Search Results (463)

This study explores the emergent dynamics of knowledge sovereignty within organisations following data breach incidents. Using qualitative analysis based on Benoit’s image restoration theory, this study shows that employees do more than relay official messages—they actively shape information governance after a cyberattack. Employees adapt Benoit’s response strategies (denial, evasion of responsibility, reducing offensiveness, corrective action, and mortification) based on how authentic they perceive the organisation’s response, their identification with the company, and their sense of fairness in crisis management. This investigation substantively extends extant crisis communication theory by showing how knowledge sovereignty is shaped through negotiation, as employees manage their dual role as breach victims and organisational representatives. The findings suggest that employees are key actors in post-breach information governance, and that their authentic engagement is critical to organisational recovery after cybersecurity incidents. Full article

Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred and difficult to identify. The purpose of this study is to describe this concept, to draw attention to one of the “pandemics” of the 21st-century world, and to find methods by which we can defend ourselves against them. Materials and methods. A cross-sectional study was conducted based on a sample of 442 respondents. Results. For 77.8% of the people surveyed, the concept of “fake news” is important in Romania. Regarding trust in the mass media, a clear dominance (72.4%) was observed among participants who have little trust in the mass media. Although 98.2% of participants detect false information found on the internet, 78.5% are occasionally deceived by the information provided. Of the participants, 47.3% acknowledged their vulnerability to disinformation. The main source of disinformation is the internet, as 59% of the interviewed subjects believed. As the best measure against disinformation, the study group was divided almost equally according to the three possible answers, all of which were considered to be equally important: imposing legal restrictions and blocking the posting of certain news (35.4%), imposing stricter measures for authors (33.9%), and increasing vigilance among people (30.5%). Conclusions. According to the statistics based on the participants’ responses, the main purposes of disinformation are propaganda, manipulation, distracting attention from the truth, making money, and misleading the population. It can be observed that the main intention of disinformation, in the perception of the study participants, is manipulation. Full article

In the present study, 96 teams’ performances across 48 matches in the group stage of the FIFA Club World Cup 2025 were analyzed. Teams scoring first won 62.5% of matches (p < 0.05), while goals were evenly distributed between halves (p > 0.05) and showed marginal variation across six 15 min intervals, peaking near the 30–45 and 75–90 min marks. Parametric analyses revealed a significant effect of match outcome on possession, with winning teams exhibiting higher average possession (53.3%) compared to losing and drawing teams. Non-parametric analyses identified significant differences between match outcomes for goals scored, attempts at goal, total and completed passes, pass completion rate, defensive line breaks, receptions in the final third, ball progressions, defensive pressures, and total distance covered. Winning teams scored more goals and registered more attempts on target than losing teams, although some metrics showed no significant difference between wins and draws. Logistic regression analysis identified attempts at goal on target, defensive pressures, total completed passes, total distance covered, and receptions in the final third as significant predictors of match success (AUC = 0.85), correctly classifying 80.2% of match outcomes. These results emphasized the crucial role of offensive accuracy and possession dominance in achieving success in elite football. Full article

As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime risk levels in Galați County, Romania. The analysis is based on a newly compiled dataset of 132 monthly observations from January 2014 to December 2024, which combines a broad array of social, economic, and environmental data points. The main variable, ‘Crime risk’, is based on normalized counts of offenses per capita and divided into five balanced levels: very low, low, moderate, high, and very high. The hybrid ARIMA-ANN model merges the strengths of statistical time series analysis with the flexible learning ability of artificial neural networks. Performance is evaluated against multinomial logistic regression, decision trees, random forests, and support vector machines. Overall, the results show that an ARIMA-ANN model consistently outperforms traditional methods, especially in recognizing patterns over time, seasonal trends, and complex nonlinear relationships in crime data. This study not only sets a new benchmark for crime analytics in Romania but also offers a flexible, scalable framework for classifying crime risk levels across different regions. Full article

Henri Lefebvre’s “right to the city” has rarely been examined through an intersectional feminist lens, leaving unnoticed the uneven burdens that urban design and policy place on women. This article bridges that gap by combining constitutional analysis, survey data (n = 736), in-depth interviews, and participatory observation to assess how Quito’s public spaces affect women’s safety and mobility. Quantitative results show that 81% of respondents endured sexual or offensive remarks, 69.8% endured obscene gestures, and 38% endured severe harassment in the month before the survey; 43% of these incidents occurred only days or weeks beforehand, underscoring their routine nature. Qualitative narratives reveal behavioral adaptations—altered routes, self-policing dress codes, and distrust of authorities—and identify poorly lit corridors and weak institutional presence as spatial amplifiers of violence. Analysis of Quito’s “Safe City” program exposes a gulf between its ambitious rhetoric and its narrow, transport-centered implementation. We conclude that constitutional guarantees of participation, appropriation, and urban life will remain aspirational until urban planning mainstreams gender-sensitive design, secures intersectoral resources, and embeds women’s substantive participation throughout policy cycles. A feminist reimagining of Quito’s public realm is therefore indispensable to transform the right to the city from legal principle into lived reality. Full article

American citizens are significantly more likely to experience property crime victimization than violent crime victimization. During a staffing crisis, police prioritize limited resources in combating serious crime; however, property crimes remain impactful to the community. Therefore, agencies need to consider innovative ways to control property crime, such as “super-cocooning” strategies that alert residents to recent offenses. These strategies intend to empower the community to implement guardianship and crime prevention measures. For these strategies to be effective, they require public buy-in and support. The present study implements a preregistered information provision survey experiment (N = 2412), similar to the strategy of super-cocooning, to assess whether the public is more likely to support such strategies to combat property crime. Although the sample held overall high support of this strategy, exposure to a super-cocooning door hanger prime produced no significant changes in perceived effectiveness. However, there was observed racial heterogeneity in the treatments: non-White respondents assigned to the treatment relative to White respondents experienced significantly increased support of super-cocooning strategies. Implications for light-footprint crime control strategies, particularly during a staffing crisis, are discussed. Full article

The challenge of detecting nuanced, context-dependent offensive language highlights the need for Large Language Model (LLM) adaptation strategies that can effectively address inherent data and task asymmetries. Standard Parameter-Efficient Finetuning (PEFT) methods like Low-Rank Adaptation (LoRA), while efficient, often employ a more uniform, or symmetric, update mechanism that can be suboptimal for capturing such linguistic subtleties. In this paper, we propose Dynamic Sparse LoRA (DS-LoRA), a novel technique that leverages asymmetric adaptation to enhance LLM finetuning for nuanced offensive language detection. DS-LoRA achieves this by (1) incorporating input-dependent gating mechanisms, enabling the asymmetric modulation of LoRA module contributions based on instance-specific characteristics, and (2) promoting asymmetric sparsity within LoRA update matrices via L1 regularization. This dual asymmetric strategy empowers the model to selectively engage and refine only the most pertinent parameters for a given input, fostering a more parsimonious and contextually aware adaptation. Extensive experiments on benchmark datasets demonstrate DS-LoRA’s significant overperformance over standard LoRA and other strong baselines, particularly in identifying subtle and contextually ambiguous offensive content, underscoring the benefits of its asymmetric adaptive capabilities. Full article

The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channels. Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB)—the best performance was achieved by the same SVM. DL models involved evaluating Bi-LSTM and CNN models, where the CNN model outperformed the others. Moreover, transformer variants such as LLaMA 2 and ModernBERT (MBERT) were instantiated and fine-tuned with LoRA (Low-Rank Adaptation) for better efficiency. LoRA has been tuned for large language models (LLMs), a family of advanced machine learning frameworks, based on the principle of making the process efficient with extremely low computational cost with better enhancement. According to the experimental results, LLaMA 2 with LoRA attained the highest F1-score of 96.58%, greatly exceeding the performance of other approaches. To elaborate, LoRA-optimized transformers perform well in capturing detailed subtleties of linguistic nuances, lending themselves well to Roman Urdu offensive language detection. The study compares the performance of conventional and contemporary NLP methods, highlighting the relevance of effective fine-tuning methods. Our findings pave the way for scalable and accurate automated moderation systems for online platforms supporting multiple languages. Full article

Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). Method: A total of 859 games from the 2021 to 2023 regular seasons were analyzed, using both traditional baseball statistics and advanced sabermetric indicators such as the Weighted Runs Created Plus (wRC+), Weighted Runs Above Average (wRAA), and Percentage of Leadoff Batters on Base (PLOB%). Five machine learning models—decision tree, logistic regression, Neural Network, Random Forest, and XGBoost—were constructed and assessed through a five-fold cross-validation. Evaluation metrics included accuracy, F1 scores, sensitivity, specificity, and the AUC-ROC. Results: Among the models, logistic regression and XGBoost achieved the highest performance, with an accuracy ranging from 0.89 to 0.93 and an AUC-ROC from 0.97 to 0.98. The feature importance and SHapley Additive exPlanations (SHAP) analysis revealed that the wRC+ and PLOB% were the most influential predictors, reflecting the offensive efficiency and pitching control. Conclusion: The results suggest that combining interpretable machine learning with sabermetrics provides valuable insights for coaches and analysts in professional baseball. Furthermore, incorporating performance weighting based on game context may further enhance model accuracy. This research demonstrates the potential of data-driven strategies in sports analytics and decision-making. Full article

Professional training in elite team handball academies is key to developing top players, yet its direct impact on physical performance remains unclear. This study aimed to (1) provide professional training to elite adolescent players and (2) assess performance improvements using a team handball-specific test. Thirty elite male players (six goalkeepers, 24 field players) participated in an 11-week program, with nine under-19 (17.2 ± 1.3 years) and nine under-17 (15.6 ± 0.9 years) field players completing at least 80% of sessions. All underwent pre- and post-testing using the game-based performance test. A two-way ANOVA analyzed differences between tests and age groups as well as playing positions. Significant improvements (p < 0.05) were found in defense and offense time and body weight for both groups. Under-17 players also showed a significant increase in peak oxygen uptake (+9%), ball velocity (+7%), and jump height (+20%). Agility in defense and offense improved in under-19 (+3%) and under-17 (+6%) players, aligning with training goals. Positional differences were observed between backcourt players and wings (p < 0.01) in the ball velocity, while all players showed improvements in both defense and offense performance. We suggest that professional and targeted specific training at this age has a significant impact on the long-term development of adolescent team handball players and is the basis for a professional handball career. Full article

Garlic is a spice widely used worldwide, but ingestion of garlic can cause unpleasant breath odor that can be offensive in interpersonal interactions. Among several sulfur-containing components of garlic, allyl methyl sulfide is considered the primary causative agent of unpleasant garlic breath and body odor. We discovered that hop cone powder exhibits potent deodorizing activity against allyl methyl sulfide. Oxidation products of the hop bitter acids humulinone and hulupone were detected in a partially purified sample of hop cone powder. Oxidation products of the α-acids cohumulinone and n-humulinone showed approximately 10- and 15-fold stronger deodorizing activity than the parent α-acids, respectively. The deodorizing activity of oxidation products of β-acids was comparable to that of n-humulinone. It is presumed that the oxidation products of hop powder play an important role in the strong deodorizing activity of hop cone powder against allyl methyl sulfide. Full article

Thrips can be attracted or repelled by volatiles from different host plant species. Houttuynia cordata is a common plant species with a strong, offensive smell, and few pests have been detected on this plant. Here, the olfactory responses of Frankliniella occidentalis to H. cordata volatiles were tested using electroantennography (EAG) and behavioral bioassays in different types of olfactometers, and the behavioral responses of Orius similis, a natural enemy of F. occidentalis, to the related main volatile compounds were also evaluated. Y-tube olfactometer bioassays showed that F. occidentalis performed negative responses to H. cordata volatiles. Decanal (47.21%), 1-decanol (11.02%), dodecanal (7.13%), β-myrcene (5.12%), and decanoyl acetaldehyde (3.76%) were the more abundant components in the H. cordata volatile profile in gas chromatography–mass spectrometry analysis. EAG recordings showed that the antennae of female thrips could perceive these five compounds at a wide range of concentrations. In six-arm olfactometer bioassays, F. occidentalis exhibited negative responses to decanal, dodecanal, and decanoyl acetaldehyde at various doses but performed positive responses to 1-decanol and β-myrcene at certain doses. Furthermore, decanal, dodecanal, and decanoyl acetaldehyde at all concentrations showed no significant influences on the behavioral responses of O. similis. According to the results above, H. cordata can be a repellent plant species to F. occidentalis, and decanal, dodecanal, and decanoyl acetaldehyde show great potential for development as repellents for the control of F. occidentalis. In short, our results suggest that an integrated pest management system combining H. cordata-derived biopesticides with releases of the predator O. similis could effectively control F. occidentalis. Full article

As offensive cyber operations have become more commonplace, cyber collateral damage (CCD) to society and to civilian infrastructure has expanded in impact and severity. Several research contexts, frameworks, and methods apply to these collateral effects, especially as they pertain to reducing them. To investigate and map this area of research, five leading scientific databases (Scopus, IEEE Xplore, Springer Link, ScienceDirect, and ProQuest) were searched for papers on CCD. From 716 search results, 74 relevant papers were selected. Using surface categories as well as thematic analysis, these were grouped into the main emergent categories of legal, ethical, targeting-oriented, and econometric papers, with each category showing a recent research trend. The papers were qualitatively assessed for importance and coverage and compared bibliographically to identify key papers and authors. Within the identified areas of research, significant gaps remain. While CCD is becoming increasingly well understood from a legal and operational perspective, this accounts only for a fraction of the civilian harm caused by offensive cyber operations. This study identifies potential pathways for the synthesis of the current research areas (targeting, taxonomy, econometrics) with broader definitions of collateral damage to include civilian harm. These include updating national cyber doctrines to require collateral damage estimates, as well as exploiting emerging open datasets to understand which cyber capabilities cause the greatest collateral effects. Finally, we observe that the research definitions and taxonomy of CCD differ widely and have been subjected to limited scrutiny and challenge to date. Full article

In volleyball, successful offensive performance is influenced not only by physical muscle function but also by injury status. The purpose of this study was to analyze the relationship between muscle function—including strength, balance, and symmetry—and injury history in relation to offensive performance (OP) and ultimately sought to find factors required to improve OP. The final analysis included 60 players in attacking positions (36 in the symmetry group and 24 in the asymmetry group). Muscle strength was assessed using isokinetic testing for shoulder and knee extension. Balance was evaluated using the Upper Quarter Y-Balance Test (UQ-YBT) and the Lower Quarter Y-Balance Test (LQ-YBT). The asymmetry index (AI, ≥10%) was calculated by comparing the dominant and non-dominant sides. The results showed that the asymmetry group had a higher injury rate and lower offensive performance (OP) than the symmetry group (p < 0.05). In multiple regression analysis, no significant predictors were found on the non-dominant side, whereas significant variables were identified only on the dominant side. The key variables influencing OP were shoulder and knee extension strength, UQ-YBT scores, and the AI of knee extension. A 13.8% improvement in shoulder extension strength on the dominant side increased the likelihood of enhanced offensive performance (OP) by 2.54 times. A 10.5% improvement in the asymmetry index (AI) of knee extension was associated with a 1.52-fold increase in OP (p < 0.05). Shoulder and knee flexion did not significantly affect OP in any of the tests (p > 0.05). In conclusion, offensive performance in volleyball is associated with the greater shoulder and knee extension strength of the dominant side, as well as positive changes in UQ-YBT scores and the AI of knee extension. Full article

Hate speech is a complex topic that transcends language, culture, and even social spheres. Recently, the spread of hate speech on social media sites like Facebook has added a new layer of complexity to the issue of online safety and content moderation. This study seeks to minimize this problem by developing an Arabic script-based tool for automatically detecting hate speech in Roman Urdu, an informal script used most commonly for South Asian digital communications. Roman Urdu is relatively complex as there are no standardized spellings, leading to syntactic variations, which increases the difficulty of hate speech detection. To tackle this problem, we adopt a holistic strategy using a combination of six machine learning (ML) and four Deep Learning (DL) models, a dataset from Facebook comments, which was preprocessed (tokenization, stopwords removal, etc.), and text vectorization (TF-IDF, word embeddings). The ML algorithms used in this study are LR, SVM, RF, NB, KNN, and GBM. We also use deep learning architectures like CNN, RNN, LSTM, and GRU to increase the accuracy of the classification further. It is proven by the experimental results that deep learning models outperform the traditional ML approaches by a significant margin, with CNN and LSTM achieving accuracies of 95.1% and 96.2%, respectively. As far as we are aware, this is the first work that investigates QLoRA for fine-tuning large models for the task of offensive language detection in Roman Urdu. Full article