followed a longitudinal cohort of Nigerians (at baseline) drawn from all 37 states in Nigeria over a 10-month period. This was done at 3 time points to evaluate psychosocial predictors of vaccination and vaccination outcomes following a theory of change based on Diffusion of Innovations, Social Norms Theory, and the Motivation, Opportunity, Ability (MOA) Framework. In a quasi-experimental design, participants in 6 Nigerian states where the social media campaign was run (treatment) were compared to participants from non-treatment states. This study highlights new social media-based data collection techniques. The study found that vaccination rates increased in treatment states compared to non-treatment states, and that these effects were strongest between baseline and first follow up (December 2021 to March 2022). We also found that more pro-vaccination social norms at one time point are associated with higher vaccination rates at a later time point. Social media campaigns are a promising approach to increasing vaccination at scale in LMICs, and social norms are an important factor in promoting vaccination, which is consistent with the Social Norms Theory. We describe implications for future vaccination campaigns and identify future research priorities in this area.
Modification of surface characteristics of ophthalmic biomaterial-polymethyl methacrylate induced by cobalt 60 gamma irradiation
Authors: Qin Dong , Guo Juan , Liang Ming , Qin Ling , Chen Ling , He Weimin
Published: September 2023

This study aims to observe the accelerated aging effect of 60Co gamma (γ) irradiation on poly (methyl methacrylate) (PMMA) under extreme conditions and determine the influence of different media states on aging. PMMA samples were prepared at room temperature under varying media conditions, including air and deionized water immersion. Then, the samples were irradiated with different doses (50, 250, 500, and 1000 KGy) of 60Co γ-rays. The compositional changes of the PMMA samples exposed to the rays at different periods were determined via Fourier transform infrared spectroscopy. The light transmission of the samples was characterized through ultraviolet–visible spectrophotometry, and the surface wettability of the samples was assessed via water contact angle measurements. Surface and microscopic changes in material morphology were analyzed using optical microscopy, ImageJ software, and scanning electron microscopy. Relative molecular mass and glass transition temperature were analyzed via gel permeation chromatography and differential scanning calorimetry. Thus, a comprehensive analysis of the effect of 60Co γ irradiation on the aging properties of PMMA was performed.
Computational phenotypes for patients with opioid-related disorders presenting to the emergency department
Authors: Taylor R. Andrew , Gilson Aidan , Schulz Wade , Lopez Kevin , Young Patrick , Pandya Sameer , Coppi Andreas , Chartash David , Fiellin David , D’Onofrio Gail
Published: September 2023

Objective

We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.

Methods

This was a retrospective study of ED visits from 2013–2020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients ≥18 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.

Results

In total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2–96.8%, rates of one-year ED returns from 9–34%, rates of one-year opioid event 10–17%, rates of medications for opioid use disorder from 17–43%, and a median Carlson comorbidity index of 2–8. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.

Conclusions

Our results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.
Study of various machine learning approaches for Sentinel-2 derived bathymetry
Authors: Chybicki Andrzej , Sosnowski Paweł , Kulawiak Marek , Bieliński Tomasz , Korlub Waldemar , Łubniewski Zbigniew , Kempa Magdalena , Parzuchowski Jarosław
Published: September 2023

In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical models. The particular proposed machine learning models differ also in a set of satellite data bands used as an input as well as in applying or not geographical weighting in the learning process. The obtained results point towards the best performance of the regression tree algorithm that incorporated as inputs information about data localization, raw reflectance data from four satellite data bands and a quotient of logarithms of B2 and B3 bands. The study for the paper was performed in relatively optically difficult and spatially variant conditions of the south Baltic coastline starting at Szczecin, Poland on the west (53°26’17’’ N, 14°32’32’’ E) to Hel peninsula (54°43’04,3774’’ N 18°37’56,9175’’ E). The reference bathymetry data was acquired from Polish Marine Administration. It was obtained through profile probing with single-beam sonar or direct in-situ probing.
Reinforcement Learning-based Wi-Fi Contention Window Optimization
Authors:   National Institute of Telecommunications (Inatel), Cruz Sheila , Ouameur Messaoud ,   Université du Québec à Trois-Rivières (UQTR),, Figueiredo Felipe ,   National Institute of Telecommunications (Inatel)
Published: September 2023

The collision avoidance mechanism adopted by the IEEE 802.11 standard is not optimal. The mechanism employs a binary exponential backoff (BEB) algorithm in the medium access control (MAC) layer. Such an algorithm increases the backoff interval whenever a collision is detected to minimize the probability of subsequent collisions. However, the increase of the backoff interval causes degradation of the radio spectrum utilization (i.e., bandwidth wastage). That problem worsens when the network has to manage the channel access to a dense number of stations, leading to a dramatic decrease in network performance. Furthermore, a wrong backoff setting increases the probability of collisions such that the stations experience numerous collisions before achieving the optimal backoff value. Therefore, to mitigate bandwidth wastage and, consequently, maximize the network performance, this work proposes using reinforcement learning (RL) algorithms, namely Deep Q Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), to tackle such an optimization problem. In our proposed approach, we assess two different observation metrics, the average of the normalized level of the transmission queue of all associated stations and the probability of collisions. The overall network’s throughput is defined as the reward. The action is the contention window (CW) value that maximizes throughput while minimizing the number of collisions. As for the simulations, the NS-3 network simulator is used along with a toolkit known as NS3-gym, which integrates a reinforcement-learning (RL) framework into NS-3. The results demonstrate that DQN and DDPG have much better performance than BEB for both static and dynamic scenarios, regardless of the number of stations. Additionally, our results show that observations based on the average of the normalized level of the transmission queues have a slightly better performance than observations based on the collision probability. Moreover, the performance difference with BEB is amplified as the number of stations increases, with DQN and DDPG showing a 45.52% increase in throughput with 50 stations.
Randomized Trial of Prophylactic Antibiotics for Endoscopic Retrograde Cholangiopancreatography in Patients With Biliary Obstruction
Authors: Leem Galam , Sung Min Je , Park Ji Hoon , Kim So Jeong , Jo Jung Hyun , Lee Hee Seung , Ku Nam Su , Park Jeong Youp , Bang Seungmin , Park Seung Woo , Song Si Young , Chung Moon Jae
Published: September 2023

INTRODUCTION:

The incidence of postendoscopic retrograde cholangiopancreatography (ERCP) infections is reported to be up to 18% in patients with biliary obstruction. Antibiotic prophylaxis may reduce the risk of infectious complications after ERCP; however, the clinical value of prophylactic antibiotics in ERCP remains controversial.

METHODS:

We conducted a double-blind, placebo-controlled, randomized trial to investigate whether the use of prophylactic antibiotics would reduce infectious complications after ERCP in patients with biliary obstruction. We randomly assigned patients in a 1:1 ratio to receive either a single dose of 1 g intravenous cefoxitin or normal saline as a placebo 30 minutes before undergoing ERCP. The primary outcome was the incidence of infectious complications after ERCP.

RESULTS:

We enrolled 378 patients, and 189 patients were assigned to each group. The risk of infectious complications after ERCP was 2.8% (5 of 176 patients) in the antibiotic prophylaxis group and 9.8% (17 of 173 patients) in the placebo group (risk ratio, 0.29; 95% confidence interval [CI], 0.11–0.74, P = 0.0073). The incidence rates of bacteremia were 2.3% (4 of 176 patients) and 6.4% (11 of 173 patients), respectively (risk ratio, 0.36; 95% CI, 0.12–1.04; P = 0.0599). The incidence rate of cholangitis was 1.7% (3 of 176 patients) in the antibiotic prophylaxis group and 6.4% (11 of 173 patients) in the placebo group (risk ratio, 0.27; 95% CI, 0.08–0.87; P = 0.0267).

DISCUSSION:

Antibiotic prophylaxis before ERCP in patients with biliary obstruction resulted in a significantly lower risk of infectious complications, especially cholangitis, than placebo (ClinicalTrials.gov trial number NCT02958059).
Contaminant curiosity and pollutant puzzles: Conceptual insights in ecotoxicity and practical implementation of higher-tiered risk assessment.
Authors: Vink Jos P.M. , Vijver Martina G. , Hendriks A. Jan
Published: September 2023

Since the soil quality Tool for Risk Identification, Assessment and Display (TRIAD) approach introduced the “three lines of evidence” accounting for chemical, toxicological and ecological stressors to explain adverse effects in biota, the assessment of contaminant risks in the environment has significantly evolved. The concept of chemical speciation, related to water characteristics, boosted the understanding of the role of free-ion activities in the overall accumulation of pollutants in biota. New modeling concepts (e.g. biotic ligand models) and measuring techniques were developed. This in turn triggered widespread research addressing the quantitative role of sediment in the overall water quality, focusing on redox interfaces. For contaminant mixtures in river catchments, complex relations between (bio)availability of compounds, including nutrients, help to explain aquatic toxicity. Variation in ecological patterns and processes across environmental or spatiotemporal gradients occur, which may identify ecological factors that influence contaminant fate and effects. Empirical evidence by meta-analysis and theoretical underpinning by modelling showed relationships between population growth rates and carrying capacities, across chemicals and across species. The potentially affected fraction of species may be related to the mean species abundance, an often-used indicator in global change studies. Knowledge gaps remain on how pollutants travel through ecological communities and which species and species-relationships are affected. Outdoor experimental systems that examine the natural environment under controlled conditions may be useful at the higher biological level to investigate the impact of stressors on a variety of species, including mutual interactions.
Pengembangan masyarakat berbasis Gender: Analisis Program Dana Desa dan Program Keluarga Harapan
Authors: Alfrojems Alfrojems , Chairani  , Anugrahini Triyanti
Published: September 2023

The purpose of this research is to examine gender issues in community development efforts through poverty alleviation programs, particularly the Village Fund and the Conditional Cash Transfer (PKH) program. The method employed in this writing is a literature review. Data collection was carried out by gathering journals, reports, books, and documents relevant to the research objectives. Based on the findings of the review, it is evident that gender-related problems in poverty alleviation efforts are not limited to within the community; they also extend to the implementation of gender concepts within government programs. This can be observed in the two aspects discussed in this paper: gender in community development and gender in government programs. The recommendations derived from this study that need to be implemented include the necessity for policy reviews, policy adjustments, capacity building for facilitators, the development of gender-related instruments, the promotion of collaborations with universities, and the encouragement of more publications relevant to gender issues.
Keywords: Gender, Poverty Alleviation, Community Development, PKH
 
Tujuan penelitian ini mengkaji isu gender dalam upaya pengembangan masyarakat melalui program pengentasan kemiskinan khususnya pada program Dana Desa dan PKH. Metode yang digunakan dalam penulisan ini adalah kajian literatur. Pengumpulan data dilakukan melalui pengumpulan data berupa jurnal, laporan, buku, dan dokumen yang relevan dengan tujuan penelitian. Berdasarkan hasil pengkajian maka diperoleh bahwa persoalan gender dalam upaya pengentasan kemiskinan bukan hanya terjadi di tengah masyarakat, bahkan juga pada implementasi konsep gender pada program pemerintah, hal ini dapat dilihat pada dua aspek pembahasan penulisan ini yakni gender pada pengembangan masyarakat dan gender dalam program pemerintah. Adapun rekomendasi dari penelitian ini yang perlu untuk dilaksanakan adalah perlunya upaya untuk melakukan reviu kebijakan, penyesuaian kebijakan, peningkatan kapasitas terhadap pendamping, penyusunan instrumen terkait dengan gender, mendorong kerjasama dengan perguruan tinggi, dan mendorong untuk semakin banyaknya publikasi yang relevan dengan isu gender.
Kata Kunci: Gender, Pengentasan Kemiskinan,  Pengembangan Masyarakat, PKH
Mengukur Perilaku Manusia dalam Skala Besar dan Secara Real-time: Studi Kasus Pola Mobilitas Penduduk dan Fase Awal Pandemi COVID-19 di Indonesia
Authors: Ramadona Aditya Lia , Arisanti Risalia Reni , Fuad Anis , Imron Muhammad Ali , Indriani Citra , Ahmad Riris Andono
Published: September 2023

Background: Good decisions in policy-making rely on acquiring the best possible understanding at the fast pace of what is happening and what might happen next in the population. Immediate measurements and predictions of disease spread would help authorities take necessary action to mitigate the rapid geographical spread of potential emerging infectious diseases. Unfortunately, measuring human behavior in nearly real-time, specifically at a large scale, has been labor-intensive, time-consuming, and expensive. Consequently, measurements are often unfeasible or delayed in developing in-time policy decisions. The increasing use of online services such as Twitter generates vast volumes and varieties of data, often available at high speed. These datasets might provide the opportunity to obtain immediate measurements of human behavior. Here we describe how the patterns of population mobility can be associated with the number of COVID-19 cases and, subsequently, could be used to simulate the potential path of disease spreading.Methods: Our analysis of country-scale population mobility networks is based on a proxy network from geotagged Twitter data, which we incorporated into a model to reproduce the spatial spread of the early phase COVID-19 pandemic in Indonesia. We used aggregated province-level mobility data from January through December 2019 for the baseline mobility patterns from DKI Jakarta as the origin of the 33 provinces' destinations in Indonesia.Result: We found that population mobility patterns explain 62 percent of the variation in the occurrence of COVID-19 cases in the early phases of the pandemic. In addition, we confirm that online services have the potential to measure human behavior in nearly real time.Conclusion: We believe that our work contributes to previous research by developing a scalable early warning system for public health decision-makers in charge of developing mitigation policies for the potential spread of emerging infectious diseases.
Faktor Sosio-Demografi yang Berhubungan dengan Penggunaan Suplemen dan Jamu selama Pandemi: Studi Kasus pada Mahasiswa UNIPMA Tahun 2022
Authors: Kartini Puri Ratna , Kusumawati Desi , Kusuma Ajeng Probo , Wisnubroto Agung Prabowo
Published: September 2023

Background: Corona Virus Disease 19 (COVID-19) is a global pandemic declared in 2020 by WHO. The high incidence and death rates of COVID-19 affect human social activities in all countries, including Indonesia. This pandemic has had an impact on various sectors including education. Students are one of the groups prone to contracting COVID-19 from the campus cluster. So it is necessary to strengthen one's immunity by consuming supplements and herbs. This study aims to analyze socio-demographic factors related to the use of supplements and herbs in UNIPMA students.Methods: This research is an analytic observational study (cross-sectional design) in which the researcher conducted indirect interviews using a questionnaire instrument distributed through the media google form. This study involved 361 UNIPMA students using a purposive sampling technique. Then the results of the study were analyzed using the chi square test.Result: Based on statistical tests, the results obtained for the gender variable were p = 0.014, the type of study program variable was p = 0.011, the income variable of both parents was p = 0.679 and the access variable to pharmacies and drug stores was p = 0.193.Conclusion : Based on the research results, it can be concluded that socio-demographic factors related to the use of supplements and herbs in UNIPMA students are gender and type of study program. Meanwhile, socio-demographic factors such as the income of both parents and access to pharmacies and drug stores are not related to the use of supplements and herbs in UNIPMA students.
Progressive Partitioning for Parallelized Query Execution in Google's Napa
Authors: Tatemura Junichi , Zou Tao , Sankaranarayanan Jagan , Huang Yanlai , Chen Jim , Zhang Yupu , Lai Kevin , Zhang Hao , Manoharan Gokul Nath Babu , Graefe Goetz , Agrawal Divyakant , Adelberg Brad , Kolhar Shilpa , Roy Indrajit
Published: September 2023

Napa holds Google's critical data warehouses in log-structured merge trees for real-time data ingestion and sub-second response for billions of queries per day. These queries are often multi-key look-ups in highly skewed tables and indexes.

In our production experience, only progressive query-specific partitioning can achieve Napa's strict query latency SLOs. Here we advocate good-enough partitioning that keeps the per-query partitioning time low without risking uneven work distribution. Our design combines pragmatic system choices and algorithmic innovations. For instance, B-trees are augmented with statistics of key distributions, thus serving the dual purpose of aiding lookups and partitioning. Furthermore, progressive partitioning is designed to be "good enough" thereby balancing partitioning time with performance. The resulting system is robust and successfully serves day-in-day-out billions of queries with very high quality of service forming a core infrastructure at Google.
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges
Authors: Yan Zhengtong , Uotila Valter , Lu Jiaheng
Published: September 2023

Join Order Selection (JOS) is a fundamental challenge in query optimization, as it significantly affects query performance. However, finding an optimal join order is an NP-hard problem due to the exponentially large search space. Despite the decades-long effort, traditional methods still suffer from limitations. Deep Reinforcement Learning (DRL) approaches have recently gained growing interest and shown superior performance over traditional methods. These DRL-based methods could leverage prior experience through the trial-and-error strategy to automatically explore the optimal join order. This tutorial will focus on recent DRL-based approaches for join order selection by providing a comprehensive overview of the various approaches. We will start by briefly introducing the core concepts of join ordering and the traditional methods for JOS. Next, we will provide some preliminary knowledge about DRL and then delve into DRL-based join order selection approaches by offering detailed information on those methods, analyzing their relationships, and summarizing their weaknesses and strengths. To help the audience gain a deeper understanding of DRL approaches for JOS, we will present two open-source demonstrations and compare their differences. Finally, we will identify research challenges and open problems to provide insights into future research directions. This tutorial will provide valuable guidance for developing more practical DRL approaches for JOS.
To UDFs and Beyond: Demonstration of a Fully Decomposed Data Processor for General Data Wrangling Tasks
Authors: Schäfer Nico , Gjurovski Damjan , Davitkova Angjela , Michel Sebastian
Published: September 2023

While existing data management solutions try to keep up with novel data formats and features, a myriad of valuable functionality is often only accessible via programming language libraries. Particularly for machine learning tasks, there is a wealth of pre-trained models and easy-to-use libraries that allow a wide audience to harness state-of-the-art machine learning. We propose the demonstration of a highly modularized data processor for semi-structured data that can be extended by means of plain Python scripts. Next to commonly supported user-defined functions, the deep decomposition allows augmenting the core engine with additional index structures, customized import and export routines, and custom aggregation functions. For several use cases, we detail how user-defined modules can be quickly realized and invite the audience to write and apply custom code, to tailor provided code snippets that we bring along to own preferences to solve data analytics tasks involving sentiment analysis of Twitter tweets.
ADOps: An Anomaly Detection Pipeline in Structured Logs
Authors: Song Xintong , Zhu Yusen , Wu Jianfei , Liu Bai , Wei Hongkang
Published: September 2023

Anomaly detection has been extensively implemented in industry. The reality is that an application may have numerous scenarios where anomalies need to be monitored. However, the complete process of anomaly detection will take much time, including data acquisition, data processing, model training, and model deployment. In particular, some simple scenarios do not require building complex anomaly detection models. This results in a waste of resources. To solve these problems, we build an anomaly detection pipeline(ADOps) to modularize each step. For simple anomaly detection scenarios, no programming is required and new anomaly detection tasks can be created by simply modifying the configuration file. In addition, it can also improve the development efficiency of complex anomaly detection models. We show how users create anomaly detection tasks on the anomaly detection pipeline and how engineers use it to develop anomaly detection models.
Odyssey: An Engine Enabling the Time-Series Clustering Journey
Authors: Paparrizos John , Reddy Sai Prasanna Teja
Published: September 2023

Clustering is one of the most popular time-series tasks because it enables unsupervised data exploration and often serves as a subroutine or preprocessing step for other tasks. Despite being the subject of active research across disciplines for decades, only limited efforts focused on benchmarking clustering methods for time series. Unfortunately, these studies have (i) omitted popular methods and entire classes of methods; (ii) considered limited choices for underlying distance measures; (iii) performed evaluations on a small number of datasets; or (iv) avoided rigorous statistical validation of the findings. In addition, the sudden enthusiasm and recent slew of proposed deep learning methods underscore the vital need for a comprehensive study. Motivated by the aforementioned limitations, we present Odyssey, a modular and extensible web engine to comprehensively evaluate 80 time-series clustering methods spanning 9 different classes from the data mining, machine learning, and deep learning literature. Odyssey enables rigorous statistical analysis across 128 diverse time-series datasets. Through its interactive interface, Odyssey (i) reveals the best-performing method per class; (ii) identifies classes performing exceptionally well that were previously omitted; (iii) challenges claims about the use of elastic measures in clustering; (iv) highlights the effects of parameter tuning; and (v) debunks claims of superiority of deep learning methods. Odyssey does not only facilitate the most extensive study ever performed in this area but, importantly, reveals an illusion of progress while, in reality, none of the evaluated methods could outperform a traditional method, namely,
k
-Shape, with a statistically significant difference. Overall, Odyssey lays the foundations for advancing the state of the art in time-series clustering.
Lingua Manga : A Generic Large Language Model Centric System for Data Curation
Authors: Chen Zui , Cao Lei , Madden Sam
Published: September 2023

Data curation is a wide-ranging area which contains many critical but time-consuming data processing tasks. However, the diversity of such tasks makes it challenging to develop a general-purpose data curation system. To address this issue, we present Lingua Manga, a user-friendly and versatile system that utilizes pre-trained large language models. Lingua Manga offers automatic optimization for achieving high performance and label efficiency while facilitating flexible and rapid development. Through three example applications with distinct objectives and users of varying levels of technical proficiency, we demonstrate that Lingua Manga can effectively assist both skilled programmers and low-code or even no-code users in addressing data curation challenges.
Kant’s Semiotics and Hermeneutics in the 1760s
Authors: Costantini Marco
Published: September 2023

In this contribution, we first discuss the aspects of the analytic method conceived by Kant in the Deutlichkeit that differentiate it from the Wolffian method and relate it to the Newtonian method. Compared to the philosophical tradition, the task of analysing concepts appears profoundly changed. Since Kant aims philosophy towards the world, he considers concepts as something given and intends to discern their characteristic marks by observing their usual applications. Although Kant abandons any attempt to define concepts nominally, he still gives great relevance to words to the extent that concepts, according to him, acquire their meaning in the linguistic usage. We will also point out how for Kant the linguistic usage makes the analysis of concepts anything but easy and how a hermeneutic process is necessary for its completion. In distinguishing philosophical signs from mathematical signs, Kant provides the semiotic reasons that underlie his recourse to hermeneutics.
Kant on Language and the (Self‐)Development of Reason
Authors: Lu-Adler Huaping
Published: September 2023

The origin of languages was a hotly debated topic in the eighteenth century. This paper reconstructs a distinctively Kantian account according to which the origination, progression, and diversification of languages is at bottom reason’s self-development under certain a priori constraints and external environments. The reconstruction builds on three sets of materials. The first is Herder’s famous prize essay on the origin of languages. The second includes Kant’s explicit remarks about language – especially his notion of “transcendental grammar,” his argument that language cannot be innate, his contrast of “Oriental” symbolic (intuitive) and “Occidental” discursive languages, and his treatment of the latter as a sine qua non of humanity’s cultural and moral progress. The third includes the concepts that we need to make sense of those remarks, such as Kant’s epigenetic theory of biological formation and his account of categories as originally acquired.