Initial Phase Research Articles

Use and co-use of alcohol and cannabis following physical pain in the daily life of community adults engaged in regular substance use.

Alcohol and cannabis are often perceived as pain-relieving. However, minimal work has examined whether people use and co-use these substances following pain in daily life. Forty-six adults reporting weekly use of alcohol and/or cannabis completed a 60-day ecological momentary assessment protocol, answering at least four daily reports on their alcohol and cannabis use and pain (nassessments = 10,769 over 2,656 days). We examined whether self-reported pain so far that day (cumulative-average pain) was associated with subsequent alcohol and cannabis use and same-occasion co-use. Models also addressed whether associations differed for initiating versus continuing a use episode. Hypotheses were preregistered. A multinomial multilevel model found that cumulative-average pain was associated with a greater likelihood of same-occasion co-use in the continuation phase but not the initiation phase, compared to no use (OR = 1.48,95% CI [1.06, 2.06], p = .023) and alcohol use (OR = 1.52, CI [1.03, 2.26], p = .037). Cumulative-average pain was largely not associated with alcohol-only and cannabis-only use. After alcohol use, greater pain was associated with cannabis use (OR = 1.37, CI [1.11, 1.70], p = .004), but not the reverse. Secondary analyses found greater previous-occasion (not cumulative) pain was associated with initiation of alcohol use and number of drinks, and initiation and continuation of cannabis use, but not number of cannabis hits. Although not all hypotheses were supported, pain was associated with subsequent substance use in this sample engaged in regular substance use and not recruited for chronic pain. Cumulative pain may be particularly related to alcohol-cannabis same-occasion co-use, which may increase the risk of substance use-related problems over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neurophysiological evidence of sensory prediction errors driving speech sensorimotor adaptation.

The human sensorimotor system has a remarkable ability to quickly and efficiently learn movements from sensory experience. A prominent example is sensorimotor adaptation, learning that characterizes the sensorimotor system's response to persistent sensory errors by adjusting future movements to compensate for those errors. Despite being essential for maintaining and fine-tuning motor control, mechanisms underlying sensorimotor adaptation remain unclear. A component of sensorimotor adaptation is implicit (i.e., the learner is unaware of the learning process) which has been suggested to result from sensory prediction errors-the discrepancies between predicted sensory consequences of motor commands and actual sensory feedback. However, to date no direct neurophysiological evidence that sensory prediction errors drive adaptation has been demonstrated. Here, we examined prediction errors via magnetoencephalography (MEG) imaging of the auditory cortex (n = 34) during sensorimotor adaptation of speech to altered auditory feedback, an entirely implicit adaptation task. Specifically, we measured how speaking-induced suppression (SIS)--a neural representation of auditory prediction errors--changed over the trials of the adaptation experiment. SIS refers to the suppression of auditory cortical response to speech onset (in particular, the M100 response) to self-produced speech when compared to the response to passive listening to identical playback of that speech. SIS was reduced (reflecting larger prediction errors) during the early learning phase compared to the initial unaltered feedback phase. Furthermore, reduction in SIS positively correlated with behavioral adaptation extents, suggesting that larger prediction errors were associated with more learning. In contrast, such a reduction in SIS was not found in a control experiment in which participants heard unaltered feedback and thus did not adapt. In addition, in some participants who reached a plateau in the late learning phase, SIS increased (reflecting smaller prediction errors), demonstrating that prediction errors were minimal when there was no further adaptation. Together, these findings provide the first neurophysiological evidence for the hypothesis that prediction errors drive human sensorimotor adaptation.

Endocrine Adverse Events Related To Immune Checkpoint Inhibitor Treatment: Relationship Between Antibodies and Severity of Thyroid Dysfunction.

The purpose of this study was to identify predictive and risk factors for the development of immune-related endocrinopathies and to analyze the incidence and characteristics of immune-related endocrinopathies in our population. A retrospective, single-centre cohort carried out at Gregorio Marañón Hospital between January 2018 -December 2019. A total of 163 patients were enrolled. In January 2018 and December 2019, we treated patients who underwent ICI treatment in the Medical Oncology Department of General University Hospital Gregorio Marañón, a tertiary care public hospital in Madrid, as part of an observational, retrospective, single-center cohort study. Endocrinopathies were diagnosed in 19.5% of the patients (n = 32). The tumours with the highest incidence of endocrinopathies were non-small cell lung cancer (25,9%), kidney cell cancer (25%) and hepatocarcinoma (20%). Among the 32 patients who developed endocrinopathy, 18,8%, 19,13%, and 21,28% received anti-CTLA-4, anti-PD-1 and anti-PDL-1, respectively. Thyroid dysfunction was the most frequent endocrinopathy (12,8%). A higher percentage of patients with negative antiTPO and antiTG antibodies developed G1 hypothyroidism compared to patients with positive antibodies who developed a higher proportion of G2 hypothyroidism. The presence of an initial phase of thyrotoxicity was not related to greater severity. We observed longer progression-free survival in patients who developed thyroid dysfunction. Pre-existing antibodies were independently associated with endocrinopathies. Moreover, our study let us conclude that the presence of thyroid autoantibodies may be related to its severity. It is important to determine anti-thyroid antibodies prior to the start of immunotherapy as a risk factor for thyroid dysfunction, which in turn is a prognostic marker.

Predicting Factors of Worse Prognosis in COVID-19: Results from a Cross-sectional Study on 52 Inpatients Admitted to the Internal Medicine Department.

The initial phases of the COVID-19 pandemic posed a real need for clinicians to identify patients at risk of poor prognosis as soon as possible after hospital admission. The study aimed to assess the role of baseline anamnestic information, clinical parameters, instrumental examination, and serum biomarkers in predicting adverse outcomes of COVID-19 in a hospital setting of Internal Medicine. Fifty-two inpatients consecutively admitted to the Unit of Internal Medicine "Baccelli," Azienda Ospedaliero - Universitaria Policlinico of Bari (February 1 - May 31, 2021) due to confirmed COVID-19 were grouped into two categories based on the specific outcome: good prognosis (n=44), patients discharged at home after the acute phase of the infection; poor prognosis, a composite outcome of deaths and intensive care requirements (n=8). Data were extracted from medical records of patients who provided written informed consent to participate. The two study groups had similar demographic, anthropometric, clinical, and radiological characteristics. Higher interleukin 6 (IL-6) levels and leucocyte count, and lower free triiodothyronine (fT3) levels were found in patients with poor than those with good prognosis. Higher IL-6 levels and leucocyte count, lower fT3 concentration, and pre-existing hypercholesterolemia were independent risk factors of poor outcomes in our study population. A predicting risk score, built by assigning one point if fT3 < 2 pg/mL, IL-6 >25 pg/mL, and leucocyte count >7,000 n/mm3, revealed that patients totalizing at least 2 points by applying the predicting score had a considerably higher risk of poor prognosis than those scoring <2 points (OR 24.35 (1.32; 448), p = 0.03). The weight of pre-existing hypercholesterolemia did not change the risk estimation. Four specific baseline variables, one anamnestic (pre-existing hypercholesterolemia) and three laboratory parameters (leucocyte count, IL-6, and fT3), were significantly associated with poor prognosis as independent risk factors. To prevent adverse outcomes, the updated 4-point score could be useful in identifying at-risk patients, highlighting the need for specific trials to estimate the safety and efficacy of targeted treatments.

Die vier Phasen von Citizen Development-Initiativen: Treiber, Herausforderungen und Handlungsempfehlungen

ZusammenfassungModerne Low-Code/No-Code-Plattformen transformieren die Art und Weise, wie Unternehmen digitale Lösungen entwickeln und Innovationen voranbringen. Durch den Fortschritt in Technologien wie Cloud-Computing, künstlicher Intelligenz und maschinellem Lernen ermöglichen diese Plattformen Fachbereichsmitarbeitern ohne umfassende IT-Kenntnisse – den sogenannten Citizen Developern – das Entwerfen und Implementieren eigener leichtgewichtiger digitaler Lösungen. Dieser Trend motiviert immer mehr Unternehmen, Citizen Development-Initiativen ins Leben zu rufen und somit einen kulturellen Wandel hin zu einer demokratisierten Entwicklung digitaler Innovationen zu fördern. Trotz des erkennbaren Potenzials stehen viele Unternehmen jedoch vor der Frage, wie sie ihre Citizen Development-Initiativen effektiv starten und unternehmensweit skalieren können. Dieser Artikel beleuchtet diese Fragestellung basierend auf Interviews mit 21 Verantwortlichen für Citizen Development aus 16 verschiedenen Unternehmen, die früh in Low-Code/No-Code investiert haben. Wir identifizieren vier Phasen, die Unternehmen im Rahmen von Citizen Development durchlaufen, und die spezifischen Herausforderungen, die in jeder Phase bewältigt werden müssen. Unsere Forschungsergebnisse eröffnen Unternehmen Einblicke in aktuelle Praktiken und Zielzustände, um die Herausforderungen einer erfolgreichen Implementierung von Citizen Development zu meistern. Unternehmen sollen befähigt werden, ihre Ressourcen effizienter zu nutzen und Citizen Development nahtlos in ihre Digitalstrategie zu integrieren.

Compact City and Mayoral Entrepreneurship: A Study of Success and Setbacks in Two Japanese Cities

Controlling urban sprawl are the overarching challenges facing cities worldwide. This article delves into the pivotal role of policy entrepreneurship throughout the planning and implementation phases of compact city initiatives. Drawing insights from the experiences of Aomori City and Toyama City in Japan, this study scrutinizes the drivers behind the shift toward urban compactness and its consequent outcomes. The incorporation of policy studies theories into the realm of compact city development has been scant. To bridge this gap, the research leverages process-oriented theories from policy studies to dissect the decision-making processes guiding compact city development in these two pivotal municipalities. Through this investigation, it is revealed that the effective transition to compact urbanization in Japanese municipalities predominantly stems from policy entrepreneurship. The author posits that this achievement is primarily attributed to adept local mayors who possess the acumen to cater to specific local needs.

Curiosity Satisfaction Increases Event-related Potentials Sensitive to Reward.

Successful learning depends on various factors such as depth of processing, motivation, or curiosity about information. A strong drive to learn something or the expectation of receiving a reward can be crucial to enhance learning. However, the influence of curiosity on the processing of new information and its similarity with reward processing is not well understood. This study examined whether states of curiosity influence specific ERPs associated with reward processing and whether these ERPs are related with later memory benefits. In an initial screening phase, participants indicated their curiosity and confidence in prior knowledge about answers to various trivia questions. In a subsequent study phase, we targeted different time windows related to reward processing during the presentation of trivia answers containing the reward positivity (RewP; 250-350 msec), the P3 (250-500 msec), and the late-positive-potential (LPP; 600-1000 msec). In a following surprise memory test, we found that participants recalled more high- than low-curiosity answers. The RewP, P3, and LPP showed greater positive mean amplitudes for high compared with low curiosity, reflecting increased reward processing. In addition, we found that the RewP and the P3 showed more positive mean amplitudes for later recalled compared with later forgotten answers, but curiosity did not modulate this encoding-related results. These findings support the view that the satisfaction of curiosity resembles reward processing, indicated by ERPs.

Clinical efficacy of the Invisalign mandibular advancement appliance: A retrospective investigation

This retrospective investigation aimed to determine whether the achieved changes in the treatment of Class II malocclusion were the same as those planned after the prescribed wear of an initial phase of Invisalign treatment with the mandibular advancement appliance (MAA) (Align Technology, Santa Jose, Calif). All patients treated with the MAA and who satisfied inclusion/exclusion criteria were selected from a database of 16,500 patients treated with clear aligners by 16 orthodontists. The pretreatment, planned, and achieved overjet measurements were documented from data provided in Align Technology's software facility, ClinCheck. Changes in intermaxillary anteroposterior (AP) first permanent molar (FPM) relationships were evaluated using Geomagic Control X (3D systems, Rock Hill, SC) metrology software. Most of the 195 patients who satisfied the inclusion criteria were females (n= 104; 53.3%). The mean age was 12.62 ± 2.20 years. The pretreatment overjet was reduced from 6.49 ± 2.86 mm to 4.61 ± 2.22 mm after prescribed MAA wear, which was 42.5% of the planned outcome. The pretreatment AP FPM reduced from 3.14 ± 1.95 mm to 2.24 ± 2.51 mm, which was 31.3% of that planned. Thirty-eight (19.5%) patients experienced an increase in overjet where a reduction was planned. Less than half of the planned overjet reduction and less than a third of the planned AP FPM correction were achieved with the MAA. Almost 20% of patients completed the MAA phase of treatment with an increased overjet despite a reduction being planned.

High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithms

The commonly used high utility itemsets mining method for massive data is the intelligent optimization algorithm. In this paper, the WHO (Whale-Hawk Optimization) algorithm is proposed by integrating the harris hawk optimization (HHO) algorithm with the beluga whale optimization (BWO) algorithm. Additionally, a whale initialization strategy based on good point set is proposed. This strategy helps to guide the search in the initial phase and increase the diversity of the population, which in turn improve the convergence speed and algorithm performance. By applying this improved algorithm to the field of high utility itemsets mining, it provides new solutions to optimization problems and data mining problems. To evaluate the performance of the proposed WHO, a large number of experiments are conducted on six datasets, chess, connect, mushroom, accidents, foodmart, and retail, in terms of convergence, recall rates, and runtime. The experimental results show that the convergence of the proposed WHO is optimal in five datasets and has the shortest runtime in all datasets. Compared to PSO, AF, BA, and GA, the average recall rate in the six datasets increased by 32.13%, 49.95%, 12.15%, and 16.24%, respectively.

CHEMICAL OCULAR INJURY BY POTASSIUM PERMANGANATE

Abstract Introduction &amp; Objectives : Chemical ocular injury is an emergency case and commonly occured among children. Alkaline chemical ocular injury is more destructive especially due to saponification. One of the rare chemical ocular injury is alkaline trauma by Potassium Permanganate (KMNO4) Methods : A case of a three-year-old boy patient brought by his mother in emergency department at Cicendo Eye Hospital with severe pain, redness, and swelling of the left eye. Ten hours earlier, the patient was playing and accidentally exposed to KMNO4. Visual acuity of the right eye was 6/6 and the left eye was difficult to examine. Ophthalmological examination revealed superior and inferior palpebra are swelling, KMNO4 adhered tightly on conjunctival tarsal superior, inferior, and conjunctival bulbi inferior until fornix and temporal area. Cornea was hazed and iris shadow test positive so he was diagnosed with Chemical Trauma Ropper Hall grade II OS. Treatment begins with irrigation 2 litres of normal saline until pH 7 along with debridement of KMNO4 from conjunctival tarsal superior inferior and bulbi. The patient underwent installation of transplanted amniotic membrane and debridement under anasthesia Results : Management in the initial phase is irrigation. The definitive therapy requires surgery to speed up the healing process. Complications that often occur are corneal limbal cell deficiency, dry eye, symblepharon formation, secondary glaucoma, and complicated cataracts Conclusion : Alkali chemical injury is hazardous and can lead to severe vision threatening complications because of saponification. Prompt and aggressive management is required in such cases to give them the best chance of a more favourable outcome

Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.

Joint Effects of Winds and Tides on Near-Inertial Internal Waves in the Northern South China Sea: A Three-Dimensional Numerical Study

Abstract Joint effects of winds and tides on near-inertial internal waves (NIWs) are numerically investigated via a series of three-dimensional quasi-realistic simulations in the northern South China Sea (NSCS). Model results demonstrate that in the presence of wind-induced NIWs, more tidal energy is transferred to NIWs, while in the presence of tide-induced NIWs, the extreme wind (cyclone) would inject less near-inertial kinetic energy (NIKE). The interaction between wind-induced and tide-induced NIWs produces total NIKE more (or less) than a linear superposition of that generated by wind and tide forcing alone at different sites in the NSCS. Specifically, near the Luzon Strait, both tides and winds make positive contributions to the local near-inertial energy input, resulting in more than 30% enhancement of total NIKE (&gt;0.5 kJ m−2). However, in some deep-water regions along the cyclone paths, energy is transferred from cyclones to NIWs and also from NIWs to internal tides. Due to this “energy pipeline” effect, tide- and wind-induced NIWs contribute to weakening of total NIKE (∼0.3 kJ m−2 or 30%). Additionally, sensitivity experiments with varying initial tidal phases indicate that the interaction between wind-induced NIKE and tide-induced NIKE is robust in most of the model domain (over 80%) under different phase alignments between wind- and tide-induced NIWs. From the perspective of cyclones, tide-induced NIKE is comparable to wind-induced NIKE in the Luzon Strait before the arrival of cyclones, while tide-induced NIKE is two orders of magnitude smaller than wind-induced NIKE in most of the NSCS after the arrival of cyclones. Overall, our results highlight the joint effects of wind and tide forcing on the local NIW dynamics in the NSCS. Significance Statement Near-inertial internal waves (NIWs) are ubiquitous phenomena in stratified water, which can significantly influence the ocean mixing, especially across the thermocline. NIWs are generated mainly by wind forcing near the sea surface, but also by internal tide breaking in the ocean interior. Hence, a question that arises is whether winds or tides play dominant roles in generating NIWs. In fact, due to the interaction between wind- and tide-induced NIWs, the total near-inertial kinetic energy (NIKE) is not merely a linear superposition of that generated by winds and tides forcing alone. By carrying out numerical experiments, we find that extreme winds can help to transfer more energy from tides to NIWs, while tides would suppress energy transferring from winds to NIWs. As a result, this fact is crucial in accurately reproducing NIW dynamics in a targeted region, thereby determining redistribution of local ocean mixing intensity.

A FEA-Based Fast AC Steady-State Algorithm for a Voltage-Driven PMSM by a Novel Voltage-Flux-Driven Model

Finite element analysis (FEA) has been used more frequently than ever before in electrical machine design covering different disciplines such as electromagnetics, rotor dynamics, thermodynamics etc. The accuracy and speed of FEA are in high demands, especially when the time-stepping FEA is employed in the electromagnetic design of the machine. In this paper, to accelerate numerical transient in FEA of a voltage-driven permanent magnet synchronous machine (PMSM), a fast AC steady-state algorithm has been proposed, in which the voltage supply is converted into the stator flux linkage and the machine can be regarded as driven by the flux linkage directly. The electromagnetic transient is eliminated by injecting additional voltage signals to compensate the oscillating stator flux linkage in transient and the time-stepping error caused by the backward Euler in FEA. And the additional voltage signals will finally disappear after the steady state is achieved within about a half electrical time period. In addition, the maximum torque per ampere (MTPA) control logic can also be realized by adjusting the initial phase angle of the voltage supply based on the vector diagram. The simulation speed of the proposed method in this paper was compared to other traditional models and the accuracy was verified by experimental results.

Floral Initiation, Organogenesis, and Flowering of Mature Paphiopedilum Clair de Lune ‘Edgard Van Belle’ Award of Merit/American Orchid Society Plants Derived from Divisions

Paphiopedilum Clair de Lune ‘Edgard Van Belle’, an excellent Maudiae-type hybrid that has been propagated by artificial division for a long time. We studied its flower bud initiation, development of floral organs, and flowering habits with a view to providing information for flowering control and efficient commercial production. According to our research, the flower bud initiation phase of this cultivar begins in February every year, and 80% of the plants completed sepal primordium differentiation in March, The flower bud differentiation lasts for 6 to 7 months, until flowering in August. Within 1 to 3 months after flower bud differentiation, all tested plants differentiated lateral buds. After 5 to 6 months, the new, aboveground vegetative shoots reached their maximum growth, with an average plant height of 20 cm, five leaves, and a shoot dry weight of more than 3 g. From February to April of the following year, a new cycle of flower development and vegetative growth began. In addition, this cultivar was notably sensitivity to high ambient temperature during the late phase of flower development, with a flower bud drop rate as high as 33.3% under average day/night temperatures of 29.0/26.5 °C.

Understanding government social media communication strategies and public engagement during the COVID-19 crisis in Lebanon

The COVID-19 pandemic has prompted governments to adopt various online communication strategies, including the use of social media platforms. Drawing on the crisis and emergency risk communication model, this study investigates the Facebook communication strategies employed by the Ministry of Public Health (MoPH) during the COVID-19 crisis in Lebanon and examines the public engagement with these efforts. A content analysis was conducted on 2,001 posts related to COVID-19 from January 2020 to June 2021. The posts were classified into categories and the frequency and median were used to measure government posting activity and engagement. The study analyzed additional post features such as hashtags, mentions, captions, and language to provide further context to the findings. Trend analysis was also conducted to examine how the metrics varied with key events. The results show that MoPH predominantly used photos (72.00%) and the majority of its posts were published during the initial phase of the outbreak (74.20%). The most prevalent categories of posts were uncertainty reduction (54.57%), self-efficacy (30.33%), and vaccines and immunization (16.89%). MoPH’s Facebook page witnessed a surge in followers during the post-COVID-19 period, increasing by over 300.00% from the pre-COVID-19 years. Posts content, media type, and crisis stage influenced the level of public engagement. The study reveals that Facebook is effective for strategic health crisis communication. Furthermore, it provides practical insights for public sector emergency managers responsible for online communication during outbreaks.

Composing MPC With LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and is capable of improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amortized</i> running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the need to reduce the amortized cost of MPC in repetitive industrial robotic applications, where long-term operational cost is important and safety is critical. Examples of such applications include factory robotic arm manipulation and fixed-route quadcopter payload transport. Unlike explicit MPC or function approximation, our approach does not require any pre-computation or pre-training. Rather, it attains task proficiency over time by learning a surrogate neural network on the spot and by gradually replacing the costly MPC with the more efficient surrogate model so long as safety permits. Consequently, the proposed scheme incurs a learning cost during the initial phase of the deployment but usually becomes more adept on the task afterwards, leading to amortized efficiency.

Digitization Process of Archives: A Case Study of the Punjab Archives, Lahore

The digitization process of archives involves the conversion of physical documents, records, and artefacts into digital formats, enabling easier access, preservation, and dissemination of valuable historical information.The present study aimed to explore the digitization and preservation of archives: a case study of the Punjab Archives, Lahore. This study uses a qualitative research methodology in order to achieve the study's goals. An inductive approach was used to collect the data. Data was collected from the staff of the Punjab archives team, the project team and primary sources. Data was converted into written transcripts with the help of NVivo software. The benefits of digitization, such as improved accessibility, enhanced searchability, increased security, efficiency of accessing resources, and the protection of cultural heritage, are discussed, along with potential benefits, challenges, and solutions. The findings of the study revealed that the Punjab archives are the richest and oldest archives in the region. Seven million archives are available in different forms (documents, manuscripts, government records, coins, stamps, lithography, books, pictures, maps, etc.) in the Punjab Archives, and 114,592 documents have been selected for the initial phase of digitization. The purpose of the project was to preserve damaged, forged, and torn records in digital form.

Artificial intelligence and feature based transfer learning framework for diagnosis of chest diseases using radiology images

The subject of artificial intelligence-assisted diagnosis and design in the medical industry is very exciting due to considerable developments in medical imaging. In real-world applications, previous manual feature extraction strategies were inefficient in achieving the required results. The number of medical image databases is quickly increasing to accommodate hospital-based diseases as a result of the numerous uses of medical images in healthcare facilities, pathology, and medical diagnostic fields. The primary objective of this study is to create a computerized Artificial intelligence system that can accurately diagnose different diseases and reduce mistakes in the testing process. The study has two primary aspects. In the initial phase, we utilized the deep transfer learning method to extract the pertinent and crucial features from the image x-ray. Subsequently, the support vector machine employs these crucial extracted features to diagnose diseases from the x-ray14 dataset. The imbalanced dataset problem was also addressed with the utilization of the Synthetic Minority Oversampling Technique (SMOTE). The authors conduct a comparative analysis of the findings from this study in relation to other cutting-edge studies and employ cross-dataset experiments to evaluate its efficacy. The results demonstrate that the proposed approach has a detection accuracy of 95.2% for the disease. The VGG-16 model achieved 78.4% accuracy and an AUC of 90%. The proposed model can be applied to other diseases for further experiments.

Relative Yield of Thermal and Nonthermal Emission during Weak Flares Observed by STIX during 2021 September 20–25

The disparate nature of the thermal–nonthermal energy partition during flares, particularly during weak flares, is still an open issue. Following the Neupert effect, quantifying the relative yield of X-ray emission in different energy bands can enable the inferring of the underlying energy release mechanism. During 2021 September 20–25, the Solar Orbiter mission—being closer to the Sun (∼0.6 au) and having a moderate separation angle (<40°) from the Sun–Earth line—offered a unique opportunity to analyze multiwavelength emission from ∼200 (mostly weak) flares, commonly observed by the Spectrometer Telescope for Imaging X-rays (STIX), STEREO-A, GOES, and the Solar Dynamics Observatory. Associating the quotient (q f ) of hard X-ray fluence (12–20 keV) and soft X-ray flux (4–10 keV) with the peak soft X-ray flux enabled us to identify strongly nonthermal flares. A multiwavelength investigation of spectral and imaging-mode observations of the 20 strongly nonthermal weak flares reveals an inverse relationship of q f with the emission measure (and density), and a positive relationship with the flare plasma temperature. This indicates that the plasma in tenuous loops attains higher temperatures compared to that in the denser loops, in response to nonthermal energy deposition. This is in agreement with the plasma parameters of the coronal loops, as derived by applying the one-dimensional Palermo–Harvard hydrodynamical code to coronal loop plasma with different initial coronal loop base pressures when subjected to similar heating inputs. Our investigation, therefore, indicates that the plasma parameters of the flaring loop in the initial phase have a decisive role in the thermal–nonthermal energy partitioning.

Unveiling success determinants for AMB-assisted phase expansion of fusion proteins in ARP/wARP

Fusion proteins (FPs) are frequently utilized as a biotechnological tool in the determination of macromolecular structures using X-ray methods. Here, we explore the use of different protein tags in various FP, to obtain initial phases by using them in a partial molecular replacement (MR) and constructing the remaining FP structure with ARP/wARP. Usually, the tag is removed prior to crystallization, however leaving the tag on may facilitate crystal formation, and structural determination by expanding phases from known to unknown segments of the complex. In this study, the Protein Data Bank was mined for an up-to-date list of FPs with the most used protein tags, Maltose Binding Protein (MBP), Green Fluorescent Protein (GFP), Thioredoxin (TRX), Glutathione transferase (GST) and the Small Ubiquitin-like Modifier Protein (SUMO). Partial MR using the protein tag, followed by automatic model building, was tested on a subset of 116 FP. The efficiency of this method was analyzed and factors that influence the coordinate construction of a substantial portions of the fused protein were identified. Using MBP, GFP, and SUMO as phase generators it was possible to build at least 75 % of the protein of interest in 36 of the 116 cases tested. Our results reveal that tag selection has a significant impact; tags with greater structural stability, such as GFP, increase the success rate. Further statistical analysis identifies that resolution, Wilson B factor, solvent percentage, completeness, multiplicity, protein tag percentage in the FP (considering amino acids), and the linker length play pivotal roles using our approach. In cases where a structural homologous is absent, this method merits inclusion in the toolkit of protein crystallographers.