bluuebunny/crossref_metadata_2025 huggingface dataset:
Les trames et les sens du travail psychanalytique en temps de guerre
Authors: Uvarova Svetlana
Published: May 2023

Les processus mondiaux contemporains menacent la pratique psychanalytique, du moins dans sa forme traditionnelle. Après tout, les événements de ces dernières années (par exemple, la pandémie) ont changé brutalement et presque partout dans le monde la clinique psychanalytique, tout d’abord, son fondament - le cadre psychanalytique - en transférant le travail psychanalytique dans l’espace virtuel. Nous sommes témoins du fait que la pratique psychanalytique (ses règles, son cadre et ses principes) subit aujourd’hui des changements radicaux et nécessite donc une révision totale. Dans ces situations extrêmes, il est nécessaire de repenser certains aspects de l’éthique clinique et du cadre, ainsi que d’introduire quelques règles et mesures supplémentaires. Dans cet article, l’auteure tente de cerner la place et les possibilités de la pratique psychanalytique en temps de guerre.
Emotionsregulation in Krisenzeiten: die sozio-interpersonelle Perspektive
Authors: Horn Andrea B.
Published: May 2023

Es gibt gute Gründe anzunehmen, dass Emotionsregulation über die Lebensspanne hinweg ein sozialer Prozess ist – eine Perspektive, die gerade hinsichtlich der derzeitigen kollektiven Krisen wichtige Implikationen hat. Dieser Beitrag zeigt, wie intrapersonelle Emotionsregulationsstrategien sich auch sozial manifestieren können – und was das für die Emotionsregulation bedeutet.
Psychologische Third-Mission-Projekte im Kontext von COVID-19-Pandemie und Ukraine-Krieg
Authors: Brakemeier Eva-Lotta , Stapel Sarah
Published: May 2023

Globale Krisen bedrohen nicht nur die physische, sondern auch die psychische Gesundheit. Welche gesellschaftspolitische Verantwortung hat die Klinische Psychologie und Psychotherapie in Krisenzeiten? Wie kann sie ihr gerecht werden? Die Initiative „Gemeinsam für psychische Gesundheit“ (GPG) hat niedrigschwellige psychologische Hilfsprojekte ins Leben gerufen. Erste Daten sprechen dafür, dass die Implementation solcher Third-Mission-Projekte, also die Verflechtung der Hochschule mit ihrer Umwelt durch Verantwortungsübernahme, gelingen kann, die Akzeptanz bei den Klient*innen gegeben zu sein scheint und die Ziele ebenfalls erreichbar erscheinen.
Aus Chaos wird Struktur – Anmerkungen zur Dynamik von Verschwörungserzählungen
Authors: Senf Wolfgang
Published: May 2023

Was sind Verschwörungstheorien? Warum treten sie aktuell so in Erscheinung? Der Beitrag versucht, ein Verständnis über das komplexe Phänomen „Verschwörung“ zu vermitteln. Mit dem Konzept der sinnstiftenden Erzählungen, mit denen individuelle oder kollektive Wirklichkeitskonstruktionen transportiert werden, eröffnet sich eine mögliche Annäherung an die Ursachen für das stabile Auftreten von Verschwörungserzählungen.
Trauma- und Belastungsfolgestörungen: Differenzialdiagnostik ICD-11 und DSM-5
Authors: Bachem Rahel
Published: May 2023

Die aktualisierte 11. Version der Internationalen statistischen Klassifikation der Krankheiten (ICD-11) brachte eine Reihe bedeutender Änderungen in der Konzeptualisierung der belastungsbezogenen psychischen Störungen mit sich. Dieser Beitrag benennt die wichtigsten Unterschiede zwischen den Belastungsfolgestörungen des Erwachsenenalters in ICD-11 und DSM-5 und leitet zentrale klinische Implikationen der ICD-11 Konzepte ab.
Fallbericht soziale Phobie
Authors: Leinert Sven , Möhr Teresa , Preuschoff Irene , Voigt Daniel
Published: May 2023

Ein 35-jähriger Mann weist ausgeprägte Symptome einer sozialen Phobie auf und verlässt deshalb kaum noch das Haus. Psychotherapeutische Hilfe sucht er jedoch nicht aus eigener Initiative.
Peritonealdialyse
Authors: Alscher Mark
Published: May 2023

Die Peritonealdialyse (PD) hat insbesondere in den ersten 3–5 Jahren gegenüber der Hämodialyse Vorteile 1. Leider finden sich weiterhin bei einer längeren Zeitdauer des Verfahrens technische Probleme und die Notwendigkeit, zu einem nicht geringen Prozentsatz das Verfahren zu wechseln 2. Deshalb wird die Peritonealdialyse in der Regel als ideales Verfahren zur Überbrückung bis zur Transplantation gesehen 3.
Ärztlich assistierter Suizid für Menschen mit psychischen Störungen
Authors: Müller Katja , Stang Philipp
Published: May 2023

Gegenstand/Ziel: Mit dem Urteil des Bundesverfassungsgerichts von 2020 ist der rechtliche Rahmen für assistierten Suizid grundsätzlich gegeben; jedoch sind Fragen der Indikationsstellung bei Personen mit psychischer Störung noch ungeklärt. Der vorliegende Beitrag geht daher der Debatte nach, inwiefern der Forschungsstand den Zugang zu einem ärztlich assistierten Suizid auf Grundlage einer psychischen Störung als vertretbar bewertet.

Methode: Die Fragestellung wurde mittels einer selektiven Literaturrecherche in den Datenbanken PubPsych und PubMed sowie durch das Schneeballsystem gefundener Studien der Länder mit Legalisierung beantwortet.

Ergebnisse: Die psychiatrische Personengruppe kann anhand der gegenwärtigen Zugangsvoraussetzungen nicht kategorisch von ärztlich assistiertem Suizid ausgeschlossen werden. Der Rechtsanspruch sollte nur unter strengen Bewertungsverfahren im Rahmen zweier psychiatrischer unabhängiger Gutachten, professionellen Standards und zusätzlichen Sicherheitsvorkehrungen geltend gemacht werden.

Schlussfolgerung: Es sollten berufsrechtliche Regelungen und Standards im Sinne von Leit- und Richtlinien geschaffen werden. Zudem kann ein themenspezifischer Bedarf an Aus-, Fort- und Weiterbildung von (Fach-)Ärzten und (Fach-)Psychotherapeuten postuliert werden.

Klinische Relevanz: Das komplexe Unterfangen verlangt transparente Regelungen und Unterstützungsangebote für das Fachpersonal.
Migräne und weibliche Geschlechtshormone
Authors: Evers Stefan , Israel-Willner Heike , Raffaelli Bianca
Published: May 2023

In einer Gruppe aus Expertinnen und Experten aus dem Bereich der Gynäkologie und der Kopfschmerzmedizin wurden die verschiedenen Aspekte des Zusammenhangs zwischen weiblichen Geschlechtshormonen und Kopfschmerzen, insbesondere Migräne, diskutiert. Auf Grundlage einer systematischen aktuellen Literaturrecherche wurde dann ein gemeinsames Konsensuspapier erarbeitet, das diese verschiedenen Aspekte in klinischer und therapeutischer Hinsicht umfasst. Insbesondere werden Informationen und Empfehlungen zur Einnahme der oralen hormonalen Kontrazeption bei Frauen mit Migräne, zur menstruellen Migräne, zur Situation in der Schwangerschaft und Stillzeit, zu postmenopausalen Kopfschmerzen und zu anderen hormonellen Situationen gegeben. Es handelt sich ausdrücklich nicht um eine Leitlinie, sondern um eine selbst initiierte Konsensusfindung.
Assistierte Dialyse: Was entwickelt sich in Deutschland und darüber hinaus?
Authors: Kitsche Benno , Iseke Kerstin Brigitte , Oleimeulen Ursula
Published: May 2023

Die Anwendung der Heimdialyseverfahren ist in Deutschland nach wie vor gering 1–3. Das trifft insbesondere auf die assistierte Heimdialyse zu. Dabei könnte vor dem demografischen Hintergrund, wenn in den kommenden Jahren die Babyboomer in den Ruhestand gehen, die assistierte Peritonealdialyse (asPD) eine gute Therapieoption für die steigende Zahl der dialysepflichtigen älteren Patienten sein. Dieser Artikel beschreibt die aktuelle Versorgungssituation in Deutschland und weist auf Aktivitäten hier und in den Nachbarländern hin.
Veranstaltungsankündigung
Authors: Bundschu Marion
Published: May 2023

Informationen zu den Veranstaltungen finden Sie auf www.wbs-ulm.de.
Migräne: Kinderwunsch, Schwangerschaft, Stillzeit
Authors: Schultze-Mosgau Askan , Holle-Lee Dagny , Keck Christoph , Segerer Sabine , Evers Stefan
Published: May 2023

Meist bessert sich die Migräne in der Schwangerschaft von allein. Sollte eine Akuttherapie der Migräne in der Schwangerschaft notwendig sein, sind Paracetamol und Ibuprofen (letzteres bis Woche 28) Mittel der Wahl. Alle vorliegenden Daten sprechen gegen ernsthafte Hinweise auf Teratogenität oder Embryotoxizität beim Menschen. ASS ist in der Schwangerschaft Analgetikum der zweiten Wahl. Eine „Low-dose“-Behandlung kann bei entsprechender Indikation in der ganzen Schwangerschaft durchgeführt werden. Auf die Verwendung von Metamizol sollte während der Schwangerschaft möglichst verzichtet werden. Sumatriptan ist Mittel der Wahl aus der Gruppe der Triptane zur Therapie von Migräneattacken, falls ein Triptan verwendet werden muss. Es ersetzt nicht die medikamentöse Migräneprophylaxe. Wenn eine medikamentöse Prophylaxe während einer Schwangerschaft notwendig ist, sind Betablocker Substanzen der ersten Wahl (mit der geringsten Gefährdung des Embryos oder Fötus). Amitriptylin ist Mittel der zweiten Wahl in dieser Situation, insbesondere bei komorbiden Depressionen. Alle anderen prophylaktischen Substanzen sollten nur mit äußerster Zurückhaltung eingesetzt werden.
OCT in der Neuroophthalmologie
Authors: Naxer Sabine , Schittkowski Michael
Published: May 2023

Die optische Kohärenztomografie (OCT) wird bei der Diagnostik retinaler und glaukomatöser Erkrankungen routinemäßig eingesetzt. Seitdem eine so hohe Auflösung möglich ist, dass die
einzelnen Netzhautschichten darstellbar und auch segmentierbar sind, hielt die OCT auch Einzug in die Neuroophthalmologie. Dieser Beitrag zeigt aktuelle und zukünftige Einsatzmöglichkeiten
in der Neuroophthalmologie und vermittelt Kenntnisse über mögliche Tücken.
Der Teilhabeverfahrensbericht als Steuerungsinstrument –
Arbeiten mit Kennzahlen
Authors: Schüring Stefan
Published: May 2023

Seit der Einführung des Bundesteilhabegesetzes geben mehr als 1200
Rehabilitationsträger einen umfassenden Einblick in ihr Leistungsgeschehen.
Die Träger erheben hierzu unterjährig Prozesskennzahlen und
übermitteln die Daten im Folgejahr an die Bundesarbeitsgemeinschaft
für Rehabilitation e.V. zur Veröffentlichung im
Teilhabeverfahrensbericht nach § 41 SGB IX.
Scalable and ultralow power silicon photonic two-dimensional phased array
Authors: Chalupnik Michelle , Singh Anshuman , Leatham James , Lončar Marko , Soltani Moe
Published: May 2023

Photonic integrated circuit based optical phased arrays (PIC-OPAs) are emerging as promising programmable processors and spatial light modulators, combining the best of planar and free-space optics. Their implementation on silicon photonic platforms has been especially fruitful. Despite much progress in this field, demonstrating steerable two-dimensional (2D) OPAs that are scalable to a large number of array elements and operate with a single wavelength has proven a challenge. In addition, the phase shifters used in the array for programming the far-field beam are either power hungry or have a large footprint, preventing the implementation of large scale 2D arrays. Here, we demonstrate a two-dimensional silicon photonic phased array with high-speed (∼330 kHz) and ultralow power microresonator phase-shifters with a compact radius (∼3 µm) and 2π phase shift ability. Each phase-shifter consumes an average of ∼250 µW of static power for resonance alignment and ∼50 µW of power for far-field beamforming, a more than one order of magnitude improvement compared to prior OPA works based on waveguide-based thermo-optic phase shifters. Such PIC-OPA devices can enable a new generation of compact and scalable low power processors and sensors.
Directed chemical dimerisation enhances the antibacterial activity of the antimicrobial peptide MSI-78(4–20)
Authors: Li Rong , Handley Thomas N.G. , Li Wenyi , O’Brien-Simpson Neil M. , Hossain Mohammed Akhter , Wade John D.
Published: May 2023

Antimicrobial resistance (AMR) is on the rise, leading to 700 000 deaths worldwide in 2020. Antimicrobial peptides (AMPs) are antibiotic agents that are active against multi-drug resistant pathogens and also have a reduced risk of AMR development. Previous studies have shown that dimerisation of the proline-rich antibacterial peptide (PrAMP) Chex1–Arg20 can enhance its antimicrobial activity while also reducing its toxicity. To determine if dimerisation via a simple disulfide bond can similarly improve other classes of AMPs, the α-helical cationic peptide MSI-78(4–20) was used as a model. The monomer alone, an S-carboxamidomethyl-capped N-terminal Cys–MSI-78(4–20) analogue and the disulfide-linked dimer were successfully synthesised and their antimicrobial activity and toxicity were determined. It was shown that dimerisation enhanced antimicrobial activity against the Gram-positive opportunistic pathogen Staphylococcus aureus ATCC 29213, the Gram-negative bacteria Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 47615. The peptides showed no significant haemolytic activity with red blood cells and only induced 50% lactate dehydrogenase (LDH) release in mammalian cells at the highest tested concentration, 15 µM. The MSI-78(4–20) dimer was less cytotoxic than the monomer and S-alkyl monomer. Together, the data support the strategy of AMP chemically directed dimerisation as a means of producing potentially more therapeutically useful antimicrobial agents.
Diabetes in the Cook Islands: a clinical audit
Authors: Tepai Machaela , Nosa Vili , Herman Josephine , May Yin Yin , Kiadarbandsari Atefeh , Sluyter John
Published: May 2023

Introduction The global burden of diabetes mellitus (diabetes) is significant and of increasing concern with more pregnant women being diagnosed with gestational diabetes mellitus (GDM). The Cook Islands face mounting pressures to address diabetes alongside competing population health needs and priorities. Cook Islands residents frequently travel to New Zealand to access health services. Inadequate information systems also make it difficult for countries to prioritise preventative measures for investment. In the absence of good data to inform effective diabetes preventative and treatment measures, people with diabetes are likely to progress to complications which will burden society and health systems in the Cook Islands and New Zealand. Aim To determine the prevalence of diabetes and prediabetes, and incidence of GDM, in the Cook Islands. Methods We analysed two Te Marae Ora Cook Islands Ministry of Health datasets, the Non‐Communicable Diseases (NCD) register examining demographic data for the period 1967 to December 2018 and same for the GDM register from January 2009 to December 2018. Results Of the 1270 diabetes cases, 53% were female and half were aged 45–64 years. There were 54 pre-diabetes cases and 146 GDM. Of the 20 GDM cases who developed type 2 diabetes, 80% were diagnosed before the age of 40 years. Data quality was poor. Discussion The Cook Islands diabetes registers provide important data to inform priorities for diabetes-related preventative and treatment measures. A data analyst has been employed to ensure quality, regularly audited data and information systems.
Characterization of PillarHall test chip structures using a reflectometry technique
Authors: Danilenko Aleksandr , Rastgou Masoud , Manoocheri Farshid , Kinnunen Jussi , Korpelainen Virpi , Lassila Antti , Ikonen Erkki
Published: May 2023

Thin film samples where one of the thin layers consists of a vacuum or air are called PillarHalls due to their support structure in silicon wafers. Custom PillarHall samples were provided by Chipmetrics Ltd and characterized by reflectometry with a Cary 7000 spectrometer. Data at 8° of angle of incidence were collected with p-polarization of the incident light within the wavelength range of 550–1800 nm. These data were then analyzed with a dedicated MATLAB code, using fitting software accompanying the transfer matrix method for calculation of the reflectance spectrum. Layer thicknesses and unknown refractive indices were chosen as fitted parameters. The oscillating reflectance spectrum of the PillarHall test chip yielded an air gap thickness of 86 nm with an estimated standard uncertainty of 5 nm. This is close to the nominal value of 100 nm. The results demonstrate that reflectometry data are sensitive to the thickness of the thin air layer deep inside the silicon structure.
An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution
Authors: Li Xueyi , Yuan Peng , Wang Xiangkai , Li Daiyou , Xie Zhijie , Kong Xiangwei
Published: May 2023

Bearings are an essential component of rotating mechanical equipment. Traditional signal processing-based fault diagnosis methods usually require a massive labeled data for training, but bearings generally operate in the equipment under normal fault-free conditions. This paper proposes an improved adaptive batch normalization (AdaBN) transfer learning bearing fault diagnosis method for batch normalization (BN) in traditional deep learning architecture. The AdaBN network preprocesses the raw vibration signals, and then the preprocessed features are input to a depthwise sparable convolution neural model for training. Features are extracted by depthwise convolution and point convolution in the network. AdaBN can freeze all the parameters in the network except the BN layer. Finally, a small amount of labeled data is classified using transfer learning methods. A laboratory data set was used for validation, and the experimental validation showed that the accuracy of the bearing fault diagnosis method using AdaBN reached 85%.
Fracture strength and failure mechanism of graphene-containing grain boundaries and pores
Authors: Wang Shuaiwei , Zhang Cun , Chen Shaohua , Peng Zhilong
Published: May 2023

Grain boundaries and pores commonly manifest in graphene sheets during experimental preparation. Additionally, pores have been intentionally incorporated into graphene to fulfill specific functions for various applications. However, how does the simultaneous presence of pores and grain boundaries impact the mechanical properties of graphene? This paper establishes uniaxial tension models of single-layer graphene-containing pores and three types of experimentally observed. The effect of interaction between pores and grain boundaries on the fracture strength of graphene was studied respectively for three types of grain boundaries by employing molecular dynamics simulations and considering factors such as pore size, the distance between pores and grain boundaries, and loading angle. A competitive mechanism between the intrinsic strength of pristine graphene with grain boundaries (referred to as pristine GGBs), which varies with the loading angle and the fracture strength of graphene sheets with pores that changes with the size of the pores, governs the fracture strength and failure modes of GGBs with pores. When the former exceeds the latter, the fracture strength of GGBs with pores primarily depends on the size of the pores, and fractures occur at the edges of the pores. Conversely, when the former is lower, the fracture strength of GGBs with pores relies on the loading angle and the distance between pores and grain boundaries, leading to grain boundary rupture. If the two strengths are comparable, the failure modes are influenced by the distance between pores and grain boundaries as well as the loading angle. The findings further elucidate the impact of coexisting grain boundaries and pores on the fracture behavior of graphene, providing valuable guidance for the precise design of graphene-based devices in the future.
Combined experimental and theoretical investigation on SONOS pFLASH switch
Authors: Wei Yidan , Liu Guozhu , Wei Jinghe , Liu Xudong , Liu Yong , Sun Jinping , Zhao Wei , Wei Yingqiang , Sui Zhiyuan , Liu Meijie
Published: May 2023

In this paper, a new push–pull pFLASH switch is designed and fabricated based on a 110 nm eFLASH process, which includes two 2 T-pFLASH transistors and one signal transmission transistor. The pFLASH transistors are programmed and erased by band-to-band tunneling-induced hot electron and Fowler–Nordheim tunneling to realize its ‘on/off’ function, and the current of the signal transmission transistor can be effectively tuned by the drain voltage of the 2 T-pFLASH. In order to clarify the degradation mechanism of the electronic properties, first principles calculations are performed from the atomic scale. Nitrogen vacancies have been proven to play a crucial role in nitrides. In addition, the effects of vacancies on the charge retention are investigated in terms of electronic structure. The nitrogen vacancies have proven to be detrimental to the electron storage by the simulated localization energies. Therefore, this study will provide a newly designed field-programmable gate array configuration unit, whose electrical mechanisms are revealed by theoretical simulations, which can also become the design foundation for future FLASH switches.
Depth-resolved attenuation coefficient estimation from optical coherence tomography data in case of incomplete signal attenuation in the imaging depth range
Authors: Moiseev Alexander , Potapov Arseniy , Sherstnev Evgeny , Gelikonov Grigory , Gelikonov Valentin , Sirotkina Marina , Shilyagin Pavel , Ksenofontov Sergey , Gladkova Natalia
Published: May 2023

Attenuation coefficient estimation from optical coherent tomography (OCT) data is one of the emerging methods for additional information extraction from the OCT data. With the reasonable assumptions of uniform proportion of the light, scattered backwards, relative to the light, scattered in all directions and the assumption of complete light attenuation within the imaging depth range, the attenuation coefficient can be estimated in every pixel of the OCT data volume, i.e. with the depth resolution. In the present paper the numerically effective method for lifting the second assumption was proposed. With numerical simulations and experiments it was shown, that the proposed method allows attenuation coefficient estimation even if OCT signal was not completely attenuated within the imaging depth range. Since the proposed method lifts one of the requirements for depth-resolved attenuation coefficient estimation, it allows the extension of the depth-resolved attenuation estimation method to the new applications.
Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm
Authors: Wang Chenggong , Li Gen , Wang Ruiqi , Li Lin
Published: May 2023

According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the interframe motion estimation part, and the two-dimensional Iterative Closest Point (ICP) algorithm was used for camera motion estimation. The experiments indicated that the proposed algorithm effectively improved the computational speed and precision of planar motion wheeled robot visual odometer. This research indicates that the dimension reduction processing of ICP algorithm can effectively improve the operation speed and calculation accuracy of planar motion wheeled robot visual odometry, which provides a good reference and data support for the subsequent research of wheeled robot visual odometry in the future.
AD-STGCRN: A Model for Traffic Flow Prediction
Authors: Tang Jie , Wen Pei , Sun Cheng , Xu Dahong , Xiong Bing , Zhao Jiajia , Zhang Jin
Published: May 2023

Aiming at the problem that the existing prediction models fail to utilize the serial correlation, temporal correlation and spatial correlation of data from multiple perspectives to achieve accurate prediction, a dual-mode spatiotemporal graph convolutional neural network model combined with attention mechanism is proposed. This paper has been tested and validated in very stochastic traffic forecasting. experiments on two publicly available traffic datasets, PeMSD04 and PeMSD08, show that AD-STGCRN has higher prediction accuracy, with the model’s prediction errors reduced by an average of 18%, 16% and 25% for MAE, RMSE and MAPE respectively on PeMSD04 compared to other prediction models. On PeMSD08 they were reduced by an average of 18%, 16% and 20% respectively.
Deep Learning Based Cross-View Human Detection System
Authors: Zhang Jining , Yang Yihan , Gao Di , Wang Ruijie , Wang Jun
Published: May 2023

Human detection is an important research branch in the field of computer vision, and it is widely used in vehicle-assisted driving, intelligent monitoring, intelligent transportation and other aspects. In order to solve the problems of poor real-time performance, low detection accuracy and inability to detect under occlusion in traditional human detection methods. This paper proposes a deep learning based trainable cross-view human detection system, where the encoder-decoder network focuses on solving the feature association problem after perspective transformation. Then, the system was tested on the WildTrack dataset. The results show that our cross-view human detection system outperforms conventional systems in terms of speed and accuracy across the board, achieving a satisfactory performance rate of 0.71 MODA in the presence of extensive occlusion.
Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning
Authors: Jia Weibing , Wei Zhengying
Published: May 2023

More than two-thirds of freshwater consumed worldwide are used for irrigation, and the existing control system for irrigation and fertilization is not intelligent and efficient enough. It is necessary to improve the utilization efficiency of irrigation water and fertilizer, and the intelligent level of irrigation and fertilization system. We have built tomato fertilizer deficiency and fertilization decision model based on leaf images and deep learning, this paper develops an Raspberry Pi-embedded intelligent control system for irrigation and fertilization based on previous deep learning models. The system included sensors, image acquisition, control part, control panel and remote control, the type of main hardware and sensors were selected, and the circuit of the system were designed. The C language is used to compile the source code of the deep learning framework, and the Python language is used to compress the deep learning model. The result shows that the compression rate of the deep learning model can reach 36.7% and the decision-making time can be shortened by 36% under the condition of ensuring the model accuracy. The Raspberry Pi-embedded intelligent control system can help farmers improve the utilization efficiency of irrigation and fertilizer.
A single-stage automatic license plate recognition network with Balanced-IoU loss
Authors: Liu Sen , Xie Yufei , Wu Longbin , Song Kunpeng , Gong Kaiwen , Duan Xiaohui
Published: May 2023

In this paper, we propose a license plate recognition model, which can detect and recognize the license plate in a single forward. The features of the input image are extracted by our 15-layer convolutional neural network. In detection branch, we use a loss function with better nonlinear to fit the detection process of license plate. To catch the location of license plate with less information loss, we add Intersection over Ground-truth (IoG) into the Intersection over Union (IoU) and get Balanced-IoU loss (BIoU loss). The combination of these two loss functions can make the model get a better predictive result. In recognition branch, we introduce an attention mechanism to make better recognition results. The experimental results show that our network can get 99.91% recognition precision, which is over 3% higher than state-of-the-art single-stage ALPR network RPnet.
Single Camera Person Re-identification with Self-paced Joint Learning
Authors: Zhang Rumeng , Li Mengyao , Lv Xueshuai , Gao Ling
Published: May 2023

Existing re-identification (re-ID) methods rely on a large number of cross-camera identity tags for training, and the data annotation process is tedious and time-consuming, resulting in a difficult deployment of real-world re-ID applications. To overcome this problem, we focus on the single camera training (SCT) re-ID setting, where each identity is annotated in a single camera. Since there is no annotation across cameras, it takes much less time in data acquisition, and enables fast deployment in new environments. To address SCT re-ID, we proposed a joint comparison learning framework and split the training data into three parts, single-camera labeled data, pseudo labeled data, and unlabeled instances. In this framework, we iteratively (1) train the network and dynamically update the memory to store the three types of data, (2) assign pseudo-labels to the unlabeled images using a clustering algorithm. In the model training phase, we jointly train the three types of data to update the CNN model, and this joint training method can continuously takes advantages of both labeled, pseudo labeled or unlabeled images. Extensive experiments are conducted on three widely adopted datasets, including Market1501-SCT and MSMT17-SCT, and show the superiority of our method in SCT. Specifically, the mAP of our method significantly outperforms state-of-the-art SCT methods by 42.6% and 30.1%, respectively.
A Failure Diagnosis Method of the Disc Cutter Based on ARIMA
Authors: Yang Yun , Zhang Yiyun , Wu Dun , Li Yumei , Lan Hao
Published: May 2023

As one of the main rock-breaking tools on the cutterhead of the shield machine, the disc cutter often fails due to the abnormal wear, which in turn affects the tunneling efficiency of the shield machine, in order to find and replace the failed disc cutter in time, a means to effectively detect the wear state and a method to diagnose the failure of the disc cutter are required.In this paper, a failure diagnosis method combining the disc cutter wear measurement and ARIMA prediction is proposed, the theoretical model of wear change law under three common wear forms such as the normal wear, the eccentric wear and the polygon wear were established, and realize the prediction of the disc cutter wear by data cleaning, time reconstruction, model ordering, residual test and other treatments of wear time series, and the error threshold of the disc cutter failure was set by experiment, and a failure diagnosis method of the disc cutter is constructed. The method was applied to an actual case, and the diagnostic accuracy is 83.3%.
RP-Bert: An approach to detect and classify network intrusions based on a combination of Transfer learning and rules
Authors: Lei Shuxin , Zhang Xiaosong , Yi Junkai
Published: May 2023

We investigate the model generalization problem that exists for network traffic intrusion detection. Existing works have begun to apply some advanced algorithms and models from NLP domain to network security domain, but have some challenges. One of the biggest problem is that the amount of data for some existing network intrusions is not sufficient to train the current oversized models. For this purpose, we improve on ET-Bert and propose a method to detect and classify network intrusion based on the combination of migration learning and rules (RP-Bert). And we test and validate it on a large open source dataset BoT-loT, which Recall is achieved, Precision is achieved, F1 Score is achieved, Accuracy is achieved.
Micro-expression Recognition Based on DCBAM-EfficientNet Model
Authors: Qi Fengyu , Yang Chuanying , Shi Bao , Ma Shaoying
Published: May 2023

To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression features to be highlighted, and the CBAM attention is improved into a DCBAM model, where the large convolution kernel in the spatial attention module of CBAM is replaced by a dilated convolution with the same receptive field, reducing the network parameters while better preserving the spatial features of the image. The integration of the DCBAM model into the main structure of EfficientNet enables better integration of contextual information. Data enhancement is used to process the micro-expression dataset to decrease the occurrence of overfitting and improve the generalization ability of the model. The results demonstrate that the optimized model DCBAM-EfficientNet can effectively promote the recognition accuracy of micro-expressions, significantly reduce the quantity and volume of model parameters, and provide a reference for the deployment of mobile micro-expression recognition models.
Energy-Efficient Virtual Machine Migration Algorithm in Data Centers
Authors: Wu Xiaodong , Zeng Taisheng
Published: May 2023

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations.
A Design of a Miniaturized Receiver Front-end of an X-band Radar
Authors: Liu Shangguang , Gao Yong
Published: May 2023

Based on the development and practice of a high-frequency carrier-based aircraft radar receiver front-end, a design of a high-density miniaturized X-band receiver front-end with its environmental adaptability is introduced. The system scheme introduces the key technologies of technical scheme, specifications allocation, circuit realization and test system design. The front-end of the designed receiver has the characteristics of small volume, high density and good maintainability. The practice shows that the environmental adaptability and test features of the receiver meet the requirements of carrier-based aircraft radar.
A Genetic Algorithm-based Proactive Caching Policy in Edge Networks
Authors: Yuan Xianrui , Wang Fuchao , Wang Zhi , Wang Xin , Wang Yantong
Published: May 2023

The incredible growth in mobile traffic lays down a challenge to network design. Therefore, proactive caching technology has been proposed as an effective method to reduce network congestion and improve the quality of service. In this paper, we focus on the caching position problem in edge networks and use a mixed integer linear programming (MILP) model to provide the optimal selection. Due to the curse of dimensionality, a genetic algorithm-based caching policy is given to make a balance between performance and time complexity. Numerical investigations reveal that the proposed approach can reduce the total cost by around 20% compared with other widely-used caching methods.
Study of the characterizationn of CdTe thin films prepared by the pulsed laser deposition technique with different laser energies
Authors: Kamel Noora Sabah , Aadim Kadhim A. , Kadhim A.
Published: May 2023

This research studied the effects of different laser energies on structural and optical properties. We prepared CdTe thin films by PLD technique using an Nd:YAG laser with a wavelength of 1064 nm and different energies (400, 500, 600, and 700 mJ). The practical experiments were carried out at a temperature of 200 °C. XRD results revealed that all the prepared thin films have polycrystalline structures and cubic systems with average crystalline sizes of 34, 42, 54, and 57 nm for 400, 500, 600, and 700 mJ, respectively. We observed that the optical energy gap of CdTe thin films decreases with the increase of laser energy (1.87–1.58 eV). The topography of the surfaces of CdTe thin films deposited on glass substrates was studied by the AFM technique, and it was shown that the average diameter of all CdTe films increases with increasing laser energies. The mentioned properties were studied for the application of this thin film as the absorber layer in constructing a solar cell.
Green human resource management and job pursuit intention: mediating role of corporate social responsibility and organizational reputation
Authors: Wang Qiuhong , Gazi Md. Abu Issa , Sobhani Farid Ahammad , Masud Abdullah Al , Islam Md Aminul , Akter Tahmina
Published: May 2023

This study aimed to evaluate whether green human resource management practice encourages prospective candidates to join environmentally responsible enterprises through organizational reputation and corporate social responsibility. The sample was gathered from 402 students from various universities in Bangladesh using a pre-structured questionnaire. The link between the latent components has been explained by analyzing the obtained data using the Partial Least Squares Method (PLSM) of Structural equation Modeling (SEM). The findings indicated that green human resources management has a positive relationship with job pursuit intention and organizational reputation and that corporate social responsibility plays a significant mediating role in this relationship. Students studying in universities in Bangladesh reported having higher job pursuit intentions towards organizations that practice environmental responsibility. The results also show that green human resource management is a source for luring graduated students to pursue careers in green firms and will enable practitioners to combine green human resource management with their conventional human resource practices to employ qualified applicants. The results provide valuable insights into the nature of green human resource management and how the top management is committed to green initiatives in an emerging economy like Bangladesh. Also, the results will help the top management understand how to use green human resource management projects to create corporate social responsibility initiatives and an excellent corporate reputation and attract highly qualified, socially responsible employees to improve environmental performance in organizations. The national policymakers may also get input in designing a green human resource management policy for the economy. Bangladesh is a climate-vulnerable country with a minimal practice of green human resource management in the economy, so the empirical findings of this study will attract researchers, academicians, and practitioners of human resource management to carry out further research in the economy of this area. Finally, this study contributes to the body of knowledge in the still-emerging subject of green human resource management by empirically showing worthy outcomes.
Families of Galois representations and (𝜑,𝜏)-modules
Authors: Karnataki Aditya , Poyeton Léo
Published: May 2023

Let

p
p

be a prime, and let

K
K

be a finite extension of

Q

p

\mathbf {Q}_p

, with absolute Galois group

G

K

\mathcal {G}_K

. Let

π
\pi

be a uniformizer of

K
K

and let

K
∞

K_\infty

be the Kummer extension obtained by adjoining to

K
K

a system of compatible

p
n

p^n

-th roots of

π
\pi

, for all

n
n

, and let

L
L

be the Galois closure of

K
∞

K_\infty

. Using these extensions, Caruso has constructed é tale

(
ϕ
,
τ
)

(\phi ,\tau )

-modules, which classify

p
p

-adic Galois representations of

K
K

. In this paper, we use locally analytic vectors and theories of families of

ϕ
\phi

-modules over Robba rings to prove the overconvergence of

(
ϕ
,
τ
)

(\phi ,\tau )

-modules in families. As examples, we also compute some explicit families of

(
ϕ
,
τ
)

(\phi ,\tau )

-modules in some simple cases.
The Relationship between Diurnal Measures of Tonic Alertness and Self-Reported Fatigue in Persons with Multiple Sclerosis—A Retrospective Data Analysis
Authors: Weinrich Jonas O , Saliger Jochen , Eschweiler Mareike , Karbe Hans , Kalbe Elke , Nielsen Jörn
Published: May 2023

Objective

Fatigue in multiple sclerosis (MS) is common, burdensome, and usually assessed by self-report measures. This retrospective data analysis of the twice-daily Alertness test (Test battery of Attentional Performance) examined the extent to which this assessment procedure is associated with MS-related fatigue.

Method

Two-hundred and thirteen German inpatients (136 women) aged 18–69 years with predominantly relapsing MS (72.8%) were included. Based on reaction time (RT) differences between morning tonic alertness (8:30–11:00 a.m.) and afternoon tonic alertness (3:00–4:30 p.m.), patients were divided into an “improver,” “maintainer,” or “decliner” group. Multinomial logistic regression (MLR) was calculated to predict the likelihood of belonging to one of these performance groups, taking into account cognitive fatigue (Fatigue Scale of Motor and Cognition, FSMCcog), disease severity (Expanded Disability Status Scale, EDSS), depression (Center for Epidemiologic Studies Depression Scale, CES-D), gender, and tonic alertness (a.m.).

Results

The final MLR model (R2 = .30) included tonic alertness (a.m.) (<.001), FSMCcog (.008), EDSS (.038), CES-D (.161), and gender (.057). Using this model, correct assignment to alertness performance groups was 56.8%. Tonic alertness (p.m.) demonstrated the greatest potential for differentiation among the three performance groups (<.001).

Conclusions

These results show a relationship between subjective fatigue and tonic alertness. However, other variables also contribute to this association, suggesting that the RT differences between twice-daily measures of tonic alertness is not related to increased subjective fatigue in a substantial number of pwMS, which diminishes the diagnostic value. Further studies including relevant variables such as sleepiness are urgently needed.
Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease
Authors: Felício Daniela , Alves-Ferreira Miguel , Santos Mariana , Quintas Marlene , Lopes Alexandra M , Lemos Carolina , Pinto Nádia , Martins Sandra
Published: May 2023

Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information.

Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3′ UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor.

This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.
Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification
Authors: Zhang Chuanchao , Li Xinxing , Huang Wendong , Wang Lequn , Shi Qianqian
Published: May 2023

Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial transcriptional heterogeneity needs to effectively incorporate spatial information accounting for the substantial spatial correlation of expression measurements. Here, we develop a computational method, SpaSRL (spatially aware self-representation learning), which flexibly enhances and decodes spatial transcriptional signals to simultaneously achieve spatial domain detection and spatial functional genes identification. This novel tunable spatially aware strategy of SpaSRL not only balances spatial and transcriptional coherence for the two tasks, but also can transfer spatial correlation constraint between them based on a unified model. In addition, this joint analysis by SpaSRL deciphers accurate and fine-grained tissue structures and ensures the effective extraction of biologically informative genes underlying spatial architecture. We verified the superiority of SpaSRL on spatial domain detection, spatial functional genes identification and data denoising using multiple SRT datasets obtained by different platforms and tissue sections. Our results illustrate SpaSRL’s utility in flexible integration of spatial information and novel discovery of biological insights from spatial transcriptomic datasets.
DataCurator.jl: efficient, portable and reproducible validation, curation and transformation of large heterogeneous datasets using human-readable recipes compiled into machine-verifiable templates
Authors: Cardoen Ben , Ben Yedder Hanene , Lee Sieun , Nabi Ivan Robert , Hamarneh Ghassan
Published: May 2023

Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator, a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable, machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine-verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.